Population aging is increasing demand for unobtrusive in-home monitoring, while cameras raise privacy concerns, and wearables depend on user compliance. This critical survey and design study compares WiFi channel-state information sensing and millimeter-wave FMCW radar for fall detection, activity recognition, and vital-sign monitoring. It identifies cross-environment generalization, multi-occupant attribution, and privacy evaluation as key barriers to deployment. An ISAC-inspired architecture is proposed that combines broad, low-cost WiFi coverage with radar in high-risk areas and uses cross-modal consistency checking to detect modality-specific unreliability. A controlled synthetic simulation illustrates that this mechanism can prevent an unstable modality from degrading fusion under asymmetric domain shift, but does not establish real-world or clinical performance. The paper also distinguishes among visual, behavioral, and health data privacy and identifies priorities for synchronized datasets, cross-home evaluation, and privacy-aware alert governance.

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In modern baseball, the relative contributions of velocity, spin, and pitch location to swinging-strike outcomes are still not well understood, and many earlier studies mix up location-driven outcomes with actual pitch quality. This study uses MLB Statcast pitch-level data from 2024–2025, drawn from 221,982 pitches thrown by World Baseball Classic 2026 roster pitchers across 16 countries, to analyze what physical factors predict pitch effectiveness. To separate pitch quality from location effects, this study defines a zone-controlled binary target: in-zone swinging strikes versus in-zone hard-hit contact, while excluding out-of-zone pitches, called strikes, foul balls, and other ambiguous outcomes. This yields a modeling sample of 24,710 pitches with a balanced 45.0%/55.0% class split that needs no resampling. Using a stratified 70/30 split that preserves this class ratio, three classification models are trained and compared: logistic regression, decision tree and random forest, with the two tree models tuned by cross-validated grid search. Exploratory data analysis shows that pitch velocity and vertical plate location separate the two classes most clearly. The random forest classifier achieves the best performance with an accuracy of 67.84%, an ROC AUC of 0.7359, and an F1-score of 0.62, outperforming the decision tree (accuracy 65.72%, AUC 0.7042) and the logistic regression baseline (accuracy 57.37%, AUC 0.5759). Notably, feature importance analysis shows that stuff-related features together account for 46.7% of importance in the random forest—nearly as much as the two location features (53.3%)—showing that pitch quality, not just placement, is an important and often overlooked driver of swinging strikes.

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With the continuous expansion of food processing and biomedical industries, the consumption of disposable packaging rises every year. Petroleum, plastic and laminated paper packaging cause microplastic pollution, resource waste and other ecological risks. Driven by global carbon neutrality and plastic restriction policies, biodegradable eco-packaging has become a key research field. This paper adopts two ways, literature review and life cycle assessment (LCA), as research methods, which sort out the evolution of packaging types, for instance, traditional plastic, plain paper, polylactic-acid (PLA) and polyhydroxyalkanoates (PHA) biopolymers, rice straw composite substrates and curcumin-zein active films, comparing their biodegradation mechanisms, physical performance, merits and drawbacks. With LCA experimental data of blueberry containers, environmental burdens under four waste treatments, including recycling, industrial composting, landfill and incineration, are quantified. Relevant experimental results prove that rice straw-reinforced polylactic acid (RPLA) composite packaging generates the lowest carbon emission in all scenarios. Curcumin-zein composite films integrate antibacterial, UV-blocking and fully biodegradable functions, yet the cost of mass commercial production remains high. Multi-layer coated paper boxes bring far higher marine ecotoxicity than other materials. According to special research on pharmaceutical packaging, fresh food and medical products require completely different packaging standards. Thus, optimization strategies can be proposed in terms of waste classification, modification of materials and policy support, providing reliable theoretical guidance for green packaging application and development of circular systems in the food and medical industries.

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With high-speed railway networks continuously expanding to areas with extreme cold climates, the impact of ambient temperature on the aerodynamic properties of high-speed trains has gradually become a research hotspot. This paper adopts computational fluid dynamics (CFD) numerical simulation technology to test the aerodynamic parameter variation law of high-speed trains within the temperature range of −20 °C to 20 °C. The calculation results show that air density will rise when the ambient temperature drops, which further leads to an obvious increase in the overall aerodynamic resistance of the train; the total drag value measured at −20 °C is roughly 14.9% higher than that under 20 °C working condition. Meanwhile, the downward pressure borne by the head car and the lift force of the tail car both grow, which will break the original balance state of the longitudinal pitching moment of the vehicle. The subsequent flow field analysis results indicate that low-temperature environments can expand the high-pressure stagnation area on the head car surface and strengthen the negative pressure area distributed on the train roof, and these two flow field changes are the fundamental reasons for the growth of each aerodynamic load. The research conclusions of this paper can provide reliable theoretical support for energy consumption evaluation and safe operation guarantee of high-speed trains operating in frigid zones.

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