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The challenging task of diagnosing gastrointestinal (GI) tracts recently became a popular research topic, where most researchers performed extraordinary feats using numerous deep learning (DL) and computer vision techniques to achieve state-of-the-art (SOTA) diagnostic performance based on accuracy. However, most proposed methods relied on combining complex computational methods and algorithms, causing a significant increase in production difficulty, parameter size, and even training cost. Therefore, this method proposes a straightforward approach to developing a vision-based DL model without requiring heavy computing resources or reliance on other complex feature processing and learning algorithms. This paper included the step-by-step procedure consisting of network compression, layer-wise fusion, and the addition of a modified residual layer (MResBlock) with a self-normalizing attribute and a more robust regularization. In addition, the paper also presents the performance of the proposed method toward the diagnosis of four GI tract conditions, including polyps, ulcers, esophagitis, and healthy mucosa. The paper concludes that the proposed method did radiate a significant improvement in the overall performance, cost-efficiency, and especially practicality compared to most current SOTA methods.

 

• The proposed method combined profound techniques like feature fusion, residual learning, and self-normalization to develop a lightweight model that accurately diagnoses gastrointestinal (GI) tract conditions.
• The model produced from the proposed method generated better performance than most pre-existing state-of-the-art Deep Convolutional Neural Networks that diagnosed the presented four GI tract conditions.
• Aside from its competitive performance, the model based on the proposed method only had 1.2M parameters and only consumed 1.5 GFLOPS, making it significantly more cost-efficient than most existing solutions.

Read full-text  Breast cancer is the second most common cancer and has the second-highest mortality rate in women among all cancer types. Accurate cancer diagnosis plays a great part in breast cancer treatment. The application of machine learning methods in cancer classification has grown popular and has provided an accurate classification of malignant (cancerous) and benign (non-cancerous) breast cancer. This paper presents the application of three machine learning methods to classify malignant and benign breast cancer. The three machine learning methods used in this study are Support Sector Machine (SVM), Logistic Regression (LR), and Neural Network (NN) for breast cancer classification. For each machine learning method, multiple models had been tested with every model having a unique set of parameter values. This study used the breast cancer Wisconsin diagnostic (BCWD) dataset. The performance of the models is evaluated using the k-fold cross-validation technique and confusion matrix. The result shows that SVM outperformed both LR and NN in terms of classification accuracy, precision, recall, and specificity with k-fold cross validation technique. On the other hand, when the train-test split was used to validate the proposed model, the NN outperformed both SVM and LR achieving accuracy of 99.4%.

Read full-text Mangrove forests are a complex assemblage of flora that plays significant roles in the sustainability of human and environmental well-being for numerous reasons. However, a lot of mangrove forests have been converted for other purposes and uses in the past years. Limited knowledge about unsustainable aquaculture, coastal infrastructure, urban development, pollution, and overexploitation of mangrove timber resources has led to the loss of mangroves globally. Thus, to address these challenges regarding the insufficiency of mangrove and conservation-related knowledge and awareness, this study was conducted to identify the community stakeholders’ knowledge and awareness as regards the ecological and socioeconomic uses of mangroves in the fringing mangrove forest in Barangay Kilitisan, Calatagan, Batangas, Philippines. The study employed the mixed method of research and the purposive sampling technique with the interview, focused group discussion (FGD), and survey questionnaire as the data gathering instruments. The respondents of the study include 20 fisherfolks who served as key informants purposively chosen from the barangay. The findings of the study revealed that the stakeholder’s knowledge of mangrove taxonomy and biology was evident. They have moderate ecological and socio-economic awareness as regards the uses of mangroves. There is a significant difference in the level of awareness according to sex while no significant difference was noted in terms of educational attainment and monthly income; extension projects and activities may be recommended to scale up sustainability initiatives of the mangrove forest in Barangay Kilitisan, Calatagan, Batangas and calls for collaboration among the local government officials and stakeholders in redefining and strengthening the sustainability initiatives for the fringing mangrove forest to preserve ecological balance and to reduce the ongoing deterioration of other associated marine resources.

Read full-text  Graphs considered in this paper are simple finite undirected graphs without loops or multiple edges. A simple graphs where each vertex has degree 3 is called cubic graphs. A cubic graphs, that is, 1-connected or cubic bridge graph is traceable if its contains Hamiltonian path otherwise, non-traceable. In this paper, we introduce a new family of cubic graphs called Non-Traceable Cubic Bridge Graph (NTCBG) that satisfy the conjecture of Zoeram and Yaqubi (2017). In addition, we defined two important connected component of NTCBG that is, central fragment that give assurance for a graph to be non-traceable and its branches. Some properties of NTCBG such as chromatic number and clique number were also provided.

Read full-textThis investigation demonstrates a 10-bit SAR-ADC with low power dissipation per conversion step at 50 MS/s sampling rate. The proposed ADC is featured with the split-capacitance array by using unity-gain amplifiers and designed for fibre optic gyroscope (FOG) systems. The buffers based on the unity-gain amplifiers replace the bridge capacitors of the split-capacitance array in conventional SAR ADCs to resist process variation and enhance linearity of the capacitor-array. Aside from those, improvements in time response, that is, settling time, and capacitor size have also been proved. The design was implemented using a typical 40-nm CMOS process. The INL (integral non-linearity) and the DNL (differential non-linearity) were found to be 0.56 LSB and 0.51 LSB, respectively. The SNDR and SFDR are 51.23 dB and 61.46 dB, respectively, at an input of 12.5 MHz showing ENOB to be 9.79 bits at a sampling rate of 50 MS/s.

Read full-textThis study examined the social climate of fully synchronous online English classrooms and its effects on students’ learning outcomes in a mixed-methods design, engaging 196 first-year non-English major students experiencing their first fully synchronous online English classes on Zoom in one academic term (12 weeks), taught by foreign lecturers. The results reveal a very positive learning experience with seven factors contributing to the social climate of the online English classrooms. Gender did not affect students’ perceptions and learning experiences, but English proficiency levels did, with a medium effect size. The social climate was a predictor of online English learning outcomes with a substantial effect size. Individual insights were extracted from the qualitative data.

Implications for practice or policy:

• Online English courses should provide the same learning quality as in a face-to-face environment by enhancing the seven factors constructing a positive online English classroom climate.
• Educational policymakers should emphasise building positive social climates in online classrooms as it will lead to both better learning outcomes and experiences.
• Teachers may be encouraged to develop the skills students need to recognise and manage emotions, establish and maintain relationships and achieve positive goals in online English learning.

As regionalism has become a prime interest within the Asia Pacific, participating member states of the Association of Southeast Asian Nations (ASEAN) aspire to consolidate unity despite cultural diversity through forging an ASEAN identity. Institutions of higher learning are expected to explore educational innovations and curriculum reforms to facilitate the regional identity formation. Hence, this correlational research determined the awareness of ASEAN of teaching personnel (N=153) handling language, literature, humanities and social studies courses among state universities in the Southern Tagalog region of the Philippines. It also examined their assessment on the importance of literature curriculum practices as pedagogic tools in institutionalizing ASEAN identity in higher education. Through utilizing a researcher-made questionnaire and appropriate statistical measures, the results revealed that the teaching personnel are aware of the dimensions of ASEAN and considered the literary curriculum practices very important in instilling regional identity. Moreover, awareness of political integration in ASEAN of the teaching personnel significantly predicted their assessment on the importance of literary curriculum practices as this dimension of regional awareness is relevant and supportive to the cultural modelling tradition to literary study.

Read full-textThe role of technology in the delivery of education in this time of pandemic help teachers in the delivery of lessons and student learning. This study examined the competence on online technological pedagogical content knowledge (TPACK) of a sample of faculty members and their online teaching attitude and ability and develop a professional development framework for teachers under the new normal in education. This quantitative research used a questionnaire to collect data from 501 faculty members from Batangas State University as respondents. Findings indicate that pedagogical knowledge, technological knowledge and technological content knowledge are the predictors of faculty members. Online attitude and ability were also considered very important by most faculty members. Thus, a professional development framework in the new normal based on technological pedagogical content knowledge of teachers was prepared.

Read full-textEleusine indica belongs to the Pocaceae family and is abundantly found in many tropical countries. Because of its anti-malarial, antioxidant, anti-viral, and antidiabetic properties, among many others, studies are being aimed on developing time and costeffective methods that could efficiently extract its active components. This study focused on the optimization of the microwaveassisted extraction (MAE) of phenolic contents from E. indica. A response surface methodology (RSM) using a Box-Behnken design (BBD) of experiment was employed to determine the optimized condition for the extraction method. The extraction was performed with three varying factors (ethanol concentration, microwave power, and irradiation time) and one response (total phenolic content or TPC). A maximum TPC of 74.81 ± 5.22 GAE mg/g was obtained through MAE, using 57.23% (v/v) ethanol with microwave irradiation of 217.77 W for 4.53 minutes. The optimized condition had an extraction yield of 11.62 ± 1.97%. The ethanolic E. indica extract obtained using the optimum condition contained mostly of triterpenes, saponins and glycosides; and moderately of flavonoids and tannins.

Read full-text Conservation of biodiversity is critical for the coexistence of humans and the sustenance of other living organisms within the ecosystem. Identification and prioritization of specific regions to be conserved are impossible without proper information about the sites. Advanced monitoring agencies like the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) had accredited that the sum total of species that are now threatened with extinction is higher than ever before in the past and are progressing toward extinct at an alarming rate. Besides this, the conceptualized global responses to these crises are still inadequate and entail drastic changes. Therefore, more sophisticated monitoring and conservation techniques are required which can simultaneously cover a larger surface area within a stipulated time frame and gather a large pool of data. Hence, this study is an overview of remote monitoring methods in biodiversity conservation via a survey of evidence-based reviews and related studies, wherein the description of the application of some technology for biodiversity conservation and monitoring is highlighted. Finally, the paper also describes various transformative smart technologies like artificial intelligence (AI) and/or machine learning algorithms for enhanced working efficiency of currently available techniques that will aid remote monitoring methods in biodiversity conservation.

Read full-text Low-power and high-speed calculation is very important nowadays for energy-efficient demand of electronic devices. With the usage of ANT (All-N-transistor) logic, the speed constraint caused by PMOS transistors can be overcome through an auxiliary current path across NMOS transistors. This study presents a 800-MHz 28.8-mW 8-bit carry look-ahead adder (CLA) using ANT logic implemented on chip. FinFET technology is utilized to improve carrier mobility and increase device speed. R-C parasitic capacitance in FinFET devices is considered in the analysis of the delay time for the 8-bit CLA to improve PDP (power-delay product). The proposed design is implemented in 16-nm FinFET process with core area of 206.403 ×152.506 μm2. It has the lowest normalized PDP at 60 pF load by far.

Read full-text This brief research report aimed to explore the concept of pedagogical resilience and how teachers in Thailand and the Philippines build resilience in pedagogy during the COVID-19 pandemic. Employing the qualitative research design, the data were obtained from the focus group discussion (FGD) and semi-structured interviews with 12 teachers, 8 of whom were from 1 of the universities in the Philippines and another four teachers from a university in Thailand. The thematic analysis revealed that pedagogical resilience is influenced by teachers’ personal, professional, and social attitudes toward teaching and learning during the COVID-19 pandemic. Although teachers acknowledged that they had no experience in remote and online teaching, they demonstrated resiliency by being flexible and adaptive to the situation. Such an attitude allowed them to build a relationship with their colleagues and design teaching and learning pedagogy that addresses the issues in their online and remote teaching. The findings provide a clear understanding of the conceptualization of pedagogical resilience from the perspectives of teachers in the Philippines and Thailand.

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Bilimbi (Averrhoa bilimbi) is an underutilized fruit usually grown in tropical countries and is widely added as souring agent in many Filipino dishes. However, it is undervalued and its utilization is limited due to short shelf life. Drying is one of the more popular and cheapest means of prolonging its storage life. This study aimed to investigate the effects of drying methods on the physicochemical properties and antioxidant activity of bilimbi. Analysis of proximate composition, pH, total sugar, ascorbic acid content, oxalic acid content and antioxidant activity was conducted on the fresh and dried fruit samples.

Data showed that drying significantly affected the proximate composition and pH of bilimbi (p < 0.05). The oxalic acid content, total sugar content and antioxidant activity of dried bilimbi generally increased while significant loss in ascorbic acid was observed in solar dried and oven dried bilimbi. Freeze drying removed the most amount of moisture while preventing significant decrease in ascorbic acid level in bilimbi. Dried bilimbi is a good source of macronutrients, ascorbic acid and possessed good antioxidant activity regardless of the method of drying utilized.

Read full-text The interplay of groups and graphs has been a subject of interest by mathematics researchers nowadays. One particular instance is the identity graph of a group introduced by Kandasamy [5]. Moreover, the concept of a central graph of any graph is widely used by many graph theorists. The central graph of a graph G denoted by C(G) can be obtained by subdividing the edge of G exactly once and joining all the nonadjacent vertices of G in C(G). In this paper, we construct the central graph of the identity graph of finite cyclic group and investigate some of its graph properties.

Read full-text A control system architecture design for an underwater ROV, primarily Class I – Pure Observation underwater ROV is presented in this paper. A non-linear plant model was designed using SolidWorks 3D modeling tool and is imported to MATLAB as a 3D model. The non-linear modeled plant is linearized using the MATLAB linear analysis toolbox to have a linear approximate model of the system. The authors designed controllers for the linear plant model of underwater ROV. PID controllers are utilized as a controller of the modeled plant. The PID tuning tools by MATLAB are utilized to tune the controller of the plant model of underwater ROV. The researchers test the control design of underwater ROV using MATLAB Simulink by analyzing the response of the system and troubleshoot the control design to achieve the objective parameters for the control design of underwater ROV.

Read full-textLighting systems for indoor crop production facilities face consistency, quality of lighting, and energy efficiency challenges. In this work, these challenges were addressed by developing a natural light collection system that allows sunlight to be concentrated and transmitted via optical conduits into the growing areas of an indoor crop production facility and supplementing it with artificial lighting. The daily light integral (DLI) estimator measures the natural light received while the supplemental lighting provides the additional photosynthetic photon flux to achieve the crop’s lighting requirements. Lux meter and spectrometer characterized the light sensors used to ensure accurate light measurements. The proposed system obtained a mean relative error of 1.78% between the DLI estimator and light instruments. In one testing activity, the system had collected 13.64 mol·m-2·d-1, while the artificial lighting supplemented 6.53 mol·m-2·d-1. The proposed system is vital in maintaining the DLI required by crops throughout the day.

Read full-text This study clarifies the data error rates optimization for OFC/OWC channels based on different transmission codes. These codes that are namely multi bits/symbol digital pulse interval modulation (DPIM), multi bits/symbol pulse position modulation (PPM), nonreturn to zero inverted (NRZI), 4 bit data symbol/5 bit code (4B5B), and Manchester for upgrading optical wired/wireless communication systems. The optical power through OFC/OWC channels, S/N ratio, the output power at the receiver side are stimulated with high bit transmission rates. The effects of coding complexity on the Q-factor, BER, optical power, and electrical received power are also stimulated using both DPIM and PPM coding.

Read full-text This paper aims to simulate performance efficiency of carrier suppressed non return to zero line coding based FSO transceiver systems under light rain conditions with amplification units at 40 Gbps. The max. Q, BER and total optical power are simulated and demonstrated after FSO channel and PIN Photodetector Receiver under light rain weather conditions at maximum reach of 1.2 km at 10 Gbps. As well as the max. Q Factor variations versus max reach variations are clarified after PIN photodetector receiver under light rain weather conditions at 10, 40 Gbps with/without amplification units. Besides the total optical power variations versus max reach variations are assured after FSO channel under light rain weather conditions at 10, 40 Gbps with/without amplification units.

Read full-text Drones, or Unmanned Aerial Vehicles (UAVs), are already being utilized to help people in need worldwide. In this report, the authors designed an unmanned vehicle composed of a flight mechanism & power supply, frame, and different payloads for different missions. These also include the design of different landing gears that are mechanically replaceable depending on the mission. This method allowed the drone to move in multi-mission or other environments (ground, air, and surface water) in normal conditions by changing its landing gears. A mapper payload that performs image stitching is also presented. The drone has an average flight time of 15 minutes, 4 kg. weight and can support payload weighing 5 kg., it was tested to operate at a maximum altitude of 100 m. It can communicate through a remote controller via radio frequency signals. It can land and take off in ground and surface water and maneuver in different environments; air, ground (flat area), and surface water, depending on the attached payload. The tests were conducted in remote and actual environments given the three different landing gears, and the results show that the drone is ready for a real mission. Future payloads such as a medical kit and detachable communications links are considered for the next phase.

Read full-text The demand for clean and safe water together with increasingly strict environmental regulations in both developed and developing countries has necessitated the need for a highly efficient yet low-cost water treatment technology to prevent the negative effects of pollutants on the human health and the environment. Nanotechnology holds great potential as a novel and promising field in water treatment. This review presents the recent development in nanotechnology for water and wastewater treatment. The review includes discussion on the nanomaterials- its properties and mechanism that allows its use in the remediation of pollutants in both water and waste water.

Read full-textMercury (Hg2+) ion is considered as one of the most lethal heavy-metal contaminants accounting for its extreme persistence, bioaccumulation, and toxicity. The objective of this study is to synthesize hybrid carbon quantum dots (h-CQDs) for the detection of Hg2+ ion in purified water and simulated seawater using ratiometric fluorescence assay between blue carbon quantum dots(b-CQDs) produced from Tamarindus indica leaves and green carbon quantum dots(g-CQDs) produced from Mangifera indica leaves. The optical properties of the produced CQDs were determined using UV-Vis spectrophotometer which explained the quenching of g-CQDs’ photoluminescence and stability of b-CQDs upon addition of Hg2+. The research also includes the effect of volume ratio and time of microwave irradiation in the photoluminescence intensity ratio of h-CQDs and the influence of increasing Hg2+ concentration in both purified water and simulated seawater. The mathematical correlation between PL intensity ratio and concentration was found to be a rational equation for both purified and simulated seawater. Using simulated seawater, the result showed in the model has no significant difference with the detected concentrations using atomic absorption spectroscopy. Furthermore, the generated model exhibits dynamic range between 394 ppb and 23.85 ppm.

Read full-text Pest infestation is one of the serious problems of the agricultural sector in crop production and may even pose health hazards to humans. Aphis fabae, normally known as black bean aphid, is one of the main culprits that predominantly damage growing crops particularly beans, celery, and peas and may carry diseases as they multiply. Kakawate (Gliricidia sepium) leaves extracts was prepared using microwave assisted extraction and used as pesticide against black bean aphids in string beans (Phaseolus vulgaris). Microwave assisted extraction (MAE) is a rapid method of extraction of active components of plants which employ microwave energy to heat sample-solvent mixture at a short period of time. A central composite design (CCD) was employed to evaluate the effect of irradiation time (1.5 minutes) and microwave power (210W, 350W) on the phenolic content of the extract. Response surface methodology (RSM) determined the optimized conditions of MAE of total phenolics in dried G. sepium leaves. The optimized conditions were obtained at irradiation time of 2.44 minutes and microwave power of 275W with total phenolic content of 9.022 mg-GAE/g-dry sample. The presence of bioactive compounds of alkaloids, flavonoids, tannins and phenols were confirmed in the extract. The mortality or average life span of A. fabae upon application of G. sepium pesticide and commercially available pesticide are 5.17 seconds and 4.73 seconds, of which the difference is insignificant with the p-value one tail (0.002) and two tail (0.005) effect. No significant effect on the height of plant and number of P. vulgaris leaves at 10 days after the application of G. sepium pesticide (p = 0.022) and commercially available pesticide (p = 0.026) which make it a great potential biopesticide.

Read full-text Agriculture is one of the most important sectors of any country, especially the Philippines. Pechay is the sixth-ranking crop for agricultural production in Batangas, according to the Philippine Statistics Authority (PSA). However, pechay is constantly threatened by different pests, such as the occurrence of cutworm (Spodoptera litura), which primarily feeds on its leaves, stalks, and stems and even cuts off the plants that destroy the entire leaf in no time at all. Detection of the presence of pests at the earliest can help farmers to employ the necessary intervention to mitigate the spread of the infestation. This paper proposes a system that can classify whether pechay plants are healthy or damaged and assess the severity of the damage using image processing techniques and machine learning. Images of pechay plants are gathered and pre-processed to remove the background, resize, and enhance. Then statistical measures derived from GLCM (gray level co-occurrence matrix) are used with diagnostic feature explorer (DFE) to select the most appropriate features for the classification. The clustering to assess the severity of damage used k-means to segment the damaged areas from the entire leaf. The system can classify healthy and destroyed plants and grade the severity of damage with an accuracy of 88.31%.

Read full-text This research illustrates a DC–DC buck converter with a pulse width modulation (PWM) feedback control loop and capable of power supply voltage range from VDD to 2.5× VDD, which is equivalent to 5–14 V. It is a single chip with area of 1.379 × 0.813 mm2 using 0.5 µm HV CMOS process, where high voltage (HV) MOSFETs, a Dead-time detector, a PWM feedback loop, a control circuit and HV driving transistors are included. The main feature of our design is its capability of shutting off an optimal number of power MOSFETs during light load operation, resulting in a very high conversion efficiency. Most important of all, the optimal solution is analytically proved. The light load efficiency is raised from 31.71% by traditional methods to 67.94% by the proposed design.

Read full-text Depression is one of the most common mental health disorders with over 264 million people suffering from it. Improvement in screening depressive disorders can lead to earlier treatment. However, some of the depression screening tools today have constraints and could be difficult to administer due to a lack of cooperation between patient and professional. It is indicated in some studies that there is a correlation between frequent use of social media and increased depression. With this finding, the authors aimed to develop a novel screening tool system incorporated with an artificial neural network that analyzes the patient’s tweets. A design of the screening tool software application was proposed, and an ANN model was developed using a dataset curated from Kaggle. The dataset was cleaned, and features were extracted using the TF-IDF approach. PCA was also used to lessen the number of features for faster training and testing time. Four algorithms were used in training – SVM, Logistic Regression, Perceptron, and KNN. Even though PCA lessens the time for training and testing, it didn’t greatly affect the performance of each model. The SVM model achieved the best performance followed by the Perceptron model. Both the SVM model and the Perceptron model achieved the highest accuracy (98%), but the SVM model achieved better results on the other parameters. However, the SVM model is very slow which prompted the authors to choose the Perceptron model that has a faster speed.

Read full-textIn this study, a metal–organic framework (MOF) adsorbent from recycled polyethylene terephthalate (PET) bottles was developed to effectively remove Eriochrome Black T (EBT) dye present in synthetic wastewater. A sustainable, “greener” route of MOF synthesis was adopted involving microwave-enabled depolymerization of PET to terephthalic acid (TPA) and conversion of the latter to its organic salt linker form. Results from differential scanning calorimetry (DSC), X-ray diffraction (XRD) analysis, Fourier transform infrared (FTIR) spectroscopy, and thermogravimetric analysis (TGA) not only revealed the structure of the as-prepared MOF, but also proved the presence of EBT molecules on the adsorbent during the dye removal process. A ∼77% maximum removal was reached after 90 min and the adsorption was best explained by Langmuir model having 82.5 mg/g maximum monolayer capacity. Kinetic studies also showed the adsorption to be pseudo-second order with a two-stage intraparticle diffusion process. The effective performance of MOF on EBT removal was also verified by the presence of potential adsorbent-adsorbate interactions such as ionic, hydrogen bonding, and π–π stacking. Thus, the as-fabricated PET-derived MOF is considered a decent, value–added material acting as a sustainable adsorbent for organic dyes present in contaminated water.

Read full-text The study described a mentoring program of a state-funded research and its effects on research and reflective practices to graduate students. Participatory Action Research (PAR), designed as a methodical and program framework, engaged nine mentors (researchers of a state-funded research) and 29 graduate students (purposively invited) to training-based mentoring (workshops and field work), small group mentoring (within research cells), and peer mentoring (field work and software-aided coding analysis). Observations, mentors’ narrations, and reflection journals extracted the experiences of the participants on the mentoring program. These reflections revealed that mentors and mentees learned many skills in the mentoring program. They had transformed challenges and difficulties (time management, field work) into learning episodes leading to reflection-in-action and reflection-on-action. They realized the importance of the theory-practice-reflection paradigm in all research endeavors. Hence, PAR-influenced mentoring helped develop their research skills. However, low engagement of the others may be due to time aspect, which may be looked into in a replicated study

Read full-text In this work, a proposed Multi-Fused Residual Convolutional Neural Network (MFuRe-CNN) with Auxiliary Fusing Layers (AuxFL), a Fusion Residual Block (FuRB) both with Alpha Dropouts (αDO) diagnosed various endoscopic images of gastrointestinal (GI) conditions or diseases. The proposed MFuRe-CNN handled four cases, including colons with ulcerative colitis, polyps, esophagitis, and a healthy colon, sourced from reliable databases like the KVASIR and ETIS-Larib Polyp DB. The proposed model consisted of three state-of-the-art models fused into a single feature extraction pipeline with their partially frozen and truncated layers, which helped propagate robust features and improved the diagnostic performance without consuming a hefty fraction of computing cost compared to most existing state-of-the-art models. In addition, the MFuRe-CNN incorporated with AuxFLs, αDOs, and FuRB have shown a significant contribution in reducing overfitting and performance saturation compared to those without the said components. Upon evaluation, the proposed model achieved an outstanding 97.25% test accuracy with only 4.8 M parameters and consumed 7.8 GFLOPs during inference, making it more efficient and accurate than most conventionally trained DCNNs. As concluded, the proposed MFuRe-CNN can potentially improve the diagnosis of the GI tract more cost-efficiently than ensembles and perform better diagnosis than most conventional pre-trained and fine-tuned DCNNs.

Read full-textIn this study, stereolithography (SLA) 3D printing was used to prepare toughened composites by facile blending of chemically compatible nanoscale polyhedral oligomeric silsesquioxanes (POSS) to commercial photoreactive resin. Due to the complex nature of 3D printing, the mechanical performance of the final parts cannot be simply determined or even estimated until they are manufactured and tested. Thus, response surface methodology (RSM) and artificial neural network (ANN) were used to build regression models for determining the toughness of fabricated composites as function of toughener (POSS) amount and printing conditions (layer thickness and annealing temperature). The influence of the mentioned process parameters on toughness were investigated through a 17-run three-factor three-level Box-Behnken RSM design (BBD). The same experimental design was also used to acquire a data set for ANN. Finally, both the modeling methodologies were compared by coefficient of determination (R2) and residual distribution values. Results reveal that ANN possesses a better data fitting and predictive power as compared to RSM.

Read full-text Additive manufacturing, or simply 3D printing (3DP), where objects are built through layer-wise material deposition, has gained significant academic and industrial attention as a result of the development of advanced and functional materials requiring rapid, customized, and flexible solutions. In the context of green manufacturing, diversifying environmentally and economically sustainable material portfolios is an essential endeavor for the success of 3DP technology that uses widely available, highly valuable, and renewable materials. In this study, we used stereolithography (SLA) for processing methacrylate-based photocurable resins containing crab shell-derived chitin nanowhiskers (CNWs), which are surface-functionalized by reactive acrylate groups. Results from full spectral, thermal, structural, and topological analyses corroborate not only the surface functionalization of CNWs but also indicate the presence of these photocurable CNW (pCNW) fillers in the 3D-printed nanocomposites. Owing to the strong interfacial bond induced by the physical and chemical crosslinking between the pCNW and methacrylate (MA) polymer matrix, the internally formulated nanocomposites displayed enhanced thermomechanical properties (e.g., storage modulus and glass transition temperature) compared to those of commercially available pure SLA resins. For instance, the inclusion of 0.5 wt % pCNW improved the tensile strength and stiffness to up to 78 and 71%, respectively, without compromising the toughness and ductility of the printed material. Accordingly, this result also evidences the compatibility between the filler and resin materials. Consequently, the formation of a crosslinked network in the nanocomposite structure results in a higher thermal stability and activation energy (i.e., up to ∼79%) for all the hybrid materials than the pristine MA. The high-resolution SLA print features, dimensional accuracy, and enhanced mechanical performance of our microstructure-forming functionalized chitin-based nanocomposites make them promising materials for a wide range of robust and high-performance industrial applications.

Read full-text The increasing diversity in schools requires in-depth understanding of pedagogical approaches to teach diverse populations. This requirement demands Teacher Education Institutions to develop among pre-service teachers positive attitude towards inclusion and prepare and train them to deliver inclusive practices in a mainstream classroom. This study investigated the attitude towards inclusive education, perceived level of readiness to implement inclusive education and assessment on the effectiveness of teacher education program for inclusive education in terms of collaborative teaching, techniques and strategies and collaborative experiences among pre-service teachers in one state university in Batangas Province, Philippines. Using descriptive-survey as research design with researcher-made questionnaire as tool to gather data, results have shown that the respondents have high positive attitude (x̄=3.57) towards inclusive education. Further, the study also revealed that the respondents have high perception (x̄=3.63) on their readiness to implement inclusive education. Lastly, teacher education program for inclusive education along with collaborative teaching and techniques and strategies were highly effective (x̄=3.51 and x̄=3.55 respectively), while effective (x̄=3.49) only in terms of collaborative experiences. Analyses of the findings afforded the researcher to formulate recommendations that higher authorities may use as inputs in formulating and designing programs to better empower future teachers to embrace diversity.

Read full-text In this paper, a career track recommender system was proposed using the Deep Neural Network model. This study aims to assist guidance counselors in guiding their students in the selection of a suitable career track. It is because a lot of Junior High school students experienced track uncertainty and there are instances of shifting to another program after learning they are not suited for the chosen track or course in college. In dealing with the selection of the best student attributes that will help in the creation of the predictive model, the feature engineering technique is used to remove the irrelevant features that can affect the performance of the DNN model. The study covers 1500 students from the first to the third batch of the K-12 curriculum, and their grades from 11 subjects, sex, age, number of siblings, parent’s income, and academic strand were used as attributes to predict their academic strand in Senior High School. The efficiency and accuracy of the algorithm depend upon the correctness and quality of the collected student’s data. The result of the study shows that the DNN algorithm performs reasonably well in predicting the academic strand of students with a prediction accuracy of 83.11%. Also, the work of guidance counselors became more efficient in handling students’ concerns just by using the proposed system. It is concluded that the recommender system serves as a decision tool for counselors in guiding their students to determine which Senior High School track is suitable for students with the utilization of the DNN model

Read full-text In such a brief period, the recent coronavirus (COVID-19) already infected large populations worldwide. Diagnosing an infected individual requires a Real-Time Polymerase Chain Reaction (RT-PCR) test, which can become expensive and limited in most developing countries, making them rely on alternatives like Chest X-Rays (CXR) or Computerized Tomography (CT) scans. However, results from these imaging approaches radiated confusion for medical experts due to their similarities with other diseases like pneumonia. Other solutions based on Deep Convolutional Neural Network (DCNN) recently improved and automated the diagnosis of COVID-19 from CXRs and CT scans. However, upon examination, most proposed studies focused primarily on accuracy rather than deployment and reproduction, which may cause them to become difficult to reproduce and implement in locations with inadequate computing resources. Therefore, instead of focusing only on accuracy, this work investigated the effects of parameter reduction through a proposed truncation method and analyzed its effects. Various DCNNs had their architectures truncated, which retained only their initial core block, reducing their parameter sizes to <1 M. Once trained and validated, findings have shown that a DCNN with robust layer aggregations like the InceptionResNetV2 had less vulnerability to the adverse effects of the proposed truncation. The results also showed that from its full-length size of 55 M with 98.67% accuracy, the proposed truncation reduced its parameters to only 441 K and still attained an accuracy of 97.41%, outperforming other studies based on its size to performance ratio.

Read full-text Due to the struggles of developing countries in coping with widespread coffee leaf diseases and infestations, the quality and quantity of coffee-based commodities have reduced significantly. This paper proposes a solution to this problem using Deep Convolutional Neural Networks (DCNN) that classifies seven coffee leaf conditions. Unlike other studies, this work proposed a novel Triple-DCNN (T-DCNN) composed of three aggregated DCNN models formed in an ensemble to produce lesser bias and better accuracy than standard models. Added to the proposed T-DCNN, an employed stage-wise approach narrowed down the classification options through a multi-staged structure and diversified the entire feature pool. Upon evaluation, the proposed Stage-Wise Aggregated T-DCNN (SWAT-DCNN) yielded successful diagnoses of diverse coffee leaf conditions in various environmental settings. Furthermore, with an overall accuracy of 95.98%, the SWAT-DCNN outperformed most state-of-the-art DCNNs that performed the same task.

Read full-text For a reaction network with species set S, a log-parametrized (LP) set is a non-empty set of the form E(P,x∗)={x∈RS>∣logx−logx∗∈P⊥} where P (called the LP set’s flux subspace) is a subspace of RS, x∗ (called the LP set’s reference point) is a given element of RS>, and P⊥ (called the LP set’s parameter subspace) is the orthogonal complement of P. A network with kinetics K is a positive equilibria LP (PLP) system if its set of positive equilibria is an LP set. Analogously, it is a complex balanced equilibria LP (CLP) system if its set of complex balanced equilibria is an LP set. An LP kinetic system is a PLP or CLP system. This paper studies concentration robustness of a species on subsets of equilibria. We present the “species hyperplane criterion”, a necessary and sufficient condition for absolute concentration robustness (ACR) for a species of a PLP system. An analogous criterion holds for balanced concentration robustness (BCR) for species of a CLP system. These criteria also lead to interesting necessary properties of LP systems with concentration robustness. Furthermore, we show that PLP and CLP power law systems with Shinar-Feinberg reaction pairs in species X in a linkage class have ACR and BCR in X, respectively. This leads to a broadening of the “low deficiency building blocks” framework to include LP systems of Shinar-Feinberg type with arbitrary deficiency. Finally, we apply our results to species concentration robustness in LP systems with poly-PL kinetics.

Read full-textOne important question that interests those who work in chemical reaction network theory (CRNT) is this: Does the system obtained from a reaction network admit a positive equilibrium and if it does, can there be more than one within a stoichiometric class? The higher deficiency algorithm (HDA) of Ji and Feinberg provided a method of determining the multistationarity capacity of a CRN with mass action kinetics (MAK). An extension of this, called Multistationarity Algorithm (MSA), recently came into the scene tackling CRNs with power law kinetics (PLK), a kinetic system which is more general (having MAK systems as a special case). For this paper, we provide a computational approach to study the multistationarity feature of reaction networks endowed with kinetics which are non-negative linear combinations of power law functions called poly-PL kinetics (PYK). The idea is to use MSA and combine it with a transformation called STAR-MSC (i.e., S-invariant Termwise Addition of Reactions via Maximal Stoichiometric Coefficients) producing PLKs that are dynamically equivalent to PYKs. This leads us to being able to determinine the multistationarity capacity of a much larger class of kinetic systems. We show that if the transformed dynamically equivalent PLK system is multistationary for a stoichiometric class for a set of particular rate constants, then so is its original corresponding PYK system. Moreover, the monostationarity property of the transformed PLK system also implies the monostationarity property of the original PYK system.

Read full-textThe Philippine government’s effort to transcend agriculture as an industry requires precision agriculture. Remote- and proximal-sensing technologies help to identify what is needed, when, and where it is needed in the farm. This paper proposes the use of vision-based indicators captured using a low-altitude unmanned aerial vehicle (UAV) to estimate weed and pest damages. Coverage path planning is employed for automated data acquisition via UAV. The gathered data are processed in a ground workstation employing the proposed methods in estimating vegetation fraction, weed presence, and pest damages. The data processing includes techniques on sub-image level classification using a hybrid ResNet-SVM model and normalized triangular greenness index. Sub-image level classification for isolating crops from the rest of the image achieved an F1-score of 97.73% while pest damage detection was performed with an average accuracy of 86.37%. Also, the weed estimate achieved a true error of 5.72%.

Read full-textIn this work, we empirically evaluated the efficiency of the recent EfficientNetB0 model to identify and diagnose malaria parasite infections in blood smears. The dataset used was collected and classified by relevant experts from the Lister Hill National Centre for Biomedical Communications (LHNCBC). We prepared our samples with minimal image transformations as opposed to others, as we focused more on the feature extraction capability of the EfficientNetB0 baseline model. We applied transfer learning to increase the initial feature sets and reduced the training time to train our model. We then fine-tuned it to work with our proposed layers and re-trained the entire model to learn from our prepared dataset. The highest overall accuracy attained from our evaluated results was 94.70% from fifty epochs and followed by 94.68% within just ten. Additional visualization and analysis using the Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm visualized how effectively our fine-tuned EfficientNetB0 detected infections better than other recent state-of-the-art DCNN models. This study, therefore, concludes that when fine-tuned, the recent EfficientNetB0 will generate highly accurate deep learning solutions for the identification of malaria parasites in blood smears without the need for stringent pre-processing, optimization, or data augmentation of images.

Read full-textIn this work, we empirically evaluated the efficiency of the recent EfficientNetB0 model to identify and diagnose malaria parasite infections in blood smears. The dataset used was collected and classified by relevant experts from the Lister Hill National Centre for Biomedical Communications (LHNCBC). We prepared our samples with minimal image transformations as opposed to others, as we focused more on the feature extraction capability of the EfficientNetB0 baseline model. We applied transfer learning to increase the initial feature sets and reduced the training time to train our model. We then fine-tuned it to work with our proposed layers and re-trained the entire model to learn from our prepared dataset. The highest overall accuracy attained from our evaluated results was 94.70% from fifty epochs and followed by 94.68% within just ten. Additional visualization and analysis using the Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm visualized how effectively our fine-tuned EfficientNetB0 detected infections better than other recent state-of-the-art DCNN models. This study, therefore, concludes that when fine-tuned, the recent EfficientNetB0 will generate highly accurate deep learning solutions for the identification of malaria parasites in blood smears without the need for stringent pre-processing, optimization, or data augmentation of images.

Read full-textThis paper proposes transfer learning and fine-tuning techniques for a deep learning model to detect three distinct brain tumors from Magnetic Resonance Imaging (MRI) scans. In this work, the recent YOLOv4 model trained using a collection of 3064 T1-weighted Contrast-Enhanced (CE)-MRI scans that were pre-processed and labeled for the task. This work trained with the partial 29-layer YOLOv4-Tiny and fine-tuned to work optimally and run efficiently in most platforms with reliable performance. With the help of transfer learning, the model had initial leverage to train faster with pre-trained weights from the COCO dataset, generating a robust set of features required for brain tumor detection. The results yielded the highest mean average precision of 93.14%, a 90.34% precision, 88.58% recall, and 89.45% F1-Score outperforming other previous versions of the YOLO detection models and other studies that used bounding box detections for the same task like Faster R-CNN. As concluded, the YOLOv4-Tiny can work efficiently to detect brain tumors automatically at a rapid phase with the help of proper fine-tuning and transfer learning. This work contributes mainly to assist medical experts in the diagnostic process of brain tumors.

Read full-textIsland in the Philippines. The passage is named after Verde Island, which is located at its middle. Although the island is located within a key biodiversity area, studies on its marine macrophyte biodiversity are scant. The present study was conducted to determine the composition, distribution, and dominance of marine macrophytes, specifically seaweeds and seagrass, during the northeast monsoon, summer, and southwest monsoon in four coastal areas in Verde Island using the line transect-quadrat method. Results revealed 63 macrophyte species, of which 92% were seaweeds and 8% were seagrass. The majority of the seaweeds were green (41%), followed by red (35%) and brown (16%) seaweeds. In most sites, the brown seaweed Padina sp. was dominant during summer and cover decreased during both monsoons. The green seaweed Neomeris annulata was present in all sites and seasons. The differences in cover across sites may be due to substratum type and topography where a relatively wider intertidal zone with different substratum such as rocky and sandy to muddy provides complex habitat promoting higher macrophyte cover. Temporal differences in marine macrophyte composition were more pronounced in macroalgae-dominated sites than in the seagrass-dominated site. Several important seaweeds that could be studied as bioindicators were recorded, such as Padina sp., which registered high cover especially in sites near populated areas and backyard pig pens. Ulva spp., which are known to form green tide blooms, and Caulerpa verticillata were also noted and should be monitored. Some red seaweeds with potential for cultivation were observed (i.e., Halymenia durvillei and Portieria hornemannii). Claudea sp., an uncommon red seaweed with limited distribution in the Philippines, was recorded and needs verification. This study is the first extensive marine macrophyte assessment at the heart of the Verde Island Passage.

Read full-textThis work reports a simple approach to prepare toughened 3D-printed polymethacrylate (PMA) composites using surfactant-modified chitosan (SMCS) particles at loadings between 2–10 wt%. Chitosan (CS) is modified with anionic surfactant, sodium dodecyl sulfate, via ionic complexation to facilitate compatibility and dispersion of CS to PMA matrix by non-covalent interactions between the components. The study successfully demonstrates high-accuracy 3D printing of composites with significant improvements in the overall mechanical properties. The composite with the best loading of 8 wt% SMCS shows a tensile modulus of 1.23 ± 0.05 GPa, a tensile strength at 49.8 ± 0.96 MPa, a yield stress at 33.3 ± 1.48 MPa, and a strain-at-failure 10.3 ± 0.61%, which are 45%, 40%, 32%, and 68% higher than neat PMA, respectively. This provides a significant improvement in toughness at 4.92 ± 0.55 MJ m−3 for the composite, 184% higher than that of neat PMA. The marked increase in toughness is due to enhanced filler-matrix interactions which improve the ability of the 3D printed composite to absorb energy under tensile load. The results from this work provide new understandings into the strategies for design and preparation of stereolithography 3D printed materials reinforced with toughening fillers from renewable resources.

Read full-textThe educational system today is anchored with serious challenges. The administration, together with the teachers, has a focal factor on the constant changes happening in th educational setting. To address the needs of the teachers to grow personally and professionally, administrative support is one of the vital factors that have given emphasizes. The purpose of the study was to assess the administrative support and job satisfaction of private school elementary teachers in Tanauan City, Batangas, Philippines.This also identified the significant relationship of administrative support to the job satisfaction of teachers that serve as basis for administrative support scheme for elementary private school teachers. The study used the descriptive-correlational method of research which involved 133 elementary school teachers from the private elementary schools in Tanauan City, Batangas and researcher-made questionnaire was used to gather the necessary data. The answers were tallied, tabulated and analyzed through the use of statistical tools such as Mean, Pearson’ r and T-test. The findings of the study asserted that school administrators were providing the allowances for registration, transportation, lodging and meals when the teacher attends seminars, trainings and conferences, were facilitating improvements of the classroom instructionalpractices and were promoting harmonious relationship among the teachers. The study also revealed that school administrators were providing budget for advancement of academic qualification, were giving constructive feedback after the classroom observation and recognition

Read full-textConstruction industry covers more than 70 percent of the total work strength of Philippines. This entails clear regulations and working conditions among its workers. In an attempt to discover the current condition of the workers, this study determined the demographic and socio-economic profile of construction workers in Malvar, Batangas, Philippines. Further, this also determined the various diseases and problems experienced by the respondents in the performance of their job. By employing descriptive research with a checklist questionnaire, this study reveals that most of the respondents belonged to age bracket of 38 and above, married, high school graduate, with a monthly income of Php 6,844.00-26,739.00, resides in barangays, with house type of both cement and wood and de buhos toilet facility, observing extended family type with 3-5 children, Php 6,001- Php 10,000 monthly expenditure worked as laborer with 8-10 hours of work per day, owned television and cellphones, experienced fever and common cold, and met problems on the absence of social security. Based on the results of this study, the researcher proposed various projects, activities and programs to empower the small-scale construction industry workers.

Read full-textOnline grammar checker and plagiarism detectors are online tools or software which allows users to correct their works providing a reliable basis of right grammar and avoid plagiarism. This study used a descriptive research method to assess the perspectives of selected university instructors and professors handling research and writing courses on the effectiveness of online tools. Results showed that using online tool or software such as grammar checkers and plagiarism detectors can improve students’ research and writing skills, style, and vocabulary skills. This result is also supported by several studies that using online grammar checkers and plagiarism detectors is effective and avoid copy and paste attitudes and behavior of the students. Aside from these, software  has been found better and time efficient for an easy checking to grammar corrections and easy tracking of plagiarism since they are just one click away compare to manual checking that demands more time and efforts of the researcher and of the research and writing instructors and professors.

Read full-textThe present study is focused on the use of activated carbon derived from water hyacinth (WH-AC) as adsorbent for the removal of Cr(VI) from aqueous solution. The optimized WH-AC was found to be mesoporous and considered as granular. The surface area of 11.564 m 2 /g was found to have a good adsorption capacity. The adsorption data of the optimized WH-AC followed a pseudo-second order kinetics and the Freundlich isotherm model. Based on the correlation coefficient obtained from pseudo-second-order kinetic model, the R 2 values were all above 0.99, which is closer to unity of one (1) indicating that it followed a chemisorption process. The adsorption capacity of WH-AC increased from 1.98 to 4.68 mg/g when adsorbate concentration increased from 20 to 50 mg/l. The overall study proved that the adsorption by activated carbon derived from water hyacinth can be an alternative and efficient technique in hexavalent chromium removal.

Read full-textThis paper presents a new mathematical feedback model to demonstrate how direct     observations of the epidemiological compartments of population could be mapped to     inputs, such that the social spread of the disease is asymptotically subdued. Details of the     stabilization and robustness are included. This is a pivotal restructuring of modelling the     control of corona virus from the current models in use world-wide which do not utilize     feedback of functions of epidemiological compartments of population to construct the     inputs. Although several vaccines have received Emergency Use Authorization (EUA) massive vaccination would take several years to reach herd immunity in most countries.     Furthermore, the period of efficacy of the vaccination may be approximately one year     only resulting in an unending vaccination. Even during the vaccination, there would be an     urgent need to control the spread of the virus. When herd immunity is reached and vaccination is discontinued, there would be new surges of the disease. These surges of disease are not possible in appropriately designed stable feedback models. However,     extensive testing, contact tracing, and medical treatment of those found infected, must be maintained.

Read full-textThe tropical red seaweed Halymenia durvillei Bory de Saint Vincent is a potential source of the high-value pigments, r-phycoerythrin (RPE) and r-phycocyanin (RPC). The unique properties of these pigments find many applications—from food to cosmetics, pharmaceuticals, and biomedical research. This study aimed to improve the land-based culture technology of H. durvillei by determining the appropriate combination of light, water motion, and stocking density that would result in high growth performance and high RPE and RPC content. Combinations of two light levels (full light, 100% and reduced light, 67%), two water motion levels (low and moderate), and three stocking densities (50, 100, and 200 g) were studied using 64.5-L glass tanks with flow-through seawater and aeration systems at the outdoor land-based seaweed nursery in Bolinao, Pangasinan, Philippines. After 6 weeks of culture, growth performance (growth rate and productivity) of H. durvillei and phycobiliprotein content were best under a combination of full light (100%), moderate water motion, and 50-g stocking density. The generally low light regime during the experimental period, which coincided with the cold, dry season characterized by shorter days and overcast skies, may have influenced H. durvillei to maximize the use of available resources such as light and possible increased nutrient availability from water motion to promote growth and phycobiliprotein synthesis. Treatments with low stocking densities generally showed satisfactory growth and phycobiliprotein content. This study provides groundwork for future research into mechanisms by which these and other factors affect the growth and physiology of H. durvillei in culture.

Read full-textBopyrid isopods and rhizocephalan barnacles are obligate parasite crustaceans which harm their decapod hosts. However, to the best of our knowledge, studies have not compared which of these parasites has a greater parasitic effect on its hosts. Here, the parasitic effect of the bopyrid isopod, Allokepon hendersoni, and an unidentified sacculinid rhizocephalan species, infesting the same population of portunid crabs, Charybdis bimaculata, was investigated and compared for the first time. Samples were collected from the bycatch of a trawl fishery in Tosa Bay, Japan. A total of 2601 crabs were collected, of which 14 (0.55%) were parasitized by the bopyrid and 21 (0.82%) by the rhizocephalan. One of the two female crabs parasitized by the bopyrid was ovigerous (with much fewer eggs than unparasitized females). No ovigerous crab was found from the eight females parasitized by the rhizocephalan. Because only two female crabs were parasitized by the bopyrid, the following analyses were made using the male crabs. Both parasites reduced the wet weight (crab condition) and the cheliped size (secondary growth) of C. bimaculata, but the impact of the parasitism did not differ between the parasite species. The size of the abdominal flap of male hosts was reduced by the bopyrid infestation; however, rhizocephalan infestation caused enlargement of the abdominal flap, which is an indication of feminization. The present study provides information on how the effect of these two parasitic castrators on the same host crab varies. A moderate decrease in crab condition and cheliped development was common among the parasites, suggesting that the degree of impact might be favorable for the survival of the two parasites species.

Read full-textDeep learning and computer vision revolutionized a new method to automate medical image diagnosis. However, to achieve reliable and state-of-the-art performance, vision-based models require high computing costs and robust datasets. Moreover, even with the conventional training methods, large vision-based models still involve lengthy epochs and costly disk consumptions that can entail difficulty during deployment due to the absence of high-end infrastructures. Therefore, this method modified the training approach on a vision-based model through layer truncation, partial layer freezing, and feature fusion. The proposed method was employed on a Densely Connected Convolutional Neural Network (CNN), the DenseNet model, to diagnose whether a Chest X-Ray (CXR) is well, has Pneumonia, or has COVID-19. From the results, the performance to parameter size ratio highlighted this method’s effectiveness to train a DenseNet model with fewer parameters compared to traditionally trained state-of-the-art Deep CNN (DCNN) models, yet yield promising results. •This novel method significantly reduced the model’s parameter size without sacrificing much of its classification performance. •The proposed method had better performance against some state-of-the-art Deep Convolutional Neural Network (DCNN) models that diagnosed samples of CXRs with COVID-19. •The proposed method delivered a conveniently scalable, reproducible, and deployable DCNN model for most low-end devices.

Read full-textA clear understanding of genetic diversity in weedy rice is important in improving protocols for its conservation, ex situ. To this end, Asian collections of weedy rice together with accessions of the wild AA genome species Oryza nivara Sharma et Shastry and Oryza rufipogon Griffith, and the cultivated species, Oryza sativa L. were characterized using 16 qualitative morphological traits and 18 SSR markers. The Shannon-Weaver index (H’) revealed higher diversity in the weedy (mean H’=0.76) and wild O. nivara (mean H’=0.79) and O. rufipogon samples (mean H’=0.69) compared to the cultivated samples (mean H’=0.44). The weedy forms showed high preference (> 90%) for high grain shattering and awn presence. Cluster analysis based on the 16 qualitative characters revealed three major clusters where weedy accessions grouped with (1) O. rufipogon samples (2) O. nivara, O. rufipogon and indica rice samples, and (3) indica, aus, and japonica and a few O. nivara and O. rufipogon rice samples. Cluster analysis based on SSR data concurred largely with morphological analysis. Genetic diversity indices (number of alleles, observed heterozygosity, expected heterozygosity) detected by SSR markers were comparable between the weedy (1.64, 0.11, 0.23) and O. rufipogon (1.53, 0.19, 0.23) samples. Analysis of molecular variance (AMOVA) among the weedy forms revealed that differentiation within accessions accounted for 79% of the total variation.

Read full-textThis study developed a teaching and learning material (TLM) in chemistry entitled eS2MART TLM with integrated augmented reality (AR) technology and assessed its effect on students’ learning gains in terms of spatial skills and students’ learning experience on the use of augmented reality as a tool in understanding atomic theory, chemical bonding and molecular structure. The present study utilized the designbased research paradigm in the development of AR-enabled teaching and learning material while the assessment of the effect of using the developed material on learners’ spatial skills employed the pre-experimental single group design. To provide the AR component of the TLM, this study also developed a database of intuitive virtual 3D objects and animations that can be manipulated by tactile controls, a dedicated mobile application software called eS2MART TLM designed to retrieve, display, and control the virtual 3D objects, and unique trackers for each virtual 3D object and animation that were printed in eS2MART TLM. A pretest and posttest using standardized spatial skills test composed of seven Purdue Spatial Visualization Test (PSVT) items and 13 content-specific items were administered to the participants before and after using eS2MART TLM. Using Hake factor test, the study found out that student participants obtained a normalized gain value of 0.50 on the PSVT spatial visualization test which signifies medium learning gain and a normalized gain value of 0.76 on the Content Specific Spatial Skills Test which indicates high learning gain. In terms of learning experience, there is an overall very positive learning experience on the use of augmented reality in eS2MART TLM as implied by the composite mean value of 3.54.

Read full-textAndrographolide is a crystalline diterpene lactone present in Andrographis paniculata (AP) and considered as the most active and important secondary metabolite component of the said plant. Few reports are available regarding the biological activities of the AP plant extract. In this study andrographolide content of ethanolic extracts of different AP plant parts was determined using UPLC. COX-2 inhibitory activity and anti-oxidant property of the extract with highest andrographolide content were examined. COX-2 inhibition assay was done using fluorometric method and antioxidant activity was determined using DPPH method. Highest content of andrographolide was observed in the ethanolic leaf extract. 150, 300, 600, 800 and 1000 µg/ml of ethanolic leaf extract of AP exhibited above 50% COX-2 inhibition and statistical analysis showed significant difference between their respective percent inhibition and the negative control. On the other hand, 100, 500, 750, 1000 and 2000 µg/ml of ethanolic leaf extract of AP exhibited 12.23, 27.08, 40.9, 46.85 and 51.67 % DPPH scavenging activity.

Read full-textLanguage acquisition takes a long process as it requires initiatives upon having set good attitudes and positive learning behavior. It is said that learning the language particularly English would not be automatic or innate, even born native speakers should have to learn the basic rules through varied skills and strategies to be communicative competent. This descriptive research aimed to improve a language instructional portfolio which includes a Purposive Communication course syllabus with specifications and module outline/course outline for higher education curricula. A self-constructed survey questionnaire was used to gather the data from 223 respondents considering the conflict of interests and ethical considerations. Results show that the graduates have moderately acquired communicative skills primarily in speaking and writing; they highly acquired reading, listening, and presentation/multimedia skills. It implies that graduates are more likely to perform their work in the company when they have learned the communicative skills based on the given tasks or work, but they are more likely to apply good attitudes and positive working behavior to integrate new things for productive, innovative, and transformative, and sustainable outcomes and output which are beneficial to the company’s targets and accomplishments. Based on the results, an action plan and strategies to create a language instructional portfolio have been proposed.  Since this study concentrated on acquired communicative skills of BSBA and BSHRM   graduates, future studies may consider other degree graduates of internal and external higher educational institutions in the Philippines.

Read full-textFluctuations in photosynthetic characteristics related to chloroplast translocation have been known in the foliose green algae Ulva lactuca var. latissima and var. rigida and U. mutabilis according to photoperiod. In Ulva, the single parietal chloroplast is found along the thallus surface wall and exhibits high photosynthetic activity during the light period; the chloroplast then moves towards the side walls and shows low photosynthetic activity during the dark period. In this study, we re‐investigated whether the photosynthetic ability is dependent on chloroplast location in a unialgal culture of U. conglobata grown and maintained under low irradiance regime by analyzing in vivo absorption spectra, oxygen evolution, and pulse amplitude modulation chlorophyll fluorescence. The light absorbance of the thallus was higher when chloroplast was along the thallus surface wall than when along the side walls and, therefore, the thallus absorbance at 680 nm was used as an index of chloroplast position. Some photosynthetic parameters such as maximum net photosynthetic rate per thallus area and effective quantum yield of photosystem II correlated with the thallus absorbance. The highest rate and absorption were reached 3 to 9 h after the beginning of light period (12 h), which gradually decreased at 10 to 15 h after the beginning of light or dark period. The lowest rate and absorption was observed after the beginning of the dark period. The chloroplast movement was inhibited for 12 h by colchicine, and photosynthetic parameters were found to be dependent on various chloroplast positions in the thallus. Interestingly, the chloroplast moved to the surface and the side walls of the cells under continuous dark and light conditions, respectively, at approximately 48 to 72 h. However, decreased maximum quantum yield was observed under both continuous dark and light conditions.

Read full-textFluctuations in photosynthetic characteristics related to chloroplast translocation have been known in the foliose green algae Ulva lactuca var. latissima and var. rigida and U. mutabilis according to photoperiod. In Ulva, the single parietal chloroplast is found along the thallus surface wall and exhibits high photosynthetic activity during the light period; the chloroplast then moves towards the side walls and shows low photosynthetic activity during the dark period. In this study, we re‐investigated whether the photosynthetic ability is dependent on chloroplast location in a unialgal culture of U. conglobata grown and maintained under low irradiance regime by analyzing in vivo absorption spectra, oxygen evolution, and pulse amplitude modulation chlorophyll fluorescence. The light absorbance of the thallus was higher when chloroplast was along the thallus surface wall than when along the side walls and, therefore, the thallus absorbance at 680 nm was used as an index of chloroplast position. Some photosynthetic parameters such as maximum net photosynthetic rate per thallus area and effective quantum yield of photosystem II correlated with the thallus absorbance. The highest rate and absorption were reached 3 to 9 h after the beginning of light period (12 h), which gradually decreased at 10 to 15 h after the beginning of light or dark period. The lowest rate and absorption was observed after the beginning of the dark period. The chloroplast movement was inhibited for 12 h by colchicine, and photosynthetic parameters were found to be dependent on various chloroplast positions in the thallus. Interestingly, the chloroplast moved to the surface and the side walls of the cells under continuous dark and light conditions, respectively, at approximately 48 to 72 h. However, decreased maximum quantum yield was observed under both continuous dark and light conditions.

Read full-textThe Philippines is said to be the center of the center of marine shore fish biodiversity because of a key marine biodiversity corridor known as the Verde Island Passage (VIP), which has up to 1,736 species over a 10 x 10 km area (Carpenter and Springer 2005). The VIP harbors a vast and rich marine life and is home to numerous other marine organisms, from microorganisms to seagrass, seaweeds, mangroves, corals and other invertebrates, cetaceans, and other species of fish, many of which have yet to be documented. Given the known threats on the marine environment, studies have noted that the VIP is an area of extremely high priority for biodiversity conservation (PAWB 2009; Asaad et al. 2018).

Read full-textThe precipitation and deposition of asphaltene are among the most pressing issues in the petroleum industry. This flow assurance issue may cause formation damage in porous media, the plugging and coking of wellbore and production pipelines and difficulties in the refining process. Such events lead to additional operating costs for the company. Therefore, knowing the conditions at which asphaltene may precipitate and can be stable may enable its management. Existing models of asphaltene stability are dependent on saturate, aromatic, resin and asphaltene (SARA) analysis which can be very expensive and time-consuming. Therefore, using regression analysis as well as MATLAB curve fitting and optimization procedures, a new set of correlations and asphaltene stability envelops (ASEs) based on the newly developed Density-Based Asphaltene/Resin ratio (DBAR), Density-Based Saturate/Aromatic ratio (DBSAr) and the Density-Based Colloidal Instability Index (DBCII) all as functions of oil density have been developed to consistently predict asphaltene stability. The new criteria were developed for the correlations by considering the boundary between stable and unstable regions in their respective plots. The new envelopes showed 83% reliability by accurately predicting asphaltene stability in 24 (eight stable and 16 unstable samples) out of 29 (13 stable and 16 unstable) crude oil samples. The correlation coefficient (R²), average absolute relative error (AARE) and the root-mean-square error (RMSE) for the new DBAR correlation are 0.9456, 0.8357 and 0.1219 respectively. The DBSAr correlation resulted in R² of 0.9437, AARE of 0.1860 and RMSE of 0.2450. For the DBCII, the statistical indicator results were 0.9639 for the R², 0.1376 for the RMSE and 0.1038 for the AARE.

Read full-textGraphs considered in this paper are  finite simple undirected graphs. Let G = (V(G), E(G)) be a graph with E(G) = {e₁, e₂,…, e_m}, for some positive integer m. The edge space of G, denoted by ℰ(G), is a vector space over the  field ℤ₂. The elements of ℰ(G) are all the  subsets of E(G). Vector addition is defined as X+Y = X ∆ Y, the symmetric difference of sets X and Y, for X,Y ∈ ℰ(G). Scalar multiplication is defined as 1.X =X and 0.X = ∅ for X ∈  ℰ(G). Let H be a subgraph of G. The uniform set of H with respect to G, denoted by E_H(G), is the set of all elements of ℰ(G) that induces a subgraph isomorphic to H. The subspace of ℰ(G) generated by  ℰ_H(G) shall be denoted by ℰ_H(G). If E_H(G) is a generating set, that is ℰ_H(G)= ℰ(G), then H is called a generator subgraph of G. This study determines the dimension of subspace generated by the set of all subsets of E(G) with even cardinality and   the subspace generated by the set of all k-subsets of E(G), for some positive integer k, 1 ≤ k ≤ m. Moreover, this paper  determines all the generator subgraphs of star graphs. Furthermore, it gives a characterization  for a graph G so that star is a generator subgraph of G.

Read full-textThis quantitative investigation involved 1,054 pre-service teacher respondents with mean age = 20.21 (SD=3.124). This cross-sectional study gathered data through the creation of four research tools validated and pilot tested. Moreover, three of the research tools (AChavQ, AFilQ, AEngQ) were determined to be of ‘excellent’ reliability, while one is of ‘acceptable’ reliability (CogMT). The study purposed to derive a model explaining the possible relationships, mediation or moderation, of language attitude towards Chavacano with ethnicity, cognition towards mother tongue and language attitude toward Filipino and English. The model with “best fit” showed that that language attitude toward Filipino and English has a direct influence on cognition in the mother tongue. In contrast, ethnicity’s influence is mediated by language attitude toward Chavacano. The results and findings of this study provide vital information and insights in the implementation of the Mother Tongue Based Education in a multilingual context.

Read full-textIn this study, the authors design and implement a real time system as an autonomous robot–camera to capture many targets in the scene. The robot has only one camera, but it is capable of capturing more than one moving object through proper movement. The system uses Gaussian Filtering for motion detection and then performs partitioning to grab location of all targets in the scene. Due to partitioning, the scene has three major regions while each of which has different sub-regions. Based on the partitioning and position of all targets, the system might be in three states of unsafe state, safe state, and over-safe state. In each state regarding specific regions or sub-regions, the system picks appropriate movement not only to be capable of capturing all moving objects, but also to give equal chance of capturing to new targets entering to the scene from different direction. The system is tested in both of indoor and outdoor with different values for different parameters such as resolutions, fps (frame-per-second), minimum number of motion frames, and minimum areas of motion.

Read full-textRust is a severe disease affecting many productive coffee regions. It is caused by a pathogenic fungi that attacks the underside of coffee leaves and it is characterized by the presence of yellow-orange and powdery points. If not treated, rust can cause a drop in coffee production of up to 45%. In this sense, this paper presents a contribution to the problem of rust identification that doesn’t use “handcrafted” features, i.e., features extracted according to rules established by human programmers. Instead, we propose to train a Convolutional Neural Network (CNN) to learn to identify rust infection. We evaluated our CNN in a set of images provided by an expert and comparison results show that our approach is able to to detect the infection with a high precision, as corroborated by the high Dice coefficient obtained.

Read full-textAlkali precipitation and electrodeposition for copper removal in artisanal gold smelting wastewater were evaluated. Alkali precipitation was done by dosing the wastewater with sodium hydroxide solution. Electrodeposition was conducted at operating current of 4 A for 3 and 6 h. Alkali treatment exhibited 99.83% removal efficiency, albeit residual copper concentration was still above effluent standards. Lead was no longer detected at pH ≥ 6 while 87.82% of cadmium was removed at maximum precipitation. Generation of metal hydroxide sludge implied the need for a post-treatment. Electrodeposition demonstrated superior copper removal efficiency (99.98%). With appropriate selection of electrolysis time, it could achieve complete copper removal. It had low energy consumption and indicative treatment cost, and no sludge generation. Electrodeposition current efficiency was relatively low due to other wastewater components which could have consumed a fraction of applied current. Kinetics evaluation showed a three-step electrodeposition process, each stage having a different reaction rate order. Lead was not detected after 3 h while 50.00% of cadmium was removed after 6-h electrolysis. Considering removal efficiency without sludge generation, indicative treatment cost, and value recovery of deposited copper, results revealed that electrodeposition has some advantage over alkali precipitation for the remediation of artisanal gold smelting wastewater.

Read full-textAs the power of Learning Management System (LMS) in supporting classroom instruction has been observed in Higher Education Institutions (HEIs), only a few have been documented in military environment using this kind of learning technology. This paper is the first attempt to investigate the satisfaction of air force officers on the use of Squadron Officer Course Virtual Classroom (SOCVC) in the Philippines. The popular Modular Object-Oriented Dynamic Learning Environment (MOODLE) was used to implement the SOCVC. A total of 47 Philippine Air Force (PAF) officers enrolled in the SOC participated in this study. Satisfaction in terms of learner interface, learning community, content and usefulness on the use of SOCVC was obtained using a questionnaire. The respondents are seen comfortable and satisfied with the use of the virtual classroom. Profile variables used in the study when taken separately do not show significant difference as to the respondents’ satisfaction on the use of the virtual classroom. The findings oppose what are already established pertaining to the variables that affect the use of Virtual Learning Environment (VLE) in civilian setting.

Read full-textEducators have the responsibility of assessing their students’ metacognitive knowledge and identifying students who may need support in developing effective metacognitive skills and providing them with necessary learning intervention. This study assessed the customs administration students’ metacognitive knowledge and its usage in their internships. The dimensions, declarative knowledge, procedural knowledge and conditional knowledge were correlated to academic performance. Differences in metacognitive knowledge and its dimensions and their usage were also ascertained. The study used the descriptive-survey method, using a questionnaire for data gathering. The respondents were 99 randomly selected final year BS in Customs Administration students. The results showed that the majority of students were female, had a capital city based internship and had an average academic performance. The majority of high performers had an internship in a capital city while the majority of low performers had an internship in their home city. Male and female students had similar performance while capital city internship students had better performance than home city internship students. The declarative, procedural and total metacognitive knowledge and their usage were high while conditional knowledge and its usage were very high. The dimensions of metacognitive knowledge and usage, except procedural knowledge had significant relationships with academic performance. Statistical differences in the responses of students when they were grouped according to sex were found only with procedural knowledge and its usage but when they were grouped by internship location – on declarative, procedural and total metacognitive knowledge usage; and when grouped by academic performance – on declarative knowledge, total metacognitive knowledge, conditional knowledge usage and total metacognitive knowledge usage.

Read full-textThis study documents the threats of the critically endangered Tectona philippinensis in the backdrop of the past conservation policies and projects. Twelve 20m x 50m plots were distributed in three altitudinal strata (S1= 50 – 100 m asl, S2= 150 to 200 m asl, and S3= 250 – 300 m asl) using stratified random sampling. Every tree was examined to detect presence of pest and diseases on foliage, stem, buttress and exposed root system. Threats of anomalous weather patterns like intense drought and human disturbances were also recorded. Leaf skeletonizers, shotholes, buttrot, heartrot, rootrot, illegal harvesting, charcoal making, wind damages, and intense dry season are among the most alarming threats of T. philippinensis. Germinants and wildlings are most susceptible to wilting during intense drought during dry season. A number of interesting species of arthropods and macrofungi within the stand were also encountered. There are variations on the incidence and infection across altitudinal habitat and across diameter classes. Poles and standards at lower altitudinal habitat (<100 m asl) are the most disturbed and susceptible to the disturbances. Existing conservation and protection policies should be strictly implemented especially in hotspot habitats.

Read full-textThe photosynthesis and growth of the commercially important and edible green seaweed, Monostroma latissimum (Ulvophyceae), from a naturally occurringpopulation in the intertidal area were examined in the laboratory. The natural population inhabits the middle to upper intertidal area and is usually attached to rocks. Cultivation occurs mostly in the shallow and calm waters under full light. The species was subjected to 10, 15, 20, or 25° C temperatures, and irradiances ranging from 0 to 1200 μmol photon m-2 s -1 in order to evaluate the effect of photosynthesis on growth. Photosynthesis was determined and characterized usingphotosynthesis-irradiance curves from oxygen production and PAM fluorometry. The maximum net photosynthetic rate (per thallus area and total chlorophyll content) did not differ significantly at the different temperatures, which were chosen to reflect the autumn to mid-springtemperatures that the seaweed would be expected to experience. The growth rates over 5 and 10 days were the same for all temperatures, but it decreased at 25° C after 15 days of culturing, which suggested that prolonged exposure to higher temperatures might have an adverse effect on growth. Similarly, the maximum quantum yield decreased as the temperature increased, which indicated that photosystem II (PSII) was under physiological stress at higher temperatures. In contrast, the light compensation point, saturating irradiance, initial slope, and PSII light absorption efficiency increased as the temperature rose. This suggested that the species optimized photosynthesis to accommodate the low and high light conditions that it may experience through its growing season. These conditions are characterized by irradiance limitation and low temperatures in winter, and higher irradiance and temperatures in spring. There were no photoinhibitory responses, which suggested that this alga was tolerant to higher irradiance. The growth rate significantly increased as irradiance rose, which may indicate higher growth rate responses at higher irradiances. Overall, the photosynthetic responses were in parallel with the growth rate response by M.latissimum. Therefore, the results from this study on M.latissimum, photosynthetic characteristics can be used to improve its cultivation.

Read full-textThis research paper aimed to determine the crop bearing capability of bitter melon or bitter gourd more commonly called “Ampalaya” in the Filipino language. Images of bitter melon leaves were gathered from Ampalaya farms and these were used as main data of the research. The leaves were classified as good and bad through their description. The research used Machine Learning Algorithm through Convolutional Neural Network. Training of data was through the capabilities of Keras, Tensor Flow and Python worked together. In conclusion, increasing number of images could enable a machine to learn the difference between a good and a bad Ampalaya plant when presented an image for prediction.

Read full-textMorphological and phylogenetic analysis showed that the Gambierdiscus isolate from Bolinao, Philippines belongs to the species of G. carpenteri. It was morphologically more similar to the Merimbula strain than the subtropical Florida Keys strain. Growth and toxin production were also investigated at varying levels of temperature, salinity, and irradiance. Gambierdiscus are known to grow favorably in a low light environment. However, this study showed high growth rates of G. carpenteri even at high irradiance levels. Generally, cells produced more toxins at lower treatment levels. Highest cellular toxin content recorded was 7.48 ± 0.49 pg Pbtx eq/cell at culture conditions of 25 °C, 100 μmol photons m^-2 s^-1, and salinity of 26. Growth rate and toxin production data suggest that cells produced more toxins during the slowest growth at certain range of treatments. This information gives insight into how changes in environmental conditions may affect toxin production and growth of G. carpenteri.