Search Results (480)

Walnut (Juglans regia) is an economically significant woody oil tree species widely cultivated in China. However, its production is increasingly threatened by extreme low-temperature events, such as unseasonal frosts and late-spring cold. Salicylic acid (SA) is a key phytohormone known to enhance cold tolerance in plants, yet its underlying mechanism in walnut remains unclear. In this study, we present the first integrated analysis combining physiological measurements, transcriptomics, and metabolomics to investigate how exogenous SA improves cold tolerance in walnut leaves. Our results showed that SA treatment significantly increased the accumulation of soluble sugars, chlorophyll, and proline, enhanced peroxidase (POD) activity, and reduced malondialdehyde (MDA) levels under cold stress. Multi-omics analysis revealed that SA modulated the expression of genes involved in multiple hormone signaling pathways, including those of SA, auxin, jasmonic acid, and abscisic acid, and altered corresponding hormone levels. Notably, carbohydrate metabolism emerged as a central pathway mediating SA-induced cold adaptation. Weighted gene co-expression network analysis (WGCNA) further identified several core candidate genes, such as JrTGA, JrPP2C, JrTPS, and JrBAM, which may play key roles in this process. Collectively, this study provides the first multi-omics perspective on the regulatory network underlying SA-enhanced cold tolerance in walnut. These findings offer both a theoretical and technical foundation for applying SA in cold-resistant walnut cultivation and contribute to the development of stress-resilient production strategies. Full article

Background: Breakpoints in the pre-examination processes and at organizational interfaces are a significant source of failures in specimen identification and tracking in anatomic pathology. While ISO 15189 emphasizes end-to-end traceability and risk-based quality management, implementing these principles in complex, multi-actor specimen pathways remains challenging. This study explores expert perspectives on specimen process chains, tracking mechanisms, and ISO 15189-oriented quality and risk management in pathology. Methods: We conducted 10 semi-structured expert interviews across three settings. Interviews were audio-recorded, transcribed, pseudonymized, and analyzed using structured qualitative content analysis (Mayring) supported by MAXQDA. A deductive category system derived from the theoretical framework and interview guide comprised six main categories and twelve subcategories. Results: Across 512 coded text segments, participants identified several factors as critical for effective implementation, including: (i) interface management along the specimen pathway, with recurrent vulnerabilities at handovers between operating theater/ward/transport and accessioning; (ii) the central role of barcode-based identification and the need for closed-loop traceability; (iii) the importance of measurable quality indicators and incident learning systems to operationalize risk management; (iv) persistent paper–digital handoffs and heterogeneous IT landscapes that undermine data integrity; (v) the need for clearly assigned responsibilities, training, and SOP governance; and (vi) implementation barriers including resources, change management, and vendor integration, alongside practical enablers such as incremental roll-out and cross-professional governance. Conclusions: Experts converge on a pragmatic ISO 15189-aligned roadmap: prioritize interface risks, standardize identifiers and handover rules, define a minimal KPI set for tracking and misidentification events, and reduce paper–digital handoffs by interoperable IT. Future work should quantify baseline error rates and evaluate the impact of digital tracking interventions on patient safety and turnaround times. Full article

Aqueous zinc-ion batteries (AZIBs) are promising for large-scale grid storage due to inherent safety, low cost, environmental compatibility, high theoretical capacity (820 mAhg⁻¹), and suitable redox potential (−0.763 V vs. SHE). However, practical deployment is hindered by coupled challenges at the zinc anode–hydrogen evolution, dendrite growth, and corrosion/passivation, which severely limit cycle life and coulombic efficiency. This review systematically summarizes key advances in AZIB research. It first elucidates working principles and four cathode energy storage mechanisms: Zn²⁺ insertion/extraction, H⁺/Zn²⁺ co-insertion, chemical conversion, and dissolution/deposition. Second, it examines four mainstream cathodes (manganese-based, vanadium-based, Prussian blue analogs, and organic compounds), analyzing performance bottlenecks and corresponding optimization via structural modification. Third, it explores functional mechanisms of advanced separators (polymer, inorganic/ceramic composite, MOF-based, and cellulose-based) in regulating uniform Zn²⁺ deposition and suppressing dendrites. Fourth, it summarizes anode optimization strategies: artificial protective layers for interface stabilization, electrolyte additives to modulate Zn²⁺ solvation/deposition, and 3D porous structures to reduce local current density and provide nucleation sites. Finally, key scientific challenges and future directions are discussed—multi-strategy synergy, in situ characterization, practical battery construction, and sustainable technological development, offering theoretical guidance for advancing AZIBs toward large-scale applications. This review aims to provide a comprehensive perspective spanning from materials to systems, and from mechanisms to applications. Its core objective is not merely to list the types of cathode materials, but to establish a logical bridge directly connecting “key challenges” to “optimization strategies,” with a particular emphasis on the issues and solutions related to the cathode side. Full article

Entrepreneurial ecosystems are multi-actor, uncertain, and dynamic environments in which policymakers and investors must balance innovation, resilience, and cost. Despite the growing literature on entrepreneurial ecosystems, much of the existing research has focused on identifying the components and relationships among actors and has provided less prescriptive frameworks for evaluating resource allocation policies before implementation. To address this gap, this study presents a digital twin-based and fuzzy multiobjective optimization framework for resource orchestration in entrepreneurial ecosystems. The proposed framework combines dynamic ecosystem representation with multiobjective decision-making under uncertainty and allows for the testing of different resource allocation and policy scenarios before actual intervention. To solve the model, exact optimization in GAMS was used for small- and medium-sized samples, and NSGA-II and ACO algorithms were used for large-scale problems. The advantage of the proposed method is that, unlike purely descriptive approaches or deterministic models, it simultaneously considers uncertainty, time dynamics, and trade-offs between innovation, resilience, and cost in an integrated decision-making framework. Experimental evaluation was conducted based on simulated data calibrated with reliable public sources, and the performance of the algorithms was compared with reference methods in terms of computational time, solution quality, and stability. The results showed that metaheuristics, especially NSGA-II, significantly reduced the solution time in large-scale problems and at the same time produced solutions closer to the Pareto frontier and with greater stability. Sensitivity analysis also showed that in the designed scenarios, policy budgets have a more prominent effect on innovation, while resource capacity and structural diversification play a more important role in enhancing resilience. Also, improving resource efficiency has had the greatest effect on reducing the total system cost. From a theoretical perspective, the present study operationally models the logic of resource orchestration in entrepreneurial ecosystems through the integration of digital twins and fuzzy multi-objective optimization. From a managerial perspective, this framework acts as a decision-making engine that allows for ex ante testing of policies, clarification of trade-offs, and extraction of resource allocation rules under uncertainty. Full article

Deep learning has achieved remarkable advancements in medical image segmentation, yet its generalization capability across unseen tasks remains a significant challenge. The variety of task objectives, disease-dependent labeling variations, and multi-center data contribute to the high uncertainty of task-specific models on unseen distributions. In this study, we propose PromptSeg, an innovative Transformer-based unified framework for universal 2D medical image segmentation. From an information-theoretic perspective, PromptSeg formulates the segmentation process as a conditional entropy minimization problem, utilizing visual prompts as side information to reduce the uncertainty of the target task. Guided by the information bottleneck principle, PromptSeg aims to utilize the provided visual prompts to filter out redundant noise and learn contextual representations, thereby breaking the restrictions of the task-specific paradigm. When faced with unseen datasets or segmentation targets, our method only requires a few annotated visual prompt pairs to extract task-specific semantics and segment the query images without retraining. Extensive experiments on CT and MRI datasets demonstrate that PromptSeg not only outperforms state-of-the-art methods but also exhibits strong multi-modality generalization capabilities. Full article

Gastrointestinal malignant tumors account for approximately one-third of global cancer-related deaths, primarily including colorectal, gastric, pancreatic ductal adenocarcinoma, and hepatocellular carcinomas. These tumors have a high incidence, are often asymptomatic, and are prone to metastasis and recurrence, posing a significant public health burden. Although traditional methods such as radiotherapy and chemotherapy can delay disease progression, their nonspecific effects often lead to severe side effects and drug resistance, resulting in limited efficacy. Therefore, developing novel treatment strategies with high target specificity and favorable biological safety is a critical scientific issue in this field. Peptide drugs offer advantages such as good biocompatibility, low immunogenicity, diverse structures, and ease of modification, collectively demonstrating unique potential for tumor treatment. They can not only achieve precise delivery by specifically recognizing tumor receptors but can also directly interfere with signal transduction, metabolism, and immune regulation, producing multi-target antitumor effects. This article systematically reviews the research progress of peptide drugs in gastrointestinal tumors, focusing on their molecular mechanisms, delivery modification strategies, and the latest applications. It also summarizes the challenges and future directions for clinical translation, providing a theoretical foundation and future perspectives for the precise treatment of gastrointestinal tumors and the design of new drugs. Full article

This study employs bibliometric analysis, utilizing the visualization tools CiteSpace 6.3.R1 and VOSviewer 1.6.18, to systematically examine 8727 documents from the Web of Science Core Collection (2000–2024) related to “resilient cities” and “urban resilience.” It explores the evolution of resilient city research, current international trends, practical developments in China, and future directions. The study addresses key questions concerning the theoretical foundations of resilient cities, research advances in the security field, China’s implementation pathways, and emerging trends. Findings indicate that resilient city discourse has evolved from a narrow focus on engineering-based disaster prevention toward a multidimensional, socio-ecological–economic adaptive system. This progression can be divided into three phases: the theoretical foundation period (2000–2008), the technological integration period (2009–2018), and the complex crisis response period (2019–present). Internationally, practices are increasingly centered on climate change adaptation, supported by multi-level governance frameworks such as the MCR2030 initiative. China demonstrates a “dual-track” approach that combines policy-driven initiatives with localized innovations, advancing through international pilot projects, domestic policy experimentation, and grassroots exploration. The study also highlights differences between Chinese and Western research in perspectives, methodologies, and theoretical frameworks. Future resilient city development is expected to emphasize systematization, digitalization, and equity, leveraging technologies such as digital twins and artificial intelligence while fostering community participation and multi-scale collaborative governance. By systematically outlining the theoretical evolution and practical logic of resilient cities, this study offers insights for urban resilience building in developing countries and provides a methodological reference for enhancing resilience capabilities across different administrative levels. Full article

Adipose tissue serves as a critical metabolic and endocrine organ, essential for maintaining systemic energy homeostasis and inter-organ communication. During the aging process, it undergoes significant structural remodeling and functional decline, characterized by dysregulated lipid metabolism, chronic low-grade inflammation, reduced insulin sensitivity, and adipokine imbalance. These alterations not only compromise the physiological integrity of adipose tissue but also contribute to the progression of various age-associated metabolic disorders, including type 2 diabetes, atherosclerosis, and nonalcoholic fatty liver disease. In recent years, natural products have emerged as a focal point in anti-aging research, owing to their broad accessibility, high biological safety, and capacity for multi-target regulation. Polyphenolic and polysaccharide, in particular, have demonstrated robust antioxidant, anti-inflammatory, autophagy-modulating, and mitochondrial-protective effects in cellular and animal models, indicating their promise in attenuating adipose tissue aging. Although the anti-aging effects of these natural compounds are well documented in the neural, hepatic, and cardiovascular systems, their specific mechanisms in adipose depots—especially differential regulatory patterns between white and brown adipose tissues, which may inform depot-specific therapies—and the development of targeted delivery approaches remain inadequately explored. This review, grounded in the three primary hallmarks of adipose tissue aging (oxidative stress, chronic inflammation, and dysregulated lipid metabolism), systematically elucidates the molecular mechanisms and recent advancements in the application of polyphenols and polysaccharides as natural modulators. This review establishes a cohesive theoretical foundation and delivers innovative perspectives to guide the advancement of natural product-based nutritional and therapeutic strategies for combating adipose tissue aging. Full article

This study explores how the multifaceted servicescape of wellness hotels influences customers’ intentions to revisit and recommend, with customer satisfaction acting as a mediating mechanism. Drawing on the Stimulus–Organism–Response (S-O-R) model and experiential marketing theory, this study conceptualizes servicescape across four dimensions: sensory, social, wellness-related activities, and cultural experiences. Survey responses were gathered from 483 Korean adults who had visited a wellness hotel within the last six months. The data were processed using SPSS (version 27.0) and AMOS (version 23.0). Findings suggest that while sensory, social, and wellness activity experiences have a significant positive impact on satisfaction, cultural experience does not yield the same effect. Satisfaction mediates both revisit and recommendation intentions. Moreover, multi-group analysis confirmed that wellness interest moderates the influence of sensory and wellness activity experiences on satisfaction. Notably, individuals with a high interest in wellness report increased satisfaction through active participation in wellness programs, whereas those with low wellness interest show greater responsiveness to sensory aspects. Theoretically, this study contributes to the existing body of literature by embedding wellness psychology and sustainable development goals (SDGs 3 and 12) into servicescape research. In terms of managerial implications, this study emphasizes the need for wellness hotels to improve sensory-based designs and experiential service quality, while also segmenting their strategies based on customers’ wellness profiles. By presenting an integrated model that connects experiential value, satisfaction, and behavioral intention, this study provides deeper insights into sustainable wellness tourism from both academic and practical perspectives. Full article

Cliff carvings, as significant art forms bearing historical, cultural, and religious connotations, face dual threats from natural weathering and human-induced damage. Their protection and restoration of the artistic style present pressing challenges. In recent years, the rapid advancement of digital technologies has offered new opportunities for preserving and reproducing cultural heritage. Particularly, 3D style transfer techniques are emerging as crucial tools for digital safeguarding. The advantages of three-dimensional style transfer in cultural heritage applications include dynamic stylized rendering, simulation of styles from multiple historical periods, alternative modes of exhibition, and facilitating a paradigm shift in conservation practices from static digital archiving to dynamic revitalization. This study proposes a novel 3D stylization method for cliff carvings by integrating 3D Gaussian Splatting (3DGS) and Nearest Neighbor Feature Matching (NNFM) loss metric. The method represents ancient cliff carvings as a set of optimizable 3D Gaussians representation, enabling efficient capture and processing of complex geometric structures and rich textural details. By integrating the textural and geometric characteristics of the target artistic style, 3DGS facilitates high-quality transfer of diverse artistic styles while effectively preserving the original intricate details of the carvings. Additionally, we employ the NNFM loss function to transfer 2D visual details into 3D representations while maintaining multi-perspective style consistency. Experimental results demonstrate that the proposed method exhibits significant advantages in texture fidelity, style consistency, and rendering efficiency. This research showcases the potential of our model for the digital preservation and presentation of cliff-carved cultural heritage, offering an innovative technological approach with theoretical value and practical significance. Full article

As industrial internet platforms increasingly play a central role in the digital transformation of manufacturing, they have become crucial areas for manufacturing enterprises to pursue digital innovation. Current academic research has paid relatively little attention to the digital innovation of participating enterprises within industrial internet platforms, failing to fully reveal the driving mechanisms of such innovation in this context. Based on the TOE framework and adopting a platform participant perspective, this study employs fuzzy set qualitative comparative analysis (fsQCA). By surveying 169 manufacturing enterprises participating in industrial internet platforms, it integrates seven key antecedents—technology availability, technology fit, digital leadership, organizational structural flexibility, resource orchestration, policy support, and competitive pressure—to systematically explore the complex influence pathways of multi-factor concurrent interactions on digital innovation. The research results show that the high digital innovation of manufacturing enterprises on the industrial internet platform includes precise implementation type, exploration-oriented type and co-evolution type, while the non-high digital innovation paths include technology blocking type, dual-core absence type and system disorder type. These conclusions expand the theoretical framework for digital innovation in manufacturing enterprises within industrial internet platforms and offer practical recommendations for their digital innovation practices. Full article

Background: Inflammatory bowel disease (IBD) involves chronic intestinal inflammation, epithelial barrier disruption, and dysbiosis, with environmental factors playing a significant role in its pathogenesis. Previous work revealed that cold exposure alleviates colitis in mice; this study extends that finding by demonstrating that cold exposure enhances intestinal regeneration even in healthy mice, upregulating proliferation markers (Mki67, PCNA, Cyclin D1). Methods: Applying this pro-regenerative effect to a colitis model, we investigated the underlying mechanisms through multi-omics analysis, transmission electron microscopy (TEM), immunofluorescence, and pathological staining as well as 16S rRNA sequencing. Results: We found that cold exposure activates intestinal epithelial proliferation pathways. Further analysis indicated that cold exposure induces colonic stem cell regeneration, upregulating stem cell markers Lgr5 and Ascl2. Notably, colonic transcriptomic profiling revealed the emergence of a Paneth-like cell phenotype, characterized by altered expression of specific lineage genes. Furthermore, cold exposure simultaneously promoted the accumulation of secretory granules and upregulated the expression of antimicrobial peptide genes (such as Lysozyme and Defa) in ileal Paneth cells. This enhanced ileal antimicrobial defense effectively reshaped the gut microbiota in inflamed intestines. Conclusions: This research elucidates a mechanism whereby cold adaptation promotes mucosal repair by integrating localized colonic epithelial regeneration with enhanced ileal Paneth cell-mediated antimicrobial defense. This offers compelling new perspectives on how environmental factors, such as cold exposure, could influence the pathophysiology of IBD and contribute to intestinal regeneration, which may provide foundational theoretical support for the future diagnosis and treatment of IBD. Full article

With the advancement of digital technology and Industry 4.0, artificial intelligence (AI) is gradually embedded in human resource management and has become an important digital foundation to support the sustainable transformation of enterprises. However, the research in the manufacturing context, particularly through the challenge perspective at different levels, remains fragmented. This work represents a systematic review of 347 articles from Scopus and Web of Science from 2000 to 2025 and employs a dual-method analysis strategy embracing metrics and in-depth coding on 100 core publications. Excel, Bibliometrix, CiteSpace, Latent Dirichlet Allocation (LDA), and VOSviewer were utilized for quantitative analysis, while open–axial–selective coding of the Grounded theory approach was applied to generate qualitative results. The findings revealed six key challenges in integrating AI-HRM within manufacturing and six approaches to solve the identified issues. The Challenge–Approach Matching Matrix was constructed, illustrating the suitability of different pathways for addressing specific challenges. Analysis of thematic evolution in AI-HRM research resulted in the identification of three distinctive phases and demonstrated a consistent shift from technology-centric approaches towards human–machine collaboration. The primary contribution of this research lies in proposing a Multi-Level Embedded Framework providing a complex view of AI-HRM in a manufacturing sector at micro, meso, and macro levels. The absence of sustainable HR transformation through AI integration was identified as the critical challenge at the macro level. This research provides theoretical and practical implications for designing the sustainable HRM system based on ESG principles and favors the United Nations Sustainable Development Goals 9 and 12. Full article

Most 2D human pose estimation frameworks utilize static designs for multi-scale feature fusion, where information from various scales is integrated using fixed weights. A drawback of these approaches is that they often lead to localization biases in complex scenarios. This paper addresses the issues of multi-scale feature mismatch and joint localization biases in pose estimation. From the perspective of feature processing, multi-scale weights must be adapted to the size and position of joints, while joint predictions should adhere to human anatomical constraints. Existing methods lack effective dynamic adaptation, structural constraints, and bidirectional complementarity between high-level semantics and low-level details. They often experience localization biases in occluded scenarios, and the peaks of their heatmaps demonstrate insufficient consistency with the actual positions of the joints. Through theoretical analysis, we identify the causes of performance gaps and propose directions for narrowing them. We propose Bidirectional Multi-Scale Collaborative Pose Estimation (BiMS-Pose), a framework that introduces dynamic weights to adjust feature proportions, establishes bidirectional topological constraints for joint relationships, and integrates a bidirectional attention flow. The framework filters key information from three dimensions, adjusts filtering strategies in real time, and is enhanced by heatmap optimization to improve localization accuracy. Extensive experiments conducted on COCO, MPII, and our self-built Orchard Spraying Pose Dataset (OSPD) demonstrate the effectiveness of BiMS-Pose. In general scenarios, it achieves a significant 1.2 percentage-point increase in average precision (AP) on the COCO val2017 dataset compared to ViTPose while utilizing the same backbone. In agricultural orchard spraying scenarios, it effectively addresses interference factors such as changes in illumination, occlusion, and varying shooting distances, achieving 75.4% average precision (AP) and 90.7% percent of correct keypoints (PCKh@0.5) on the OSPD dataset. Additionally, it maintains an average frame rate of 18.3 FPS on embedded devices, effectively meeting the requirements for real-time monitoring. This highlights the model’s potential for precise, stable, and practical human pose estimation in both general and agricultural application scenarios. Full article

To address the contradiction between high flexibility and low stiffness in continuum robots, as well as the problems of complex structure, slow response, and narrow stiffness adjustment range in existing variable stiffness methods, this paper proposes a variable stiffness approach based on Twisted Multi-String Actuators (hereinafter referred to as TSA) for bionic spine-like continuum robots. Firstly, a bionic spine-like configuration was designed to support the force-locking variable stiffness mechanism. Secondly, the proposed TSA-based variable stiffness method was analyzed theoretically from the perspectives of geometric relationships and stiffness characteristics, laying a foundation for establishing other mathematical models such as that of string-twisting behavior. Finally, an experimental prototype was fabricated and subjected to flexibility tests. Furthermore, TSA variable stiffness experiments were conducted under two-strand, three-strand, and four-strand configurations to investigate the retraction and stiffness performance under different torsion turns and external loads. The results demonstrate that the stiffness of the robot is effectively enhanced by the TSA method, and increasing the number of string strands raises the failure load of the robot. Characteristic curves confirm that the proposed design and model exhibit superior performance to the traditional single-cable force-locking scheme. The design features a simple structure, fast response, and wide stiffness adjustment range, which provides a valuable reference for the stiffness modulation research of continuum robots. Full article