The Eurasia Proceedings of Science, Technology, Engineering and Mathematics
The Eurasia Proceedings of Science, Technology, Engineering and Mathematics (EPSTEM) is a reviewed scholarly online international journal. The manuscripts which are accepted for publication in the EPSTEM are invited from the conferences. The Eurasia Proceedings of Science, Technology, Engineering and Mathematics (EPSTEM) welcomes any research papers on technology, engineering and basic sciences using techniques from and applications in any technical knowledge domain: original theoretical works, literature reviews, research reports, and review articles. The articles should be original, unpublished, and not in consideration for publication elsewhere at the time of submission to the EPSTEM. The full paper versions of the conference papers are published in the EPSTEM. The EPSTEM is published in July, August, September, October, November and December. The full paper versions of the conference papers are published in the EPSTEM. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM) is indexed or abstracted in Scopus, Google Scholar, Scientific Indexing Services, Directory of Research Journals Indexing, ASOS index.
ISRES Publishing - International Society for Research in Education and Science (ISRES)
Intelligent Vehicle Gas and Fire Detection and Suppression System
The increasing complexity of vehicle electronics and conventional fuel systems have elevated the risks of gas leaks and fires. In addition, high-density batteries in Electric Vehicles (EVs) raises the risk of fire. Vehicle fires represent a significant safety hazard, causing hundreds of deaths and billions in property damage annually. One of the main dangers related with firefighting operations is the harmful ambience resulted from combusting materials. The four main hazards related to these cases are smoke, the oxygen insufficient atmosphere, high temperatures, and toxic ambience. Hence, fire is a real danger and fire safety is a critical issue in vehicle’s industry. This paper proposes a real-time intelligent vehicle gas and fire detection and suppression system. The system uses Arduino Microcontroller based-system that integrates multi-sensor data fusion capable of detecting gas, smoke, over-temperature and combustion. The system uses alarm signaling devices, solenoid valve, fire-extinguishing equipment, and feeders for the fire-extinguishing substance. This comprehensive vehicle safety system provides multi-layered protection against gas leaks, fires, and overheating and automatic response without driver intervention. Hence, it is capable of detecting and suppressing fire. In addition, the system is interfaced to GSM network to enable wireless communication through SMS.
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Application of Physics-Informed Neural Networks (PINNS) to Solve a Dynamic Model of Drug Abuse
Drug abuse remains a complex and persistent issue in Indonesia, requiring a systematic approach to understand its underlying dynamics. This study applies Physics-Informed Neural Networks (PINNs) as an alternative method for solving the nonlinear differential equations of the SnSeIR drug abuse dynamics model. The model divides the population into four compartments: susceptible individuals without education (Sn), susceptible individuals with education (Se), active users (I), and individuals who have quit or completed rehabilitation (R). The focus of this research is the forward problem, in which the solution of the model is approximated using parameter values obtained from the literature. The PINNs framework combines a data loss term, which measures the discrepancy between predicted solutions and synthetic data, and a physics loss term that enforces consistency with the governing differential equations. The performance of PINNs was first validated by comparing its results with synthetic data generated using the classical fourth-order Runge–Kutta (RK4) method. The model was then further tested using additional synthetic datasets to evaluate generalization. The findings demonstrate that PINNs can successfully approximate the solution of the SnSeIR model, yielding continuous and mathematically consistent trajectories that align with the system’s dynamics. Moreover, the mesh-free nature of PINNs provides flexibility and computational efficiency, highlighting its potential as a promising tool for analysing complex population-based models.
A Study of Juglone in Georgian Walnuts (Juglon regia) by Chromato-Mass Spectrometry
The study concerns the identification and quantification of the biologically active naphthoquinone, juglone (5-hydroxy-1,4-naphthoquinone), in the walnut (Juglans regia), a species widespread in Georgia, using an ultra-high-performance liquid chromatograph. The relevance of this research lies in the need to valorize agri-food waste and adopt zero-waste principles, which constitute an important aspect of the circular economy. Walnut peel obtained during processing is considered a potentially rich source of juglone, which is used for its antimicrobial, anti-inflammatory, antifungal, and other effects. Mass spectrometric analysis identified juglone with a characteristic molecular mass (m/z 173 [M-H]). The effects of different extraction methods (ultrasound, classical, high pressure/temperature, hot extraction with reflux, lyophilization) and solvent on the yield of the target compound were compared. Quantitative data showed that the maximum juglone content was observed in the ethyl acetate extract of lyophilized walnut skin, exceeding that of the ethyl acetate extract of the sample dried at 40°C and of the raw skin. Relatively low concentrations were detected in the ultrasonic and classical extraction using 50% ethanol, while the minimum amount was found in the condensate formed during lyophilization. The results obtained indicate that walnut peel represents a promising raw material for juglone, and that lyophilization combined with ethyl acetate extraction can be considered the optimal technological approach for maximizing the extraction of the target compound. This study provides a basis for the targeted utilization of walnut agro-waste and for the optimization of technological processes aimed at obtaining bioactive compounds.
Physics-Informed Neural Networks (PINNs) for Parameter Estimation in the SIRS-D Epidemiological Model
Infectious diseases exhibit complex and rapidly evolving transmission dynamics, requiring modeling approaches that can accurately capture these mechanisms. The SIRS-D compartmental model provides a suitable framework, as it incorporates temporary immunity and disease-induced mortality within the epidemic process. Accurate parameter estimation is essential for quantifying the transmission rate, recovery rate, waning immunity rate, and mortality rate, which collectively govern the system behavior. Among existing estimation methods, Physics-Informed Neural Networks (PINNs) offer significant advantages by integrating observational data with the underlying structure of differential equations, thereby preserving physical consistency while maintaining robustness under imperfect data conditions. In this study, PINNs are employed to estimate the parameters of the SIRS-D model using synthetic data generated through the fourth-order Runge–Kutta (RK4) method to ensure stable and consistent numerical solutions. To better represent real-world measurement conditions, 5% noise is added to the synthetic data, introducing realistic variability into the training process. The results demonstrate that PINNs successfully reconstruct the trajectories of S(t), I(t), R(t), and D(t) with low prediction errors. The model achieves MAE values of 0.0065 (S), 0.0067 (I), 0.0208 (R), and 0.0043 (D), with corresponding RMSE values of 0.0090, 0.0074, 0.0253, and 0.0058. Moreover, the estimated parameters closely match the true values, yielding ????????=0.5031, ????=0.0996, ????=0.0095, and ????=0.0149, demonstrating strong parameter identification capability. These findings confirm that PINNs constitute a reliable and accurate framework for analyzing infectious disease dynamics and offer promising potential for extension to more complex epidemiological models and real-world datasets.
Experimental Investigation on Tensile Property of Plain-Woven Glass/Polyester Composite
Due to their cost efficiency and superior mechanical properties, Glass Fiber-Reinforced Polymer (GFRP) composites are attracting increasing attention across various industrial sectors, including aeronautics, automotive, marine, chemical, and sporting goods industries. This study investigates the tensile behavior of plain-woven GFRP laminates through experimental characterization. The laminates were fabricated using unsaturated polyester resin via the Vacuum Bagging (VB) technique, employing a single stacking sequence of [0/90°]₅. Uniaxial tensile tests were conducted in accordance with ASTM standards. In addition, Hooke's law equations for two-dimensional composites were applied to determine the local stresses and strains in each ply. The results demonstrated a high sensitivity of tensile properties to fiber orientation. The [0/90°]₅ laminates tested at 0° orientation exhibited the highest tensile strength of 317 MPa and a Young's modulus of 18.94 GPa, while specimens tested at 30° orientation showed significantly reduced performance, yielding a tensile strength of 114 MPa and a Young's modulus of 10.6 GPa. Furthermore, the maximum and minimum local stresses (σ₁) in the 1st and 2nd plies were approximately 4.085 × 10⁴ Pa and −1.63 × 10⁴ Pa, respectively, for the glass/epoxy lamina, while the corresponding maximum and minimum strains (ε₁) in the 1st and 2nd plies were approximately 9.9 × 10⁻⁷ and 7.785 × 10⁻⁸, respectively.
Comparative Analysis of Clustering Algorithms for Meteorological Regime Characterization Over Türkiye Using ERA5 Reanalysis Data
Precise analysis and region-specific classification of meteorological parameters constitute a critical prerequisite for advanced scientific processes, ranging from artificial intelligence modeling to complex atmospheric research. The multidimensional and inherently volatile nature of these parameters often hinders the extraction of latent patterns through conventional statistical methods. At this juncture, clustering analysis facilitates the derivation of optimized analytical domains by automatically classifying data points with shared climatological signatures. This study adopts a comparative perspective by evaluating data-driven (K-means and K-medoids), spatial-based (Spatially Constrained Multivariate Clustering: SCMC), and density-based (DBSCAN) clustering approaches to reveal regional variability and spatial dependency across Türkiye. The primary motivation of this research is to determine which approach reflects the heterogeneous structure with high representational power across diverse topographical settings, thereby establishing an optimal clustering strategy for atmospheric modeling. This study aims to characterize and regionally decompose Türkiye’s meteorological profile by leveraging high-resolution European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis parameters, including temperature, pressure, relative humidity, instantaneous moisture flux, geopotential, 2m temperature and dewpoint temperature. Methodologically, the ERA5 were downscaled to the geographical coordinates of meteorological stations via point-based extraction and validated against in-situ observations provided by the Turkish State Meteorological Service. Performance analyses indicate that the SCMC approach achieved the highest precision in delineating meteorological similarities and disparities. The comparative success ranking was established as SCMC>DBSCAN>K-medoids>K-means. Findings demonstrate that while K-means and K-medoids are effective in identifying broad climatic zones, the SCMC algorithm generates significantly more consistent and spatially continuous clusters. Furthermore, DBSCAN successfully isolated microclimatic regions with extreme values, such as the Eastern Black Sea, from the primary clusters. Consequently, this research demonstrates that integrating high-resolution reanalysis data with advanced spatial clustering techniques provides a more precise and dynamic decision-support mechanism for regional meteorological analysis and environmental planning compared to conventional methodologies.
In this study, Fibonacci, Lucas, and generalized forms of these sequences are examined. In the first chapter, definitions of Fibonacci and Lucas number sequences are provided, and some of their properties are analyzed. In the second chapter, incomplete Fibonacci and Lucas numbers are analyzed. Finally, in the third chapter, incomplete Fibonacci and Lucas numbers with arithmetic indices are defined and new relations are obtained
Solutions and Solution Behavior of Difference Equation Systems
The behaviour of the solutions of the following system of difference equations is examined. In this study, the solutions of the initial conditions in the range (0,1) and the behavior of the solutions were examined. Investigations on the recent studies show that researches nature of nonlinear difference equations have been an object of great interest. Although difference equations are relatively simple in form, it is, unfortunately, extremely difficult to understand thoroughly the periodic and non periodic behavior of their solutions. Nonlinear difference equations of the max type has been investigated by many authors. These equations are particularly utilized in various fields, including population dynamics, economics, finance, and control systems. Max-type difference equations are not only interesting from a theoretical perspective but also possess a wide range of practical applications. They are frequently employed to model discrete dynamical systems in control theory, economic fluctuations, and biological population models with delayed feedback mechanisms. Understanding the periodic behavior and stability of such systems provides vital insights into the long-term dynamics of these real-world phenomena. In addition, Mathematica software was employed to generate the graphical illustrations and numerical examples presented in this study.
Neutrosophic Nano Regular Generalized Star Star B – Continuous andIrresolute Mappings in Neutrosophic Nano Topological Spaces
Neutrosophic sets, proposed by Smarandache, extend fuzzy sets and intuitionistic fuzzy sets to better manage imprecise, incomplete, and inconsistent information frequently encountered in real-world problems. A neutrosophic set is characterized by three independent components: truth, indeterminacy, and falsity. Based on this framework, Salama introduced the notion of neutrosophic topological spaces. The concept of a Neutrosophic Nano Topological Space further integrates neutrosophic set theory with nano topology to effectively model uncertainty, indeterminacy, and vagueness in highly granular or fine-scale systems and structures. In 2020, N. Rajeshwari, G. Vasanthakannan, and J. Subashini introduced the concepts of neutrosophic nano regular generalized star star b-closed sets and studied some basic properties in neutrosic nano topological space In this research paper, we introduce the concepts of mappings, mappings, mappings, mappings, strongly mappings, perfectly mappings, mappings, mappings and totally mappings in neutrosophic topological spaces We investigate and obtain several properties and characterizations concerning these mappings in ( ) eut anorb closed N g-N ( ) eut ano Top Spaces −. -N N eut anorbcontinuous N g-N eut anorbirresolute N g-N eut anorbclosed N g-N eut anorbopen N g-N eut anorbcontinuous N g-N eut anorbcontinuous N g-N eut anocontra rbcontinuous N -g-N eut anocontra rbirresolute N -g-N eut anorbcontinuous N g-N ( ) eut ano Top Spaces . --N N eut ano TopSpaces. N --N
A Binary Hiking Optimization Algorithm for 0/1 Knapsack Problem
The 0/1 knapsack problem is one of the prominent NP-hard binary optimization problems. Solving such problems using exact methods can be computationally expensive. Therefore, metaheuristic methods which offer optimal or near optimal solutions in a reasonable time, are commonly utilized for such tasks. The Hiking Optimization Algorithm (HOA) is a recently proposed metaheuristic optimization algorithm that is inspired by the similarity between the hikers’ navigation of steep terrain and the search process within the optimization problem’s search space. However, the basic algorithm is initially proposed for continuous optimization problems. To apply the algorithm to binary optimization tasks, certain adaptations are required. One of the commonly used binarization techniques involves using a transfer function to convert continuous decision variables into binary counterparts. In this study, HOA is binarized using four different variants for each of the following transfer function types: S-shaped, U-shaped, Z-shaped, and T-shaped. Furthermore, a repair function is adopted to tackle the infeasible solutions produced by the algorithm. The performance of the proposed binary HOA (binHOA) algorithm is assessed on two standard benchmark datasets. The results are compared with the binary versions of the well-known optimization algorithms. The experimental results indicate that the proposed binary version of HOA is a competitive alternative for the 0/1 knapsack problem.
Solution of the Binary Knapsack Problem Using the Secretary Bird Optimization Algorithm
Many nature-inspired metaheuristic algorithms proposed in the literature are initially designed to operate in continuous search spaces. However, real-world problems are not limited to continuous domains; they also include discrete, combinatorial, and binary problem types.In this study, the performance of the Secretary Bird Optimization Algorithm (SBOA), a recently introduced metaheuristic method, is investigated on the knapsack problem, which belongs to the class of binary optimization problems. Numerous transfer functions have been proposed in the literature to transform continuous search spaces into binary ones. In this work, S-shaped transfer functions are employed for this purpose.Through this transformation, SBOA—originally designed for continuous domains—is adapted to the binary search space and referred to as the Binary SBOA (BSBOA). The performance of BSBOA is evaluated on well-known benchmark knapsack problem instances. While BSBOA achieves optimal solutions on 10 small-scale benchmark problems, it produces near-optimal results for 18 large-scale binary knapsack instances.This study aims to address the SBOA binary optimization problem and evaluate its performance in such an environment.
Matheuristics in Scheduling Problems: A Global Bibliometric Analysis
Matheuristic approaches, which seamlessly combine exact mathematical programming with heuristic algorithms, have gained significant momentum in solving complex, NP-hard scheduling problems. Despite their growing popularity and effectiveness, there is a notable lack of comprehensive bibliometric mapping in this specific subfield. This study conducts a quantitative bibliometric analysis to uncover the structural and dynamic characteristics of literature at the intersection of matheuristics and scheduling. A total of 370 peer-reviewed articles and conference papers, published between 2007 and 2025, were extracted from the Scopus database and analyzed using the bibliometrix R package. The findings reveal a steady, logarithmic increase in scientific production, driven by the demands of complex, large-scale industrial applications. Co-occurrence network analyses confirm that current research is heavily centered on the integration of Mixed-Integer Linear Programming (MILP) with heuristic frameworks. The analysis identifies critical research gaps, suggesting that future studies should focus on energy-efficient scheduling, dynamic environments with stochastic constraints, and algorithm hybridization with machine learning.
Common Fixed-Point Results for Geraghty-Ciric Contraction in Fuzzy Metric Space
The theory of fixed points has undergone significant development over the years through the generalization of contraction conditions and the modification of the axioms of metric spaces. These advancements have broadened the applicability of fixed-point results across various branches of mathematics and applied sciences. In particular, fuzzy metric spaces have provided a flexible and effective framework for dealing with uncertainty, leading to a growing interest in the study of contractive mappings within this setting. In this paper, we investigate common fixed-point results for a pair of weakly compatible mappings satisfying a Geraghty–Ćirić-type contraction condition in the context of complete fuzzy metric spaces. By using the properties of Cauchy sequences and convergence in fuzzy metric spaces, we establish sufficient conditions for the existence and uniqueness of a common fixed point for the mappings under consideration. The results obtained in this work extend and generalize several known theorems in the existing literature, especially those related to Geraghty- and Ćirić-type contractions. To demonstrate the applicability of the main theorem, an illustrative example is provided. Furthermore, a corollary is derived as a special case of the main result, highlighting its versatility and potential for further generalizations.
A Machine Learning Framework for Long-term Precipitation Prediction Using Gradient Boosting and Ensemble Techniques
Long-term precipitation prediction remains a challenging task due to the nonlinear and dynamic interactions among meteorological variables, particularly in regions with complex climatic conditions. This study proposes a Machine Learning framework based on XGBoost combined with temporal feature engineering for predicting rainfall, snowfall, and total precipitation in North Macedonia. Given the limitations of traditional numerical weather prediction models and the lack of region-specific studies focused on the Balkan climate, this research investigates whether advanced supervised learning algorithms can improve the accuracy and stability of precipitation forecasting.A comprehensive dataset covering the period from 2000 to 2025 was obtained from Visual Crossing, followed by extensive preprocessing and feature engineering. The engineered features include lag variables, rolling statistics, seasonal indicators, and interaction terms to better capture temporal dependencies and complex relationships in the data.Three predictive models were developed: a rainfall classifier, a snowfall classifier, and a general precipitation classifier. The primary algorithm employed was XGBoost, while the snowfall prediction model utilized an ensemble stacking approach combining XGBoost, Random Forest, and LightGBM to address class imbalance and enhance model robustness. Model validation was conducted using a TimeSeriesSplit strategy with temporal gaps to prevent data leakage.The results demonstrate significant performance improvements using gradient boosting-based methods. The snowfall model achieved an accuracy ranging from 90% to 97%, rainfall prediction reached 81% to 84%, and the general precipitation model achieved 80% to 81% accuracy. Feature importance analysis identified temperature, humidity, atmospheric pressure patterns, and cloud cover as the most influential predictors. These findings confirm that machine learning provides a reliable and effective approach for long-term precipitation forecasting in the region, with potential applications in agriculture, transportation, and environmental planning.
Power Supply Technologies for Collaborative Service Robots: Characteristics and Comparative Overview
The article examines the power supply of a Service Collaborative Robot, powered by a hydrogen fuel cell, intended for transporting seriously injured people and serving them in medical facilities. The qualities of this power supply are analyzed, highlighting its advantages in terms of environmental characteristics and its long "life" during the activities of serving patients in serious health conditions and transporting them to a hospital. Some main characteristics of this type of robot, powered by a hydrogen fuel cell, are analyzed, compared with robots powered by other types of power supplies, and its advantages when working in a hospital are shown.
Evaluation and comparison of the Seismic and Environmental Performance of Plane Trusses and Flat Space Frames: A Parametric Study on Soil and Climatic Influence
This study evaluates the analysis and design of two steel roof structures according to current practices in Turkey, including Eurocode 1 (2007) and the Turkish Building Earthquake Code of 2018 (TBEC 2018), with the aim of comparing the efficient roof system configuration from both a structural perspective and a structural behavior perspective. In this study, 8 structural models were examined, which investigated a combination of 2 different structural roof types, Plane Trusses (Type A) and Flat Space Frames (Type B), in two different climatic regions (Zone II and Zone IV), one seismic level, and two site classes (ZB and ZC). Structural modelling and analyses were analyzed and simulated using SAP2000 software. Calculations were carried out in accordance with the Turkish Steel Structures Design, Calculation and Construction Regulations (SDCCSS, 2016). The results indicate that the sensitivity of the structure to lateral loads is largely dependent on the classification of the lands and soil types, so that for type II lands, wind-induced displacements predominate, while for zone IV lands and site class (ZD), the behavior of the structure changes and becomes earthquake-oriented, with seismic displacements being predominant. Economically, Type A systems achieved the maximum weight for 20 m spans, and Type B systems recorded the minimum material consumption. Seismic displacements were recorded at higher levels for ZC site class, as well as for all models 2 and 4. This study shows that the choice of roof geometry significantly affects the total weight of the structure and consequently the cost of the project. The results of the analysis confirm that the performance of the Type B systems in limiting the lateral displacement in weak soils.
ERP Strategic Framework for Vietnam’s Precision Mechanical Engineering: A Hybrid MCDM Approach to Digital Transformation
For precision mechanical engineering firms in emerging economies, adopting an Enterprise Resource Planning (ERP) system presents a distinct challenge. Decision-makers frequently struggle to reconcile complex technical operational requirements with strict economic limitations. This research navigates this tension by developing a contextualized, mixed-methods evaluation framework for Vietnamese Small and Medium Enterprises (SMEs). Integrating qualitative expert consultation with a combined Best-Worst Method (BWM) and VIKOR analytical model, the study offers a rigorous, data-driven pathway to selecting the most viable ERP system. Applying BWM, the results indicate that Technical Capability emerges as the dominant evaluation criterion, substantially outweighing both Economic Feasibility and Organizational Readiness. The subsequent VIKOR compromise ranking further confirms Epicor Kinetic as the most suitable solution, as it provides the strongest overall performance (i.e., maximized group utility) while maintaining low individual regret—particularly in relation to complex requirements such as multi-level Bill of Materials (BOM) integration and Make-to-Order (MTO) synchronization. By contrast, alternatives emphasizing high modularity and lower upfront cost were ranked least favorable, primarily because they entail considerable operational risk in engineering-intensive settings. Taken together, these findings challenge conventional cost-led adoption logics and suggest an evidence-based implication for practice: decision-makers should prioritize foundational technical alignment over initial capital savings to enable a more resilient and sustainable digital transformation.
Numerical Analysis and Optimization of Hydroforming Parameters of a 2024-T3 Aluminum Alloy Interface Part for Aerospace Applications
This study investigates the production of a 2024-T3 aluminum alloy interface component, designed for the mounting of a commercial antenna onto a helicopter tail, using the hydroforming process. Hydroforming is a widely utilized sheet metal forming technique in aerospace applications, operating on the principle of utilizing high fluid pressure to induce plastic deformation of the blank into a male or female die. Compared to conventional forming methods, it enables the production of complex geometries with superior quality. Within the scope of this work, the interface part was first designed to conform to the surfaces of both the antenna and the helicopter tail, followed by the development of the female hydroforming die geometry. The effects of critical process parameters, such as fluid pressure, blank holder force (BHF), and initial blank dimensions, on thinning and stress distribution were examined through Finite Element Analysis (FEA) based on the Forming Limit Diagram (FLD) and material hardening criteria. Through iterative simulations, the optimal process parameters required to prevent potential failure modes, such as tearing and wrinkling, were identified. The results indicate that fluid pressures reaching 50 MPa are necessary for successful forming, with a minimum achievable corner radius of 22 mm. Consequently, it is demonstrated that 2024-T3 alloy can be effectively formed into complex aerospace components using the hydroforming method.
Optimization of 3D Printing Parameters for Compressive Strength of PLA Using the Taguchi Method
Fused Deposition Modeling (FDM) has significantly advanced in the additive manufacturing of complex geometrical and customized parts, particularly for thermoplastics like Polylactic Acid (PLA). The purpose of the present study is to optimize FDM process parameters to improve the compressive strength of 3D-printed PLA using the Taguchi method. Four process parameters such as infill density, printing speed, layer thickness, and infill pattern type are selected. Among these parameters applied, printing speed had a significant impact on compressive strength. Analysis of variance results revealed that printing speed was dominant, followed by layer thickness in analyzing the compressive strength, but the infill density and infill pattern were not significant. Confirmation tests were provided by Taguchi method. Further, it is found that the optimal compressive strength was achieved with a printing speed of 300 mm/s, a layer thickness 0.3 mm, an infill density of 10, and a pattern shape of grid. Therefore, trial 3 yielded the highest compressive strength results at approximately 42.45 MPa, which is slightly higher than those reported in prior studies.
DSOGI-PLL and Moving Average Filter Based Control Method for Shunt Active Power Filter
The increasing penetration of nonlinear loads and power electronic converters in modern power systems has led to a significant rise in current harmonics and power quality problems. The shunt active power filter (SAPF) has emerged as an effective solution to mitigate these issues. However, the performance of SAPF systems largely depends on the accuracy of the reference current generation method and the effectiveness of the synchronization algorithm. In conventional synchronous reference frame (SRF–dq) based SAPF control methods, low-pass filters (LPFs) are commonly used to extract the fundamental component, while high-pass filters (HPFs) are employed to obtain harmonic components. Nevertheless, these filtering structures may introduce undesirable effects such as time delay and phase shift, which degrade the dynamic performance of the control system.In this study, an improved dq-based control strategy is proposed for SAPF applications by combining a Dual Second-Order Generalized Integrator Phase-Locked Loop (DSOGI-PLL) based synchronization technique with a Moving Average Filter (MAF) based harmonic extraction approach. In the proposed method, the DSOGI-PLL structure ensures accurate phase angle estimation even under distorted grid conditions, while the MAF structure enables the extraction of the fundamental current component without introducing the delay and phase shift problems associated with conventional LPF and HPF filters. The effectiveness of the proposed control method is evaluated through simulation studies under different operating conditions. In the case of sinusoidal grid voltage and nonlinear load, the total harmonic distortion (THD) of the source current is reduced from approximately 19.19% to 0.79%. Furthermore, under distorted grid voltage and nonlinear load conditions, the source current THD values decrease to the range of 1.55–1.89%. These results demonstrate that the proposed method significantly enhances harmonic mitigation performance and ensures compliance with the limits specified in the IEEE 519-2014 standard.
Next-Generation Multifunctional Composites: The Role of MXene Nanosheets in Enhancing Fiber-Reinforced Epoxy Polymers
Fiber-reinforced polymer composites are indispensable in high-performance engineering sectors. This study investigates the potential of MXenes as advanced reinforcing agents for carbon, glass, basalt, and Ultra High Molecular Weight Polyethylene (UHMWPE) epoxy composites. It presents a comparative analysis evaluating the effects of the primary MXene synthesis methods: Direct HF etching method and the Minimally Intensive Layer Delamination (MILD) method using LiF/HCl. Literature synthesis reveals that MXenes significantly enhance Interlaminar Shear Strength (ILSS), flexural modulus, and fracture toughness even at ultra-low concentrations through mechanisms such as crack deflection, bridging, and mechanical interlocking. Notably, interfacial modifications in UHMWPE composites have demonstrated a reported increase in Interfacial Shear Strength (IFSS). Beyond mechanical gains, MXenes provide multifunctional capabilities, including electromagnetic interference shielding and superior resistance to corrosive environments. This study concludes that, despite being an emerging area, the synergy between MXenes and fiber-reinforced epoxy polymers represents a promising approach for the development of next-generation multifunctional composites with high durability.
High Mass Ratio Tuned Mass Dampers Obtained Through Building Superelevations for Seismic Retrofit
Tuned Mass Dampers (TMDs) are widely adopted passive vibration control devices used to mitigate structural oscillations induced by wind and seismic actions. Conventional TMD systems typically involve relatively small auxiliary masses installed near the top of tall buildings. This paper investigates an alternative strategy for seismic retrofitting of existing structures based on large mass ratio TMDs obtained through building superelevations. In this approach, one or more additional storeys supported by seismic isolation devices behave as large tuned masses capable of reducing structural response during earthquake excitation. A comprehensive parametric numerical investigation is carried out in order to evaluate the influence of the mass ratio, structural period and damping characteristics on the effectiveness of the system. The results are summarized in a set of design charts that allow engineers to determine optimal tuning parameters. In particular, by using the obtained design abaci they can find a solution which minimizes both displacement and acceleration.
Scheduling Analysis in Digitizing Factories: An Application
The manufacturing companies should be dynamic and flexible for the customers’ demands nowadays. Thus, scheduling plays an important role in these dynamics and flexibility. The manufacturing companies should make digitizing the operations and processors for online scheduling. In this research, the fourth industrial revolution is explained, a literature search is done for scheduling in the smart manufacturing environment, and the tools that are used during the digitized factories are defined. A simulation-based model is proposed for the smart factory in Industry 4.0 environment. Then an application is done in an office furniture manufacturing company in the smart factory environment. The physical model is transformed to virtual model by the proposed simulation approach at the production line where the office chair for executive and operational produced. With the three dispatching rules such as first in first out, earliest due date, and scatter search approach, a scheduling optimization is done in the office furniture manufacturing company. The efficient solution method is proposed for furniture manufacturing companies in the digitizing factories.
Analysis of MEMS IMU Errors Impact on UAV Positioning Accuracy
This paper proposes a comprehensive study conducted over one of the primary sources of positioning errors in unmanned aerial systems (UAVs) navigation systems: the inherent limitations of embedded miniaturized inertial measurement units (IMUs). In recent years, MEMS technologies have come to dominate the electronics market due to their promising advantages of reduced cost and size, low power consumption and series production. These aspects make them particularly attractive for the applications in which compactness and autonomy are critical, such as in the UAV industry. However, the drawbacks of MEMS devices are their susceptibility to environmental factors and their intrinsic imperfection which result in errors and noises contaminating the output acceleration and angular velocity signals. Improper sensing of vehicle dynamics may consequently lead to erroneous computation within the strap-down navigation algorithm. For this reason, errors and noises originating from the inertial subsystem may substantially degrade the precision of the final global coordinates and compromise the overall mission success. The current research aims to provide a clear examination of the aspects influenced by IMU errors throughout the navigation algorithm, as well as to identify potential approaches for further research in the field of positioning accuracy enhancement
A Truncated Spline Nonparametric Logistic Regression: Simultaneous Testing and Model Comparison
Regression is widely used in statistical analysis. For binary response variables, Logistic Regression has been widely applied due to its interpretability. However, conventional Binary Logistic Regression (BLR) generally assumes a linear relationship between predictors and the response, which may limit its ability to capture complex data patterns. To address this limitation, this study examines a Truncated Spline Nonparametric Logistic Regression (TSNLR) model that provides greater flexibility in modeling nonlinear relationships through the use of knot points. Statistical inference of the proposed model is examined using simultaneous hypothesis testing to assess the significance of predictor variables. The TSNLR model is applied to Human Development Index (HDI) data of cities/districts in Indonesia, which are classified into high and low HDI categories. Model performance is evaluated and compared with BLR using classification performance metrics. These findings suggest that TSNLR is an effective alternative for modeling binary response data with complex predictor relationships and can offer improved interpretability in applied statistical analysis.
3D Accuracy Assessment of an Indoor UWB Positioning System for UAV Emergency Applications Using VICON Ground Truth
In recent years, amid the growing frequency of natural disasters and large-scale emergency scenarios, unmanned aerial vehicles (UAVs) have become essential tools for executing risky missions, without jeopardizing the lives of search and rescue personnel. While GNSS represents the most mature and robust positioning method, it becomes unreliable in indoor scenarios, as it is affected by multipath and non-line-of-sight (NLOS) conditions. This paper evaluates the 3D positioning accuracy of an ultra-wideband (UWB) indoor positioning system. The main purpose of this assessment is to decide whether the system can assure robust UAV positioning during emergency situations in GNSS-denied areas. The proposed system is rapid to deploy, cost-effective, and requires minimal hardware and software configuration, making it suitable for emergency response scenarios. The 3D trajectory estimated by the UWB positioning system is evaluated against ground truth positioning data provided by a VICON motion capture system. The system is tested in an uncontrolled indoor environment, with obstacles and NLOS conditions. In order to produce the best possible trajectory estimate, offline filtering techniques such as Kalman Filter, Savitzky-Golay filter and Rauch–Tung–Striebel smoothing are applied. The main purpose of these filters is to mitigate noise and the impact of outliers, and obtain a smoother and more reliable trajectory. A comparative analysis of Kalman Filter, Savitzky-Golay filter and Rauch–Tung–Striebel smoothing is presented by computing the Root Mean Square Error (RMSE) on each axis between the UWB measurements and the ground truth trajectory both before and after applying the offline filtering techniques.
Reasserting the Epistemic Role of Non-Parallel Line Contexts in Introducing Angle Relationships
Angle relationships are an important topic in Euclidean plane geometry and form a conceptual basis for understanding line parallelism. In scholarly geometry, these relationships were introduced through configurations of two non-parallel lines intersected by a transversal. However, in several instructional contexts, angle relationships were presented first through the context of parallel lines. This practice risked obscuring the epistemic status of angle relationships within the structure of Euclidean geometry. This study aimed to reaffirm the epistemic role of non-parallel line contexts as the conceptual foundation for introducing angle relationships. A qualitative study was adopted. Data were collected through both of the analysis of mathematical documents such as geometry textbooks and Focus Group Discussion (FGD) involving scholars in geometry, pure mathematics, and advanced mathematics education. The analysis focused on logical coherence and structural relationships among concepts. The results showed that the configuration of non-parallel lines provided the most conceptually neutral framework for introducing angular relationships. This can be seen, among other things, from the use of the term special pair of angles (in this case same-side interior angles) in Euclid's parallel postulate. Consequently, angle congruences or supplementary were interpreted as consequences of Euclid’s fifth postulate rather than as their conceptual origin. These findings highlight the epistemic necessity of introducing angle relationships through non-parallel contexts to preserve coherence and conceptual hierarchy in Euclidean geometry.
A Comparative Study of Metaheuristics on Raspberry Pi 5: Results to Guide Optimization on Constrained Edge Devices
This study investigates the efficiency of five metaheuristic algorithms, namely Differential Evolution (DE), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Harmony Search (HS), and Particle Swarm Optimization (PSO), when deployed on a Raspberry Pi 5 edge device. The evaluation focuses on both optimization quality and computational cost, using four standard benchmark functions that represent a range of landscape characteristics: Sphere, Rosenbrock, Rastrigin, and Ackley. Each function is tested at dimensions 10, 30, and 50 to probe scalability. In addition to objective values, the experiments collect per-iteration processor usage and memory (RAM) to provide a practical view of runtime overhead under constrained resources. Among the five candidates, GWO consistently delivers the fastest or near-fastest convergence while keeping variability tight. Its trajectories show smooth descent across functions and dimensions, paired with comparatively modest CPU and RAM footprints. PSO typically ranks second in speed with stable dynamics, though brief CPU spikes often appear at early iterations as swarms synchronize. DE demonstrates resilience on rugged functions but generally requires more iterations to close the final gap. GA and HS can reach competitive objective values on some settings, yet they display wider dispersion and higher overhead at larger dimensions, which reduces their suitability for small devices. Overall, the evidence indicates that GWO is the most efficient choice for edge deployment on Raspberry Pi 5, striking a favorable balance between convergence speed, stability, and resource usage. PSO is a strong alternative when slightly higher processor activity is acceptable. These findings support the adoption of lightweight, variance-stable metaheuristics for edge optimization workloads where CPU and memory budgets are tight.
Optimal Sizing of a Sustainable and Climate Resilient Microgrid System
The research investigates the potential of an off-grid microgrid system powered by renewable energy sources in Barangay Ransang, a remote village in Palawan. The study aims to determine the renewable energy potential, identify the most economical combination of renewable energy sources, and evaluate the financial benefits of the proposed system. The researchers used HOMER software to simulate and optimize the microgrid system, identifying a 2.0 MWp solar PV array, 0.840 MW diesel generators, and a 1.0 MWh battery energy storage system as the optimal solution. This configuration provides 40% renewable fraction and a cost of energy of $0.287/kWh. The Simplified Planning Tool (SPT) validated the results, confirming the feasibility of the proposed microgrid system. The hybrid power plant's levelized cost of electricity (LCOE) is $0.287/kWh, significantly cost-saving compared to a diesel-only system with an LCOE of $0.424/kWh. Implementing this off-grid microgrid system in Barangay Ransang is projected to result in annual savings of $578,219.10 in Universal Charge on Missionary Electrification (UCME) subsidy, further contributing to the project's economic viability. The findings can serve as a reference for other off-grid electrification projects in the Philippines and beyond, offering potential benefits to remote areas waiting to be electrified.
Evaluation of a Plant Quality Monitoring System Based on Visible and Near-Infrared Spectroscopy Sensors in a Controlled Environment
The increasing demand for agricultural production requires reliable and non-destructive methods for monitoring plant physiological conditions in real time, particularly in controlled environments. Spectral sensing in the visible to near-infrared (VIS–NIR) region offers a promising approach; however, the performance of low-cost sensors is often limited by calibration accuracy, wavelength-dependent sensitivity, and insufficient validation against plant physiological indicators. This study aims to evaluate the performance of a VIS–NIR plant monitoring system based on the AS7265x multispectral sensor in a controlled hydroponic environment. Lettuce (Lactuca sativa L.) was grown under two nutrient concentrations (600 ppm and 1200 ppm), and spectral reflectance data were collected across the 410–760 nm range. Sensor measurements were calibrated and validated against a LI-COR LI-180 reference spectrometer using linear regression, with performance assessed using the coefficient of determination (R²), root mean square error (RMSE), and spectral response consistency. The results show strong calibration performance, with wavelength-specific R² values ranging from 0.9682 to 0.9870 and RMSE values between 0.26965 and 5.19772. Although a systematic offset was observed, the AS7265x sensor preserved key spectral patterns, particularly in the green (510–560 nm) and red-edge (705–730 nm) regions. Differences in nutrient concentration were consistently reflected in both spectral responses and SPAD measurements, indicating sensitivity to plant physiological variations. These findings demonstrate that the AS7265x sensor provides reliable spectral information for relative plant monitoring and has strong potential as a cost-effective tool for plant quality assessment in controlled environments.