Search Results (10,347)

Environmental commitments disclosed in merger and acquisition (M&A) announcements have become an important channel through which firms signal their green governance intentions. However, systematic empirical evidence remains limited regarding whether and how capital markets respond to such commitments. Using a sample of M&A events involving Chinese A-share listed firms from 2010 to 2023, this study develops a multidimensional framework to measure environmental commitment quality and examines its association with market performance while exploring potential channels through which capital markets respond to such disclosures. The results show that: (1) high-quality environmental commitments are associated with significant short-term and long-term abnormal returns, suggesting that investors respond positively to such disclosures. (2) Increased public attention and enhanced green innovation emerge as key channels linking environmental commitments to market performance. (3) More importantly, firms issuing high-quality commitments subsequently exhibit improvements in long-term financial, environmental, market, investment, and governance performance, suggesting that these commitments may function as credible signals rather than mere “greenwashing” rhetoric. (4) These observed patterns are structurally heterogeneous and more pronounced in firms with abundant resource endowments and stronger executive environmental awareness. Overall, this study provides new evidence on how event-driven environmental disclosures are associated with firms’ resource acquisition processes and offers insights for policies aimed at improving disclosure regulation and guiding capital toward green transformation. Full article

The emergence of social media platforms as commercial environments has necessitated the reconceptualization of service quality frameworks in electronic commerce research. This investigation analyzes and empirically validates social media quality as a multidimensional construct encompassing clarity, attractiveness, interactivity, and relevance dimensions. Grounded in means-end chain theory and uses and gratifications theory, we propose and test a comprehensive nomological network examining customer service and privacy as antecedents and their consequent effects on brand image, electronic word-of-mouth, repurchase intention, and customer satisfaction within Instagram commerce ecosystems. Using structural equation modeling with 258 Chilean Instagram commerce users, we find empirical support for all hypothesized relationships. Results reveal that both customer service and privacy are significant determinants of social media quality perceptions, with privacy demonstrating a substantially stronger effect. Social media quality, in turn, demonstrates considerable predictive power for brand image, customer satisfaction, electronic word of mouth, and repurchase intention. Importantly, customer satisfaction functions as a critical mediating mechanism, translating quality perceptions into behavioral outcomes. This research extends existing service quality literature by delineating a theoretically grounded framework for assessing quality perceptions in social commerce contexts and offering strategic guidance for optimizing social media commerce initiatives. Full article

To address the limited accuracy of speech-based Alzheimer’s Disease (AD) screening and the shortage of paired multimodal data, this paper proposes a detection framework based on feature alignment and Rectified Flow-driven latent representation generation. The EEG dataset consists of 36 AD patients and 29 Healthy Controls (HC). The speech dataset contains 399 samples, which include 114 AD cases, 132 Mild Cognitive Impairment (MCI) cases, and 153 HC cases. We extracted multidimensional features of EEG signals, such as time-domain and frequency-domain characteristics, alongside behavioral representations of speech. A heterogeneous alignment network was used to map these features into a common semantic subspace, where an adaptive interpolation strategy reconstructed the missing pathological trajectories of MCI within the latent space. On this basis, a conditional Rectified Flow model was introduced to learn the optimal transport mapping from speech to EEG. This model generated physiological-information-rich latent representations to compensate for semantic gaps. Experimental results showed that the fused features from speech and latent representations achieved a three-class classification accuracy of 89.08%, a precision of 88.77%, and a recall of 88.71%. This performance represented an accuracy improvement of 9.28% compared with the speech-based baseline system. Our method combines the convenience of speech screening with the high reliability of neurophysiological signals, and it provides a new approach for low-cost early detection of AD. Full article

This study uses panel data from 30 Chinese provinces spanning 2010–2023. It applies Fuzzy Set Qualitative Comparative Analysis (fsQCA) to examine how different aspects of Smart Logistics affect New Quality Productive Forces. Analysis covers three areas: overall configuration, changes over time, and regional differences. The findings show: (1) New Quality Productive Forces develop from the interaction of Smart Logistics factors, not just one. System coordination limits development more than hardware does. (2) There is a strong link between Smart Logistics and New Quality Productive Forces. The connection moves from basic support to innovation and then to broader ecosystem development. (3) Regions differ: Eastern areas benefit from digital tools and innovation; central areas rely on system change and efficiency; Western areas focus on building up basics and capabilities. Full article

Urban expansion is a key driver of land-use change and environmental pressure in rapidly urbanizing regions. Existing assessments of urban expansion often rely on predefined indicator systems and fixed weighting schemes, which limits their adaptability to evolving research priorities and regional contexts. This study develops an adaptive framework for urban expansion assessment by integrating a transformer-based language model with multi-source spatial data. A BERT-based semantic extraction process is used to identify relevant indicators and derive their relative weights from the scientific literature, enabling the construction of a literature-driven Urban Expansion Index (UEI). The framework is applied to the Central Plains Mega-city Region (CPMR), China, to examine spatial patterns and temporal dynamics of urban expansion between 2010 and 2020. Results show that UEI is primarily driven by land-use expansion indicators, while socioeconomic, infrastructure, and environmental indicators jointly reflect the multidimensional nature of expansion processes. Spatial patterns reveal a persistent concentration of high expansion intensity in core cities, alongside heterogeneous environmental responses and gradual outward growth. Changes in UEI display weaker spatial coherence than static levels, indicating differentiated local expansion dynamics. Local spatial autocorrelation analysis further identifies shifting clusters of urban expansion intensity, suggesting a reorganization of expansion centers within the agglomeration over time. By linking transformer-based indicator extraction with spatial analysis, this study advances urban expansion assessment beyond outcome-oriented mapping toward a more adaptive and knowledge-informed approach. The proposed framework is transferable to other mega-city regions and provides a useful tool for supporting territorial spatial planning and sustainable urban development. Full article

To address the issues of poor adaptability to complex scenarios, high computational complexity, and difficulties in terminal deployment of existing steel surface defect detection models, a novel lightweight detection network named LHO-net is proposed, with the Lightweight Multi-Backbone (LM Backbone), the Hierarchical Scale-based Pyramid Attention Network (HSPAN), and the Occlusion-aware Detection Head (OAHead). The LM Backbone adopts a dual-branch structure with shared HGStem and a dynamic feature fusion mechanism, effectively capturing multi-dimensional features of irregular defects while extremely compressing model parameters. The HSPAN module realizes efficient fusion of multi-scale features through dynamic feature selection and adaptive upsampling strategies, balancing background noise suppression and defect detail preservation. The OAHead completes adaptive compensation of features in occluded regions by means of deep feature aggregation and exponential normalization technology, significantly enhancing the ability to recognize complex defects. On the NEU-DET dataset, LHO-net achieves a mAP@0.5 of 75.0%, a mAP@0.5:0.95 of 44.0%, and a recall of 73.6%, with a computational complexity of only 2.3 GFLOPS. Compared with the baseline model YOLOv12, it reduces parameters by 64% and computational cost by 60.3%. On the GC-10 dataset, its mAP@0.5 reaches 67.2%, and its detection stability for complex defects such as slender creases and low-contrast water spots is superior to that of mainstream lightweight YOLO variants. Visualization results confirm that the model can effectively avoid common problems such as redundant annotations and false detections and maintains stable recognition performance for various defects. It solves the core contradiction between detection accuracy and lightweight deployment in industrial scenarios, providing an efficient and practical technical solution for real-time steel surface defect detection on resource-constrained terminal devices. Full article

Background and Objectives: Thirty-day hospital readmissions are a major clinical and economic challenge, particularly among frail older patients. Integrated protected discharge models, coordinated by nurse case managers and including multidimensional hospital-to-community interventions, may improve continuity of care and reduce inappropriate readmissions. Materials and Methods: We conducted a retrospective observational study on 200 consecutive patients aged ≥65 years, discharged between January and December 2024 from a public hospital in Italy. Frailty was assessed using BRASS scores (≥11), ADL, and IADL. The primary outcome was 30-day hospital readmission. Logistic regression models evaluated the impact of individual interventions (Model 1) and combined interventions (Model 2), defined as the simultaneous presence of four components: nurse case manager involvement, telephone follow-up, activation of home care (ADI) within 24 h, and social worker support. Results: Overall, 65 patients (32.5%) were readmitted within 30 days. In the multivariate analysis, nurse case manager involvement was associated with lower odds of readmission (OR = 0.023; 95% CI 0.008–0.064; p < 0.001). Early ADI activation was not associated with readmission in the bivariate analysis (p = 0.195) but showed higher odds of readmission in the multivariable model (OR = 3.475; 95% CI 1.384–8.725; p = 0.008). The combined interventions variable was significantly associated with readmission in Model 2. Patients who did not receive combined interventions had higher odds of 30-day hospital readmission compared with those who did (OR = 26.1; 95% CI 10.1–67.5; p < 0.001). Conclusions: An integrated protected discharge model coordinated by a nurse case manager and including combined interventions was associated with lower odds of 30-day hospital readmission among frail older patients. These findings suggest that the potential benefit of transitional care may lie not in isolated interventions but in the multidimensional integration and coordination of clinical, functional, and social support strategies, highlighting the central role of nurse case managers in transitional care pathways. Full article

Oral squamous cell carcinoma (OSCC) is a prevalent form of head and neck cancer that typically develops on the lip or within the oral cavity. Although there have been advances in early detection and treatment, the prognosis for patients, particularly those with advanced-stage disease, remains poor. Liquid biopsy, particularly through the analysis of cell-free DNA (cfDNA) in plasma and urine, has emerged as a promising tool for non-invasive cancer detection and monitoring. This study assessed cfDNA concentration dynamics in plasma and urine samples from 32 OSCC patients, with 5 undergoing genomic characterization by targeted next-generation sequencing (NGS). CfDNA levels were higher in patients compared to healthy controls and showed transient increases following treatment initiation, likely reflecting tumor cell death, followed by a gradual return to baseline. However, cfDNA concentrations were not significantly associated with tumor stage, recurrence, or progression-free survival. Targeted NGS analysis revealed a heterogeneous mutational landscape, identifying 76 variants across tumor tissue and initial cfDNA, with 30.3% shared between both sources. Recurrent hotspot mutations were detected in several important genes, including TP53, PIK3CA, KRAS, APC, and FBXW7. Urine cfDNA also captured several mutations absent from plasma or tissue, supporting its complementary value. These findings demonstrate that cfDNA analysis can dynamically reflect treatment response and capture tumor heterogeneity in OSCC. While informative, cfDNA quantification alone offers limited prognostic reliability, reinforcing the need for a multidimensional approach that includes genomic and clinical evaluation. Overall, this study supports the potential of liquid biopsy as a real-time, non-invasive tool for molecular monitoring and personalized management of OSCC patients. Full article

Assessing cosmetic quality, safety, and effectiveness demands advanced in situ, real-time, and multi-dimensional analytical technologies, while conventional methods suffer from complex sample preparation and incomplete analysis. Raman spectroscopy, with its non-invasiveness, specific molecular fingerprint, and micron-level spatial resolution, has become a key tool for cosmetic analysis. Based on a comprehensive review of the literature from 2000 to 2025, this article systematically examined the application of Raman spectroscopy in cosmetic analysis. The systematic search and screening process ensured the comprehensiveness and rigor of the review’s research foundation, as the 69 high-quality studies covered all core application areas of Raman spectroscopy in cosmetic analysis, providing solid literature support for subsequent technical summaries and trend analysis. This article systematically reviews the application of Raman spectroscopy in the cosmetic industry for ingredient detection (approved ingredients and hazardous substances), quality control (authenticity verification and production traceability), transdermal mechanism analysis (penetration pathways and interaction with skin barriers), and efficacy assessment. Combined with typical research cases, this study examined the technical principles and practical value, as well as the limitations and shortcomings of Raman technology applications. It ultimately provides suggestions for future developments in terms of portability, intelligence, and standardization, offering references for researchers to enable technological innovation and regulatory improvements in the cosmetics industry. Full article

Background/Objectives: Virtual surgical planning (VSP) allows three-dimensional assessment of complex dentofacial deformities and has become integral to modern orthognathic surgery. However, evidence remains limited regarding how skeletal characteristics and malocclusion patterns translate into surgical movement selection. This study aimed to evaluate demographic features, skeletal malocclusion patterns, and clinical treatment strategies in patients undergoing VSP-guided orthognathic surgery. Methods: This retrospective study included 158 patients who underwent VSP-assisted orthognathic surgery between 2019 and 2025. Sagittal skeletal classification, vertical growth pattern, facial asymmetry, and maxillary crossbite were evaluated together with planned maxillary and mandibular movements. Surgical procedures were analyzed according to skeletal malocclusion classes (Class I, II, and III). Group comparisons were performed using chi-square and Kruskal–Wallis tests. Multivariable logistic regression analysis was conducted to assess factors associated with bimaxillary surgery (p < 0.05). Results: Skeletal Class I malocclusion was most prevalent (46.8%), followed by Class III (29.7%) and Class II (23.4%). Hyperdivergent growth patterns were predominantly observed in Class II patients, whereas normodivergent patterns were most common in Class III cases (p < 0.05). Mandibular advancement and setback generally followed expected class-based trends but were also observed across non-corresponding skeletal classes. Maxillary impaction and mandibular autorotation were frequently incorporated. Bimaxillary surgery was performed in 84.2% of cases. Logistic regression analysis showed no independent predictors of bimaxillary surgery (p > 0.05). Conclusions: VSP-assisted orthognathic surgery demonstrates that surgical planning cannot be reduced to sagittal skeletal classification alone. Treatment decisions are shaped by combined sagittal, vertical, transverse, and patient-specific factors, supporting a multidimensional and individualized planning approach. Full article

With the expansion of global oil and gas resource exploration and development into deep and ultra deep layers, the efficient development of deep carbonate rock fracture cave reservoirs has become the key to ensuring energy security. However, this type of reservoir commonly faces high temperatures, high salinity, and extremely strong heterogeneity, leading to increasingly severe water content spikes caused by dominant water flow channels. Although the existing traditional inorganic plugging agent has good temperature resistance, it has the defects of great brittleness and easy cracking, while the organic polymer gel is prone to degradation failure under high temperature and high salt environments. In order to solve the above problems, a new biochar-enhanced inorganic composite gel system was constructed by using biochar prepared from agricultural and forestry waste pyrolysis as a functional enhancement component. Through rheological testing, high-temperature and high-pressure mechanical experiments, long-term thermal stability evaluation, and dynamic sealing experiments of fractured rock cores, the reinforcement and toughening laws and rheological control mechanisms of biochar on inorganic matrices were systematically studied. Research has found that a biochar content of 0.5 wt% can significantly improve the micro pore structure of the matrix. By utilizing its micro aggregate filling effect and interfacial chemical bonding, the compressive strength of the solidified body can be increased to over 2 MPa, and there is no significant decline in strength after aging at 130 °C for 30 days. More importantly, the unique “adsorption slow-release” mechanism of biochar effectively stabilizes the hydration reaction kinetics at high temperatures, extending the solidification time of the system to 15 h and solving the problem of flash condensation in deep well pumping. This system exhibits excellent shear thinning characteristics and crack sealing ability, and presents a unique “yield reconstruction” toughness sealing feature. This study elucidates the multidimensional strengthening mechanism of biochar in inorganic cementitious materials, providing technical reference for stable oil and water control in deep fractured reservoirs. Full article

Digital platforms became central to institutional participation during the COVID-19 pandemic, yet little is known about how participants experience digitally mediated decision-making processes and which conditions foster high-quality deliberation. Guided by an ecological perspective from community psychology, this study examined university staff’s experiences with platform-based participation, focusing on the relationship between online participation practices, digital empowerment, and perceived deliberative quality. In November 2021, faculty and technical/administrative staff at an Italian university (N = 673) completed an online questionnaire assessing platform use (Microsoft Teams), participation practices, perceived benefits and limitations of digital platforms, digital empowerment, and deliberative quality (critical awareness and engagement). Exploratory factor analyses supported multidimensional measures of platform perceptions and deliberative quality. Hierarchical regression analyses showed that interaction rules and perceived improvements in participation processes were the strongest predictors of deliberative quality. Platform benefits related to participatory quality were positively associated with both outcomes, while efficiency-related benefits showed a small negative association with critical awareness. Digital empowerment uniquely predicted deliberative engagement above and beyond participation practices. Overall, results suggest that deliberative quality in digital institutional settings depends more on structured interaction and empowerment-supportive conditions than on platform use frequency, with implications for designing sustainable online and hybrid participatory processes. Full article

Background: Hypoxic–ischemic encephalopathy (HIE) remains a major cause of neonatal mortality and long-term neurodevelopmental impairment despite advances in perinatal care and the widespread use of therapeutic hypothermia. Reliable early prognostic markers are essential for risk stratification and long-term follow-up planning. This study aimed to evaluate long-term neurodevelopmental outcomes and associated prognostic factors in neonates with HIE treated in the era of therapeutic hypothermia. Methods: This retrospective cohort study was conducted in a tertiary neonatal intensive care unit between January 2020 and June 2024. Neonates with gestational age ≥ 35 weeks diagnosed with HIE were included. Clinical characteristics, laboratory parameters, neurophysiological findings, neuroimaging results, and indicators of multiorgan dysfunction were recorded. Long-term neurodevelopmental outcomes were assessed at 18 to 24 months of age. The primary outcome was death or severe neurodevelopmental impairment. Multivariable logistic regression analysis was performed to identify independent predictors of adverse outcomes. Results: A total of 99 neonates were included. Therapeutic hypothermia was administered to 86 (86.9%) infants. Severe neurodevelopmental impairment or death occurred in 18 (18.2%) patients. Cerebral palsy was diagnosed in 19 (20.9%) survivors, developmental delay in 12 (13.2%), epilepsy in 16 (17.6%), and feeding difficulties in 9 (9.9%). In multivariable analysis, higher lactate levels (adjusted OR = 1.239, 95% CI = 1.052–1.458), lower Apgar score at 5 min (adjusted OR = 0.570, 95% CI = 0.344–0.944), and renal dysfunction (adjusted OR = 7.947, 95% CI = 2.027–31.164) were independently associated with severe neurodevelopmental impairment or death. Multiorgan dysfunction and abnormal neurophysiological and neuroimaging findings were significantly associated with adverse outcomes. Conclusions: Early biochemical markers, neurological assessments, neurophysiological recordings, neuroimaging patterns, and systemic organ dysfunction are closely associated with long-term neurodevelopmental outcomes in neonates with HIE. A multidimensional approach to early prognostic evaluation may improve risk stratification and guide targeted follow-up and intervention strategies. Full article

Background: Acute kidney injury (AKI) is a common and high-risk complication of hospitalization that frequently occurs in patients with chronic cardiometabolic disease. Although heart failure (HF) and diabetes mellitus (DM) are prevalent among hospitalized adults and may differentially modify AKI-associated outcomes, their joint impact on in-hospital risk profiles and cumulative burden remains incompletely characterized. Methods: We conducted a retrospective analysis of adult hospitalizations complicated by AKI using a nationally representative inpatient database. Hospitalizations were classified into four cardiorenal metabolic phenotypes: AKI alone, AKI with HF, AKI with DM, and AKI with both HF and DM. Primary outcomes included in-hospital mortality, dialysis initiation, and mechanical ventilation. Survey-weighted multivariable logistic regression models incorporating HF, DM, and their interaction were used to estimate adjusted associations and model-based predicted probabilities. Adjusted risks were visualized across outcomes, and a composite burden metric was constructed to summarize cumulative in-hospital adverse events. Results: AKI outcomes varied substantially across cardiorenal metabolic phenotypes. HF was consistently associated with higher adjusted mortality and mechanical ventilation risk, whereas DM alone was associated with lower adjusted mortality. A significant interaction between HF and DM was observed regarding dialysis initiation, with a disproportionately higher adjusted risk when both conditions coexisted. Integrated visualization across outcomes demonstrated distinct risk profiles by phenotype, with the combined HF and DM group exhibiting the highest cumulative burden of adverse in-hospital events. Conclusions: Among hospitalizations complicated by AKI, the underlying cardiorenal metabolic status is associated with marked heterogeneity in in-hospital outcomes. HF appears to be a dominant modifier of AKI-associated risk, while DM exerts outcome-specific effects and synergistically increases the risk of dialysis initiation when combined with HF. These findings highlight the importance of incorporating cardiometabolic context into AKI risk stratification approaches and underscore the value of multidimensional in-hospital assessments. Full article

A multi-dimensional quantitative assessment of high-quality development (HQD) in China’s livestock industry and the identification of its main constraints are essential to understanding its current stage and future direction. Guided by global sustainability targets and the United Nations’ Sustainable Development Goals (SDGs), an evaluation system was constructed by this study. This system integrates five key aspects: product safety, output efficiency, resource conservation, environmental friendliness, and regulatory effectiveness. Using provincial panel data from China for 2013–2022, this research applies the entropy-weighted TOPSIS method, kernel density estimation (KDE), and an obstacle degree model for analysis, the goal is to support food security and foster environmentally sustainable growth. The findings indicate the following: (1) Notable inter-provincial disparities exist in the HQD of China’s livestock industry, revealing a spatial pattern of “leading in the east, stable in the center, and lagging in the west.” (2) The nationwide evolution exhibits a “convergence followed by divergence” pattern: from 2013 to 2017, the primary peak of the KDE rose and its width narrowed; from 2018 to 2022, the primary peak declined and its width widened, indicating that inter-provincial disparities first narrowed and then expanded. At the regional level, the development pattern is characterized by eastern polarization, central stability, and western lock-in. (3) Obstacle factor analysis identifies product safety and environmental friendliness as the principal constraints on HQD in the livestock industry. Addressing these bottlenecks is crucial for ensuring the supply of livestock products (SDG 2: Zero Hunger), promoting resource conservation and green production (SDG 12: Responsible Consumption and Production), and alleviating the ecological and environmental pressures of the livestock industry (SDG 15: Protection of Terrestrial Ecosystems). The challenges related to resources, the environment, and quality safety confronting China’s livestock industry are common among developing countries. Consequently, the evaluation framework established in this study can offer methodological references for relevant nations. Full article