A polymer is a molecule made by joining many small molecules called monomers. The word "polymer" can be broken down into "poly" and "mer". This shows how the chemical composition of a polymer consists of many smaller units (monomers) bonded together into larger molecules. Rubbers are elastomeric polymers that are not cross-linked but can be cross-linked. There are natural and synthetic rubbers in the literature. Acrylonitrile butadiene rubber (NBR) is a type of synthetic rubber widely used in various industries. Rubber formulations consist of different components with various properties. The plasticizers are one of the significant components of rubber formulation. In this study, Alkylsulphonic acid ester, Diisononyl Phthalate, Trioctyl Trimellitate Triethyleneglycol ester, Phthalic acid esters were used for comparison. Mechanical and thermal analyses were conducted for all formulations containing different plasticizers. The most resistant to low temperature in the Differential Scanning Calorimeter test is triethylene glycol ester at -45°C. On the other hand, this refers to the hardness change. tri-plasticizer has high change value than others. Tensile strength values increased after the ASTM oil aging tests. All the plasticizers improved the tensile strength values of the compounds in ASTM oils. The plasticizer with the lowest initial mechanical values. Aging tests in hot air and in ASTM oils were also investigated. The loss of elongation was parallel to the hardness change results. For all compounds, ASTM 3 effects more than ASTM 1 effects.

This study investigates the technoeconomic feasibility of integrating photovoltaic (PV) and wind turbine-based renewable energy systems with various storage technologies for a university lodging consisting of 50 houses in İzmir, Türkiye. To address the rising demand for sustainable and cost-effective residential energy solutions, four energy storage options—lead-acid, lithium-ion, vanadium redox flow (VRF), and hydrogen-based systems—were evaluated. Their economic performance was assessed using indicators such as Levelized Cost of Electricity (LCOE), Levelized Cost of Storage (LCOS), and Net Present Value (NPV). The annual electricity load profile was constructed hourly, reflecting monthly consumption and user behavior. PV and wind energy outputs were estimated using PV-Sol software and turbine power curves, respectively. A model was developed in MATLAB, targeting a Loss of Power Supply Probability (LPSP) below 1% over 20 years. Results showed that vanadium redox flow batteries had the best economic performance, due to their long lifespan and low capital cost, achieving the lowest LCOE (0.31 $/kWh), LCOS (0.24 $/kWh), and highest NPV (0.42 million $). PV-based systems were more favorable than wind-based ones, mainly due to wind turbines' higher costs and lower capacity factors in the region. Sensitivity analysis highlighted that storage cost, LPSP, grid electricity price, and interest rate are the most influential parameters. This research provides valuable guidance for developing economically viable and technically reliable off-grid renewable energy systems, supporting informed decision-making by researchers and policymakers working on localized energy transition strategies.

In this study, biocomposite materials resistant to various effects (heat and humidity) were produced by using olive seed powder and basil leaf powder as reinforcing elements at different ratios (30%-0%, 25%-5%, 20%-10%, 15%-15%, 10%-20%, 5%-25%, 0%-30%) and the effects of these ratios were obtained as a result of tests. In the samples produced using olive kernel powder and basil leaf powder, it was observed that the powder ratio added to the mixture had significant effects on the properties in tensile, impact, hardness, water absorption, and ignition loss tests. In the tensile strength test, the sample produced using 30% olive pomace powder showed the highest tensile strength with 28.892 MPa, while a value of 23.39 MPa was observed with 30% basil leaf powder. In the impact energy test, the BLP5 sample produced using 5% basil leaf powder had the highest resistance with 0.808 J. In the Vickers hardness test, the BLP30 sample with 30% basil leaf powder reached the highest value (150.4 HV) among the samples produced using basil leaf powder, while the BLP0 sample produced using olive pomace powder with the same 30% pomace powder ratio reached 120.11 HV. In the water absorption test, a decrease in water absorption capacity was observed as the basil leaf powder ratio increased and the lowest absorption capacity was observed in BLP30 specimen produced using 30% basil leaf powder. However, it was observed that the specimens produced using olive seed powder by weight had less water absorption capacity than the specimens produced using basil leaf powder by weight. In the ignition loss test, the weight loss increased with higher olive seed or basil leaf powder content, reflecting the greater organic fraction in these samples. Based on these results, it is aimed to be used in the automotive sector in areas where water and heat contact is high.

Oxygen levels in the body/and organs significantly influence the regulation of metabolic reactions. Hypoxia, a decrease in oxygen levels, can potentially trigger various signals leading to serious health issues. This study aimed to develop an electrochemical immunosensor platform for rapidly and accurately detecting the hypoxia biomarker, HIF-1α protein. In this regard, screen-printed gold electrodes were modified using 11-mercaptoundecanoic acid and 3-mercapto-1-propanol (as a spacer) to generate functional carboxyl groups. Employing EDC-NHS chemistry facilitated the immobilization of HIF-1α antibodies, which were then utilized for the selective and specific recognition of their target. Electrochemical voltametric measurements were conducted using a potassium ferri/ferrocyanide redox couple in both hypoxia-cultured cell lysates and phosphate-buffered saline spiked with HIF-1α protein.In addition to electrochemical measurements, Western blotting (WB) was performed to compare findings with electrochemical results and to confirm the presence of HIF-1α in hypoxic cell lysates. While WB results only exhibited the qualitative presence of the respective antigen in lysates, significant signal decreases were observed in both Cyclic Voltammetry (CV) and Differential Pulse Voltammetry (DPV) measurements due to specific antibody-target binding, emphasizing the electrochemical sensor’s performance for more rapid and quantitative protein detection. The low limit of detection (1.46 nM) suggests the potential of our proposed immunosensor platform for detecting HIF-1α protein within a clinically significant range, which is highly desired for point-of-care applications. This study is one of its kind in the literature to develop an electrochemical immunosensor platform for the rapid detection of HIF-1α. The sensor's ability to provide inexpensive, rapid, and quantitative measurements is a significant advancement for HIF-1 α detection.

IEnhancing urban resilience in seismically active regions is essential for reducing disaster risks and ensuring sustainable development. This study characterizes the dynamic engineering parameters of soils in Eskisehir’s city center, a region situated in Central Anatolia that faces high seismic risk due to its young alluvial deposits and proximity to active faults. To achieve this, detailed geophysical surveys were conducted using seismic refraction and microtremor (HVSR) methods at selected locations, and the resulting data were analyzed using Geographic Information Systems (GIS). The findings reveal significant spatial heterogeneity in soil behavior within the upper 30 meters. Specifically, Vs₃₀ values across the study area were found to range from 145 to 990 m/s, with low-velocity zones (145–315 m/s) heavily concentrated in densely populated districts. Consequently, the GIS-based Site Amplification (Fa) maps exhibited values ranging from 0.92 to 1.70, pinpointing specific zones with high seismic amplification potential. Furthermore, while fundamental site periods varied between 0.09–2.86 s, a critical concentration of periods in the 0.43–0.85 s range was identified. This range directly corresponds to the natural vibration periods of 4-8 story reinforced concrete buildings, indicating a high potential for destructive soil-structure resonance. These quantitative results provide an operational basis for multi-level planning processes specifically for defining priority zones in urban transformation and enforcing height restrictions in land-use decision-making thereby demonstrating the strategic role of geophysical methods in multidisciplinary disaster management.

This study investigates parabolas in the generalized taxicab plane, a non-Euclidean geometry where distance is measured using weighted coordinate axes with positive parameters (a,b). Using the focus-directrix definition, it examines the structures of generalized taxicab parabolas (briefly, GTPs) with respect to the positions of their directrices. It is determined that generalized taxicab parabolas are simple rectilinear figures. It further provides a detailed analysis of GTPs, including their axes, vertices, latus rectum, and focal lengths. It reveals that the latus rectum length of a GTP is four times its focal length regardless of the directrix type. Also, the algorithm is presented to visualize GTPs for all types of directrices. Additionally, the study identifies degenerate cases in which the focus is on the directrix, and it is demonstrated that the obtained geometric structures reduce to single lines or unions of planar regions defined by vertical and horizontal lines through the focus.

Behavior Trees (BTs) have emerged as a widely adopted method for modeling Non-Player Character (NPC) behavior in digital games, offering modularity, scalability, and flexibility compared to traditional approaches such as Finite State Machines. This paper explores the theoretical underpinnings of BTs and their application in NPC modeling. Behavior trees are formalized using graph theory, propositional logic, and probabilistic models, and their computational complexity is analyzed to position them within the broader mathematical framework of decision-making systems. Furthermore, practical examples are presented to demonstrate the advantages of BTs in creating adaptive and realistic NPC behaviors. The results highlight that BTs not only provide practical benefits for game developers but also offer a rigorous mathematical structure for analyzing decision-making models, enabling comparison with alternative approaches such as Markov Decision Processes and reinforcement learning.

Term weighting plays a critical role in text classification tasks. Traditional methods, with a few exceptions, make limited or inadequate use of distributional characteristics of terms across classes. The core hypothesis in this study is that a term’s weight should be proportional to its uneven distribution across classes. Therefore, the proposed methods prioritize terms concentrated around one or a few classes rather than terms almost evenly distrusted across all classes. To implement this idea, we introduce a family of novel term weighting methods based on economic inequality metrics. These metrics are typically used to measure the unfairness of income distribution in a population, and adapt them to characterize term distributions. To quantify distributional unevenness or imbalance to assess term significance, we select one representative method from each of three major categories of inequality indices: Lorenz curve-based (Schultz), entropy-based (Theil with two variants), and social welfare-based (Atkinson). Experiments with four benchmark datasets (20NG, R8, R52, and WebKB) using two classifiers (Multinomial Naïve Bayes and Support Vector Machines) on f1-micro and f1-macro evaluation metrics have been conducted. The experimental results demonstrate that the proposed term weighting methods, particularly the method based on Schultz index, consistently demonstrate superior or highly competitive performances compared to both traditional and state-of-the-art term weighting approaches. Experimental findings confirm the validity of exploiting economic inequality principles for quantifying inter-class distributional characteristics of terms in term weighting. Thus, this work not only validates the effectives of proposed methods but also demonstrate the value of interdisciplinary work in term weighting literature.

The primary aim of this work is to utilize the Fourier Transform in addressing random ordinary differential equations characterized by stochastic coefficients. The Fourier Transform Method was employed to analyze random ordinary differential equations arising from the stochastic selection of coefficients or initial conditions. The coefficients or beginning conditions were treated as random variables by applying uniform, exponential, and beta probability distributions to them. MATLAB (2013a) was employed to compute the statistical characteristics of the derived random solutions, encompassing expected value, variance, and confidence intervals. The results acquired are illustrated graphically and examined comprehensively.

Bimetallic Pt–Cu nanoparticles are promising catalysts for oxidation and hydrogenation reactions due to their tunable electronic and geometric properties. However, first-principles simulations of realistic nanoparticle sizes remain computationally prohibitive. In this study, Gaussian Approximation Potential (GAP) models were developed for Pt–Cu nanoparticles functionalized with a single O2 or CO molecule, achieving near-DFT accuracy in energies and forces while drastically reducing computational cost. The training dataset, derived from ab initio molecular dynamics (AIMD) trajectories at 300–1000 K, spans various morphologies (pure, core–shell, Janus, and ordered alloys) and particle sizes (38–260 atoms), capturing both thermal and structural fluctuations representative of realistic catalytic conditions. The resulting GAP models successfully reproduce DFT-level energetics and atomic forces with root-mean-square errors below 0.4 meV atom-1 for energies and 70 meV Å-1 for forces, without overfitting to any specific morphology. AIMD simulations reveal that alloying Pt with Cu enhances thermal and mechanical stability, with core–shell and Janus configurations maintaining ordered atomic coordination up to 1000 K. Radial distribution function (RDF) analysis confirms that short-range order persists at elevated temperatures, ensuring structural integrity under reactive conditions. These results demonstrate that machine-learning-based interatomic potentials provide a robust and transferable framework for exploring adsorption-driven restructuring, morphology evolution, and catalytic stability of Pt–Cu nanoparticles beyond the accessible limits of conventional DFT.

Producing electrode materials with high energy density and structural stability is crucial for advanced supercapacitor technologies. In this study, Co-BDC MOF material was synthesized by the solvothermal method and this material was used as an electrode material in an asymmetric supercapacitor device. The structural characterization of the produced Co-BDC MOF material was applied with XRD, FTIR analysis and FE-SEM analysis. The results of these analyses proved the successful production of Co-BDC MOF. Moreover, Co-BDC MOF was used as a material of electrode in the supercapacitor device and its supercapacitive performance was determined by electrochemical tests. According to the electrochemical test results of the supercapacitor device, the areal capacitance values at a scan rate of 5 mV.s-1 were calculated as 68 mF.cm-2. After 5000 cycles, it retained 77.6% of its initial capacitance. At a current density of 0.25 mA.cm-2, the energy and power densities of supercapacitor were determined as 13 µWh.cm-2 and 237 µW.cm-2, respectively. These results show that the Co-BDC MOF has attractive potential for supercapacitor application.

Physical accessibility constitutes a fundamental dimension of spatial justice, ensuring that all individuals can equally benefit from the built environment. This study aims to evaluate and quantify the accessibility challenges faced by wheelchair users within the Sivas Cumhuriyet University campus and to propose a model that objectively identifies spatial inequalities. For this purpose, the intra-campus pedestrian network was modelled using Python, and shortest-path analyses were conducted via Dijkstra’s algorithm for 1,000 randomly selected origin–destination pairs. These routes were further simulated within a multi-agent system (MAS) environment to estimate travel times and compare mobility performance between wheelchair users and able-bodied individuals. This study provides one of the first quantitative frameworks to integrate Dijkstra-based shortest-path computation and MAS-driven simulation for assessing wheelchair accessibility in outdoor environments. The resulting data were used to develop a numerical accessibility scoring system that expresses spatial disadvantage as an accessibility coefficient. The findings revealed that 85.8% of the routes were completely inaccessible for wheelchair users and that, where access was possible, travel distances were on average 8.5 times longer than those of non-disabled individuals. By establishing a reproducible and data-driven framework, the study connects the aim of promoting spatial equity with quantifiable outcomes, thereby providing a decision-support tool for campus redesign and urban accessibility planning. These findings provide a scalable analytical framework for promoting spatial equity, offering practical guidance for policymakers and urban planners seeking to improve accessibility in built environments.

In this study, sector-based methane emissions of European countries were modeled using a Random Forest–based machine learning approach applied to a panel dataset covering the period 2014–2023 with country–sector–year dimensions. The primary objective of the study is not to maximize predictive accuracy, but to evaluate how different validation strategies affect model performance and generalization behavior. Accordingly, three validation strategies—random training–test split, temporal (time-based) validation, and country-based group validation—were comparatively analyzed. The dataset, obtained from Eurostat, comprises 29 countries, 5 sectors, and 1,449 observations. Model performance was evaluated using root mean square error and the coefficient of determination. Under random splitting, the model achieved very low errors (mean RMSE = 0.0126 ± 0.0025; mean R² = 0.9993 ± 0.0003), although these results may be optimistic due to information leakage. Temporal validation yielded stable near-future performance (RMSE = 0.0225, R² = 0.9975). In contrast, country-based group validation resulted in a substantial performance decline (average RMSE = 0.3132 ± 0.4061), indicating strong cross-country heterogeneity. Overall, the findings demonstrate that, in panel data settings, the choice of validation strategy is as critical as the machine learning algorithm for realistic generalization assessment.