Multiple Perspectives Research Articles

A SYSTEMATIC LITERATURE REVIEW OF PREDICTIVE MODELS AND ANALYTICS IN AI-DRIVEN CREDIT SCORING

This systematic review examines the transformative role of AI-driven models in credit scoring, highlighting their advances over traditional statistical methods in terms of predictive accuracy, adaptability, and inclusivity. By synthesizing findings from 70 studies, this review demonstrates that machine learning techniques, particularly ensemble models such as random forests and gradient boosting, effectively capture complex, non-linear relationships in borrower data, providing more accurate risk assessments across diverse demographics. Deep learning models, especially convolutional and recurrent neural networks, extend credit scoring capabilities to unstructured and alternative data sources, supporting financial inclusion by enabling assessments of individuals without traditional credit histories. Hybrid models that integrate logistic regression with neural networks offer an optimal balance between interpretability and predictive power, addressing regulatory demands for transparency while maintaining robust accuracy. Ensemble techniques like stacking and blending enhance model adaptability, allowing credit scoring systems to integrate multiple perspectives and improve prediction accuracy in varied borrower contexts. Despite these advancements, challenges remain in the form of ethical concerns and the need for model interpretability, particularly with complex deep-learning architectures. The review underscores the importance of developing fairness-aware and explainable AI frameworks to ensure that as AI-driven credit scoring evolves, it remains both transparent and equitable. These insights suggest that with careful attention to ethics and transparency, AI has the potential to create a more inclusive and resilient credit scoring landscape, accommodating the needs of an increasingly diverse global population.

Connect, care, create: practical framework for physical environment design for person-centred palliative care.

This article aims to explore architectural-rich insights derived from users' experiences within everyday practice in palliative environments and provides a practical framework for healthcare organisations, architects and researchers involved in (re)designing palliative environments for person-centred care. An ethnographic study involving participatory observation was undertaken to gain insight into the influence of palliative environments on the diverse users' subjective experiences. This immersive research took place across multiple palliative environments, encompassing a palliative care unit, a day care centre for palliative care and a hospice located in Belgium. Also, informal conversations and photo-elicitation interviews were conducted with residents, family members, healthcare professionals, volunteers and maintenance staff. Analysing the subjective experiences yielded a practical framework of four architectural atmospheres (proximity, support, engagement, comfort) intended to serve as guiding principles for designing palliative environments. To shape these atmospheres, this article elaborates on 17 spatial aspects and aligns them with real-life experiences of users within palliative environments, thereby enriching and contextualising these insights. This study presents a practical framework encompassing atmospheres, spatial aspects and overarching insights. It is augmented by supplemental material featuring real-life user experiences, all directed towards guiding the design of palliative environments in pursuit of person-centred palliative care. This article advocates for a collaborative, interdisciplinary, holistic design approach that acknowledges these considerations' interconnectedness and considers the users' multiple perspectives. Ultimately, this approach serves as a means of bridging design intentions and actual experiences encountered by users in their real-life contexts to assist healthcare organisations and architects in creating environments for person-centred palliative care.

Predictive value of metabolic parameters and apparent diffusion coefficient derived from 18F-FDG PET/MR in patients with non-small cell lung cancer

BackgroundMultiple models intravoxel incoherent motion (IVIM) based 18F-fluorodeoxyglucose positron emission tomography-magnetic resonance(18F-FDG PET/MR) could reflect the microscopic information of the tumor from multiple perspectives. However, its value in the prognostic assessment of non-small cell lung cancer (NSCLC) still needs to be further explored.ObjectiveTo compare the value of 18F-FDG PET/MR metabolic parameters and diffusion parameters in the prognostic assessment of patients with NSCLC.Meterial and methodsChest PET and IVIM scans were performed on 61 NSCLC patients using PET/MR. The maximum standard uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), diffusion coefficient (D), perfusion fraction (f), pseudo diffusion coefficient (D*) and apparent diffusion coefficient (ADC) were calculated. The impact of SUVmax, MTV, TLG, D, f, D*and ADC on survival was measured in terms of the hazard ratio (HR) effect size. Overall survival time (OS) and progression-free survival time (PFS) were evaluated with the Kaplan–Meier and Cox proportional hazard models. Log-rank test was used to analyze the differences in parameters between groups.Results61 NSCLC patients had an overall median OS of 18 months (14.75, 22.85) and a median PFS of 17 months (12.00, 21.75). Univariate analysis showed that pathological subtype, TNM stage, surgery, SUVmax, MTV, TLG, D, D* and ADC were both influential factors for OS and PFS in NSCLC patients. Multifactorial analysis showed that MTV, D* and ADC were independent predicting factors for OS and PFS in NSCLC patients.ConclusionMTV, D* and ADC are independent predicting factors affecting OS and PFS in NSCLC patients. 18F-FDG PET/MR-derived metabolic parameters and diffusion parameters have clinical value for prognostic assessment of NSCLC patients.

Development Problems and Suggestions for the Giant Salamander Industry

The Chinese giant salamander (<i>Andrias davidianus</i>), the American giant salamander (<i>Cryptobranchus alleganiensis</i>), and the Japanese giant salamander (<i>A. japonicum</i>) are precious aquatic protected animals that currently exist in the world. The latter two cannot be traded locally, whether wild or cultivated, and can only be protected, so they have not formed an industry. However, China allows the second generation of artificially bred offspring giant salamander to be traded and has already formed a factory scale breeding. The Chinese giant salamander has high economic value in food, health, medicine, beauty and other fields. In recent years, its artificial breeding industry has been widely developed in China and has a certain industrial scale. However, there are also many problems in the development of the giant salamander industry. In order to promote the high-quality development of the giant salamander industry and improve its quality, this article conducts research on the giant salamander industry, identifies existing problems, and provides suggestions. Firstly, this article analyzes the problems in the development process of giant salamanders from multiple perspectives, including management, service, market, research and development, and brand awareness. Secondly, corresponding industrial development strategies are proposed, including increasing regulatory efforts, enhancing service awareness, building market order, expanding sales channels, increasing research and development investment, reducing disease occurrence, extending the industrial chain, and strengthening brand awareness. Finally, the prospects for the development of the giant salamander industry are discussed.

The Complexity of Freedom of Speech: Wittgenstein’s Language Game and Gadamer’s Hermeneutics

Freedom of speech is filled in various facets of daily life, which has long been the focus of people’s attention and debate. The concept has multiple layers, including mode, content, and timing of speech. It is worth noting that in different cultural, political and legal contexts, different groups of people interpret and apply freedom of speech in various ways. The purpose of this paper is to compare and analyze Austin’s Speech Act Theory, Ludwig Wittgenstein’s Language Game Theory and Hans-Georg Gadamer’s Hermeneutics. Also it aims to demonstrate and summarize three philosophers’ views on freedom of speech from multiple perspectives. By examining these philosophical frameworks, this paper attempts to guide people to further understand the factors that influence freedom of speech and clarify its inherent complexity. This paper not only provides a detailed analysis of the role of language in different contexts, but also provides a relatively complete model for understanding the dynamic interaction between language and society. In the era of globalization, the contradiction between the basic principle and practice of freedom of expression has become increasingly prominent. The results of the paper are intended to provide a reference for future efforts to strengthen intercultural dialogue to guarantee freedom of speech in pluralistic societies.

A multidimensional analysis of the impact of obesity on immune checkpoint inhibitor therapy efficacy

BackgroundObesity is a well-known risk factor for developing malignant tumors and promoting tumor cell growth and spread. However, recent studies have shown that obese cancer patients, who typically have a worse prognosis than nonobese cancer patients, show a significant improvement in survival after receiving immune checkpoint inhibitor (ICI) therapy. This phenomenon is known as the “obesity paradox”. However, this phenomenon is influenced by tumor type and sex. Therefore, this study aimed to explore the impact of obesity on immunotherapy efficacy from multiple perspectives, aiming to verify this paradox and provide new scientific evidence on the effect of obesity on ICI efficacy.MethodsThis retrospective study evaluated the data of patients who received ICI therapy between June 2019 and August 2023. Automatic segmentation of skeletal muscle, subcutaneous fat, and visceral fat was performed using Slice-O-Matic software, and the corresponding skeletal muscle index (SMI), subcutaneous fat index (SFI) and visceral fat index (VFI) were calculated. The neutrophil-to-lymphocyte ratio (NLR) was determined by dividing the neutrophil count by the lymphocyte count. Univariate and multivariate Cox regression analyses were used to evaluate the correlation between body mass index (BMI), body composition parameters, and the NLR with overall survival (OS) and progression-free survival (PFS) in obese patients receiving ICI therapy.ResultsWe analyzed 219 patients with a median age of 60 years (IQR 53–69 years; 155 men and 64 women). Obese patients, particularly those with visceral fat accumulation, exhibited extended OS after ICI therapy (log­rank P = 0.027). Cox multivariate analysis revealed that the NLR (HR = 1.036; 95% CI: 0.996 to 1.078; P = 0.002) was independently associated with OS. Patients with a high NLR had worse OS than those with a low NLR.ConclusionsThis study corroborates the veracity of the "obesity paradox" under specific conditions and identifies NLR as an independent prognostic factor, with elevated NLR indicative of a poor prognosis.

Application of 3D Motion Graphic Design Technology of Object Simulation Human Body in Sports Teaching

In the current era, the rapid development of computer technology has brought unprecedented opportunities for innovation in physical education teaching. The introduction of computer technology not only improves the efficiency of physical education teaching but also provides more accurate and scientific means for sports training. In response to this trend, this paper proposes an innovative human body model design method and explores in detail the key implementation techniques of computer simulation of 3D motion maps, aiming to further optimize and improve the quality of physical education teaching. The design of human body models plays a crucial role in physical education teaching and training, as it can help students and teachers better understand the mechanisms and principles of human movement. By introducing computer technology, this paper can construct a more precise, dynamic, and realistic human body model that conforms to the actual motion situation. This model can simulate various movement states of the human body, providing students with an intuitive and vivid learning experience. In the classroom, teachers can use projection or virtual reality devices to display 3D motion models, allowing students to intuitively see the details and changes of motion movements, deepening their understanding of the essentials of the movements. Teachers can demonstrate different movements in real time by operating models, and explain the technical points and precautions of each movement. Students can observe the motion trajectory and posture changes of the model from multiple perspectives to improve learning efficiency. The system generates personalized feedback reports based on students’ movement data, pointing out the problems and improvement directions that students have during the exercise process. Teachers can provide more accurate guidance to students based on these feedbacks, helping them correct incorrect movements and improve their skill levels.

Ultra 40 % optical and electrical performance enhancement of silver nanosphere array-structured radioluminescent nuclear batteries

Weak performance output is currently the major factor limiting the further development of radioluminescent nuclear batteries for sustainable and stable applications. This work proposed a method for enhancing the radiofluorescence absorption to boost the electrical output of these batteries with metal nanosphere array structures for photovoltaic modules. The radiofluorescence absorption enhancement of photovoltaic modules, the electrical output enhancement of batteries, and the fundamental enhancement mechanism of metal nanosphere array structures were investigated from multiple perspectives. Silver nanosphere array structure on the surface of the photovoltaic module could enhance the forward scattering probability of radiofluorescence generated by the radioluminescent material inside the photovoltaic module, thereby achieving a radiofluorescence absorption gain of up to 43.66 % for the photovoltaic module and an electrical output gain of up to 44.91 % for the radioluminescent nuclear battery.

3.Y.1. Genomics in EU Health Systems: Navigating the Opportunities and Challenges for Personalized Health

Abstract The rapid advancements in genomics research offer significant promise for revolutionizing healthcare, potentially reshaping how we approach disease prevention, diagnosis, and treatment. However, unlocking the full potential of genomics requires more than just scientific breakthroughs-it necessitates translating these discoveries into clinical practice. This involves bridging the gap between research and healthcare implementation, addressing challenges such as technical infrastructure, ethical and legal frameworks, healthcare professional competencies, and citizen engagement. As we navigate the evolving landscape of individualized patient care, it becomes increasingly crucial to implement effective risk stratification methodologies and personalized preventive measures. Among these methodologies, polygenic risk scores are gaining prominence. However, the adoption of these innovations necessitates careful consideration of their clinical utility, including their feasibility, cost-effectiveness, and acceptability in diverse healthcare settings. Validating these approaches from multiple perspectives is essential to ensure their effectiveness in real-world clinical practice. Integrating genomic sequencing data with health records presents an invaluable resource that can enhance patient outcomes and drive further research. The creation of infrastructures and initiatives such as those carried out by projects like the European Health Data Space and Genomic Data Infrastructure are fundamental to unleash the enormous potential of data-driven innovation. To date, the implementation of genomics in medical practice varies across European countries, with discrepancies in adoption levels across different aspects of healthcare, including prevention, diagnosis, and personalized treatment. Addressing these disparities and promoting standardized approaches to genomic medicine implementation are crucial steps toward ensuring equitable access and maximizing the benefits of genomics for all individuals across Europe. This workshop aims to provide decision-makers, healthcare professionals and researchers with a comprehensive overview of the state-of-the-art for the adoption of personalized approaches, and of the maturity of genomic medicine practices within different healthcare systems as well outlining the benefits of data accessibility for research. The speakers are coordinators and partners of EU projects as B1MG (Beyond One Million Genomes), Prophet (A PeRsOnalized Prevention roadmap for the future Healthcare), EHDS (European Health Data Space), GDI (European Genomic Data Infrastructure), GoE (Genome of Europe) and CanHeal. Key messages • Genomics has the potential to improve health outcomes. • Equal access to genomic medicine critical for personalized health. Ensuring equity, while considering feasibility.

Development of a Green Banking and Green Financing Practice Model for Enhancing Sustainability Development Goals (SDGS)

Objective: This study is to investigate a green banking model based on green banking practices, green financing, and green environment to enhance sustainability development goals (SDGs). Theoretical Framework: The research focuses on developing green banking and financing practices to improve environmental performance in the Indonesian banking sector. Green Banking emphasizes sustainability in banking operations and products, while Green Financing promotes funding environmentally friendly projects. The study also draws on Stakeholder Theory, highlighting that banks are accountable to a wide range of stakeholders. Method: The research stages include the development of instruments, questionnaire distribution, data collection, and data processing using a sample of 54 respondents, selected using convenience sampling. Data testing and processing were carried out using SmartPLS software. Results and Discussion: The analysis of banking employees and practitioners revealed that Banks' Customer-Related Practices (BCRP), Employee-Related Practices (BERP), and Operation-Related Practices (BORP) positively impact Green Environment Performance (GEP), whereas Banks' Policy-Related Practices (BPRP) do not. Additionally, while BCRP, BERP, and BORP showed no significant influence on Green Financing (GF), BPRP demonstrated a notable positive effect on Green Financing (GF). This result also show that Studies on Bank’s customer behavior change towards sustainability and its influence on the SDGs. Research Implications: Banks can implement specific green practices to improve their sustainability development goals (SDGs). The study reinforces Sustainability Theory by highlighting the importance of balancing environmental, social, and economic concerns in banking operations, confirming the need for a "Triple Bottom Line" approach in business models. Originality/Value: The originality of the research is demonstrated through its Studies on Bank’s customer behavior change towards sustainability and its influence on the SDGs by integrating multiple theoretical perspectives.

6.D. Skills building seminar: Family Vulnerability Scale: Why and how to use at Primary Health Care to promote health equity?

Abstract Family vulnerability encompasses multiple perspectives that are interconnected with the health needs of each family member. Families experiencing vulnerability face heightened risks of poor health outcomes. Nonetheless, these vulnerabilities are further compounded by systemic inequalities within healthcare systems, where biological or clinical factors are exclusively considered to organize and/or prioritize access, especially given the scarcity of valid instruments to measure family vulnerability. Primary Health Care (PHC) plays a pivotal role in addressing health inequalities, especially considering its bond with community. From this context, the Family Vulnerability Scale for Brazil (EVFAM-BR, in Portuguese) was developed and validated, been composed of 14 items about four dimensions (income, healthcare, family and violence) (2019-2023). By reducing the gap between science and practice, the implementation process of EVFAM-BR was started at the same year the scale was validated (2023). First, it occurred at 14 PHC services in São Paulo (Southeast Region, Brazil) and second, since 2024 has been implemented at Boa Vista (North Region, Brazil). Also, between 2024 and 2026, the implementation of EVFAM-BR is expected to take place in other Brazilian states participating in the third phase of the PlanificaSUS project, which is developed aiming to support State health departments to implement the Health Care Planning method to organize and integrate PHC and specialized outpatient care based on population health care needs. At this stage, capacity building is needed to align the concept of family vulnerability and to train PHC managers and health professionals on how to apply and use results from EVFAM-BR. This training has already been performed for around 1,000 health professionals from two different regions of Brazil, including managers, Community Health Workers, nurses, and physicians. The present workshop proposal aims to disseminate the EVFAM-BR and to offer the EVFAM-BR experience. It proposes knowledge transferring throughout participants engagement at three activities. Activity 1) “Family Vulnerability: From concept to a scale validity and application”: it is designed to engage participants to discuss about the family vulnerability concept and applicability at PHC, and to discuss on how EVFAM-BR implementation process has been occurring at Brazil and how can be tailored to other contexts. Activity 2) “Role Play: Practicing EVFAM-BR”: Two cases will be used for role play dynamics that will take place in trios, in which one will represent the community health worker, another will be the patient and the third will be the observer. Activity 3) That ones that assumed observer role will share their experiences of using EVFAM-BR. Finally, a discussion will be facilitated based on the participants’ impressions and a review of the main points covered throughout the workshop. Key messages • Family vulnerability stratification (through EVFAM-BR) is essential to plan services achieving equity and qualified population-based care management and to qualify patient approach and care. • Family Vulnerability Scale (EVFAM-BR) training represents efforts to reduce the gap between research and practice and contributes to address social determinants of health on healthcare.

Exploring the perceived dynamic system driving inequities in the community obesogenic environment

Abstract Introduction Persons in socioeconomically disadvantaged situations (PSEDS) are more susceptible and disproportionally exposed to obesogenic environments, resulting in poorer dietary outcomes and lower levels of physical activity. This study aimed to map the complex dynamic system within the community obesogenic environment driving unhealthy eating and insufficient physical activity among PSEDS in Flanders, and to identify potential local actions. Methods Participatory group model building (GMB), a qualitative system dynamics method, was used to engage PSEDS (n = 7), local decision makers (n = 7) and local stakeholders (n = 5) in two Flemish municipalities. A two-day GMB session was held in each municipality during which a causal loop diagram (CLD) was created and potential local actions were identified by considering causal pathways and reinforcing loops in the system. Results The CLD was designed around two main variables (i) healthy eating and (ii) physical activity among PSEDS. Seven sub-systems could be identified: 1) social environment, 2) facilities and activities, 3) mobility options, 4) information, 5) affordability, 6) safety (specifically for physical activity), and 7) industry and advertisement (specifically for eating behaviour). Several actions to improve the local environment to promote eating behaviour and physical activity among PSEDS were identified. Conclusions The CLD illustrates the complex interactions between individual and environmental determinants contributing to inequalities in eating behaviour and physical activity by including multiple perspectives of community members in socioeconomically disadvantaged situations, local decision makers and local stakeholders. Both the participatory GMB approach and the final CLD visualization can provide the basis for planning local actions for promoting healthy eating behaviour and physical activity among PSEDS. Key messages • Group model building is an effective approach to explore and understand a dynamic complex system through a shared understanding among diverse stakeholders. • A systems approach is valuable to provide a more comprehensive and context-specific understanding of the perceived factors contributing to inequities in the obesogenic environment.

Racial Governance in Postwar Chicago: A Multiple Orders Perspective

Much theorizing about patterns of urban governance portrays urban governing arrangements as unified political orders of some kind, be they progressive, neoliberal, or defined by power structures such as urban regimes or growth machines. In this study of racial governance in postwar Chicago, I borrow from literature on American political development to advance a multiple orders perspective in which racial governance is conceptualized as combinations of overlapping elements, at times in conflict with one another. Examining three distinct periods during the postwar era, I show how intersections among multiple political orders resulted in change at certain times and stability, or the appearance of stability, in others. Against arguments that portray urban political development as long periods of equilibrium punctuated at times by brief episodes of change, I find seemingly stable arrangements to be more precarious than they appear, as multiple orders impinge on one another and create frictions that can be key sources of change.

DHKFN

Knowledge tracing is effective in modeling learners' knowledge levels to predict future answering situations based on their past learning history and interaction processes. However, current methods often overlook the impact of knowledge point correlations on answer prediction. This paper design a model based on deep hierarchical knowledge fusion networks (DHKFN). It utilizes statistical approaches to construct correlation matrices to capture the correlations between knowledge points. Subsequently, it constructs a topology graph structure of questions and knowledge points and utilizes a multi-head attention network to learn student interaction information from multiple perspectives, effectively constructing adjacency matrices. Finally, it uses GCN to learn the interaction information representation between deep-level knowledge points from dynamically constructed a topology graph structures. Experimental results on three large public datasets show that DHKFN effectively considers the influence of correlations between different knowledge points, thus showing promising effectiveness.

Assessing personalized molecular portraits underlying endothelial-to-mesenchymal transition within pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a progressive and rapidly fatal disease with an intricate etiology. Identifying biomarkers for early PAH lesions based on the exploration of subtle biological processes is significant for timely diagnosis and treatment. In the present study, nine distinct cell populations identified based on gene expression profiles revealed high heterogeneity in cell composition ratio, biological function, distribution preference, and communication patterns in PAH. Notably, compared to other cells, endothelial cells (ECs) showed prominent variation in multiple perspectives. Further analysis demonstrated the endothelial-to-mesenchymal transition (EndMT) in ECs and identified a subgroup exhibiting a contrasting phenotype. Based on these findings, a machine-learning integrated program consisting of nine learners was developed to create a PAH Endothelial-to-mesenchymal transition Signature (PETS). This study identified cell populations underlying EndMT and furnished a potential tool that might be valuable for PAH diagnosis and new precise therapies.

A novel dynamic tracking method for coded targets with complex background noise

To address the issues of low tracking efficiency and poor localization accuracy of artificial coded targets under complex background interference conditions, a new method for dynamic tracking of coded targets is proposed. This method includes a lightweight feature tracker (CBAM-Slim-Net) for adaptive localization of coded circles and a large-capacity coded target solver (CSN-BSSCT). The CBAM-Slim-Net feature tracker achieves a detection accuracy of 0.987 with only 6.030 M parameters. In actual measurement environments with complex background interference, CSN-BSSCT can decode quickly and accurately, with a mean error of 0.036 mm in the three-dimensional Euclidean distance between coded circles in static measurement scenarios. Additionally, this method can analyze the motion trajectory of the target and perform dynamic stitching for 3D measurement from multiple perspectives, making it highly significant for applications in robot motion control and large-field-of-view 3D measurement.

Using Adjoint-Based Forecast Sensitivity to Observation to Evaluate a Wind Profiler Data Assimilation Strategy and the Impact of Data on Short-Term Forecasts

A wind profiler radar detects fine spatiotemporal resolution dynamical information, enabling the capture of meso- and micro-scale systems. Experience gained from observing system experiments (OSEs) studies confirms that reasonable profiler assimilation techniques can achieve improved short-term forecasts. This study further applies the adjoint-based forecast sensitivity to observation (FSO) method to investigate the quantitative impact of a profiler data assimilation strategy on short-term forecasts, and the results are consistent with those obtained from OSEs, further demonstrating that FSO and OSEs can be used to evaluate the effect of data assimilation techniques from different perspectives. Considering the unique advantage that the FSO can quantify the interactions between various observing systems and the impact on improving the model forecasts according to specific needs without costly additional calculations, we further diagnose in detail the observation impacts from multiple perspectives, including the observation platform, observation variables, and spatial distribution. And the results show that dynamical variables are more significant in improving forecasts compared to the other observed variables. Meanwhile, the dense profiler observations resulted in a more significant impact when radiosonde observations were not detected. The upper-level single winds monitored by profiler radars play a more important role in improving forecast skill. The FSO method measures the impact of an individual observing system, which can be used to enrich the evaluation of data assimilation schemes, efficiently calculate the impacts of multisource observations, and contribute to future development in adaptive observation, observation quality control, and observation error optimization.

The Impact of Green Logistics on the Sustainable Development of Enterprises

This study examines the importance of green logistics for the sustainable development performance of enterprises. It analyzes the connotation and application of green logistics, the significance of corporate sustainable development, and the relationship between the two, to clarify the decisive influence of green logistics in contemporary enterprises. At the same time, it points out the existing problems in the development process of green logistics from multiple perspectives, indicating that the further development of green logistics requires the efforts and cooperation of enterprises, governments, and multiple parties in the supply chain. Following this positioning, several conceptual solutions are proposed, such as the innovation and sustainable utilization of green technology and green management in logistics, and the government's role as a third party in regulating the application of green logistics. The research results have theoretical significance, highlighting the impact of green logistics on corporate sustainable development and the important role of the government in sustainable development. The practical significance includes improving corporate sustainable development performance indicators in the technical and management dimensions, in order to further improve the organization's sustainable operations and behavior.

A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision

Abnormal crowd behavior recognition is one of the research hotspots in computer vision. Its goal is to use computer vision technology and abnormal behavior detection models to accurately perceive, predict, and intervene in potential abnormal behaviors of the crowd and monitor the status of the crowd system in public places in real time, to effectively prevent and deal with public security risks and ensure public life safety and social order. To this end, focusing on the abnormal crowd behavior recognition technology in the computer vision system, a systematic review study of its theory and cutting-edge technology is conducted. First, the crowd level and abnormal behaviors in public places are defined, and the challenges faced by abnormal crowd behavior recognition are expounded. Then, from the dimensions based on traditional methods and based on deep learning, the mainstream technologies of abnormal behavior recognition are discussed, and the design ideas, advantages, and limitations of various methods are analyzed. Next, the mainstream software tools are introduced to provide a comprehensive reference for the technical framework. Secondly, typical abnormal behavior datasets at home and abroad are sorted out, and the characteristics of these datasets are compared in detail from multiple perspectives such as scale, characteristics, and uses, and the performance indicators of different algorithms on the datasets are compared and analyzed. Finally, the full text is summarized and the future development direction of abnormal crowd behavior recognition technology is prospected.

Metabolic regulation of intestinal homeostasis: molecular and cellular mechanisms and diseases.

Metabolism serves not only as the organism's energy source but also yields metabolites crucial for maintaining tissue homeostasis and overall health. Intestinal stem cells (ISCs) maintain intestinal homeostasis through continuous self-renewal and differentiation divisions. The intricate relationship between metabolic pathways and intestinal homeostasis underscores their crucial interplay. Metabolic pathways have been shown to directly regulate ISC self-renewal and influence ISC fate decisions under homeostatic conditions, but the cellular and molecular mechanisms remain incompletely understood. Understanding the intricate involvement of various pathways in maintaining intestinal homeostasis holds promise for devising innovative strategies to address intestinal diseases. Here, we provide a comprehensive review of recent advances in the regulation of intestinal homeostasis. We describe the regulation of intestinal homeostasis from multiple perspectives, including the regulation of intestinal epithelial cells, the regulation of the tissue microenvironment, and the key role of nutrient metabolism. We highlight the regulation of intestinal homeostasis and ISC by nutrient metabolism. This review provides a multifaceted perspective on how intestinal homeostasis is regulated and provides ideas for intestinal diseases and repair of intestinal damage.