Search Results (18,676)

This study develops an integrated decision-support framework to advance green supply chain management (GSCM) by systematically linking Environmental, Social, and Governance (ESG) practices, environmental product innovation, corporate performance, and strategic alternatives. Employing the Analytic Network Process (ANP), the proposed model captures complex interdependencies and feedback relationships across life-cycle value chain stages, enabling a holistic evaluation of sustainability-oriented strategies. A Delphi panel comprising 15 experts from academia, industry, and government is used to validate the evaluation criteria and network structure. The empirical results indicate that eco-friendly design, energy and resource efficiency, and carbon–climate management are the most influential drivers shaping green supply chain performance. Moreover, operational and sustainability performance are found to exert greater strategic importance than short-term financial performance, highlighting GSCM as a long-term capability-building approach rather than a cost-centered initiative. To enhance analytical adaptability, this study proposes a conceptual extension integrating neural feature extraction (NFE) signals with ANP-based expert weights. The NFE module is not empirically trained or validated; rather, it illustrates a theoretically consistent mechanism for incorporating data-driven feature signals into structured multi-criteria decision frameworks. Empirical validation of the NFE component is proposed as a future research direction. Full article

Cellulose nanocrystals (CNCs) are highly desirable nanomaterials for reinforcing biopolymer films. Coconut husks are generated in massive quantities after harvesting and processing, leading to waste management issues. This study isolated and characterized CNCs from young (y-CNCs) and mature (m-CNCs) coconut husks via acid hydrolysis (32% H₂SO₄, 50 °C, 5 h), comparing them with commercial CNCs (c-CNCs) to evaluate their performance in gelatin-based films. TEM confirmed rod-shaped morphology for all CNCs. Notably, m-CNCs exhibited a smaller particle size (199 nm), a higher surface charge (−46.8 mV), and superior crystallinity (63.98%), demonstrating properties comparable to c-CNCs. FTIR and XRD confirmed characteristic cellulose functional groups and crystalline structure, while TGA demonstrated excellent thermal stability above 300 °C for all samples. Incorporation of CNCs into gelatin films significantly improved tensile strength (from 15.63 to 24.93 MPa) and reduced water vapor permeability (from 2.65 to 2.43 × 10⁻¹⁰ g m m⁻² s⁻¹ Pa⁻¹; p < 0.05). These findings demonstrate how coconut husk residues can be upcycled into high-value nanomaterials fostering economic growth with innovation in sustainable manufacturing. This research also promotes responsible waste utilization, highlighting the benefits of biodegradability and a reduced carbon footprint for sustainable food packaging applications. Full article

Due to the perishable nature of products, high uncertainty, and conflicting objectives, food supply chain logistics management requires dynamic and adaptive decision-making frameworks. In this study, an integrated decision-making architecture is presented that integrates a multi-objective fuzzy optimization model into an adaptive digital twin along with an agentic AI-based dynamic goal reset mechanism. The main methodological innovation of this study is not in the separate development of each of these components but in their structured integration in the form of a self-regulating decision-making loop in which the priority of goals is dynamically adjusted based on the current state of the system. Computational results based on real and simulated data show that the proposed framework reduces the total logistics cost by about 4–5% and reduces product waste by about 13% while simultaneously improving the service level by about 4%. Resilience analysis shows faster performance recovery in the face of operational disruptions, and scalability results confirm the controlled growth of computational time with increasing problem size. These findings demonstrate the effectiveness of integrating adaptive digital twins and agentic AI in a multi-objective fuzzy optimization environment for intelligent and resilient food logistics management. Full article

Generative AI has surfaced as a key driving force for corporate sustainable development and strategic transformation, offering new perspectives for effectively enhancing corporate ESG performance practices. Utilizing panel data sourced from Chinese A-share listed firms spanning the years 2012 to 2024, this research establishes and substantiates a model elucidating the mechanism by which generative AI impacts corporate ESG performance. The findings reveal the subsequent points: First, generative AI can effectively drive improvements in corporate ESG performance. Second, the caliber of information disclosure acts, in part, as an intermediary factor influencing the correlation between generative AI and corporate ESG performance enhancement. Third, sustainable innovation partially mediates the relationship between generative AI and corporate ESG performance enhancement. Fourth, environmental regulations weaken the beneficial influence exerted by generative AI on a company’s ESG achievements. Fifth, compared to non-manufacturing firms, companies situated in the central and western parts of China, and non-technology-intensive firms, the application of generative AI exerts a more pronounced enhancing impact on ESG achievements in manufacturing firms, firms in eastern regions, and technology-intensive firms. The research findings provide new insights for improving corporate ESG performance and provide strategic guidance for businesses aiming to attain long-term sustainable growth through reliance on generative AI. Full article

Exploring effective training methods to reliably trigger scoring in electronic protective gear is a significant challenge faced by coaches and athletes, and it constitutes a critical research direction that urgently demands scientific exploration. To improve the scientific precision of daily Taekwondo training and enhance competitive performance more efficiently and to improve the effectiveness of daily Taekwondo training and enhance competitive performance, a hydrogel-based flexible pressure-sensing film was developed. This film would enable traditional Taekwondo protective gear with electronic sensing capabilities via a simple adhesion method. By attaching a low-cost, high-precision, and appropriately flexible gel-based pressure-sensing film to conventional protective gear through a straightforward adhesion approach, it can attain sensing performance comparable to that of specialized competition-grade electronic protective gear. This innovation will provide technological support for advancing the scientific rigor of Taekwondo training in China. This study focuses on the design and development of high-strength, high-toughness ionic hydrogels, offering technical backing for the creation of flexible pressure-sensing films tailored for Taekwondo applications. Full article

Resilience pads provide elastic behavior and reduce impact and vibration loads in sleeper floating tracks, but their long-term degradation affects critical track components. Their performance is currently measured using labor-intensive, expensive restricted field sampling and indoor static stiffness testing, which cannot reliably measure entire track sections. The objective of this study was to measure and analyze a rail’s supporting-point reaction force (pressure) using a skin sensor according to the train load (as the force changes with spring stiffness) and the resilience pad spring stiffness of the sleeper floating track. To evaluate the rail-supporting-point reaction force of the sleeper floating track, a skin sensor was installed at the bottom of the concrete sleeper, and the reaction force was measured according to the train load. Laboratory testing demonstrated that resilience pad spring stiffness affects the rail-supporting-point reaction force. Field measurements of the train load were used to examine the sleeper floating track’s supporting-point reaction force and the resilience pad’s spring stiffness, confirming field applicability. Therefore, this innovative skin-sensor-based assessment technique for reaction forces at rail-support points may predict resilience pad replacement and identify anomalies in real time, making it easier to assess performance and diagnose sleeper floating tracks. Full article

‘Barhi’ dates (Phoenix dactylifera L.) are highly prized and widely consumed at the khalal stage, but they are only available for a short time, which highlights the importance of extending their storage life. This study examined the effectiveness of edible coatings in delaying ripening and maintaining fruit quality during cold storage (2 °C). The treatments tested were gelatin alone or gelatin combined with chitosan, Aloe vera gel (AVG), or gum arabic, and applied in a layer-by-layer (LbL) approach. A fifth treatment consisting of deionized water was used as a reference untreated control. The fruit parameters measured included weight loss, decay, moisture content, ripening (rutab transformation), firmness, color (lightness and hue angle), total soluble solids (TSS), titratable acidity (TA), TSS/TA ratio, total sugars, total polyphenols, and enzymatic activity. Results indicated that the LbL edible coating was more effective in preserving postharvest quality. Regarding weight loss and decay rate, the results showed that the control treatment consistently had 1.5–5-fold higher deterioration indicators than the coated fruits. Among the tested treatments, the gum arabic and gelatin coating was the most effective compared to the untreated control, reducing weight loss by over 40%, lowering decay by approximately 80%, and maintaining significantly higher moisture content throughout storage. Concerning carotenoid levels, the untreated fruits exhibited approximately 1.2–1.4-fold higher carotenoid content than the coated fruits. Fruits treated with gum arabic and gelatin exhibited the best preservation effect Sby limiting TSS increase and maintaining higher TA compared with the control. This treatment best maintained antioxidant capacity and phenolic content while significantly suppressing the activities of polyphenol oxidase and peroxidase. Overall, the LbL coating strategy successfully maintained the quality of ’Barhi’ dates by mitigating oxidative and enzymatic degradation throughout storage. Principal component analysis and hierarchical cluster analysis demonstrated that treatments gum arabic and gelatin exhibited superior effectiveness in extending the date storage life in terms of physicochemical properties and antioxidant activity, followed by chitosan and gelatin, and Aloe vera and gelatin, compared to the control fruits over a 60-day storage period. Full article

This paper presents experimental and numerical investigations on thin-walled carbon-epoxy composite structures subjected to axial compression under varying thermal conditions. The primary objective of the study was to determine the influence of temperature on the stability, postbuckling behavior, and load-carrying capacity of the tested profiles. To achieve this, an innovative research methodology combining laboratory experiments and numerical simulations was developed, enabling a comprehensive assessment of the performance of compressed composite structures at different operating temperatures. The obtained results allowed for both qualitative and quantitative evaluation of the temperature-dependent behavior (from −20 °C to +80 °C) of thin-walled composite elements under compressive loading, offering new insights into their structural performance in thermally variable environments. The maximum percentage change in load capacity under variable thermal conditions was approximately 26.5%. At sub-zero temperatures (−20 °C), a slight effect on the load-carrying capacity of composite structures was observed, with a change in stiffness of a few percent. At increased above-zero temperatures (+80 °C), a significant change in stiffness (up to several dozen percent) was observed. The strengths of the work are a relatively extensive experimental program across several temperatures and stacking sequence composites, the use of digital image correlation to capture buckling and postbuckling deformations, and the parallel use of numerical modeling. Full article

Accurate and continuous outdoor pedestrian positioning using smartphones remains challenging in complex environments like urban canyons, where Global Navigation Satellite System (GNSS) signals are frequently degraded or blocked, and Pedestrian Dead Reckoning (PDR) suffers from cumulative errors. To address this, this paper proposes a novel fusion method based on a Robust Adaptive Cubature Kalman Filter (RACKF). The core of our approach is a two-stage filtering architecture: the first stage employs a quaternion-based RACKF to optimally fuse gyroscope and magnetometer data for robust heading estimation; the second stage performs the core fusion of GNSS observations with an enhanced 3D PDR solution. Key innovations include an adaptive noise estimation strategy combining fading and limited memory weighting, a robust M-estimator-based mechanism to suppress outliers, and the integration of differential barometric height measurements. Experimental results demonstrate that the proposed method achieves a horizontal positioning accuracy of 3.28 m (RMSE), outperforming standalone GNSS and improving 3D PDR by 25.97% and 10.39%, respectively. This work provides a practical, infrastructure-free solution for robust smartphone-based outdoor navigation. Full article

The primary objective of this study is to develop a specialized deep learning framework specifically adapted for the unique physical characteristics of neurovascular Optical Coherence Tomography (OCT) imaging. Although Polyp-PVT, originally designed for polyp segmentation, shows promise for OCT analysis, it faces limitations in neurovascular applications. The default RGB input wastes resources on duplicated grayscale data, while its fixed-scale fusion struggles with vascular curvature variations. Furthermore, the attention mechanism fails to capture radial vessel patterns, and geometric constraints limit thin boundary detection. To address these challenges, we propose Brain-OCT-PVT with key innovations: a single-channel input stem reducing parameters by two-thirds; a Radial Intensity Module (RIM) using polar transforms and angular convolution to model annular structures; and a Deformable Cross-scale Fusion Module (D-CFM) with learnable offsets. The Boundary-aware Attention Module (BAM) combines Laplace edge detection with Swin-Transformer for sub-pixel consistency. A specialized loss function combines Dice Similarity Coefficient (Dice), BoundaryIoU on 2-pixel dilated edges, and Focal Tversky to handle extreme class imbalance. Evaluation on 13 clinical cases achieves a Dice score of 95.06% and an 95% Hausdorff Distance (HD95) of 0.269 mm, demonstrating superior performance compared to existing approaches. Full article

Background/Objectives: In the context of its policy of “Reinvigorating Immunization as a Public Good for Universal Health,” the Pan American Health Organization (PAHO) developed a methodology and tool (MD-PAI) to help Member States of the Americas monitor and assess the performance of their national Expanded Program on Immunization (EPI) for each of the 13 technical components that make up the program. Methods: The MD-PAI was developed in several stages, including review of existing national EPI evaluation methodologies, selection and prioritization of questions for each of the 13 EPI components, piloting of the methodology, final calibration to ensure validity, completeness, reliability, standardization, usefulness, and usability across components and across countries, and publication in the four official languages of the PAHO. Results: The implementation of the MD-PAI enables countries to collect data, document lessons learned, develop action plans to close the most urgent gaps in the short and medium term and enforce the management of the EPI as part of the continuous improvement process. Since its introduction in 2023, fourteen countries in the Americas implemented the MD-PAI, using the results for their short- and medium-term planning and budgeting. Of the 13 components of the EPI, those that have performed best are political priority and planning and programming, while social communication is the component that reported the greatest number of gaps across countries. Conclusions: The PAHO has developed a methodology and tool to help countries to assess their EPIs to identify good practices, gaps and challenges, and develop an action plan to strengthen their programs. However, the impact of vaccination coverages and the epidemiology of vaccine-preventable diseases could take time. Full article

Against the backdrop of accelerating urbanization and population aging, urban parks have emerged as significant venues for enhancing the physical and mental well-being of older adults. The age-friendly quality of these spaces is directly linked to health equity and urban inclusiveness. Using the high-density historic district of Beilin in Xi’an as a case study, we developed an innovative assessment tool to evaluate the age-friendliness of park walking environments. Guided by the Health Impact Assessment (HIA) framework, this tool integrates subjective perceptions and objective data to diagnose environmental strengths and weaknesses across four dimensions: accessibility, safety, comfort, and health-related interactivity. Based on multi-source data and quantitative analysis, the study revealed key variations in the age-friendly attributes of different parks. Our field assessment focused on three representative park types: urban comprehensive, historic–cultural, and community leisure parks. The key findings are: (1) Safety was perceived by experts as the most critical dimension for older adults’ health experience, with a weight of 0.49, accounting for nearly half of the total. However, significant variations exist in safety quality across different types of parks. (2) Age-friendly performance differed profoundly among park types. Benefiting from systematic management, the urban comprehensive park achieved balanced performance and a total score of 84.87. In contrast, the historic–cultural park, constrained by its linear morphology and historical functions, scored the lowest at 66.03, exhibiting notable deficits in safety and comfort. The community leisure park, while vibrant in community activity, attained an intermediate score of 74.76 due to insufficient attention to safety details. (3) The assessment outcomes highlight the association of park typology, site selection, and design sophistication with the lived experience and potential health benefits for older adults. This study provides a refined evaluation tool and tailored optimization strategies for the age-friendly renovation of diverse park types. Full article

Climate change and tightening resource and environmental constraints are increasing the strategic importance of environmental, social, and governance (ESG) performance for firms’ long-term viability. This study examines whether ESG performance enhances organizational resilience in China and explores the roles of innovation capability and environmental uncertainty. Using an unbalanced panel of 1037 non-financial Chinese A-share listed firms from 2014 to 2022, we estimate panel models and conduct a series of robustness and endogeneity tests. The results show that ESG performance significantly improves organizational resilience. Innovation capability partially mediates this relationship, indicating that ESG enhances resilience in part by strengthening firms’ innovative capacity. Environmental uncertainty also positively moderates the effect of innovation capability on organizational resilience, although the evidence is modest. Further analysis shows that the positive ESG–resilience relationship is stronger among state-owned enterprises and small and medium-sized firms. Overall, the findings suggest that ESG functions not only as a sustainability signal, but also as an organizational capability that supports resilience under uncertainty. Full article

Remote sensing (RS) images, characterized by their large size and rich texture, require algorithms capable of effectively integrating both global and local features for compression. However, existing Learned Image Compression (LIC) approaches face distinct bottlenecks. While Transformer-based architectures typically suffer from heavy computational loads, standard State Space Models (SSMs) often incur prohibitive memory costs when processing high-resolution inputs. To address these limitations, we propose MambaLIC, a novel RS image compression network that integrates the efficient long-range modeling of SSMs with the local modeling ability of CNNs. In this paper, we introduce an innovative Remote Sensing State Space Model (RS-SSM) module, which combines visual SSM with dynamic convolution for remote sensing image compression. This integration facilitates effective interaction between local and global information, thereby enhancing the performance of RS image compression. Furthermore, we propose an SSM attention-based (SSA-based) spatial-channel context model for better entropy modeling. Compared to Transformer-CNN mixed architectures, MambaLIC reduces computational complexity by 63.9% and achieves superior rate-distortion (RD) performance. Consequently, compared to the latest SS2D-based method MambaIC, MambaLIC achieves substantial efficiency gains, saving 78.8% in memory usage. Experimental results demonstrate that MambaLIC achieves state-of-the-art (SOTA) performance, outperforming VVC (VTM-17.0) by 14.22%, 18.48%, and 17.47% in BD-rate on UC-Merced, LoveDA, and xView datasets, respectively. Full article

This study explores the dynamics of Open Innovation (OI) in manufacturing firms, with particular attention to collaboration with public research institutions. The research is performed in the Piedmont region, Northern Italy, which represents one of Italy’s leading innovation regions, with a strong manufacturing heritage and an active strategy to foster industrial transition through innovation clusters and partnerships. The research analyzes survey responses from 82 managers and decision-makers in manufacturing firms belonging to the local manufacturing ecosystem. The questionnaire investigated how company size, organizational structure for research and development (R&D), perceived importance of collaboration, innovation drivers and barriers, and trust in research institutions affect four types of innovation: product, process, marketing, and organizational. Results indicate that collaboration with other private companies is significantly associated with product innovation, while collaboration with public research institutions is associated to both product and process innovation. The level of R&D structuring in the management of innovative projects and trust in the expertise of public research organizations are also positively associated with product innovation. In addition, key drivers—such as the availability of dedicated financial resources, staff creativity, and openness to external partnerships—are significantly related to process innovation. The findings suggest that regional policymakers and industry stakeholders should promote targeted measures to strengthen OI adoption, particularly by improving the perceived competence and transparency of public research organizations. Full article