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.
