Osteoarthritis (OA) is a degenerative joint condition mostly affecting the knees characterized by cartilage degradation, synovial inflammation, and chronic pain, with limited effective treatments with minimal side effects. Traditional, complementary, and integrative medicine (TCIM), such as numerous medicinal plants, acupuncture, and folk herbalism, among others, have been historically used to treat symptoms associated with OA, which include joint pain, stiffness, and inflammation. Several herbs and their bioactive constituents have shown potential to modulate key inflammatory pathways like the NLRP3 inflammasome, which is an important regulator of innate immune responses in OA pathophysiology. This review aims to investigate the underlying mechanisms and potential of TCIM to alleviate OA-related pain by modulating the NLRP3 inflammasome. A comprehensive literature search across multiple databases, including PubMed, Cochrane Library, Embase, Google Scholar, Scopus, and Web of Science by using a set of MESH and relevant keywords like “NLRP3”, “TCIM”, “OA”, and “Animal” for articles published from January 2000 to August 2024. Twenty-two in vivo studies met the inclusion criteria for the systematic review, and 21 studies for the meta-analysis. Across these studies, TCIM interventions consistently reduced NLRP3, IL-1β, IL-18, and Caspase-1 expression compared with OA controls. Pooled effects were consistently moderate-to-large across all molecular, with low between-study heterogeneity (I² ≈ 0
Against the backdrop of population aging, community parks are important spaces for older adults' daily activities, and perceived safety plays a key role in shaping their use and spatial satisfaction. This study selected six typical community parks in central Beijing, constructed an indicator system for safety perception needs, and applied an analytical KANO-IPA (Integrated Kano and Importance-Performance Analysis) approach to identify the demand attributes and optimization priorities of safety elements. The results reveal a clear hierarchy in older adults' safety perception needs. Basic environmental and facility safety factors, such as pavement conditions and facility reliability, function as must-be needs. Elements related to spatial visibility, circulation, lighting, and wayfinding act as one-dimensional needs that steadily influence satisfaction, whereas features including natural surveillance, spatial enclosure, and activity atmosphere mainly enhance spatial experience as attractive needs. Priority analysis further indicates that circulation conditions and facility safety constitute the most critical aspects for improvement. Overall, older adults' safety perception in community parks results from the combined effects of multiple spatial factors. Hierarchical spatial optimization can enhance user experience and improve resource allocation efficiency. The findings provide theoretical support and decision-making guidance for safety-oriented planning and age-friendly renewal of urban community parks in aging societies.
The increasing frequency of torrential rainfall due to global warming has resulted in a significant rise in urban flooding and river overflows. Rainwater pumping stations, typically located near rivers, serve as buffers between sewer systems and receiving water bodies, helping to mitigate flood risks. A primary challenge in operating these stations is optimizing pump performance to prevent flooding while minimizing energy consumption and costs. Various computational methods, including meta-heuristics and deep learning, have been proposed to tackle this optimization problem. However, most studies either overlook or inadequately address pump maintenance costs, which are essential for long-term operational efficiency. This gap stems from the lack of a comprehensive model that accurately captures the full spectrum of costs involved in pump operation. This paper introduces a cost estimation model that integrates both deterministic and probabilistic elements to enhance the energy-efficient operation of rainwater pumping stations. The model focuses on pumps with capacities of 100 m3/min and 170 m3/min, which are commonly used. It takes into account electricity consumption costs as well as maintenance costs arising from frequent on/off cycles and dry-run events. Predictions of failures due to these operational stresses are modeled using the Crow-AMSAA non-homogeneous Poisson process (NHPP) and Weibull distributions-probabilistic models widely used in mechanical failure analysis. To evaluate the proposed model, simulations were conducted using the Storm Water Management Model (SWMM), comparing a deep reinforcement learning-based control strategy with the current operational method at the Gasan Pumping Station in Seoul, South Korea. The pump operating costs associated with each method were calculated and analyzed using the proposed model, demonstrating its potential for ensuring cost-effective and reliable pump operation.
Rapid advancements in high-definition CMOS and magnetic resonance transducers have led to the accumulation of complex medical imaging data that requires robust, real-time computational interpretation. However, current high-performance segmentation models require excessive computational power, making them incompatible with low-power point-of-care sensing hardware. Therefore, we improved the Self-Supervised Dynamic Gated Fusion Network (SS-DGFNet) model for resource-efficient medical image segmentation. The network utilizes automated signal calibration (self-supervised learning) and an adaptive fusion module to maintain high accuracy even with missing sensor data or limited labeled information. For the Multimodal Brain Tumor Image Segmentation Benchmark 2025 dataset, SS-DGFNet shows high spatial accuracy (a Dice score of 0.888) while maintaining 97.6% performance retention when a sensor channel is lost. Despite these gains, issues remain, including the need for validation across a broader range of clinical sensor materials and the optimization of the model for heterogeneous edge-computing hardware. The improved model demonstrates significant robustness when a sensor modality is missing. By reducing computational overhead and accelerating calibration cycles for emerging biosensors, the model leads to the transition of complex diagnostics to edge-computing sensor platforms and supports the transition of complex diagnostics to mobile sensor platforms.
Laser acupuncture (LA) has been increasingly investigated as a non-invasive therapy for knee osteoarthritis (KOA), yet its clinical efficacy remains uncertain. To evaluate the effectiveness and safety of LA for pain, function, and mobility in KOA through a systematic review and meta-analysis. Randomized controlled trials (RCTs) comparing LA with sham, placebo, electroacupuncture, or standard care were identified from major databases. Outcomes included pain (VAS, WOMAC-Pain, NPRS, PI), function (WOMAC-Function), and range of motion (flexion). Risk of bias was assessed using RoB 2, and heterogeneity explored through subgroup analyses. Thirteen RCTs of 611 participants were included. LA did not significantly improve WOMAC pain (SMD 0.04; 95
