Objectives: This research aimed to perform proper land cover classification of the Krishnagiri and Dharmapuri districts using hyperspectral imagery. This research also attempted to determine the usefulness of deep learning models in detecting large land cover classes, such as quarry, barren, forest, built-up, and agricultural land, using hyperspectral data. Method: The analysis of the land cover patterns in the study area used hyperspectral images (HSI) taken between 2015 and 2022, which had 230 spectral bands. A total of 3000 hyperspectral images were processed, 2000 of which were utilized for training and 1000 for testing. The images were then preprocessed, segmented into superpixels using superpixel segmentation, and classified. Three deep learning models, a Convolutional Neural Network (CNN), Residual Neural Network 18 (ResNet-18), and Visual Geometry Group-16 (VGG-16), were used as classification models. Standard measures of accuracy, precision, recall, and F-score were used to evaluate the performance and effectiveness of the models. Findings: The results indicate that ResNet-18 outperformed the other models in determining land cover classes using hyperspectral images. The model has shown high classification levels among various types of land, with built-up land having an accuracy level of approximately 95.09, indicating a better ability to extract and classify features than CNN and VGG-16. Novelty: The novelty of this study lies in the combination of hyperspectral image processing with deep learning frameworks to classify regions based on land cover. The relative analysis of CNN, ResNet-18, and VGG-16 demonstrated the usefulness of residual learning networks in enhancing the classification accuracy in hyperspectral remote sensing scenarios. Keywords: Hyperspectral Image (HSI), Land Cover Classification, Deep Learning, Convolutional Neural Network (CNN), ResNet-18, VGG-16, Remote Sensing, Spectral–Spatial Analysis
Objectives: This study aimed to compare the preservation efficacy of protein content and antioxidant activity in crude whole-body larval extract (WBE) products obtained from Lucilia sericata second instar (L2) larvae using four different drying protocols. Methods: The larvae were processed using lyophilisation (freeze-drying), infrared drying, microwave drying, and conventional oven drying methods. The antioxidant capacities (using the DPPH radical scavenging and FRAP iron reduction methods) and total protein concentrations (using the Bradford and Lowry methods) of the resulting extracts were then analysed. Statistical analyses (one-way ANOVA, Tukey HSD test, p<0.05) revealed that drying methods had a highly significant effect on the results. Findings: The highest antioxidant activity (DPPH: IC₅₀: 0.1350 ± 0.0242 mg/mL; FRAP: 45.4430 ± 0.0037 mg TE/g) and protein content (237.1 ± 0.0203 mg/ BSA eq/g dry weight (Bradford method), 164.8125 ± 0.0056 mg/BSA eq/g dry weight (Lowry method)) were observed in the lyophilisation group. This group was followed by infrared drying, which showed moderate values (DPPH: IC50: 0.2546 ± 0.0341 mg/mL; FRAP: 40.1603 ± 0.0082 mg TE/g; Protein: 173.0378 ± 0.0060 mg/BSA eq/g dry weight (Bradford method), 144.4583 ± 0.0065 mg/ BSA eq/g dry weight (Lowry method), followed by oven drying with significantly lower values, and microwave drying with the lowest bioactivity. The findings clearly demonstrate that the duration and intensity of thermal stress have a direct degradative effect on heat-sensitive proteins and antioxidant molecules in the larvae. Novelty: Drying methods have a significant effect on the bioactivity of larval secretions and have implications for Larva Debridement Therapy (LDT) applications. The stability of bioactive compounds responsible for wound healing and antimicrobial properties is critical in these products. By demonstrating that lyophilisation preserves up to four times more protein content and significantly higher antioxidant capacity than conventional thermal methods, this research offers a standardized, evidence-based protocol for the industrial-scale production of high-quality, bioactive larval extracts. This advancement directly addresses the critical bottleneck of biomolecule degradation during processing, paving the way for more stable, accessible, and potentially efficacious pharmaceutical products for LDT; confirmation of therapeutic efficacy will require further functional, antimicrobial, and in vivo assays. Keywords: Lucilia sericata, biotherapy, drying optimisation, thermal degradation, antioxidant capacity, protein stabilisation, lyophilisation, infrared drying
Objectives: Cloud data confidentiality is conventionally achieved by increasing the computational strength of a single encryption algorithm, which raises processing time and ciphertext size in proportion to security level. This study aims to achieve high confidentiality with low computational and storage overhead by decoupling security strength from a single heavy cipher and instead distributing protection across three lightweight, sequential transformations. Method: A three-layer framework is proposed, combining Word-based Magic Rectangle Alphanumeric Data Obfuscation (WMRADO), deterministic matrix-based scrambling, and single-key binary XOR encryption. Plaintext is first converted into a compressed alphanumeric code using a seed-driven 8×8 substitution square, then structurally disordered through positional matrix scrambling, and finally masked with a bitwise XOR operation before upload. The framework was implemented in Java (JDK 8) on an Intel Core i7-8700K workstation with 16 GB DDR4 RAM running Windows 10, and benchmarked against DES, AES, AES-Blowfish and AES-RSA using four plaintext sizes (112 B, 2,305 B, 7,894 B and 153,422 B) across ciphertext size, encryption time, decryption time and projected Google Cloud Storage cost. Findings: The proposed framework produced ciphertext 3–18 times smaller than DES/AES and up to 99.97% smaller than AES-RSA at the largest tested size (43 bytes versus 168,302 bytes). Encryption time was reduced by 9.5–52.0% relative to DES/AES and by up to 77.4% relative to AES-RSA, while decryption time was reduced by 3.0% relative to DES/AES and by up to 97.9% relative to AES-Blowfish/AES-RSA at large data sizes. Projected monthly storage cost fell by 99.5–99.8% relative to unencrypted storage and remained below both MRADO- and Moncrypt-obfuscated baselines at every tested file size. Novelty: The framework is the first to combine word-level alphanumeric obfuscation, matrix scrambling and XOR masking into a single seed/key pipeline evaluated jointly on security, latency and storage cost, rather than treating obfuscation and encryption, or security and efficiency, as separate design problems. Keywords: cloud computing, cloud storage security, data confidentiality, data obfuscation, layered encryption, WMRADO, matrix scrambling, XOR encryption
Objectives: To optimize Direct Metal Laser Sintering (DMLS) parameters for Co-Cr alloys to simultaneously enhance microhardness and compressive strength using multi-objective Grey Relational Analysis (GRA). Method: Co-Cr alloy specimens were fabricated using DMLS by varying laser power, scan speed, and hatch spacing based on a Taguchi L9 orthogonal array. Microhardness and compressive strength were measured as performance responses. Signal-to-noise ratios were calculated and normalized for GRA to obtain the grey relational grade (GRG). ANOVA was performed to determine the significance of process parameters, and confirmation experiments were conducted to validate the optimal parameter combination. Findings: The multi-objective optimization results showed that laser power was the most influential parameter with a contribution of 76.21%, followed by hatch spacing (11.17%) and scan speed (10.37%). The optimal parameter combination was identified as 200 W laser power, 1200 mm/s scan speed, and 0.10 mm hatch spacing. Under these conditions, the Co-Cr alloy exhibited a microhardness of 438 HV and compressive strength of 827 MPa with a GRG of 0.961. The confirmation experiment showed a very low error of 0.3%, confirming the accuracy of the optimization model. Compared to the initial experimental run, the optimized parameters significantly improved both hardness and compressive strength due to improved densification and melt pool stability. The results are consistent with previous studies that reported improved mechanical properties at optimal energy density; however, this study uniquely demonstrates simultaneous optimization using GRA for DMLS-fabricated Co-Cr alloys. Novelty: This study presents multi-objective optimization of DMLS-fabricated Co-Cr alloys using Taguchi-GRA with experimental validation for simultaneous improvement of hardness and compressive strength. Keywords: Direct Metal Laser Sintering, Co-Cr alloy, Multi-objective optimization, Grey Relational Analysis, Mechanical properties
Objectives: To characterise the microbial diversity associated with spoiled fruits and vegetables and to design a multifunctional biocomposite coating based on chitosan, cellulose and green tea extract for postharvest preservation and shelf-life extension. Method: Spoiled samples of Tomato, Cucumber, banana, and Grape were gathered from Sulur Market, Coimbatore, Tamil Nadu, India during the winter season (December 2025). Microbial diversity was determined by serial dilution, selective culture medium, Gram staining and biochemical assays. Chitosan was isolated from the waste of prawn shells and characterised by FTIR spectroscopy. Antioxidant activity was assessed by DPPH assay. Antibacterial activity was evaluated by agar well diffusion method. Fresh fruits and vegetables were coated with a chitosan–cellulose (coir pith)–greentea extract biocomposite and compared with uncoated controls. Findings: Spoilage bacteria consisted of Bacillus, Staphylococcus, Pseudomonas, Enterobacter, Klebsiella, Escherichia coli, Salmonella, Vibrio and Enterococcus spp. The FTIR analysis showed successful chitosan extraction with typical peaks at 3348.78, 2919.70, and 1635.34 cm-1. Chitosan displayed concentration-dependent antioxidant activity with DPPH radical scavenging of 18.18 ± 1.29% to 90.91 ± 1.29% (100–1000 μg/mL). The results showed antibacterial activity with inhibition zones of 22.0 ± 0.6 mm against Staphylococcus aureus and 21.0 ± 0.7 mm against Escherichia coli. The biocomposite covering enhanced shelf life of grapes from 5-12 days, bananas from 6-14 days, tomatoes from 10-18 days and cucumbers from 7-13 days by cutting moisture loss, delaying ripening, retaining firmness and minimising microbiological deterioration. Novelty: The research provides a multifunctional biodegradable chitosan–cellulose–green tea extract coating with antibacterial, antioxidant, and barrier capabilities. Unlike previous studies that have been mainly conducted on chitosan alone or on a single fruit commodity, the present study couples microbial profiling with the effective use of a composite coating on multiple fruits and vegetables, showing sustainable alternative to synthetic preservatives and packaging materials. Keywords: Chitosan, Microbial diversity, Cellulose, Green tea extract, Postharvest, preservation
Objectives: This study aimed to assess the levels and spatial distribution of heavy metals in road dust of various land-use areas of Aizawl and Lawngtlai, Mizoram and to assess the human health risk associated with the heavy metals. Method: Road dust samples were taken from agricultural, industrial, commercial, residential and solid waste disposal areas. Samples were digested using USEPA Method 3050B and analysed for Cr, Pb, Ni, Cu, Zn and Cd using atomic absorption spectroscopy. GIS was employed to investigate land-use and topographic factors, and the USEPA model was used to evaluate health risks to adults and children. Findings: The results showed that Zn was the predominant metal with mean concentration of 72.75 mg/kg in Aizawl and 60.60 mg/kg in Lawngtlai. The highest concentrations of Zn were 119.84 mg/kg and 80.30 mg/kg, respectively. Compared with Indian natural soil background values, Pb, Ni and Zn were enriched by 94.66%, 41.47% and 228.54% in Aizawl and 29.21%, 20.56% and 174.69% in Lawngtlai. There was no significant difference (p > 0.05) in metal concentrations between districts. Ingestion was the most important exposure pathway. The hazard indices were 0.070 and 0.218 for adults and children in Aizawl, respectively, and 0.035 and 0.176 in Lawngtlai, respectively, which are less than 1. Novelty: This study provides a baseline assessment of the integration of heavy metal concentration, land use, topography and human-health risk in two contrasting mountainous urban areas of Mizoram. Keywords: Heavy metals, road dust, land use, topography, human health risk assessment
Objectives: To compare the outcomes of co-precipitation and hydrothermal green synthesis of zirconium oxide (ZrO2) nanoparticles prepared with Moringa oleifera leaf extract as a natural stabilizing and reducing agent. Method: M. oleifera leaf extract was used to create zirconium oxide nanoparticles from zirconium nitrate using hydrothermal and co-precipitation methods. X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscopy (FESEM), and energy-dispersive X-ray spectroscopy (EDS) were used to characterize the synthesized materials in order to analyze their morphology, elemental composition, crystal structure, and functional groups. Cyclic voltammetry (CV), galvanostatic charge-discharge (GCD), and electrochemical impedance spectroscopy (EIS) were used to examine electrochemical performance. Findings: The hydrothermal synthesized electrode produces a specific capacitance of 222 Fg⁻¹, which was around 34.5% higher than the co-precipitation sample 165 Fg⁻¹. Novelty: This work suggests an efficient and ecologically sustainable method for creating high-performance electrode materials based on zirconium oxide of energy storage devices. Keywords: Zirconium oxide, Moringa oleifera extract, Hydrothermal synthesis, Co-precipitation, Supercapacitor
Objectives: To develop a reliable multi-vehicle detection-based tracking framework under unregulated traffic scenario using enhanced vision transformer model that can precisely localize and track the vehicles. Method: The proposed framework involves a three-level enhancements, (1) Adaptive frame slicing technique in the input data loader to capture the discriminative features of near and far away vehicles in the video frame (2) Geometric positional encoding to provide clear spatial cues and fusion of features, allowing the model to better separate and detect multi-scale vehicles (3) Dual inferencing of sliced and full frame with DIoU-NMS post-processing technique to remove redundant detections. The proposed framework is evaluated on our custom developed Active Learning based vehicle dataset, “AU-INV-P-PALS”, and two public traffic datasets- “IITM-HeTra and Udacity-Fixed small”. Findings: In comparison with the existing baseline DETR model, the proposed model with SGD optimization showed 9.2% increase in the average precision for IoU = 0.5:0.95 indicating its precise localization ability. The model localizes vehicles that occupy as little as 0.2% of the surveillance video frame area. The observed tracking accuracy on real-time traffic CCTV videos indicates that the model can adapt to unregulated traffic environment and achieved appreciable score of about 85.4%, maintaining the tracking stability. The average F1-score of the enhanced DETR obtained for the test frames indicate the generalization ability of the model across different frames. Novelty: The proposed work introduces adaptive frame slicing scheme in the input data loader and geometric positional encoding which enables the detection of faraway vehicles with high accuracy in wide area surveillance imagery. Further, the enhanced model is trained using active learning based custom developed vehicle dataset that significantly reduces the reliance on massive datasets. Keywords: Deep Learning, Active Learning, Unregulated Road Traffic Control, Vision Transformer
Objectives: This study critically reviews the evolution, performance, and current status of Earth Air Heat Exchanger (EAHE) technology for sustainable heating and cooling of buildings in India. It further tries to identify key design parameters, climatic suitability, current status of research, existing technological limitations, and future research directions of EAHE technology. Method: A comprehensive literature survey of peer-reviewed EAHE studies conducted in India was carried out. The reviewed studies were analysed with respect to thermal performance, coefficient of performance (COP), design parameters, climatic suitability, and integration with complementary technologies, covering experimental, numerical, CFD, optimization, and hybrid investigations. Findings: The EAHE systems make the best use of ground temperatures to precondition ventilation air and can thus be considered a sustainable replacement of traditional air-conditioning systems. Various studies carried out in different climatic regions of India found that cooling in summers was between 8 and 22 ∘𝐶 while winter heating was between 4 and 8 ∘𝐶, with COP figures ranging between 1.9 and 7.9. The hybrid EAHE systems using evaporative cooling systems resulted in the reduction of pipe lengths by up to 93.5% while EAHE-photovoltaics in adobe houses yielded energy savings of 4,183–10,321 kWh and reduction of CO2 emissions of 7–16 tonnes annually. Hot-dry and composite climate zones have been recognized as the most suitable regions for application of EAHE systems. Novelty: This review presents a comprehensive synthesis of EAHE research in India by integrating experimental, numerical, CFD, and hybrid investigations. It uniquely combines a chronological mapping of technological development with a comprehensive assessment of the current status of EAHE research in India while identifying critical technological bottlenecks. Conclusions: EAHE technology is a promising passive heating and cooling strategy for sustainable buildings in India but needs further technological development and increased awareness through demonstration projects for wider acceptance. Keywords: Earth Air Heat Exchanger; Hybrid System; CFD; Thermal Performance; Passive Cooling; India
Objectives: This review focuses on examining recent trends in automated sorting and inventory management systems which directly contributes to the industry 4.0 and smart manufacturing. Additionally, it also evaluates the operational efficiency, cost effectiveness and challenges of the next generation warehouse technologies. Method: A structured, PRISMA-informed literature review was conducted across IEEE Xplore, ScienceDirect, SpringerLink, MDPI, and Taylor & Francis Online, supplemented by Google Scholar, using defined inclusion/exclusion criteria (see Section 2). The focus was kept on research papers into the fields like Microcontroller based architecture, Internet of Things (IoT), Computer vision-based sorting systems and smart warehouse automated systems. Findings: Various automation technologies were analyzed which reveals that the accuracy rate in Arduino based system goes to 90-100% while costs reduces by 60-80% compared to industrial setups. Where IoT driven inventory system shows accuracy improvement upto 25-35% where 20-30% reduction in carrying costs and also 35-45% decrease in out-of-stock items. Computer vision-based sorting systems achieved a 95–99% classification accuracy with rapid processing speeds ranging from 0.05 to 100 ms per item. Novelty: In earlier research studies these technologies have been studied and evaluated thoroughly, but this paper adds a coherent framework that combines all these technologies together for automated sorting and inventory systems in the view of Industry 4.0. It has been evaluated by identifying the critical success factors, realistic limitations and clear future research pathways. Keywords: Automated sorting, Inventory management, Industry 4.0, IoT integration, Computer vision, Smart manufacturing, Arduino microcontrollers, Warehouse automation
Objectives: This study is aimed to determine the stress-related issues and coping mechanisms among university students in Northern Iloilo, Philippines. Method: This study employed a descriptive survey to ensure the quality of the results and included interviews, observations, and focus group discussions (FGD). The instrument was researcher-made and composed of two parts: Part A on stress level and Part B on coping level, as well as open-ended interview questionnaires. The topics on the questionnaires were all related to HyFlex Learning. The participants were 340 university students enrolled at Northern Iloilo State University (NISU), Iloilo, Philippines, during the academic year (AY) 2022-2023. One section from 16 courses was included in this study as a participant. The statistical tools were the mean and standard deviation. The qualitative parts were coded, themed, and triangulated. Findings: Almost all programs experienced severe stress, but majoring in Agriculture, Tourism, and Electricity experienced moderate stress. In terms of coping mechanisms, many programs showed average coping; the rest showed poor coping. The responses showed that university students' stress levels were widely distributed. Similarly, not all students were ready and able to cope with the educational policy. Not all of them were stressed during this post-pandemic period, but they were stressed when the pandemic started. The replies indicate that students are ready for full-capacity face-to-face instruction; they are very excited. The qualitative findings showed that major stress-related issues were associated with online learning. Gadgets and connectivity are essential during e-learning. Novelty/Recommendations: This study made an emphasis on the necessity to support and protect students' well-being in the event of casualties. The academic community should design interventions to support responses to events such as pandemics and natural and artificial calamities. Keywords: New Normal, Hybrid Learning, Flexible Learning, Stress-Related, Coping Mechanisms, University Students
Objectives: This study focuses on developing a hybrid detection model that can improve document element like signature detection, by combining singlestage and two-stage object detection architectures. The objective is to improve the detection of objects with higher processing efficiency. Method: YOLOv8n is used for fast and real-time detection, and Faster R-CNN is used for accurate classification. Post-processing method: The approach involves the combination of predictions with their respective confidence scores and IoU values. Findings: The overall model is more accurate with average precision of 100.00%, recall of 92.15%, F1-score of 95.74%, IOU of 76.29%, and Dice co-efficient of 83.77% than the individual models, especially for challenging document categories. Novelty: This work introduces a new custom dataset and the presentation of a hybrid detection model that combines YOLOv8n and Faster R-CNN for improved document element detection. In this proposed system, instead of relying on either a speed-optimized or accuracy-optimized model, the proposed system combines fast initial detection with region-based refinement in a single model. The proposed system is particularly beneficial for semistructured document organization and can be applied to real-world signature detection problems. Keywords: Document Element Detection; YOLOv8n; Faster R-CNN; Hybrid Model; Signature Detection; Object Detection
Objectives: To present a viable preprocessing pipeline of dermoscopic images to maximize lesion appearance and minimise noise and artifacts to help in the diagnosis of skin cancer. Method: The 7-step Skin Cancer Image Pre-Processing (SCIP) pipeline has been created and tested on three benchmark dermoscopic databases, i.e., ISIC, PH2, and HAM10000, with over 11,000 images. The image preprocessing steps are image resizing, hair removal, artifact removal with the DullRazor algorithm that uses Canny edge detection and Telea inpainting, Gray World based color normalization, contrast enhancement through Contrast Limited Adaptive Histogram Equalization (CLAHE), noise reduction with a median filter, and Min-Max normalization to fit a deep learning model. Findings: As it was experimentally demonstrated, the proposed SCIP pipeline significantly enhanced lesion boundaries by approximately 27%. Additionally, it improved the contrast-to-noise ratio by 32% and reduced artifacts by more than 90%, the contrast-to-noise ratio by 32 percent and diminished the interference of hair and artifacts by more than 90 percent with respect to the raw dermoscopic images. Color normalization minimized variation in illumination across datasets by around 24 percent whereas median filter minimized the level of background noise by 18 percent with no impact on lesion structures. The pipeline reduced the classification error of deep learning models by 6.8%, 5.4%, and 4.9% on ISIC, HAM10000, and PH2 datasets, respectively, compared to standard preprocessing techniques. These findings indicate that the suggested method improves feature selection and facilitates quicker model convergence of automated skin cancer detection. Novelty: The proposed Skin Cancer Image Pre-Processing (SCIP) pipeline introduces a unified and adaptive preprocessing framework that integrates multiple complementary techniques, including Canny-based DullRazor hair removal, Gray World color normalization, CLAHE-based contrast enhancement, and median filtering, into a single standardized workflow. Unlike existing approaches that address individual preprocessing challenges in isolation, the SCIP framework simultaneously handles multiple artifacts while preserving lesion structure and ensuring cross-dataset consistency. The novelty lies in its system-level integration, cross-dataset evaluation, and quantitative validation demonstrating improved structural preservation, noise reduction, and artifact minimization across heterogeneous dermoscopic datasets. Keywords: Skin cancer, Image preprocessing, CLAHE, DullRazor, Normalization
Objectives: Diabetic Retinopathy (DR) is one of the diseases that has created significant impacts among diabetic patients. Data augmentation plays a vital role in increasing the variety of the data and minimizing the DR risk. Methods: To improve the variety of data in a data collection, augmentation entails applying alterations to datasets at random. Several augmentation techniques, such as flipping, rotation, lighting, and magnification, have been used to enhance the variability of each sample in the dataset. To achieve a better level of classification accuracy, we propose Transfer Learning based Diabetic Retinopathy Detection (TLDRD), which is used to extract the features from fundus images. Findings: Based on the evaluation, the proposed model achieves 95.1% accuracy for the IDRID dataset, 96.2% for the Dirat DB1 dataset, and 97.3% accuracy for the MESSIDOR-2 dataset, as well as against existing models. Novelty: The presented method is one of the image enhancement techniques that is applied to the dataset for better prediction results. This research work introduces a depth-wise Inception V3 model for achieving better performance in classifying the severity of the disease.Convolutions that are depth-wise separable are alternatives to traditional convolutions that are significantly faster to compute. Convolution is a costly process. The introduction of depth-wise separable convolutions has reduced the cost of such a procedure. Keywords: Deep CNN, Pre-trained models, VGG16, VGG19, Transfer Learning
Objectives: The main goal of the research is to create a framework that is geometry motivated and can find and localize hidden three-dimensional defects with severity level supervision. Method: A zero-shot learning system called SDF-ZSL-DeTex is introduced that operates on dual Signed Distance Field representations. Canonical Geometry SDF is a model of defect-free morphology, whereas Defect Texture Deviation SDF is a model of local geometry-texture discrepancies to canonical structure. The Latent Attribute Discovery module automatically detects defect prototypes by using samples of severity-labeled samples. Zero-shot alignment loss is a structured separation imposed on healthy morphology and defect caused deviations. The framework gives both severity prediction and voxel-level localization predictions based on single implicit geometric reasoning. Findings: Experimental analysis of the Mango, Muskmelon, and Pomegranate datasets reveal the defect detection accuracy of 94.6%, the severity classification Macro-F1 up to 86.9%, and localization IoU up to 81.4%. Further testing on publicly available Potato and Tomato data sets attains a classification accuracy of 98.92% and 98.40% respectively. Findings are consistent with high levels of cross-produce generalization and localization performance as opposed to newer voxel-based and point-based three dimensional anomaly detection models. Novelty: This is demonstrated by the integration of canonical geometry modeling, deviation-based implicit representation, latent attribute discovery, and zero-shot alignment learning in a single dual Signed Distance Field architecture, to allow severity-dependent defect reasoning without semantic defect annotations. Representation based on continuous geometry facilitates powerful detection of unexplored patterns of defects in a variety of fruit and vegetable groups. Keywords: 3D defect detection, Zero-shot learning, Signed distance field, Agricultural inspection, Geometry-based anomaly localization, Implicit surface representation
Objectives: This study explored the development and evaluation of Bacteria Chronicle: Invisible but Mighty as an innovative instructional material for teaching science, with particular emphasis on microbiology. Method: Utilizing a mixed-methods approach, descriptive data were gathered from science teachers within the Department of Education (DepEd) in the Municipality of Ajuy, Iloilo, Philippines, and pre-service teachers enrolled in the Bachelor of Secondary Education major in science program at Northern Iloilo State University (NISU), Iloilo, Philippines. Complementary qualitative data was collected through interviews, focus group discussions (FGD), and open-ended questionnaires. Findings: The results indicated that the comic strip, as evaluated by the science teachers, was satisfactory across all criteria, with a mean score of 2.12, whereas the pre-service teachers rated it very Satisfactory across almost all criteria, and the mean score ranged from 2.48 to 2.85, except for format, writing construction, and neatness. The findings indicate generational differences in receptivity to visual and narrative learning tools. For comparing the responses of the two groups, almost all categories were significant, indicating that the innovations are appealing and have the potential to become an effective instructional material. Responses to the qualitative parts also give both strengths and areas for improvement. To make it more appealing to learners, key points should include improvements in grammar, clearer titles, varied color intensity, and humor to further engage learners. From the previous title, Prolific Organisms Called Bacteria, it was changed to Bacteria Chronicle: Invisible but Mighty. Novelty: This study makes a novel contribution by demonstrating that comic strips can simultaneously enhance conceptual understanding and motivation in science learning through science communication and multimedia pedagogy. It further highlights the role of institutional support and the potential of integrating comics with digital technologies in higher education to be utilized in basic education curricula. Keywords: Comic strips, Science education, Microbiology, Instructional materials, Teacher and student engagement
Objectives: This study analyses the growth trends in area, production, and productivity of eleven major horticultural crops in Goalpara district, located in the lower Brahmaputra Valley of Assam, India, during 2006–07 to 2020–21. It also examines the relative contribution of area and productivity to production growth and evaluates the district’s status among major horticultural producers in the state. Method: The study adopts an analytical approach using the logarithmic method to estimate compound growth rates of area, production, and productivity. A decomposition model was applied to quantify the contributions of area effect, productivity effect, and their interaction using secondary data. Findings: The area under major crops such as banana, pineapple, papaya, and potato increased at growth rates of 5.44%, 7.19%, 5.17%, and 10.00%, respectively. Production growth was highest for papaya (14.07%), pineapple (11.52%), banana (10.52%), and potato (11.41%). However, productivity growth remained relatively low, with notable increases in papaya (5.01%), banana (2.80%), and pineapple (2.08%), while lemon recorded negative growth (-1.75%). Decomposition results show that area effect contributed more than 50% of production growth in most crops (e.g., banana: 51.71%, pineapple: 62.42%, tomato: 90.65%, potato: 87.61%). Novelty: This study provides novel district-level empirical evidence by applying a decomposition framework to horticultural growth in an aspirational district. It demonstrates the dominance of area expansion over productivity improvement and identifies crop-specific productivity gaps, offering policy-relevant insights for sustainable horticultural development. Keywords: Horticulture, Production, Productivity, Socio-economy
Objectives: To identify difficulties in mathematics teaching and to develop an ethnomathematics-based, culturally relevant program for geographically isolated schools. Methods: The participants were eleven purposively selected mathematics teachers from elementary and secondary schools in Island School, Western Visayas, Philippines. In-depth interviews and focus group discussions, classroom observations, and document analysis were used to collect data. The data were thematically analyzed using the procedure of Braun and Clarke. Findings: The teachers stated that the lack of foundational skills, absenteeism, limited parental support, learning losses from modular instruction, and challenges in implementing contextualization remained a challenge. The majority of teachers reported gaps in basic numeracy skills, absences, and low parental engagement as [ongoing] barriers to continuity in learning. Furthermore, the need to review prerequisite concepts accounted for instructional delays of about 2 to 6 weeks. In response, innovations were developed, such as remedial instruction, peer tutoring, mobile learning hubs, and culturally grounded teaching. These interventions were reported to lead to significant changes in the students’ engagement, participation, and conceptual understanding. The study resulted in the development of the “Kaalam Halin sa Kultura” program, a culturally responsive program that incorporates community knowledge in teaching mathematics. Results suggest that ethnomathematics-based and contextualized approaches enhance relevance, inclusiveness, and instructional effectiveness in resource-constrained settings. Novelty: This paper provides qualitative evidence on teachers’ interactions and observations to address how a culturally responsive-ethnomathematics-based approach can narrow equity gaps and increase the relevance of instructional practices. It describes a local ethnomathematics program drawing ideas from teachers' innovations in geographically isolated schools across the Philippines. Keywords: Mathematics education, Ethnomathematics, Culturally relevant teaching, Learning gaps, Teacher innovation
Background/Objectives: Due to the carcinogenic nature of asbestos, the development of eco-friendly, lightweight brake pads using agricultural waste and natural minerals has become vital. However, such formulations often suffer from poor mechanical strength. This study investigates the optimization of particle sizes of wheat straw fiber and basalt materials to enhance the compressive strength of a novel, lightweight brake pad formulation. Method: This research investigates the optimization of brake pad friction materials via RSM, focusing on the particle size effects of constituents: basalt (B), sand (S), steel (St), wheat straw fiber (WS), and graphite (G). A novel, non-traditional casting fabrication route is introduced. Comprehensive physical and mechanical characterization, including density, porosity, and compressive strength testing, validates the performance of the produced composites. Findings: RSM analysis revealed that optimized particle sizes—basalt at 168.75 µm, sand at 225 µm, steel at 337.5 µm, wheat straw fiber at 225 µm, and graphite at 112.5 µm—yielded significant performance improvements. Notably, sample S6 achieved a compressive strength of 114.77 MPa, porosity of 3.84%, density of 1.31 g/cm³, and water absorption of 0.27%. Novelty: Specifically, the achieved compressive strength surpassed 39% higher than reported in the literature. The findings highlight the significant influence of particle size variation on porosity and compressive performance, unlike density variation. This study demonstrates that controlled particle size distribution improves the mechanical performance of eco-friendly brake pads, offering a high-performance, sustainable alternative. Keywords: Basalt, Compression Strength, Particle Size, Wheat Straw
Objectives: To investigate the proportional hazards' assumption in breast cancer survival analysis. Also to build a more flexible version of the Cox regression model that brings in time-dependent covariates, aiming for sharper hazard estimates. Method: A retrospective analysis was conducted using a publicly available dataset comprising 272 breast cancer patients with long-term follow-up. Descriptive statistics, Kaplan-Meier estimation, and multivariable Cox proportional hazards modeling were applied. The proportional hazards assumption was examined through time-dependent covariate interactions. An extended Cox model was subsequently formulated, and model performance was assessed using -2 log-likelihood, Akaike Information Criterion, and likelihood ratio statistics. Findings: Out of all patients, 77 (28.3%) events were observed during the study period. Tumor grade was identified as a significant predictor of survival (HR = 2.382; 95% CI 1.653-3.433). The proportional hazards assumption was violated for age (p<0.001) and tumor diameter (p=0.001), indicating the presence of time-dependent effects. The extended Cox model demonstrated a substantial improvement in fit, with -2 log-likelihood decreasing from 760.128 to 326.791 and AIC from 772.128 to 344.791, confirming enhanced explanatory performance. The findings confirm that incorporating time-dependent covariates improves model adequacy and provides a more reliable and realistic framework for breast cancer survival analysis, particularly in the presence of non-proportional hazards. Novelty: This study presents an integrated modeling approach that combines proportional hazards assessment with a time-dependent Cox regression model. By addressing violations of model assumptions while preserving interpretability, the proposed framework provides a practical and methodologically robust alternative to conventional survival models. Keywords: Breast cancer, Survival analysis, Cox proportional hazards, Time-dependent covariates, Non-proportional hazards, Hazard modeling
