Science & Technology Asia

Seating Derangements without Horizontal Displacement

Monrudee Sirivoravit , Utsanee Leerawat — Thu, 19 Jun 2025 00:00:00 +0700

This paper investigates the permutations of seating arrangements where individuals can only move to adjacent seats, without any horizontal displacement. We consider a grid consisting of 𝑚 rows and 𝑛 columns, with each seat occupied by a single person. The allowed movements are restricted to vertical or diagonal shifts to neighboring seats. We establish recurrence relations to determine the number of possible seating derangements for given values of m and n. Solutions to these recurrence relations are provided. Additionally, we extend our analysis to larger grids of size 2𝑚 × 𝑛, subject to the same movement constraints.

A Comparative Study of Subspace Measures for Parametric Model Reduction Using K-Medoids Clustering and Neural Networks: A Case Study for Burgers’ Equation

Norapon Sukuntee, Suvarin Phungngam, Saifon Chaturantabut — Thu, 19 Jun 2025 00:00:00 +0700

This work investigates the performance of three distance measures including Grassmann, Binet-Cauchy and Chordal distances in the complexity reduction of parameterized nonlinear dynamical systems using K-medoids clustering and neural network. The Grassmann distance provides a geometric perspective by measuring the subspace angles, while the Binet-Cauchy distance utilizes determinants to measure volume distortion between subspaces. The Chordal distance, on the other hand, considers a straightforward metric that uses the Euclidean distance between points on a unit sphere. Each of these distances is employed in the K-medoids clustering process to construct a dictionary of projection basis sets used in reduced-order modeling. The neural network is then trained to automatically select the most suitable basis sets in the dictionary for a given parameter vector. The numerical experiments are demonstrated using the parameterized Burgers’ equation, where the governing partial differential equation, initial conditions and boundary conditions are all parameter-dependent. The Grassmann distance is shown to give the most accurate result across different parameter settings when compared with the Binet-Cauchy and Chordal distances.

Involutive Weak Cubical ω-categories

Supaporn Theesoongnern, Paolo Bertozzini, Paratat Bejrakarbum — Thu, 19 Jun 2025 00:00:00 +0700

We investigate the notion of involutive weak cubical ω-categories via Penon’s approach: as algebras for the monad induced by the free involutive strict ω-category functor on cubical ω-sets. A few examples of involutive weak cubical ω-categories are provided.

Functions Whose Images are Terms of a Weakly Fixed Variable

Chortip Siwapornanan, Thodsaporn Kumduang, Prapairat Junlouchai — Thu, 19 Jun 2025 00:00:00 +0700

In this paper, the set of terms of a weakly fixed variable of type 𝜏 which is a generalization of terms of a fixed variable is introduced. Applying the generalized superposition operation, the superassociative algebra of terms of a weakly fixed variable is formed. Binary associative systems induced by such operations are obtained. We also discuss properties of functions whose images are terms of a weakly fixed variable called wfv-generalized hypersubstitions.

Within-Host Model of Dengue Viral Infection with Immune Response and Vaccination: Dynamics Analysis and Optimal Control

Ratchada Viriyapong, Pornthera Aimrod — Thu, 19 Jun 2025 00:00:00 +0700

In this study, we propose a within-host model describing dengue viral infection. The model incorporates immune response and latency stage of cells when getting infected by dengue viruses. We verify that all solutions are nonnegative and bounded. Two equilibrium points (infection-free and infected) are established, and the basic reproduction number is computed. Local stability analysis is performed and each equilibrium point is stable under some conditions. The Lyapunov functional and geometric approaches are implemented to show the global stability of infection-free and infected equilibrium point, respectively. Numerical simulation of the model is carried out to confirm the stability of both equilibrium points i.e., the case when the basic reproduction number is less than and greater than one, respectively. Further, an optimal control problem is applied into the model by adding vaccination as control variable to seek the optimal strategy in preventing dengue viral infection. Our numerical results of optimal control model demonstrate that vaccination not only reduces exposed and infected cells, viruses, B-cells and cytotoxic T lymphocytes (CTLs), but also increases antibody. Our results indicate that vaccination can delay the peak of infection, potentially mitigating disease spread. Although most of the dengue patients are not in severe case, it is still better not to get infected and being risk. Therefore, dengue vaccination measure is highly recommended for public health policy in order to reduce both number of dengue infected patients and cost of treatment.

Brain Tumor Classification With Selective Fine Tuning Using Transfer Learning

Deepa AB, Varghese Paul — Thu, 19 Jun 2025 00:00:00 +0700

The accelerated pace of contemporary life has led to a notable increase in cancer incidence, which poses a significant challenge in the field of oncology. This study introduces an innovative approach to brain tumor detection by employing fine-tuned pre-trained models with sparse data and comparing their performance to that of traditional convolutional neural networks (CNNs). The study addresses the challenge of limited medical imaging datasets in oncology, a discipline experiencing heightened demand due to rising cancer rates. By utilizing transfer learning techniques, the proposed method seeks to alleviate the overfitting issues that are commonly encountered. Fine-tuned models developed from pre-trained networks exposed to millions of diverse images have been adapted for tumor classification tasks by incorporating max-pooling and dense layers. A comparative analysis revealed that these refined models achieved superior accuracy, exceeding 90 percent even with limited data, thereby outperforming the conventional CNNs. This study evaluated the model performance using various metrics, including accuracy and precision, and demonstrated the efficacy of transfer learning in enhancing brain tumor detection capabilities. This approach holds promise for improving diagnostic tools in oncology, particularly in scenarios in which large-scale medical imaging datasets are unavailable.

Existence of Coincidence Point In 𝐶∗-Algebra Valued Metric Space

Rishi Dhariwal, Deepak Kumar — Thu, 19 Jun 2025 00:00:00 +0700

In the present manuscript, we established some results on coincidence points for two pairs of compatible and weakly compatible mappings satisfying a generalized contraction with a pair of partially weakly increasing mapping in ordered𝐶∗-algebra valued metric space. Moreover, integral type contractions in space provide a few implications for the main result. The result is an extension and generalization of several metric space results available.

A Model for the Water Saturation Dependence of Electrical Conductivity of Porous Media

Thu, 19 Jun 2025 00:00:00 +0700

Electrical conductivity (EC) measurement has been extensively applied for characterization of geological media on account of its high sensitivity to various parameters of water-filled porous media (i.e., structure, water content, water composition). It is one of most frequently used methods in geological, geotechnical, and environmental problems. Its effectiveness relies on theoretical models that relate the EC and parameters of porous media. In this work, we develop an EC model for porous media under partially saturated conditions using a bundle of capillary tubes model. The proposed model is expressed in terms of electrical conductivity of pore water, electrical surface conductivity, water saturation, microstructure parameters of porous media (e.g., porosity, tortuosity, minimum and maximum pore radii) and their pore size distribution. The model is then numerically analyzed and successfully compared with experimental data in literature and published models.

Identifying the Effects of Environmental Factors on Dengue Transmission in Dhaka, Bangladesh: A Parametric Count Regression Approach

Anamul Haque Sajib, Sahera Akter, Faysal A Chowdhury — Thu, 19 Jun 2025 00:00:00 +0700

Bangladesh faces ongoing public health challenges from dengue fever, particularly in urban areas like Dhaka. The spread of this mosquito-borne disease is influenced by climate, with temperature, rainfall, humidity, and wind speed affecting its occurrence and distribution. This study investigates the relationship between these environmental factors and dengue transmission in Dhaka from 2021 to 2023. Various count models—Poisson, Negative Binomial, discrete Lindley and Weibull, zero-inflated, and hurdle models—were applied to analyze dengue incidence. Model selection was based on AIC, dispersion, and predictive criteria. A simulation study supported the findings, consistently identifying the discrete Weibull model as the best fit. Results showed that maximum temperature and wind speed were negatively associated with dengue cases, while minimum temperature and humidity had a positive effect. Rainfall and visibility showed no significant impact. This study enhances understanding of how climate influences dengue in Dhaka and supports the development of effective prevention strategies, including a potential climate-based warning system for Bangladesh.

Optimization of Reaction Temperature and Time on the Deproteinization Process during the Extraction of Chitin from Shrimp Shell Waste

Thu, 19 Jun 2025 00:00:00 +0700

Chitin is found in abundance in shrimp shells and is a highly sought component for bioplastic development. In the Philippines, shrimp meat processing discards shrimp shell as wastes which could be valorized by extracting chitin using the chemical method. Extraction of chitin starts with the demineralization of the sample with 1M HCl, at 25◦C to 30◦C, with a solid to solvent ratio of 1g:10mL for 1 hour. The samples are then processed for deproteinization with 1M NaOH, at 30◦C to 70◦C, with a solid to solvent ratio of 1g:10mL, for 1-24 hours. This study aims to extract chitin from shrimp shells and to optimize reaction temperature and time of the deproteinization step. The deproteinization temperature (30◦C to 70◦C) and time (1 hour to 24 hours) were optimized using Response Surface Methodology (RSM) by maximizing the extent of deproteinization (DP). The optimum conditions were determined to be at 53◦C and 24 hours, where the overall yield is 19.72% (±0.19) and the DP is 50.79% (±1.33). Lastly, the extracted chitin at the optimum conditions was determined to have a 94.17% (±1.2) degree of acetylation and a molecular weight of 29.15 kDa (±2.39). These are better than the commercial chitin which has a molecular weight requirement of ≥ 5 kDa and a degree of acetylation of ≥ 90%.

Probability of the Alertness Level of Elderly Drivers before an Accident Occurs

Thu, 19 Jun 2025 00:00:00 +0700

This research aims to develop a model to predict the alertness level of elderly motorcycle riders, focusing on those aged 45–65 in Riau Province, Indonesia. Accident statistics in Indonesia show that riders aged 50 and above are more prone to accidents. Data was collected by interviewing elderly motorcycle riders who had experienced accidents at the ages of 45-65 years. The total of respondents used in this study was 564. The analysis results indicated that elderly motorcycle riders are 43% likely to have a high level of alertness and 57% likely to have a low level of alertness. Four scenarios were conducted to determine the influence of each variable. The results showed that the highest level of alertness among elderly motorcycle riders was 46%. Thus, elderly motorcycle riders are likely to be prone to accidents. The findings of this research indicate that the highest alertness level among elderly drivers is below 50%, thus several things are required to improve the safety of elderly drivers including: 1) not to ride for too long on straight road segments; 2) to ride in good physical condition; 3) sleep more than 7 hours at night; 4) not to use cell phones while riding; 5) necessary to review the eligibility of a driver’s license when motorcyclists reach the age of 45years; and 6) elderly riders need to be accompanied by another rider.

Development of Multi-Level Hot Air Temperature Dryer Cooperation with Biomass Heat Collection System for Longan Flesh

Thu, 19 Jun 2025 00:00:00 +0700

This study investigates the drying of longan flesh through hot air energy derived from a biomass heat collection system. Three heating patterns were developed and selected for study. Next, the drying rate and specific energy consumption were measured and analyzed. The heat from the burner is transferred to the hydraulic oil, and the oil is pumped to the heat exchanger in the drying chamber. The temperature control system in the drying chamber used a temperature controller to stop the operation of the hydraulic oil pump motor. Three patterns of multistep temperature with an airflow velocity of 1.15 m/s were tested. The opening air outdoor system was 10%. Pattern 1 was dried at 80◦C for 3 h, then at 70◦C for 8 h; pattern 2 was dried at 80◦C for 6 h, then at 70◦C for 4 h; and pattern 3 was dried at 80◦C for 5 h, then at 70◦C for 3 h, and at 60◦C for 4 h. 4 kg of longan flesh with an initial moisture content of 330.61 ± 7.03% dry basis was dried to a final moisture content of 17.37 ± 0.254% dry basis. The results showed that the drying rate of pattern 1 was 0.265 ± 0.0009 kg/h, and the specific energy consumption (SEC) was 68.34 ± 3.775 MJ/kg, while the drying rate of pattern 2 was 0.289 ± 0.0010 kg/h, and the SEC was 72.64 ± 5.906 MJ/kg. The drying rate of pattern 3 was 0.244 ± 0.0004 kg/h, and the SEC was 70.03 ± 6.436 MJ/kg. Finally, pattern 1 was the most suitable condition. It was low SEC, and the color quality was a golden color close to market standards.

Empowering Smart Megacities: IoT-Based Smart Metering for Enhanced Energy Efficiency and Consumer Empowerment

Rithesh P Karkera, Abhishek S Athalekar, Gajanan Thokal, Suyash Pawar — Thu, 19 Jun 2025 00:00:00 +0700

The development of smart homes, societies, communities, and metropolises has assumed a central role in the recent era of IoT-based intelligent devices. The development of the smart grid, an intelligent infrastructure for managing electricity, is a top priority for smart megacities in particular. Bidirectional communicating smart meters are at the heart of this infrastructure; they are more than just a convenience. By giving customers better information and control over their energy consumption and appliances, these modern iterations of conventional energy meters empower consumers. These features offer new levels of interaction, from remote control of home appliances to real-time consumption data. However, the use of reprogrammable microcontrollers like the Arduino Uno and Node MCU is required to implement such a capability. These microcontrollers aid manufacturers in modifying products to meet shifting consumer demands and stay relevant over time. Smart meters, the foundation of Smart Grids, coordinate the smooth operation of online devices, effectively manage energy consumption, and promote significant energy conservation. We present a novel IoTbased smart metering paradigm in accordance with this viewpoint. In addition to archiving these data for use in calculating monthly electricity bills, this gives consumer’s power by sending them real-time energy use statistics over 24-hour cycles via SMS. This comprehensive approach fosters a culture of energy conservation, which is urgently needed in our day and age, while also empowering consumers to manage their electricity consumption. The system demonstrated an energy measurement accuracy of 97.91%, with only a 2.0833% error, as validated against multimeter readings.

Design, Simulation and Analysis of a Microstrip Rampart Line Antenna (MRLA) for a MIMO-OFDM Transceiver Module in UAV Applications

Jeba Kumar RJS, G.P. Ramesh — Thu, 19 Jun 2025 00:00:00 +0700

An Unmanned Aerial Vehicle (UAV) is a swarm of Mobile Sensor Networks that operates in the recommended standard of 802.11g, which needs a miniaturized Microstrip Rampart Line Antenna (MRLA) to establish full duplex Multiple-Input Multiple-Output (MIMO) Radio Frequency Communication. Hence, a MIMO Orthogonal Frequency-Division Multiplexing (OFDM) transceiver module which operates in 432.5 MHz for UAV application is in need for compact size antenna. The innovative aspect of this research paper is to showcase the novel rampart design, i.e., Microstrip Rampart Line Antenna (MRLA) and its practical evaluation, specifically operating within the Ultra-High-Frequency (UHF), i.e., 432.5 MHz. The novelty of the recommended design is to enhance performance in terms of both bandwidth and overall radiation properties. To attest the correctness of the proposed MRLA design, the simulation findings are compared with the state-of-art research design for size reduction comparison, fabricated results were observed, and thereby measured test results via fabrication were found in line with simulated results. The observed fabricated test results of the proposed MRLA antenna for MIMO-OFDM module, constructed for the Operating-Range-of-Frequency (f0) of 432.5 MHz, yields a Reflection Coefficient (S11) value of -47 dB, an acceptable VSWR numerical value of 1.5, and an Axial Ratio (AR) of 1.8; hence it is ideally suited to establish medium-to-long range UAV RF communication. This proposed antenna will pave the way for futuristic AI based Reconfigurable Antennas for advanced UAV applications.

Real-Time Traffic Surveillance: Aerial Vehicle Detection, Tracking, and Counting with YOLOv7

Vishakha Singh, Phisan Kaewprapha — Thu, 19 Jun 2025 00:00:00 +0700

In this research, we present an advanced aerial surveillance system powered by the YOLOv7 object detection model, designed for automatic and on-demand collection of traffic data. The system uses unmanned aerial vehicles (UAVs) to capture real-time video, making it especially valuable in areas without fixed surveillance cameras, such as rural roads and busy highways. It accurately detects, classifies, and tracks eight types of vehicles, and includes vehicle counting with directional analysis (left, right, or straight). This comprehensive approach enables the extraction of detailed traffic statistics, including flow rates, movement patterns, and vehicle density. Our classification model achieved an overall accuracy of 98.6%, with some vehicle types reaching up to 99.6%, demonstrating the system’s strong performance and practical utility for traffic monitoring.

Data Preprocessing Enhancement for Artificial Neural Networks in Predicting Mechanical Properties of Dual-Phase Steel

Anan Butrat, Patiparn Ninpetch — Thu, 19 Jun 2025 00:00:00 +0700

The mechanical properties of Dual-phase (DP) steel, including toughness and hardness, are influenced by the intercritical annealing process parameters, such as temperature, holding time, and cooling rate. Traditional regression models have limitations in accurately predicting these nonlinear relationships. This study addresses the challenge by introducing a data preprocessing method, the Regression-Based Data Preprocessing (RBDP) method, aimed at improving the accuracy of Artificial Neural Network (ANN) models in predicting toughness and hardness of DP steel. The RBDP method enhances the training dataset by generating synthetic data through regression analysis, allowing for more robust ANN performance. The mechanical properties of nine DP steel specimens were analyzed using RBDP combined with ANN models and compared with predictions from pure ANN models and traditional regression approaches. For hardness prediction, the PRH + ANN model emerged as the most accurate, achieving the lowest average error of 0.72%, performing particularly well for specimens 1, 3, 4, and 9. For toughness prediction, the PRT + ANN model was the most effective, delivering the lowest prediction errors across most specimens, with particularly low errors for specimens 1, 4, 5, 7, 8, and 9. Both PRH + ANN and PRT + ANN models outperformed traditional regression approaches and standalone ANN models. These findings demonstrate that the proposed RBDP method significantly improves the accuracy of ANN models for predicting the mechanical properties of DP steel. This study highlights the potential to enhance predictive modelling in materials science.

Optimizing Ammonia Removal in Recirculating Aquaculture Systems Using NaOH-Activated Sugarcane Bagasse Carbon

Said Ali Akbar, Cut Nuzlia, Muhammad Hasan, Zulkarnain Jalil, Ilham Zulfahmi — Thu, 19 Jun 2025 00:00:00 +0700

This study evaluates the effectiveness of activated carbon produced from sugarcane bagasse as an ammonium filter in a Recirculating Aquaculture System (RAS) for Nile tilapia (Oreochromis niloticus). The research involved five different treatments with varying amounts of carbon mass. The results indicated that the highest carbon mass treatment (P5) achieved superior water quality and fish growth performance, including a specific growth rate (SGR) of 3.345% per day, the lowest feed conversion ratio (FCR) of 0.829, and a 100% survival rate. Fourier Transform Infrared (FTIR) and Scanning Electron Microscope (SEM) analyses confirmed significant pore enlargement and the presence of functional groups facilitating ammonium adsorption. The findings demonstrate the potential of sugarcane bagassederived activated carbon as a sustainable and cost-effective solution for improving water quality and promoting fish growth in aquaculture systems.

A DNA Biometric Implementation Model to Promote Thailand’s Criminal Justice System

Thu, 19 Jun 2025 00:00:00 +0700

DNA biometrics has proven to be a valuable tool in criminal justice systems worldwide, providing accurate identification and helping solve crimes effectively. However, Thailand faces several challenges in implementing DNA biometrics in its criminal justice system. This study aimed to explore the use of DNA as reliable biometric data in Thailand’s judicial and authentication processes, develop operational procedures for practical use, identify problems and obstacles, and propose a suitable model for implementation. The study employed documentary research and in-depth interviews with sixteen experts in the criminal justice system. The findings revealed that the absence of a central authority for collecting DNA biometric data results in disparate management of DNA databases and a lack of coordination between agencies. Personnel and budget issues, insufficient professional standards for forensic science officers, and the absence of a central agency responsible for forensic science work management also hinder Thailand’s forensic science operations. Issues related to crime scene protection, such as the lack of operational guidelines for collecting evidence and insufficient first-response units, were also identified. Furthermore, Thai judges often lack sufficient knowledge to assess the reliability of forensic evidence. Another major obstacle is the absence of a law to establish a central agency that administers the National DNA Database. The study proposes the establishment of a central agency to oversee the collection and management of DNA biometric data, the development of clear operational guidelines, and the provision of training for personnel involved in the criminal justice system to effectively implement DNA biometrics in Thailand.

Interaction of Host miRNA and Viral RNA of Hepatitis C Virus as New Drug Targets Against Development of Hepatocellular Carcinoma

Thu, 19 Jun 2025 00:00:00 +0700

Hepatitis C virus (HCV) infection induces significant changes in hepatocytes over time. Host-derived microRNAs (miRNAs), small non coding RNAs ( 22 nucleotides), are believed to influence the persistence or suppression of chronic HCV infection by modulating viral genome replication, transcription, and translation. However, their contribution to the development of hepatocellular carcinoma (HCC), as well as the specific interactions between miRNAs and distinct HCV genotypes, remain insufficiently understood. This study explores the potential involvement of host miRNAs in HCC progression among patients infected with different HCV genotypes. Using various bioinformatics tools—such as Clustal Omega, ChimeraX: AlphaFold, EMBOSS Transeq, UNAFOLD, and Freiburg IntaRNA—we analyzed the site-specific binding of key miRNAs (miR-122, miR-21, miR-155, and miR-193a-5p) across all seven HCV genotypes. Our findings revealed that miR-122 predominantly targets the NS5B coding region in most genotypes, suggesting a role in inhibiting viral replication and translation. Conversely, certain miRNAs that bind to the Envelope (E) region may enhance viral gene expression. These results indicate that sequence homology between host miRNAs and HCV mRNA can either suppress or facilitate viral gene translation. Targeting miRNAs that promote HCV replication may offer a novel therapeutic strategy for managing HCV-related diseases.

Molecular Docking of Laccase from Trametes Versicolor with Ligand Substrates from Textile Dyes and Antibiotics Impacting the Environment

Thu, 19 Jun 2025 00:00:00 +0700

Environmental pollution from textiles and pharmaceuticals is a primary source of concern for various environmental protection agencies. The nature of synthetic dyes and the increasing rate of antibiotic resistance by bacteria have made them a major health concern. Laccases are capable of degrading organic pollutants, including phenols, dyes, bisphenol, polyaromatic hydrocarbon (PAHs) and pharmaceuticals. This study focused on assessing the toxicity and binding interaction of eight ligands of environmental importance with laccase from a fungus, Trametes versicolor, via an in-silico approach. The canonical SMILES of the ligands were retrieved from the PubChem database and the 3D structures were obtained using UCSF Chimera 1.18. The 3D structure of laccase was retrieved from the Protein Data Bank (PDB) and blind docked with the ligands. Discovery Studio 4.5 software was used to observe the different bonding interactions between the enzyme and the docked ligands. The ADMET study of the ligands was done using their canonical SMILES on the admetlab3.0 servers. The binding energies of laccase with Indigo carmine (IC), Malachite green (MG), Remazol brilliant blue R (RBBR), Direct red 75 (DR75), Ciprofloxacin (CIP), Amoxicillin (AMO), 2,2′-azino-bis-(3-ethylbenzthiazoline-6-sulfonic acid (ABTS), and Guaiacol were −6.6, −7.0, −7.0, −7.4, −6.8, −7.0, −6.4, and −5.2 kcal/mol, respectively. All the dyes showed high probability for toxicity with values close to 1 and also environmental toxicity, with Malachite green (MG) showing the highest probability with bioconcentration factors (BCF), 50 percent growth inhibition concentration of Tetrahymena pyriformis (IGC₅₀), 96-hour fathead minnow 50 percent lethal concentration (LC₅₀FM), and 48-hour Daphnia magna 50 percent lethal concentration (LC₅₀DM) of 1.683, 3.858, 4.567, and 5.446. The active site of the enzyme that interacted with the eight ligands was found to contain one of these eleven (11) amino acid residues, namely, HIS, GLN, ASP, ALA, PHE, SER, LEU, ARG, VAL, PRO, and GLU in the docked complexes.