This study conducts a systematic comparative analysis of nine principal international seismic codes, focusing on their provisions for non-structural elements and their adequacy for protecting museum artifacts housed within buildings. While these standards share a consistent force-based framework for general non-structural components, they rarely address the distinct vulnerabilities of museum objects, such as low fragility thresholds, irreplaceability, and conservation constraints that often prohibit invasive anchoring. The results indicate that, although fundamental parameters (e.g., component weight and spectral acceleration) are universally included, advanced modifying factors, such as response reduction, resonance, and strength-related terms, are incorporated in only a limited number of codes, with overall parameter coverage ranging from 4 to 11. Even in the most parameter-rich formulations, the added complexity primarily refines inertial force scaling rather than governing artifact-specific stability mechanisms, including sliding, rocking, and overturning. The findings demonstrate that current seismic codes provide limited reliability for protecting museum objects when applied without adaptation. This research highlights the need for mechanism-aware extensions to existing code frameworks. It provides a foundation for the development of museum-specific seismic provisions to safeguard irreplaceable cultural heritage from earthquake hazards.
In this paper, the application of Direct Torque Control (DTC) to a single-inverter system driving two induction machines is investigated, incorporating an advanced control strategy. Sliding Mode Control (SMC), specifically the Super-Twisting Algorithm (STA), is employed to replace conventional PI controllers for speed, flux, and torque regulation, overcoming their inherent limitations. Additionally, to eliminate the dependency on physical sensors, a super-twisting-based observer is proposed, enabling accurate estimation of the various quantities required by the control strategy. The system implements cooperative control to synchronize and efficiently manage the operation of the two motors. This technique provides an effective solution to challenges related to robustness in the presence of uncertainties and its ability to rapidly reject disturbances. However, it has some drawbacks, including rapid control actions that can generate vibrations or noise in the controlled system. Simulation results demonstrate that the proposed sensorless DTC-STA control achieves superior performance compared to conventional DTC.
The contemporary digital advertising ecosystem faces critical technical and operational challenges, primarily driven by centralized intermediaries, data opacity, and privacy vulnerabilities. To address these systemic issues, this paper proposes "Ex-Mad," a decentralized architecture leveraging blockchain technology and the Interplanetary File System (IPFS). The proposed framework integrates three core architectural layers: the user entities (advertisers and end-users), the blockchain ledger for immutable transaction recording, and IPFS for distributed content storage. Unlike traditional centralized models, Ex-Mad utilizes smart contracts to automate campaign management and enforce trustless interactions, ensuring verifiable reward distribution based on user contribution without third-party validation. Furthermore, the integration of IPFS optimizes on-chain storage costs by handling large media files off-chain while maintaining data integrity through cryptographic hashes. The system design enhances auditability through a tamper-proof ledger, eliminates single points of failure, and provides a technically robust alternative to current ad-tech intermediaries. This study demonstrates how the proposed architecture improves system efficiency, reliability, and cost-effectiveness in the digital advertising domain.
Biological organisms in nature vary in their thermal adaptation strategies according to the cold or hot climatic conditions they live in. In this study, a building envelope design, which provides a two-way function of both gaining the heat needed in cold climate conditions and preventing the heat gain in hot climate conditions was studied. The morphological features and adaptation behaviors of organisms living in extreme temperature conditions in nature to keep their body and nest temperatures in the optimum range were examined with biomimetic design methodology approach. When the adaptation skills of biological organisms in the transition to different seasons were taken as reference, it was seen that dynamism is necessary to apply a similar adaptation to structures for the effective use of energy. The dynamism of design proposals of the kinetic building envelope is obtained by combining morphological structures with smart materials. Contrary to existing one-way adapting building envelope designs which are costly, composed of mechanical components, and bring difficulties to be implemented on buildings, a comprehensive and technology-free approach was brought. Three ideas designed with a synthesizing approach that can adapt to both conditions were proposed. In order to compare the thermal comfort performance among the designs, solar thermal analysis was carried out using computational-fluid-dynamics (CFD) analysis. In the end of the analysis, it was seen that the building envelope cells can increase heat gain in winter up to 5.9°C in the interior wall, while the thermal temperature load can be reduced up to 1.1°C in summer.
In this paper, the success of machine learning models applied to two datasets consisting of fake news was examined. The performance of these methods was measured using Accuracy, Precision, Recall, and F1-Score. Evaluation metrics for all models were calculated using TF-IDF and N-gram TF-IDF for Dataset 1 and Dataset 2, respectively. In continuation of the study, a decision-making mechanism was created to measure the success of machine learning methods. The success of these models was compared by creating a decision-making mechanism using intuitionistic fuzzy sets. The PROMETHEE method was used here. In the first stage of the study, the dataset samples evaluated were expressed using classical sets, while in the second stage, the success of the models according to the metrics was expressed using intuitionistic fuzzy values. This was to minimize the uncertainty in the model success results. The results obtained in the first stage of the study were evaluated in the second stage using a decision-making mechanism. In this mechanism, machine learning models represent the alternatives, while classification metrics represent the criteria. When evaluating machine learning models, experts provide subjective opinions based on each classification metric to determine the successful model.
Biofuels; a potential and renewable solution to climate change, fossil fuel depletion, and energy security. 1st, 2nd and 3rd generation biofuels are the main types of biofuels widely used worldwide, have different feedstock, production methods, and environmental impacts. 1st generation biofuels including ethanol and biodiesel, produced from food crops like sugar-cane, corn, and vegetable oil, are extensively used but have prompted concerns about land use and food availability. 2nd generation biofuels including cellulosic ethanol and bio-oils that mainly produced from fuel crops, agricultural wastes and forest byproducts by using enzymatic hydrolysis and thermochemical processes; are not only more ecofriendly but mitigate food-fuel conflict. On the other hand, 3rd generation biofuels derived from algae have advantage due to easy availability and high oil-content. Biofuels are renewable with carbon neutrality, reduce environmental pollution, minimize green-house gas emission, enhance energy security, stimulate economic growth, creating job opportunities, promoting rural development, minimizing water pollution as well as deforestation and beneficial for eco-systems. Except these benefits biofuels have some obstacles that hinder their efficiency like land degradation, underprivileged government policies, lack of people awareness, low energy return on investment, high production costs, unsafe production methods, limited feedstock and land utilization. Besides these challenges, biofuels are still a sustainable energy source that mitigate climate change and satisfy global energy demand.
Determining the effective stiffness of structural elements made of reinforced concrete with reliability has always been an important subject of research since it provides a reliable estimate of a building's capacity in the case of a seismic event. The load-bearing element's design parameters, such as the longitudinal reinforcement ratio and concrete compressive strength, influence the effective stiffness of the cracked section in reinforced concrete structures, even if it is not always constant. In this study, a secure and efficient approach covering all important design parameters is proposed to determine the stiffness coefficient of cracked sections of doubly-reinforced beam models. The proposed equation for the effective stiffness coefficient has been verified by comparisons with moment–curvature relationships and data provided by various researchers and standards, based on numerical results. For beam sections, the suggested equation provides values of the effective stiffness coefficient that are reasonably accurate and consistent. Therefore, it is possible that the results of analytical solutions may differ from each other according to different researchers and standards. Analysis results have shown that an important parameter influencing the nonlinear behavior of the sections and the effective stiffness in beams is the reinforcement ratio, which includes concrete strength and compression reinforcement ratios.
In this study, silicon acrylate resins were formulated using a 1:1 ratio of silicon diacrylate and dipropylene glycol diacrylate (DPGDA) as the control sample (Si-HTH0), previously tested in earlier work. To enhance mechanical properties, a novel diacrylate monomer, HEMA-terminated TDI-based urethane (HTH), was synthesized by reacting toluene diisocyanate (TDI) with 2-hydroxyethyl methacrylate (HEMA). The synthesized monomer was blended with DPGDA at varying concentrations (20–100%) and integrated into the silicon diacrylate resin. Mechanical characterization revealed significant improvements in tensile strength, Young’s modulus, impact resistance, and hardness. At 60% HTH (Si-HTH60), the material exhibited the best balance of properties, with a 393% increase in ultimate tensile strength, 11.87% elongation at break, and 3.7 kJ/m² impact resistance. SEM analysis confirmed that Si-HTH60 displayed the most ductile fracture morphology among the tested samples, characterized by rougher surfaces, a distinct river-like pattern, and deep fractures, indicating enhanced energy absorption. In contrast, the 100% HTH sample exhibited a unique morphology with a fibrous and slightly porous structure, leading to the highest tensile strength and modulus, though without superior impact resistance. These findings highlight HTH’s effectiveness in enhancing silicon acrylate-based resins for advanced DLP/LCD 3D printing applications.
: The Adaptive Camouflage Data Set (ACD1K) is a carefully curated collection of high-quality images developed to facilitate research in camouflage detection and segmentation tasks. This dataset is categorized into training, validation, and testing subsets, enabling comprehensive evaluation of deep learning models. Models including Attention U-Net, built upon the ResNet-50 architecture, and U-Net++, enhanced with attention mechanisms, were employed for robust feature extraction. Performance evaluation was carried out using common metrics such as accuracy, precision, recall, F1-score, and intersection over union. The Attention U-Net model, in conjunction with CLAHE preprocessing, Adamax optimizer, a learning rate of 1e-5, and a dropout rate of 0.2, achieved an accuracy of 96.88% and an intersection over union of 92.01%. Under similar experimental conditions, the Attention U-Net++ model using the Adam optimizer achieved an accuracy of 98.32% and an intersection over union of 82.09%. These findings highlight the effectiveness of CNN-based architectures in accurately identifying camouflaged objects within visually complex environments.
This study analyzes the possibility of producing glass-ceramics from chromite tailings, coal fly ash (Class F), and red mud industrial wastes in Türkiye. Unlike previous studies that typically used binary mixtures or different compositional ratios, this study introduces a novel ternary combination with a fixed 1:2:3 ratios (fly ash: chromite tailings: red mud). This specific ratio was optimized through experimental trials and found to enhance sinterability, mechanical strength, and microstructural stability. The glass-ceramics obtained using this ratio demonstrated compressive strength, water absorption, and density of 12.21 MPa, 0.44%, and 2.05 g/cm³, respectively. The resultant products were analyzed by XRD and leaching tests, which confirmed quartz-based crystalline phases and negligible leaching of toxic metal ions. These findings propose a new strategy for environmentally benign waste recycling, aligned with green chemistry principles and the UN Sustainable Development Goal 12.
Electricity is one of the most important sources of energy. Many devices need electrical energy to operate. In addition to the production of electrical energy from renewable sources, the fact that it can be produced from waste heat sources will increase efficiency. As in many systems, it is possible to generate electricity by using thermoelectric generators (TEGs) on the waste heat systems of vehicles using internal combustion engines. Thanks to the electricity obtained from waste heat systems, the load on the alternators and batteries in the vehicles is reduced, thus increasing their service life. In addition, since the charging time of the vehicle battery is reduced, fuel savings can be achieved. Therefore, making electricity generation predictions using machine learning algorithms in internal combustion engines will make a great contribution to the initial project planning phase of the design of automobile systems. Nowadays, research on waste heat energy recovery from automobile exhaust with TEGs using machine learning is a new topic. In this study, a data set containing the attributes of 2692 current and voltage values obtained from a thermoelectric generator on an automobile exhaust system was used. Adaboost and Random Forest machine learning algorithms were used in the estimation process of the designed model. The most successful result was achieved when estimating the current with the Adaboost algorithm. In this study, it has been shown that with the proposed model, electrical energy production estimation can be made over the waste heat sources of different systems.
The variability in photovoltaic power generation generates different negative effects on power grid systems in terms of stability, reliability, and operation planning. Therefore, an accurate estimation of PV power generation is crucial for stabilizing and securing grid operation and promoting large-scale PV power integration. Every year, new techniques and approaches emerge worldwide that reduce the uncertainty in these estimates and improve model accuracy. This study presents a comprehensive survey of solar energy prediction models while summarizing the most recent methodologies and strategies employed to enhance the precision of solar energy production forecasting. The energy sector is highly dynamic and integrates new technologies continually; hence, compilation studies should be updated with new developments. Industries are trying to benefit from artificial intelligence, including the field of solar energy prediction, and this study attempts to capture this trend. In addition to presenting a comparison of the solution methods in artificial intelligence, hybrid approaches to overcome problems are discussed. Different classification models and critical analyses of recent studies based on forecast horizons and historical data are also presented.
This paper presents an efficient single-band rectenna for radio frequency energy harvesting at the 2.45 GHz ISM band. For this purpose, a stacked cylindrical dielectric resonator antenna (SCDRA) has been used. The RF harvester has also a matching network and a rectifier. Two different dielectric materials are used to broaden the bandwidth of the antenna and improve the gain at the frequency of 2.45 GHz. The SCDRA has been fed using aperture coupled feed technique. As well known, the antenna converts the electromagnetic waves into electrical signals, however these signals have a low RF power. To overcome this limit, a multi-stage voltage doubler rectifier is used. It allows increasing the efficiency and yields a satisfactory output voltage. Besides, a simple impedance matching network, is placed between the antenna and the rectifier circuit to ensure a maximum power transfer. We used the advanced design system (ADS) simulator for the design of the rectifier and matching network circuit. To investigate the performance of the proposed rectenna, simulation results are presented and discussed. Our rectenna achieves RF to DC conversion efficiency up to 70% with an output voltage of 3 V at an input power of 10 dBm.
This study investigates the dynamic control and optimal allocation of Distribution Static Compensators (D-STATCOMs) in PV–EV integrated distribution systems to mitigate the impacts of voltage fluctuations, increased power losses, and reactive power imbalances. An Enhanced Hunter–Prey Optimization (EHPO) algorithm is proposed in the study, incorporating chaotic initialization, adaptive parameter control, and Cauchy's mutation exploration strategy to improve global search capability and convergence reliability. The proposed method is validated on the IEEE 33, 69, and 118 bus distribution test systems under varying PV generation and EV charging demand scenarios. Results show that the EHPO-based D-STATCOM placement significantly reduces active power losses and enhances voltage stability. The findings highlight the effectiveness of combining advanced metaheuristic optimization with custom power devices to ensure the resilient, reliable, and sustainable operation of future EV–PV–dominated distribution networks.
Epileptic seizures significantly impact individuals' safety, independence, and quality of life. Traditional monitoring systems are effective in clinical settings but lack portability for daily use. This project addresses these limitations by developing a wearable device for real-time epileptic seizure detection, enhancing patient safety and bridging the gap between clinical monitoring and everyday management. The low-cost, daily-use prototype improves detection accuracy and minimizes false alarms by strategically placing the device on the back for enhanced stability and sensor performance. The system uses an Arduino Mega microcontroller for control, a gyroscope for motion detection, GPS for location tracking, and GSM for emergency alerts. Each component was individually tested before integration. The device was evaluated for detecting tonic-clonic seizures, verifying audio and SMS alerts, and monitoring performance via a custom mobile app. Field testing over three weeks confirmed its effectiveness, with the buzzer alerting nearby individuals and SMS notifications enabling faster emergency response. The system demonstrated reliable detection of motor seizures with high accuracy and minimal false positives. Future enhancements include miniaturizing the device with a PCB, improving battery life, and integrating EEG monitoring for comprehensive seizure detection.
The Internet of Things (IoT) has become a major issue that has gained significant attention in the research community. Advances in IoT technologies have resulted in the emergence of various security issues and raised concerns about potential privacy breaches of IoT data. Utilizing Blockchain (BC) is seen as a promising solution for addressing security issues in the IoT. This paper offers a clear overview of IoT security threats, including the related security characteristics and the challenges that come with integrating BC with IoT. A brief discussion of various consensus protocols and existing security techniques is presented. A comparative study of several Distributed Ledger Technology (DLT) platforms based on both qualitative and quantitative evaluation criteria is also presented. This paper explores the role of BC Technology in improving security in Intrusion Detection Systems (IDS) and other applications in the IoT environment. Additionally, the paper identifies open issues and highlights potential research opportunities that can benefit future studies.
Shared data volume may hit 175 zettabytes by 2025 thus causing problems for storage solutions. Storage methods using conventional techniques encounter challenges such as data loss and short system durability and high expenditure rates. The natural storage molecule DNA provides excellent density alongside premium durableness together with minimal upkeep needs. The technology still deals with two core limitations of high expense and sluggish encoding procedures. DNA storage research focuses on three areas of improvement to achieve practical and scalable storage solutions. The research examines how DNA data storage exists at present as well as the existing obstacles and future course the technology may take.
This study presents a comprehensive approach that integrates GNSS-based measurements with web mapping technologies to enhance the spatial accuracy of public transport stops in the city centre of Karaman and digitally strengthen the existing transport information infrastructure. Increased mobility in urban public transport, rising user demand for real-time information, and the obsolescence of the existing inventory over time have necessitated the re-measurement of stop locations and their accessibility on a digital platform. In line with this, in the first phase of the research, all stops across the city were measured in the field using an RTK-supported GNSS receiver, obtaining position information with centimeter accuracy. The coordinates obtained were compared with the route-stop data set obtained from the municipality, incorrect points were corrected, and missing, duplicate, or location-uncertain records were filtered out to create an up-to-date and reliable stop database. In the second phase, real-time location data in JSON format obtained from GPS devices on buses was processed and integrated into an OpenStreetMap-based web map interface using Leaflet.js. The interface offers users an interactive experience with features such as stop search, route filtering, route viewing, and dynamic pop-up information. The results demonstrate a significant improvement in spatial accuracy and show that the system's scalable structure makes a strong contribution to urban transport management.
Reliable perception of non-NATO armored vehicles is fundamental for Unmanned Ground Vehicle (UGV) operations in safety-critical, time-constrained environments. This study proposes a UGV-oriented framework integrating lightweight You Only Look Once (YOLO) architectures with a constrained multi-objective Bayesian optimization strategy. An original hybrid dataset of 10,640 ground-level images was constructed, featuring tanks, armored personnel carrier (APCs) main battle tank (MBT), self-propelled howitzers, and hard-negatives, excluding aerial views for domain consistency. Quantitative evaluation shows YOLOv9s achieves the highest accuracy reaching 97.33% mAP@50 and 0.8478 mAP@50–95, while maintaining a high recall of 92.11% and the highest Matthews Correlation Coefficient (MCC) score (0.8312). YOLO11s provides the highest sensitivity with a recall of 92.75%, whereas YOLOv5su delivers the lowest latency (13.82 ms) and highest throughput (72.4 FPS), highlighting critical trade-offs between detection accuracy and computational efficiency. To address accuracy-efficiency trade-offs, a Multi-Objective Tree-structured Parzen Estimator (MOTPE) based Bayesian optimization framework yielded Pareto-optimal configurations for Ambush, Reconnaissance, and Balanced mission modes. This approach enables adaptive, hardware-aware model selection while preserving mission-critical detection performance.
The primary aim of this study is to address potential inaccuracies in membership values expressed by decision-makers when dealing with uncertainty in complex environments. To achieve this, the concept of virtual logic is incorporated as a refinement mechanism. Building on this foundation, we construct the notion of a virtual fuzzy parameterized fuzzy hypersoft set (vfp-fhs-set) by integrating virtual logic with hypersoft sets, which are designed to more effectively represent the components of parameter sets. Through this integration, a novel hybrid mathematical model is introduced, enhancing parameterization with fuzzy membership values and providing a more robust framework for decision-making under uncertainty. Several fundamental set operations associated with this structure are formally examined to establish its theoretical soundness. Furthermore, a decision-making algorithm based on vfp-fhs-sets is proposed and illustrated with an application, demonstrating the practical utility of the model. Comparative analysis with existing approaches highlights the advantages of incorporating virtual logic, particularly in improving precision and reliability in the ranking of alternatives.
