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Existing building management systems face critical limitations in real-time data integration, primarily relying on static models that lack dynamic updates from IoT sensors. To address this gap, this study proposes a novel system integrating MQTT over WebSocket with Three.js visualization, enabling real-time sensor-data binding to Building Information Models (BIM). The architecture leverages MQTT’s lightweight publish-subscribe protocol for efficient communication and employs a TCP-based retransmission mechanism to ensure 99.5% data reliability in unstable networks. A dynamic topic-matching algorithm is introduced to automate sensor-BIM associations, reducing manual configuration time by 60%. The system’s frontend, powered by Three.js, achieves browser-based 3D visualization with sub-second updates (280–550 ms latency), while the backend utilizes SpringBoot for scalable service orchestration. Experimental evaluations across diverse environments—including high-rise offices, industrial plants, and residential complexes—demonstrate the system’s robustness: Real-time monitoring: Fire alarms triggered within 2.1 s (22% faster than legacy systems). Network resilience: 98.2% availability under 30% packet loss. User efficiency: 4.6/5 satisfaction score from facility managers. This work advances intelligent building management by bridging IoT data with interactive 3D models, offering a scalable solution for emergency response, energy optimization, and predictive maintenance in smart cities.

This study investigated the impact of a Yard Management System (YMS) implemented at a third-party logistics distribution center in the United States. Five years of operational data (2018–2022), including 72 monthly observations of inbound and outbound freight performance (measured in pounds) and detention occurrences (measured in US dollars), were analyzed using one-way ANOVA to assess pre- and post-implementation performance. The results indicated that the YMS significantly improved inbound and outbound freight volume, reduced detention occurrences, and enhanced operational efficiency within the third-party logistics distribution center. These findings suggest that YMS can be an effective tool for enhancing yard-level operational efficiency, reducing delays, and supporting broader supply chain optimization strategies in third-party logistics environments.

Satellite videos typically employ image registration techniques for video stabilization in order to achieve persistent observation. However, existing methods largely neglect the residual stabilization offsets, particularly when exceeding the physical dimensions of target vehicles, which inevitably causes performance degradation. Furthermore, the detection pipeline struggles with hard-to-discriminate samples that exhibit low contrast, motion blur, or occlusion, while conventional sample assignment strategies fail to address the inherent annotation ambiguity for extremely small objects. We propose an end-to-end method called ViTrans for detecting moving vehicles in satellite video under stabilization offsets. ViTrans consists of three core modules: (1) a feature-aligned stabilization offset correction module (SCM) that mitigates feature misalignment by aligning features between the reference frame and the current frame; (2) a feature adaptive aggregation enhancement module (AAEM) based on vehicle trajectory consistency, which leverages the motion characteristics of objects across consecutive frames to eliminate dynamic clutter and false-alarm artifacts; and (3) a Gaussian distribution-based metric that dynamically adapts to bounding box dimensions, thereby providing more accurate positive sample feedback during model training. Extensive experiments on the VISO and SDM-Car datasets under simulated stabilization offsets demonstrate that ViTrans achieves state-of-the-art performance, improving F1-score by 14.4% on VISO and 6.9% on SDM-Car over existing methods.

As a typical grain base with a dense population and high-level urbanization, the North China Plain (NCP) faces a serious threat to its sustainable development due to water shortage. Surface water area (SWA) is a key indicator for continuously measuring the trends of regional water resources and assessing their current status. Therefore, a deep understanding of its changing patterns and driving forces is essential for achieving the sustainable management of water resources. In this study, we examined the interannual variability and trends of SWA in the NCP from 1990 to 2023 using annual 30 m water body maps generated from all available Landsat imagery, a robust water mapping algorithm, and the cloud computing platform Google Earth Engine (GEE). The results showed that the SWA in the NCP has significantly increased over the past three decades. The continuous emergence of artificial reservoirs and urban lakes, along with the booming aquaculture industry, are the main factors driving the growth of SWA. Consequently, the expansion of artificial water bodies resulted in a significant increase in water evaporation (0.16 km3/yr). Moreover, the proportion of water evaporation to regional evapotranspiration (ET) gradually increased (0–0.7%/yr), indicating that the contribution of water evaporation from artificial water bodies to ET is becoming increasingly prominent. Therefore, it can be concluded that the ever-expanding artificial water bodies have become a new hidden danger affecting the water security of the NCP through evaporative loss and deserve close attention. This study not only provides us with a new perspective for deeply understanding the current status of water resources security in the NCP but also provides a typical case with great reference value for the analysis of water resources changes in other similar regions.

Skeletal muscle is the most abundant tissue in the human body and is responsible for movement, metabolism, energy production and longevity. Muscle atrophy is a frequent complication of several diseases and occurs when protein degradation exceeds protein synthesis. Genetics, ageing, nerve injury, weightlessness, cancer, chronic diseases, the accumulation of metabolic byproducts and other stimuli can lead to muscle atrophy. Muscular dystrophy is a neuromuscular disorder, part of which is caused by the deficiency of dystrophin protein and is mostly related to genetics. Muscle atrophy and muscular dystrophy are accompanied by dynamic changes in transcriptomic, translational and epigenetic regulation. Multiple signalling pathways, such as the transforming growth factor‐β (TGF‐β) signalling pathway, the phosphatidylinositol 3‐kinase (PI3K)/protein kinase B (AKT)/mechanistic target of rapamycin (mTOR) pathway, inflammatory signalling pathways, neuromechanical signalling pathways, endoplasmic reticulum stress and glucocorticoids signalling pathways, regulate muscle atrophy. A large number of long noncoding RNAs (lncRNAs) have been found to be abnormally expressed in atrophic muscles and dystrophic muscles and regulate the balance of muscle protein synthesis and degradation or dystrophin protein expression. These lncRNAs may serve as potential targets for treating muscle atrophy and muscular dystrophy. In this review, we summarized the known lncRNAs related to muscular dystrophy and muscle atrophy induced by denervation, ageing, weightlessness, cachexia and abnormal myogenesis, along with their molecular mechanisms. Finally, we explored the potential of using these lncRNAs as therapeutic targets for muscle atrophy and muscular dystrophy, including the methods of discovery and clinical application prospects for functional lncRNAs.

A systematic study of the physical and mechanical processes of landslide development and evolution is important for forecasting, early warning, and prevention of landslide hazards. In the absence of on-site monitoring data, seismic networks can be employed to continuously record ground seismicity generated during landslides. However, landslide seismic signals are relatively weak and inevitably affected by noise interference. Furthermore, systematic characterization and reconstruction of the landslide evolution process remain poorly reported. An evaluation method to recognize landslide events based on seismic signal characteristics is therefore important. This study analyzes the 2019 “7.23” Shuicheng landslide based on data from nearby seismic stations. A landslide seismic signal recognition method is developed based on short-time Fourier transform (STFT) and band-pass filter (BP-filter) analysis. Data from 14 stations near the landslide were reviewed and the landslide data from one station was selected for analysis. The landslide seismic signal was noise-attenuated by using the empirical mode decomposition (EMD) and BP-filter methods. Fast Fourier transform (FFT), STFT, and power spectral density analyses were applied to the landslide seismic signal with higher signal-to-noise ratio (SNR) to obtain the time–frequency signal characteristics of the landslide process. Finally, combined with landslide field survey data, the dynamic process of the landslide was reconstructed based on the seismic signal, and the landslide was divided into four stages: the fracture-transition stage, the accelerated initiation stage, the bifurcation-scraping stage, and the deposition stage. The dynamic characteristics of each stage of the landslide are presented. The results indicate that the initial fracture point of the landslide is located between the bottom of the sliding source area and the top of the acceleration zone, not as traditionally thought, at the top of the sliding source area; this would be difficult to determine through field survey and analysis only. These results provide theoretical guidance for the study of seismic signal extraction, identification of landslide dynamic parameters, and characterization and reconstruction of landslide processes.

In this study, the response surface method is used to develop a model for analyzing and optimizing zinc leaching experiments. An investigation into the leaching kinetics of smithsonite in ammonium citrate solution is also conducted. A model of kinetics is studied in order to represent these effects. The experimental data show that an increase in the solution temperature, concentration, and stirring speed has a positive impact on the leaching rate, while an increase in the particle size has a negative impact on it. The optimal experimental conditions consist of a leaching temperature of 70 °C, ammonium citrate concentration of 5 mol/L, particle size of 38 µm, and rotational speed of 1000 rpm. Under these optimal conditions, the leaching rate of zinc from smithsonite is 83.51%. It is speculated that the kinetic model will change when the temperature is higher than 60 °C. When the temperature is lower than 60 °C, the leaching process is under the control of the shrinking core model of the surface chemical reactions. The calculated activation energy of the leaching reaction is equal to 42 kJ/mol. The model of the leaching process can be described by the following equation: 1−1−x1/3=k0⋅(C)0.6181⋅r0−0.5868⋅SS0.6901exp⁡−42/RT]t. This demonstrates that an ammonium citrate solution can be used in the leaching process of zinc in smithsonite as an effective and clean leaching agent.

Meige syndrome (MS) is a complex neurological disorder with unclear etiology. Accurate prediction of MS risk is essential for facilitating early diagnosis. This study aimed to develop and validate a nomogram for predicting the risk of MS based on oxidative stress markers. This retrospective, cross-sectional study included 424 patients with MS and 848 age- and sex-matched healthy controls, with data collected from January 2022 to December 2023. Clinical and laboratory data were extracted from electronic medical records. The MS patients and healthy controls were randomly allocated to the training and validation sets at a 7:3 ratio using random stratified sampling. A nomogram was developed using a multivariate logistic regression model based on data from the training set. Model performance was validated through fivefold cross-validation, receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Univariate and multivariate logistic regression analyses identified albumin, gamma-glutamyl transferase (GGT), total bilirubin (TBIL), and the urea nitrogen-to-creatinine ratio as independent predictors of MS. A nomogram was constructed based on these four variables. The cross-validation confirmed the model's reliability. The model demonstrated high predictive accuracy, with an area under the curve (AUC) of 0.930 for the training set and 0.914 for the validation set. The calibration curve and DCA results indicate that the model has strong consistency and significant potential for clinical application. This study developed a nomogram based on four risk predictors, GGT, TBIL, albumin, and the urea nitrogen-to-creatinine ratio, to forecast the risk of MS and enhance the accuracy of MS risk prediction.

This research delves into the nuances, origins, and societal effects of irrational religious orientations within China’s Generation Z, employing grounded theory methodology for a comprehensive analysis. The focus is on those born between 1995 and 2010, a demographic raised amidst rapid information technology growth and significantly influenced by digitalization and globalization. The study identifies three primary dimensions of irrational religious orientations in Generation Z: religious spiritual dependence, religious instrumental tendency, and religious uniqueness identity. These are shaped by factors such as the overwhelming influx of information via digital media, societal pressures and psychological dilemmas, conflicts in values and identity crises, as well as feelings of social isolation and the need for group belonging. To address these trends, the study suggests several interventions: enhancing multicultural and values education, implementing stricter online information regulation and literacy programs, boosting mental health awareness and support, and fostering engagement in social and cultural activities. These recommendations are essential for comprehensively understanding and effectively responding to the irrational religious orientations of Generation Z, ultimately contributing to their overall well-being and healthy development.

This study seeks to analyze the psychological construction of Unconventional Religious Orientations and their association with individual income level satisfaction within Generation Z. Generation Z, individuals born between 1995 and 2010, grew up in a socio-cultural context marked by digitization and globalization. This study identifies three key dimensions of Unconventional Religious Orientations: religious spiritual dependence, religious instrumental tendencies, and religious uniqueness identity. By combining rootedness theory, semi-structured interviews, and literature review, we constructed and refined a set of relevant scales. Using exploratory and validation factor analyses (EFA and CFA), we verified the structural validity of the scale. The results of the analyses revealed significant negative correlations between satisfaction with income level and all dimensions of Unconventional Religious Orientation for Generation Z, suggesting that Unconventional Religious Orientation tends to diminish as income satisfaction increases. In addition, the significant positive correlations between these dimensions of religious inclination imply that they may share certain underlying factors in their psychological structure. This study not only successfully developed a set of psychometric instruments for Unconventional Religious Orientations, but also provided a new psychological perspective for understanding the dynamic interaction between economic satisfaction and religious psychological attitudes in Generation Z.