The severity of falls increases with age and reduced mobility. Falls are a frequent source of domestic accidents and accidental death on the part of fragile people. They produce anatomical injuries, reduce quality of life, cause dramatic psychological effects, and impose heavy financial burdens. A growing elderly population leads to a direct increase in health service costs, and indirectly to a deterioration of social life in the long term. Unsurprisingly, socioeconomic costs have triggered new scientific health research to detect falls in older people. One of the most appropriate solutions for monitoring the elderly and automatically detecting falls is computer vision. The Kinect camera plays a vital role in recognizing and detecting activities while ensuring seniors’ comfort, safety, and privacy preferences in the fall detection system. This research surveys several Kinect-based works in the literature that cover the approaches used in fall detection. In addition, we discuss the public fall benchmark based on Kinect technology. In general, the main objective of this survey is to provide a complete description of the modules making up the fall detectors and thereby guide researchers in developing fall approaches based on Kinect.

Objective: This study provides a comprehensive global overview of surgical instrument traceability systems and accentuates their growing importance in healthcare. Background: Surgical instruments pose risks to patient safety, economic costs, logistical challenges, and environmental impact. The increasing focus on instrument traceability reflects its potential to address these issues. Methods: We performed a systematic review using PRISMA guidelines, analyzing articles from 2000 to 2023 across five digital libraries (PubMed, Web of Science, IEEE, ACM, Google Scholar). Our review concentrated on traceability systems’ lifecycle for reusable and sterile surgical instruments. Results: Out of 7189 articles retrieved, 22 were selected for evaluation, and only 6 were considered relevant after a thorough examination. These studies mainly deployed Radio Frequency Identification (RFID) technology. They enhance patient safety, reduce environmental impact, improve economic efficiency, and optimize logistics. Additionally, these systems encourage more responsible surgical practices. Conclusions: Our study underscores the limited applied research in this field and discusses system architectures and performance metrics. It proposes future research directions, including the development of public databases, integration of automation, and investment in artificial intelligence (AI) and computer vision to improve traceability and risk analysis.

The increasing use of blockchain smart contracts has introduced new security challenges, as small coding errors can lead to major financial losses. While rule-based static analyzers remain the most common detection tools, their limited adaptability often results in false positives and outdated vulnerability patterns. This study presents a comprehensive comparative analysis of machine learning (ML) and deep learning (DL) methods for smart contract vulnerability detection using the BCCC-SCsVuls-2024 benchmark dataset. Six models (Random Forest, k-Nearest Neighbors, Simple and Deep Multilayer Perceptron, and Simple and Deep one-dimensional Convolutional Neural Networks) were evaluated under a unified experimental framework combining RobustScaler normalization and Principal Component Analysis (PCA) for dimensionality reduction. Our experimental results from a five-fold cross-validation show that the Random Forest classifier achieved the best overall performance with an accuracy of 89.44% and an F1-score of 93.20%, outperforming both traditional and neural models in stability and generalization. PCA-based feature analysis revealed that opcode-level features, particularly stack and memory manipulation instructions (PUSH, DUP, SWAP, and RETURNDATASIZE), were the most influential in defining contract behavior.

Falls represent a threat to older adults, overload healthcare systems, and reduce quality of life. Vision-based fall detection has advanced recently through deep learning, yet most proposed models lack validation on physical hardware and do not report inference-time metrics. This systematic review, following PRISMA and Kitchenham guidelines, targets this gap. We focus exclusively on vision-based systems that report inference speed on a specified device. We define real-time performance using a threshold of 10 fps, based on the reported duration of the critical fall phase in real-life falls. From 588 records across IEEE Xplore, ACM Digital Library, Web of Science Core Collection, and PubMed (2019–2024), only 11 met all inclusion criteria, highlighting how few studies validate real-time performance on physical hardware. The findings show that CNN-based architectures dominate algorithm choice, edge devices dominate deployment platforms, and optimization remains central to real-time inference on constrained hardware. Across these studies, we identify two persistent limitations: no real-world testing with older adults and reliance on small, controlled datasets with simulated falls.