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This study aims to explore the impact of physical exercise on BrainRot behavior, as well as the mediating roles of Resilience and Ego depletion in the relationship between Physical exercise and BrainRot behavior. A total of 1091 adolescents from Sichuan and Hunan provinces completed self-report surveys on physical exercise, BrainRot behavior, resilience, and ego depletion. Descriptive analysis, correlation analysis, and mediation effect testing were conducted. After controlling for demographic variables, ego depletion was found to have an independent mediating effect between physical exercise and BrainRot behavior. Physical exercise was found to negatively predict ego depletion, which in turn significantly and positively predicted BrainRot behavior. The hypothesis that resilience played a mediating role between physical exercise and BrainRot behavior was not supported. Physical exercise significantly predicted resilience, but resilience did not significantly predict BrainRot behavior. Resilience and ego depletion exhibited a chained mediating effect between physical exercise and BrainRot behavior. Resilience could negatively predict ego depletion. Physical exercise could significantly predict BrainRot behavior. Moreover, during the analysis of the indirect effect, it was revealed that the negative predictive effect of physical exercise on BrainRot behavior remained significant. This study further investigates the multivariate mechanisms of influence between physical exercise and BrainRot behavior among adolescents.

With the global food consumption pattern changing to convenient and healthy, prepared food has gradually become an emerging food industry. In recent years, due to the new crown epidemic, China’s prepared food industry has developed greatly, and the market value of its industry has exceeded one trillion dollars. Differing from the traditional food industry, prepared dishes are finished and semi-finished dishes, so the cooking time required is shorter and the taste is more fixed, which is very suitable for industrialized mass production. In addition, the safety of prepared food is also widely concerned, and the debate on whether prepared food is healthy or not has not yet ended. The purpose of this paper is to systematically analyze the current development status and future development trend of the prepared food industry, as well as some of the problems of the prepared food industry that the public is generally concerned about, through multi-dimensional case studies and the collection of relevant industrial data on the Internet, to provide advice and suggestions on the sustainable, green and high-quality development of the prepared food industry, and to help the transformation of the global food industry.

Calcium-enriched compounds have great potential in the treatment of heavy-metal contaminated wastewater. Preparing stable basic calcium carbonate (BCC), which is a calcium-enriched compound, and applying it in practice is a great challenge. This work investigated the formation process of hierarchical hydroxyapatite (HAP)/BCC nanocomposites and their adsorption behaviors regarding lead ions (Pb2+). The morphology of the HAP/BCC nanocomposite was controlled by the addition of monododecyl phosphate (MDP). The carnation-like HAP/BCC nanocomposite was achieved with the addition of 30 g of MDP. The carnation-like HAP/BCC nanocomposite had a high Pb2+ adsorption capacity of 860 mg g−1. The pseudo-second-order and Freundlich model simulation results indicated that the adsorptions of Pb2+ on the nanocomposites belonged to the chemisorption and multilayer adsorption processes. The main effective adsorption components for the nanocomposites were calcium-enriched HAP and BCC. Through the Ca2+ ions exchanging with Pb2+, the HAP and BCC phases were converted to hydroxyl-pyromorphite (Pb-HAP) and hydrocerussite (Pb3(CO3)2(OH)2), respectively. The carnation-like HAP/BCC nanocomposite has great potential in the treatment of heavy metal ions. This facile method provides a new method for preparing a stable HAP/BCC nanocomposite and applying it in practice.

The light-use efficiency-based gross primary productivity (LUE-GPP) model is widely utilized for simulating terrestrial ecosystem carbon exchanges owing to its perceived simplicity and reliability. Variations in cloud cover and aerosol concentrations can affect ecosystem LUE, thereby influencing the performance of the LUE-GPP model, particularly in humid regions. In this study, the performance of six big-leaf LUE-GPP models and one two-leaf LUE-GPP model were evaluated in a humid agroforestry ecosystem from 2018–2020. All big-leaf LUE-GPP models yielded GPP values consistent with that derived from the eddy covariance system (GPPEC), with R2 ranging from 0.66–0.73 and RMSE ranging from 1.81–3.04 g C m−2 d−1. Differences in model performance were attributed to the differences in the quantification of temperature (Ts) and moisture constraints (Ws) and their combination forms in the models. The Ts and Ws algorithms in the eddy covariance-light-use efficiency (EF-LUE) model well characterized the environmental constraints on LUE. Simulation accuracy under the common limitation of Ts and Ws (Ts × Ws) was higher than the maximum limitation of Ts or Ws (Min (Ts, Ws)), and the combination of the Ts algorithm in the Carnegie–Ames–Stanford Approach (CASA) and the Ws algorithm in the EF-LUE model was optimized in combination forms, thereby constraining LUE for GPP estimates (GPPBLO, R2 = 0.76). Various big-leaf LUE-GPP models overestimated or underestimated GPP on sunny or cloudy days, respectively, while the two-leaf LUE-GPP model, which considered the transmission of diffuse radiation and the difference in photosynthetic capacity of canopy leaves, performed well (R2 = 0.72, p < 0.01). Nevertheless, the underestimation/overestimation for shaded/sunlit leaves remained under different weather conditions. Then, the clearness index (Kt) was introduced to calculate the dynamic LUE in the big-leaf and two-leaf LUE-GPP models in the form of exponential or power functions, resulting in consistent performance even in different weather conditions and an overall higher simulation accuracy. This study confirmed the potential applicability of different LUE-GPP models and emphasized the importance of dynamic LUE on model performance.

China’s prepared dishes industry has experienced rapid development and is undergoing a transformative shift from industrialized production to public recognition and acceptance. This study examines the discursive dynamics surrounding the development and promotion of prepared dishes and food in China, exploring the interaction between policy makers and consumers. Through a discourse analysis of 21 government policy documents published by official government websites, over 1000 comments from social medias, and 55 depth consumer interviews face to face and via phone call, this paper identifies three key discourses, cultural, distrust, and industrialization, that consumers have developed regarding prepared dishes and food. This study finds that: Initially, the government’s top-down promotion of prepared dishes encountered heated public opinion due to unclear definition of the terminology and lack of consumer education, as well as widespread consumer concerns over food safety, quality, and transparency. The cultural discourse centered on traditional food values and the perceived artificiality of processed foods, while the distrust discourse linked prepared dishes to past food safety scandals, eroding public trust. As consumer opposition grew, government discourse adapted, incorporating more stringent regulations, official definition and clarification of terminology and consumer-friendly measures, such as a ban on preservatives, distinguishing the terminology between “prepared dishes and food” and “central kitchen”, in response to consumer demands. By examining these shifting discourses and their impact on policy changes, the study provides insights into the role of discourse in the governance of new food initiatives and institutionalization of food industry regulation, and highlights the need for responsive, inclusive policymaking that integrates consumer perspectives to foster trust and legitimacy in emerging food industries.

The COVID-19 pandemic has drastically challenged urban food systems, has hurt the resilience and fundamental function of urban food systems and also accelerated the trends of digitization and changing preferences of consumers in cities. This research conducted a qualitative analysis of the discourses, actions and interactions of different actors in the urban food systems in China during COVID-19 using an actor-oriented approach and discourse analysis. This research finds that stricter regulations and policies have been implemented by governments to regulate the food supply chain and ensure human health. Local community service personnel, volunteers, stakeholders along the food supply chain and consumers formulated collective actions during the pandemic yet chaos and discourse distortions also emerged at different stages. The pandemic is a preamble to changes in consumers’ preferences and food supply chains in urban communities. There were significant structural changes and a dual structure of urban and rural food systems, where unbalanced supply and demand existed. Collective actions with community governance and an innovative food business model to digitize flows and easily adapt to shocks in food systems are required.

Purpose The aim of this study was to develop a deep convolutional neural network (DCNN) for detecting early osteonecrosis of the femoral head (ONFH) from various hip pathologies and evaluate the feasibility of its application. Methods We retrospectively reviewed and annotated hip magnetic resonance imaging (MRI) of ONFH patients from four participated institutions and constructed a multi-centre dataset to develop the DCNN system. The diagnostic performance of the DCNN in the internal and external test datasets was calculated, including area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, and F1 score, and gradient-weighted class activation mapping (Grad-CAM) technique was used to visualize its decision-making process. In addition, a human–machine comparison trial was performed. Results Overall, 11,730 hip MRI segments from 794 participants were used to develop and optimize the DCNN system. The AUROC, accuracy, and precision of the DCNN in internal test dataset were 0.97 (95% CI, 0.93–1.00), 96.6% (95% CI: 93.0–100%), and 97.6% (95% CI: 94.6–100%), and in external test dataset, they were 0.95 (95% CI, 0.91– 0.99), 95.2% (95% CI, 91.1–99.4%), and 95.7% (95% CI, 91.7–99.7%). Compared with attending orthopaedic surgeons, the DCNN showed superior diagnostic performance. The Grad-CAM demonstrated that the DCNN placed focus on the necrotic region. Conclusion Compared with clinician-led diagnoses, the developed DCNN system is more accurate in diagnosing early ONFH, avoiding empirical dependence and inter-reader variability. Our findings support the integration of deep learning systems into real clinical settings to assist orthopaedic surgeons in diagnosing early ONFH.

Many existing multi-modality studies are based on the assumption of modality integrity. However, the problem of missing arbitrary modalities is very common in real life, and this problem is less studied, but actually important in the task of multi-modality person re-identification (Re-ID). To this end, we design a novel dynamic enhancement network (DENet), which allows missing arbitrary modalities while maintaining the representation ability of multiple modalities, for partial multi-modality person Re-ID. To be specific, the multi-modal representation of the RGB, near-infrared (NIR) and thermal-infrared (TIR) images is learned by three branches, in which the information of missing modalities is recovered by the feature transformation module. Since the missing state might be changeable, we design a dynamic enhancement module, which dynamically enhances modality features according to the missing state in an adaptive manner, to improve the multi-modality representation. Extensive experiments on multi-modality person Re-ID dataset RGBNT201 and vehicle Re-ID dataset RGBNT100 comparing to the state-of-the-art methods verify the effectiveness of our method in complex and changeable environments.

为精确识别和测度区域土地利用冲突，清晰呈现其时空演化过程，本研究以洱海流域为研究对象，结合多尺度分割与随机森林算法精确提取四期土地利用数据，构建土地利用冲突测度模型，系统分析了1992—2022年土地利用冲突时空演变特征及规律，并创新地耦合多目标规划（MOP）与土地利用变化模拟（PLUS）模型模拟2035年4种发展情景下的土地利用格局并分析其土地利用冲突。结果表明：洱海流域1992—2022年的土地利用冲突呈先增后减的趋势，重度冲突区集中在城市的中心及周边地区，轻度冲突区域分布在苍山洱海等自然生态保护区。不同发展情景下流域土地利用结构分布中，经济发展情景（ED）的建设用地明显扩张，2035年面积占比比2022年增加了5.5个百分点，生态保护发展情景（TD）更注重流域生态环境保护，林地、湿地和水域的面积变化最大，面积占比比2022年分别增加了2.6、0.7个和10.0个百分点，可持续发展情景（SD）中各地类面积变化较为平衡，处于ED与TD情景的中间范围。SD情景下，流域整体的土地利用冲突水平最低，格局布局最优，斑块密度值为0.18，边缘密度值为7.9，各地类面积变化比其他情景更均衡，景观格局破碎度和复杂性最低。在当今政策规划下可持续发展情景为2035年洱海流域的最优土地利用布局。In order to accurately identify and measure regional land use conflicts and clearly present their spatial and temporal evolution, this study takes the Erhai basin as the research object, accurately extract four periods of land use data by combining multi-scale segmentation and random forest algorithm, construct a land use conflict measurement model, systematically analyze the characteristics and laws of spatial-temporal evolution of land use conflicts from 1992 to 2022, an

Immunometabolic intervention has been applied to treat cancer via inhibition of certain enzymes associated with intratumoral metabolism. However, small-molecule inhibitors and genetic modification often suffer from insufficiency and off-target side effects. Proteolysis targeting chimeras (PROTACs) provide an alternative way to modulate protein homeostasis for cancer therapy; however, the always-on bioactivity of existing PROTACs potentially leads to uncontrollable protein degradation at non-target sites, limiting their in vivo therapeutic efficacy. We herein report a semiconducting polymer nano-PROTAC (SPN pro ) with phototherapeutic and activatable protein degradation abilities for photo-immunometabolic cancer therapy. SPN pro can remotely generate singlet oxygen ( 1 O 2 ) under NIR photoirradiation to eradicate tumor cells and induce immunogenic cell death (ICD) to enhance tumor immunogenicity. Moreover, the PROTAC function of SPN pro is specifically activated by a cancer biomarker (cathepsin B) to trigger targeted proteolysis of immunosuppressive indoleamine 2,3-dioxygenase (IDO) in the tumor of living mice. The persistent IDO degradation blocks tryptophan (Trp)-catabolism program and promotes the activation of effector T cells. Such a SPNpro-mediated in-situ immunometabolic intervention synergizes immunogenic phototherapy to boost the antitumor T-cell immunity, effectively inhibiting tumor growth and metastasis. Thus, this study provides a polymer platform to advance PROTAC in cancer therapy. Proteolysis targeting chimeras (PROTACs) is an effective alternative to modulate protein homeostasis but can lead to uncontrollable protein degradation and off-target side effects. Here, the authors developed semiconducting polymer nano-PROTACs with phototherapeutic and activatable protein degradation abilities for photo-immunometabolic cancer therapy.