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The canopy temperature of winter jujube serves as a direct indicator of plant water status and transpiration efficiency, making its accurate prediction a critical prerequisite for effective water management and optimized growth conditions in greenhouse environments. This study developed a data-driven model to forecast canopy temperature. The model serially integrates a Long Short-Term Memory (LSTM) network and a Random Forest (RF) algorithm, leveraging their complementary strengths in capturing temporal dependencies and robust nonlinear fitting. A three-stage framework comprising temporal feature extraction, multi-source feature fusion, and direct prediction was implemented to enable reliable nowcasting. Data acquisition and preprocessing were tailored to the greenhouse environment, involving multi-sensor data and thermal imagery processed with Robust Principal Component Analysis (RPCA) for dimensionality reduction. Key environmental variables were selected through Spearman correlation analysis. Experimental results demonstrated that the proposed LSTM–RF model achieved superior performance, with a determination coefficient (R2) of 0.974, mean absolute error (MAE) of 0.844 °C, and root mean square error (RMSE) of 1.155 °C, outperforming benchmark models including standalone LSTM, RF, Transformer, and TimesNet. SHAP (SHapley Additive exPlanations)-based interpretability analysis further quantified the influence of key factors, including the “thermodynamic state of air” driver group and latent temporal features, offering actionable insights for irrigation management. The model establishes a reliable, interpretable foundation for real-time water stress monitoring and precision irrigation control in protected winter jujube production systems.

Carbon financial futures have both the characteristics of commodity futures and environmental protection and its price is affected by many factors. It is hard and complex for traditional analysis methods to get precise prediction results effectively. How to effectively predict the price trend of carbon financial futures has been focused on by both academia and traders. This study addresses the high prediction error of European allowance (EUA) futures price by constructing a novel approach by combining the support vector machine (SVM) and particle swarm optimization (PSO) algorithm. This article introduces a parameters optimization method, which provides the best parameters for SVM to improve the prediction performance of the EUA futures price. Furthermore, this research uses the realistic trading dataset containing 30,762 EUA futures closing prices to verify the effectiveness and efficiency of the PSO-SVM prediction model. The empirical results show that the prediction performance of the model, especially the radial kernel function, is significantly improved. And this approach can determine the parameters according to the characteristics of the dataset and input the parameters for training and prediction automatically. The PSO-SVM algorithm can effectively predict extreme price fluctuations and overcome the problem of high prediction error caused by parameter constraints.

Since the reform of the housing system in 1998, China’s housing distribution system has changed from welfare- to a market-based distribution, forming a unique phenomenon of housing inequality. Existing research on housing inequality mainly focuses on housing inequality in a single dimension and a single year, in which the importance of housing welfare based on household registration (hukou) has been ignored. This study examined China's housing inequality from three dimensions: housing living condition, housing wealth, and housing welfare based on hukou. Based on the data from the four-times China household finance survey and the generalized entropy measurement model, this study investigated the dynamic trends of China's housing inequality and showed that the degree of China's housing inequality has an overall upward trend from 2011 to 2017. Moreover, this research also calculated the provincial housing inequality and conducted the regression analysis to investigate the factors that affect housing inequality in each province. It was found that economic development and population migration affects provincial housing inequality significantly. Research confirms that housing inequality in China is increasing, which needs the central government to promote housing reform and alleviate the contradiction of housing interests.

A delay-induced predator–prey system with the effect of habitat complexity and Holling type functional response is proposed. The dynamical behaviors of the presented system are investigated and some critical conditions that guarantee the corresponding results are obtained based on mathematical view, such as positivity and boundedness, stability, Hopf bifurcation, direction and stability of Hopf bifurcation. Furthermore, based on ecological issue, the effect of habitat complexity on the dynamical consequences of the considered system is discussed. Finally, some numerical simulations are conducted to test the validity of the theoretical results.

Accurate differentiation between bacterial and viral-induced pharyngitis is recognized as essential for personalized treatment and judicious antibiotic use. From a cohort of 693 patients with pharyngitis, data from 197 individuals clearly diagnosed with bacterial or viral infections were meticulously analyzed in this study. By integrating detailed hematological insights with several machine learning algorithms, including Random Forest, Neural Networks, Decision Trees, Support Vector Machine, Naive Bayes, and Lasso Regression, for potential biomarkers were identified, with an emphasis being placed on the diagnostic significance of the Monocyte-to-Lymphocyte Ratio. Distinct inflammatory signatures associated with bacterial infections were spotlighted in this study. An innovation introduced in this research was the adaptation of the high-accuracy Lasso Regression model for the TI-84 calculator, with an AUC (95% CI) of 0.94 (0.925–0.955) being achieved. Using this adaptation, pivotal laboratory parameters can be input on-the-spot and infection probabilities can be computed subsequently. This methodology embodies an improvement in diagnostics, facilitating more effective distinction between bacterial and viral infections while fostering judicious antibiotic use.

Diabetic nephropathy (DN) is one of the main causes of end-stage renal disease (ESRD). Existing treatments cannot control the progression of diabetic nephropathy very well. In diabetic nephropathy, Many monocytes and macrophages infiltrate kidney tissue. However, the role of these cells in the pathogenesis of diabetic nephropathy has not been fully elucidated. In this study, we analyzed patient kidney biopsy specimens, diabetic nephropathy model animals. Meanwhile, we cocultured cells and found that in diabetic nephropathy, damaged intrinsic renal cells (glomerular mesangial cells and renal tubular epithelial cells) recruited monocytes/macrophages to the area of tissue damage to defend against and clear cell damage. This process often involved the activation of different types of macrophages. Interestingly, the infiltrating macrophages were mainly M1 (CD68+iNOS+) macrophages. In diabetic nephropathy, crosstalk between the Notch pathway and NF-κB signaling in macrophages contributed to the polarization of macrophages. Hyperpolarized macrophages secreted large amounts of inflammatory cytokines and exacerbated the inflammatory response, extracellular matrix secretion, fibrosis, and necroptosis of intrinsic kidney cells. Additionally, macrophage depletion therapy with clodronate liposomes and inhibition of the Notch pathway in macrophages alleviated the pathological changes in kidney cells. This study provides new information regarding diabetic nephropathy-related renal inflammation, the causes of macrophage polarization, and therapeutic targets for diabetic nephropathy.

Carbon neutrality has positive impacts on people, nature and the economy, and buildings represent the “last mile” sector in the transition to carbon neutrality. Carbon neutrality is characterized by the decarbonization of operations and maintenance, in addition to zero emissions in electricity and other industry sectors. Taking China’s commercial buildings as an example, this study is the first to perform an extensive data analysis for a step-wise carbon neutral roadmap of building operations via the analysis of a dynamic emission scenario. The results reveal that the carbon emissions abatement of commercial building operations from 2001 to 2018 was 1460.85 (±574.61) mega-tons of carbon dioxide (Mt CO2). The carbon emissions of commercial building operations will peak in the year 2039 (±5) at 1364.31 (±258.70) Mt, with emission factors and energy intensity being the main factors influencing the carbon peak. To move toward carbon neutral status, an additional 169.73 Mt CO2 needs to be cut by 2060, and the low emission path toward carbon neutrality will lead to the realization of the carbon peak of commercial buildings in 2024, with total emissions of 921.71 Mt. It is believed that cutting emissions from the operation of buildings in China will require a multi-sectoral synergistic strategy. It is suggested that government, residents, enterprises, and other stakeholders must better appreciate the challenges to achieve a substantial carbon reduction and the need for urgent action in the building sector in order to achieve carbon neutrality.

Due to large energy consumption and carbon emissions (ECCE) in the building sector, there is huge potential for carbon emission reduction, and this will strongly influence peak carbon emissions and carbon neutrality in the future. To get a better sense of the current research situation and future trends and to provide a valuable reference and guidance for subsequent research, this study presents a summary of carbon peak and carbon neutrality (CPCN) in buildings using a bibliometric approach. Three areas are addressed in the review through the analysis of 364 articles published from 1990–2021: (1) Which countries, institutions, and individuals have conducted extensive and in-depth research on CPCN in buildings, and what is the status quo of their collaboration and contributions? (2) What subjects and topics have aroused wide interest and enthusiasm among scholars, and what are their time trajectories? (3) What journals and authors have grabbed the attention of many scholars, and what are the research directions related to them? Moreover, we propose future research directions. Filling these gaps will enrich the research body of CPCN and overcome current limitations by developing more methods and exploring other practical applications.

There have been hundreds of millions of cases of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With the growing population of recovered patients, it is crucial to understand the long-term consequences of the disease and management strategies. Although COVID-19 was initially considered an acute respiratory illness, recent evidence suggests that manifestations including but not limited to those of the cardiovascular, respiratory, neuropsychiatric, gastrointestinal, reproductive, and musculoskeletal systems may persist long after the acute phase. These persistent manifestations, also referred to as long COVID, could impact all patients with COVID-19 across the full spectrum of illness severity. Herein, we comprehensively review the current literature on long COVID, highlighting its epidemiological understanding, the impact of vaccinations, organ-specific sequelae, pathophysiological mechanisms, and multidisciplinary management strategies. In addition, the impact of psychological and psychosomatic factors is also underscored. Despite these crucial findings on long COVID, the current diagnostic and therapeutic strategies based on previous experience and pilot studies remain inadequate, and well-designed clinical trials should be prioritized to validate existing hypotheses. Thus, we propose the primary challenges concerning biological knowledge gaps and efficient remedies as well as discuss the corresponding recommendations.

As a major contributor to global energy consumption and carbon emissions, the building sector plays a pivotal role in achieving carbon peaking and neutrality targets. This study systematically reviews the evolution of research on building stock energy conservation and emission reduction (BSECER) from 1992 to 2025, which is based on a comprehensive bibliometric analysis of 2643 publications. The analysis highlights the research contributions of countries, institutions, and scholars in the BSECER field, reveals patterns in collaborative networks, and identifies the development and shifting focus of research topics over time. The findings indicate that current BSECER research centers around four main areas: behavioral efficiency optimization, full life cycle carbon management, urban system transformation, and the integration of intelligent technologies, which collectively form a multiscale emission reduction framework from individual behavior to large-scale systems. Building on these insights, this study outlines five key future research directions: advancing comprehensive carbon neutrality technologies, accelerating the engineering application of intelligent technologies, developing innovative multi-scenario policy simulation tools, overcoming integration challenges in renewable energy systems, and establishing an interdisciplinary platform that links health, behavior, and energy conservation.