Formulating Seismic Intensity Scale (JMA-SIS) Using Response Spectrum: A New Approach for Structural Engineering Design

This study aims to formulate a calculation for earthquake shaking intensity (rs_mSIS) based on the response spectrum (RS) using the Japan Meteorological Agency-seismic intensity scale. The research investigates the relationship between the response spectrum parameters—period and maximum acceleration—and the earthquake source types, including megathrust, Benioff, and shallow crust/background sources. Artificial ground motions are generated and analyzed using Matlab to calculate shaking intensity values, which are then used to develop the rs_mSIS formula. The formulation is validated against actual response spectrum data from 15 Indonesian cities and demonstrated high accuracy, with the Wariyatno coefficient applicable across all models. This approach provides a standardized method to assess seismic intensity, offering enhanced reliability for building design in earthquake-prone areas and serving as a valuable tool for engineers and urban planners to improve earthquake resilience in diverse seismic environments

Deep Learning-Based Smart Invigilation System for Enhanced Exam Integrity

This study proposes a smart invigilation system to preserve exam integrity by detecting suspicious student behaviors using deep learning. The model consists of three phases, i.e., student identity verification using face recognition, behavioral sampling for model training utilizing gesture analysis and convolutional 3D networks for emotion analysis, and live video analysis of suspicious activities integrating gesture, emotional analysis, and face recognition. The model is evaluated using 4,000 training and 1,000 test images and identifies non-cheating activities with 99% accuracy and cheating activities with 97.6% accuracy. The proposed model outperforms other methods, achieving accuracies of 98.4% for identifying cheating behaviors and 99.2% for non-cheating behaviors, resulting in an overall accuracy of 98.8% and a low misclassification rate of 1.2%. While the system demonstrates high accuracy, challenges remain in scaling to larger classrooms due to increased computational demand and the need for additional hardware to ensure comprehensive monitoring

Investigation of Affordable Technologies for Real-Time See-Through Various Indoor Surfaces and Walls

Wireless scanning for detecting objects behind various surfaces or walls in indoor settings has garnered significant interest recently. This study presents experimental results on several widely accessible, affordable, and portable see-through technologies. The technologies evaluated include a radio frequency (RF) device, a chip-sized multiple-input and multiple-output (MIMO) radar, an ultra-wideband sensor, and a motion sensor. These can be used either as standalone transceivers or mounted on unmanned aerial vehicles (UAVs) to extend their range, particularly for emergencies in high-rise buildings. Tests on various wall and surface materials show that RF and Wi-Fi devices can detect objects through wood, glass, and plasterboard, but metal and concrete significantly block or limit signal penetration. The results suggest that affordable see-through technologies need to improve their performance against concrete and metals

Risk Management Framework-Based Failure Mode and Effect Analysis for AI Risk Assessment

As artificial intelligence (AI) technologies continue to spread into human life, developers must ensure benefits while minimizing the risk of adverse impacts. This study aims to evaluate risks in real-world AI applications using the AI Incident Database. It employs Failure Mode and Effect Analysis and the National Institute of Standards and Technology AI Risk Management Framework to identify failures, their causes and effects, and assess how current systems address them. A total of 100 incident reports were analyzed. The findings indicate frequent failures in autonomous systems and biased predictions. Seven cases were classified in the highest risk categories, including those involving physical harm and loss of life. Over 80% failures originated from algorithmic flaws or poor data quality. The method employed successfully evaluates the risks in current AI applications, revealing critical gaps in risk management and emphasizing the urgent need for targeted safeguards and proactive mitigation strategies

A Systematic Review of Coal Mine Dust Suppression Methods Based on Numerical Simulations and Experimental Investigations

Large quantities of dust are generated during coal mining and transportation, posing a threat to workers’ health. Therefore, this article conducts a systematic review of the literature on coal mine dedusting. This study examines coal mine dust suppression methods by integrating numerical simulations and experiments, focusing on four aspects: the structural improvement of the dust remover, chemical modification, the optimization of the operating environment, and the ventilation system. The structural improvement of a dust remover primarily involves optimizing the nozzle’s structure and size, particularly the Laval structure. The findings indicate that alterations in the surface structure of the Laval nozzle’s contraction section have minimal effect on the airflow velocity. Chemical modification of the dust remover can enhance the wetting properties of coal dust and includes non-phytochemical and phytochemical modification. Molecular Dynamics (MD) simulations are frequently employed in chemical modification. The optimization of the operating environment for dust removers focuses predominantly on spray pressure optimization

Analysis of Characteristics of Complaints on Parenting Q&A Sites Using pLSA and Data Augmentation

This study investigates the classification and clustering of complaints on a Japanese parenting Q&A site, aiming to identify meaningful patterns from limited labeled data. To address data scarcity, generative AI was utilized for data augmentation through prompts that reflected authentic parenting frustrations, with synthetic data validated by comparing classification performance under varying proportions of generated content. Complaint texts were vectorized using Bag-of-Words, Doc2Vec, and Sparse Composite Document Vectors, providing multiple levels of semantic representation. LightGBM was used as the classifier, and F1 scores measured performance. Clustering of predicted complaints employed probabilistic Latent Semantic Analysis, with topic numbers selected via Bayesian Information Criterion. Six distinct themes emerged, including childcare stress and family conflict. Incorporating generated data improved the F1 score from 0.824 to 0.865. The findings highlight the potential of generative AI to augment low-resource datasets and demonstrate the effectiveness of context-aware embeddings and probabilistic clustering in structuring real-world text data

A Wide-Band Millimeter-Wave On-Chip Six-Port Reflectometer

Following the previous success of measuring the reflection coefficients of devices under test at 20 GHz, this paper proposes a new six-port reflectometer (SPR) chip that aims to work at 40 GHz. The new SPR is implemented with the 0.13-μm IBM BiCMOS-8HP technology, and the overall chip area is 1.5 mm in width and 1 mm in height. To demonstrate the SPR’s excellent performance over a wide band, this study utilizes a programmable tuner to create fifteen different loads for the SPR to measure at 30 GHz, 40 GHz, and 50 GHz, respectively. Among the loads, the programmable tuner serves as an important instrument for producing various sliding terminations, which are essential for calibrating the SPR. Compared with the measurement results of a vector network analyzer, the SPR displays maximum measurement errors of -28.6 dB, -32.4 dB, and -27.7 dB while operating at 30 GHz, 40 GHz, and 50 GHz

A Review of Advances in Bio-Inspired Visual Models Using Event-and Frame-Based Sensors

This paper reviews visual system models using event- and frame-based vision sensors. The event-based sensors mimic the retina by recording data only in response to changes in the visual field, thereby optimizing real-time processing and reducing redundancy. In contrast, frame-based sensors capture duplicate data, requiring more processing resources. This research develops a hybrid model that combines both sensor types to enhance efficiency and reduce latency. Through simulations and experiments, this approach addresses limitations in data integration and speed, offering improvements over existing methods. State-of-the-art systems are highlighted, particularly in sensor fusion and real-time processing, where dynamic vision sensor (DVS) technology demonstrates significant potential. The study also discusses current limitations, such as latency and integration challenges, and explores potential solutions that integrate biological and computer vision approaches to improve scene perception. These findings have important implications for vision systems, especially in robotics and autonomous applications that demand real-time processing

A Novel Data Transmission Model Using Hybrid Encryption Scheme for Preserving Data Integrity

The objective of the study is to introduce a novel hybrid encryption scheme, combining both symmetric and asymmetric encryptions with a data shuffling mechanism, to enhance data obfuscation and encryption security. The approach uses RSA for asymmetric encryption and ChaCha20-Poly1305 for symmetric encryption. To increase the complexity, an additional phase involves reorganizing the RSA-encrypted data blocks. Furthermore, symmetric key generation using the key derivation function is employed to generate the key for symmetric encryption through an asymmetric private key. Decryption entails reversing these procedures. This model significantly enhances security through an additional shuffling step, measured by performance metrics like encryption and decryption times, throughput rate, and the avalanche effect. The method, despite increasing execution time compared to symmetric models, yields comparable results for asymmetric models and ensures robustness. The proposed method outperforms traditional methods regarding resistance to cryptanalytic attacks, including chosen-plaintext and pattern analysis attacks

Enhanced Electrocardiogram Arrhythmia Diagnosis with Deep Learning and Selective Attention Mechanism

The study aims to improve the diagnosis of arrhythmia in cardiovascular disease management. A novel approach using a deep convolutional network combined with a selective attention mechanism is proposed for electrocardiogram signal classification. The deep convolutional network extracts relevant features directly from raw electrocardiogram signals, while the selective attention mechanism focuses on the most critical regions of the signals and suppresses irrelevant or noisy components. This method achieves an accuracy of 99.70% in multi-class arrhythmia classification and 99.85% in binary classification, significantly outperforming traditional classification algorithms. Furthermore, the selective attention mechanism improves the localization of critical electrocardiogram segments, offering valuable insights for clinicians and aiding in the diagnosis process. This enhanced approach increases diagnostic accuracy and provides a clearer understanding of the electrocardiogram signals, which is crucial for effective patient management in cardiovascular diseases