Backgrounds/Objectives: Urolithiasis, characterized by the presence of calculi in the urinary tract, involves the formation of stones in any part of the urinary system, including the kidneys, ureters, bladder, or urethra. Calcium oxalate contributes to the formation of kidney stones (urolithiasis) in the urinary tract. This is a mineral belonging to the oxalate group and is found naturally in humans, animals and plants. These stones are responsible for the majority of urinary calculi. The present work examines the crystal formation of calcium oxalate using silica gel in laboratory conditions and its characterization by spectroscopical methods. Methods: Calcium oxalate crystals were grown from the sodium metasilicate using the single diffusion gel growth technique. The harvested crystals were analysed using X-Ray Diffraction (XRD), FT-IR Spectroscopy, UV-Visible spectrophotometer, Field Emission Scanning Microscopy (FE-SEM), and Thermogravimetric Differential Thermal Analysis (TG-DTA) studies. The dielectric constant and dielectric loss as a frequency function were measured for the grown crystal. Findings: The gel growth technique is extremely effective for producing high-quality crystals in a controlled and reliable way. The prevention of calcium oxalate kidney stones could be beneficial for society. Novelty: This study integrates the multispectral approach in the growth of crystals and helps in producing a focus for the prevention or dissolution of the urinary-type stones. Keywords Calcium oxalate, Gel method, Spectral Characterisation

Objective: To assess the prevalence of Glenohumeral Internal Rotation (GIRD) deficit in recreational Bowlers. Methods: This observational study consisted of recreational cricketers (18-25 years) from various cricket academies and cricket clubs in Belagavi city. The GIRD was measured with a goniometer. Both the dominant and non-dominant shoulder were assessed and compared to measure GIRD. This study involved 93 bowlers, predominantly male (90%), aged between 18 and 25 years. Findings: A positive linear association was found between GIRD and age (𝑟 = 0.369, 𝑝 = 0.001), as well as between GIRD and years of experience (𝑟 = 0.263, 𝑝 = 0.011), indicating that both increasing age and greater experience are associated with a higher likelihood of developing GIRD in bowlers. The study concluded that the prevalence of GIRD amongst cricket bowlers is 37%. The study highlights a significant prevalence of GIRD among bowlers, particularly as they age and gain experience. Novelty: The study’s findings highlight the importance of implementing targeted strategies to preserve shoulder function and prevent injury. Keywords: Cricket; Bowlers; GIRD; Shoulder injury; Glenohumeral internal rotation deficit

Background: Alzheimer’s Disease (AD) is a background of progressive neurodegenerative disorder which affects cognitive function and memory. In the traditional diagnostic approaches, there exists a lot of technical issues such as, hyperparameter tuning challenges, data imbalance and limited generality of predictive models result into the late detection, complex data interpretation and low classification performance. Objectives: The objectives are to develop an improved classification framework by enhancing the Gradient Boosting Machine (GBM) with a biologically inspired optimization algorithm and apply it to accurately and early predict AD. The desired result to accomplish this is to have a robust and adaptive learning process that can handle the above issues, including sub optimal hyperparameter selection, overfitting and sensitivity to data imbalance.Methods: The proposed model is Cormorant Swarm Optimized Gradient Boosting Machine (CSO-GBM) which is inspired from the cormorant’s cooperative hunting and adaptive foraging behavior. The hyperparameter refinement is done efficiently by balancing the global exploration and local exploitation using the optimization algorithm. In the proposed work evaluation on the OASIS Alzheimer’s MRI dataset and compared with existing models, INN-MT and FDCNN-AS. Findings: Results showed that the CSO-GBM model provided a high classification accuracy result of 82.39% and the highest F-measure of 83.65% as compared to benchmark models. It had shown better performance in reducing false positives and false negatives; at the same time, it accommodates high adaptability, robustness, and computational efficiency over complex data distribution. Novelty: This is the first research to explore swarm intelligence derived from the cormorant for optimizing GBM for predicting Alzheimer’s Disease. Dynamic convergence, high generalization ability and the possibility of integration into medical diagnostic frameworks were ensured by an adaptive search mechanism; thus, the algorithm provides an excellent basis for a computationally efficient and clinically reliable classification model. Keywords: Alzheimer’s Disease Classification, Bio-Inspired Optimization, Cormorant Optimization, GBM, Feature Selection, Healthcare

Objectives: The Philippine Technological Council – Washington Accord (PTC WA) provides internationally accepted standards for engineering education with an emphasis on outcomes-based learning and ensuring graduates are globally competent. This study investigated the perceived compliance of the Civil Engineering program at Camarines Sur Polytechnic Colleges with the nine PTC accreditation criteria. Methods: This study employed a survey method that gathers information on the level of compliance of the engineering programs along PTC accreditation standards. Likewise, documentary analysis was used to analyze the program profile and support the data gathered. Faculty members of civil engineering served as the study's respondents. They were requested to respond to the survey questions explicitly crafted to determine the level of compliance of the programs to PTC accreditation standards. Findings: The results show that the institution understands the significance of accreditation, but there are areas that need to be addressed. These are characterized by low scores obtained in the evaluation, which include the areas of student performance monitoring, faculty development, academic exchange, facilities, extension, industry linkages, and Continuous Quality Improvement (CQI). Findings also emphasized the importance of encouraging faculty members to pursue advanced studies and enhance the passing percentage in the licensure examinations of graduates. Based on these findings, the study formulated plans of action that will serve as a roadmap for the program's quest for PTC accreditation. Novelty: The developed plans of action aim to enhance the quality of the program, generate international recognition, and make graduates more competitive within the global engineering community. Keywords: PTC, Accreditation, Civil engineering, Compliance, Washington Accord

Objectives: The study aimed to synthesize and characterize a polypyrrole (PPy) nanocomposite doped with iron perchlorate [Fe(ClO4)2] and silver nanoparticles (Ag-NPs) for potential application as a room-temperature gas sensor, specifically targeting hydrogen sulfide (H2S) gas detection. Methods: The PPy nanocomposite was synthesized through in situ chemical polymerization using ferric chloride (FeCl3) as the oxidant, with a monomer-to-oxidant ratio of 1:1. Silver nanoparticles were prepared using the Turkevich method and incorporated as dopants. The structural, morphological, and chemical properties of the synthesized nanocomposite were analyzed using SEM, TEM, UV-Vis, FTIR and XRD techniques. Electrical conductivity was measured using a two-probe technique, and the material's gas-sensing performance was evaluated by exposing it to H2S gas. Findings: The synthesized PPy-Fe(ClO4)2-Ag nanocomposite demonstrated excellent sensitivity to H2S gas at room temperature. A sharp increase in current was observed upon exposure to H2S gas, which quickly reverted when the gas was removed. The characterization results confirmed the successful incorporation of Ag nanoparticles and Fe(ClO4)2 into the PPy matrix, contributing to its enhanced electrical and sensing properties. Novelty: This study highlights the innovative use of a PPy-based nanocomposite doped with iron perchlorate and silver nanoparticles for highly sensitive and reversible H2S gas detection at room temperature. The combination of these dopants enhances the material's electrical conductivity and sensing capabilities, offering a promising candidate for low-cost, efficient gas sensor applications. Keywords: Polypyrrole, Silver nanoparticles, Iron per Chlorate, H2S, Gas sensor

Objectives: To propose a method for imputing missing values within non-linear datasets with class imbalance issues. Methods: The proposed methodology includes two stages of imputation through the Polynomial with Backfill (PoBa) process. This imputation technique applies quadratic polynomial interpolation for individual class type missing value estimation as well as implementing a backfill strategy to complete remaining gaps. The evaluation process utilized six ML models to assess dataset imputations under different non-linear and class imbalance conditions within two publicly available datasets. Findings: The PoBa method showed superior performance than HPCNN baseline in all the metric evaluations for different degrees of missingness. The Wine Quality dataset received an accuracy score of 0.807 and MAE of 0.193 and RMSE of 0.439 when feeding PoBa to Multi-Layer Perceptron (MLP) under 30% missing data conditions. The PoBa method reached 0.950 accuracy along with 0.050 MAE and 0.224 RMSE in the heart disease dataset despite its imbalanced characteristics. The experiments demonstrate that PoBa effectively preserves data organization and improves predictive performance while handling large real-world datasets. Novelty: The method proposes a class-specific combination of polynomial interpolation with backfill to handle missing data in an efficient and interpretable way. Keywords: Missing Data, Polynomial Interpolation, Backfill Imputation, Machine Learning, Class Imbalance

Objectives: This study aims to compare the design strength of eccentrically loaded unequal leg single angles under compression according to IS 800:1984, IS 800:2007, IS 800:2007 (Amendment 2), and AISC 360-22 Specifications for structural steel buildings. Methods: Spreadsheets were developed to evaluate the compression capacities of unequal single-angle sections using all these codes. The analysis focused on unequal leg single angle connected by either longer or shorter legs, under eccentric loading conditions. Findings: IS 800:2007 (Amendment 2) significantly enhances the compression strength of unequal leg single angles compared to IS 800:2007, with increases of up to 40% for angles fastened through the longer leg and up to 87% for those fastened through the shorter leg. Compared to the AISC 360-22 LRFD method, IS 800:2007 (Amendment 2) provides up to 184% more strength for angles fastened through the longer leg and up to 179% more for angles fastened through the shorter leg, While IS 800:2007 provides up to 112% more strength for angles connected by the longer leg and up to 68% more for those connected by the shorter leg. IS 800:1984 also shows increased strength over the AISC 360-22 ASD method, with up to 67% more strength for angles connected by the longer leg and 27% more for those attached by the shorter leg. Additionally, IS 800:2007 (Amendment 2) reveals that unequal leg single angles connected by the shorter leg provide up to 56% more compression strength than those connected by the longer leg. Novelty: This research compares the capacity predicted by the latest amendment of the Indian standard code for steel design with its previous versions and the American code, focusing on the design of unequal-legged single-angle compression members. It offers valuable insights into the capacity prediction and overall structural safety, and thereby the resulting economy in the choice of steel sections. An important insight from the study is that unequal leg single angles connected by the shorter leg exhibit greater compression strength than those connected by the longer leg. Keywords: Unequal legs, Eccentrically loaded, Single angles, Steel sections, Indian codes, American codes

Objective: To develop a task scheduling algorithm that efficiently approximates solutions for the multi-objective task scheduling problem in a cloud environment, optimizing resource utilization, execution time, cost, and overall system performance. Method: A Modified Sunflower Optimization Algorithm for Task Scheduling (MSOTS) is proposed to improve efficiency in cloud environments. The traditional sunflower optimization algorithm is enhanced with Levy flight, crossover, and mutation operations to achieve a better balance between exploration and exploitation while preventing entrapment in local minima. These enhancements help improve convergence speed and solution quality. CloudSim is utilized for comprehensive performance evaluation, comparing MSOTS with existing algorithms in terms of execution time, cost, and resource utilization. The randomly generated dataset (500 tasks and 50 VMs) is used for analyze the performance of the MSOTS. Findings: The results demonstrate that MSOA significantly reduces makespan and cost while enhancing resource utilization. Specifically, the proposed method reduces makespan by 22.43% compared to PSO and 14.95% compared to SOA. Additionally, cost is reduced by 14.47% and 10.38% compared to PSO and SOA, respectively, while resource utilization increases by 3.89% and 2.51% over these methods. These findings indicate that MSOA effectively improves task scheduling performance in terms of makespan, cost efficiency, and resource utilization, making it a promising approach for cloud computing environments. Novelty: The MSOA is integrated with single-point crossover, swap mutation, and Lévy flight to update the solution, which prevents entrapment in local minima and premature convergence and enhances task scheduling efficiency, optimizes resource allocation, minimizes execution time, and reduces overall costs in cloud computing environments. Keywords: Meta-heuristic, Sunflower optimization, Levy flight, Crossover, Mutation

Objectives: The key objectives are to enhance the performance of Internet of Things (IoT) networks to attain a high packet delivery ratio (PDR), efficient residual energy (RE) usage, minimum energy consumption, and decrease packet loss during data transmission. Performance metrics are used to discover and choose high, top-notch, and long-living paths, increasing network lifetime and optimizing resource management by preventing the participation of non-essential nodes and inefficient paths in the network routing process. Methods: To devise an energy-efficient technique and refine it with decision-making for selecting an optimal path, which employs a composite metric path quality index (PQI) to enhance path quality, comprising RE for priority of nodes with maximum remaining energy, link quality score (LQS), packet delivery ratio (PDR) and hop count (HC) to minimize the path length must be integrated into the routing process to make data transmission more efficient. Hence, intermediate nodes ensure path quality, determine if packet flow is good or bad, eliminate unnecessary nodes, and optimize the whole routing by deciding on a path. The proposed technique simulates IoT network scenarios in which nodes dynamically compute these metrics and select optimal paths for transmitting data to the sink node. Findings: The proposed technique achieves a much higher PDR and an energy-proficient routing mechanism by balancing the quality of the optimal path, the residual energy, and the number of hops, which can not only prolong the network lifetime but also ensures that resources are being used efficiently, in turn all leading to a better network performance than existing methods. Simulation results show that the proposed technique significantly improves the PDR over the existing methods and reduces overall energy consumption. In this work, the validated results show that metrics PQI, RE, LQS, and HC collectively improve data delivery and ensure higher survivability of the network by optimizing the path choosing and eliminating the unnecessary nodes from the source to the target. The proposed technique simulation showed a 32.15% increase in network RE and a 36.44% improvement in PDR. Novelty: The technique presented in this paper introduces a routing approach designed with the unique integration of RE, LQS, PDR, and HC, deriving PQI metric, which will improve the path selection in IoT networks. Unlike existing methods, this method considers energy efficiency, path quality instead of link quality, and path length together to avoid over-utilizing energy-draining nodes while favoring stable, efficient paths. The approach fills a gap in existing IoT environments by focusing on energy-wise efficiency and path reliability and reducing the number of nodes unnecessarily concerned with the dynamic optimal path selection. As IoT deployments increase, so does the need for energy-efficient data transmission, and the proposed technique supports the sustainability of IoT networks. Keywords: Energy Efficiency, Path Quality Indicator, Residual Energy, Hop Count, Quality of Service