Search Results (243)

Driven by China’s “dual carbon” strategy, concerns about channel fairness and green investment have become key frontier issues in supply chain management. This study focuses on a two-tier supply chain under a low-carbon background and innovatively incorporates both fairness concerns and green investment perspectives. It systematically explores the impact mechanisms of fairness concern coefficients and green investment levels on channel pricing and profit distribution across four scenarios: information symmetry vs. asymmetry and the presence vs. absence of channel encroachment. The simulation results reveal the following: (1) Under information symmetry and without channel encroachment, an increase in the retailer’s fairness concern significantly enhances its bargaining power and profit margin, while the supplier actively adjusts the wholesale price to maintain cooperation stability. (2) Channel encroachment and changes in information structure intensify the nonlinearity and complexity of profit distribution. The marginal benefit of green investment for supply chain members shows a diminishing return, indicating the existence of an optimal investment range. (3) The green premium is predominantly captured by the supplier, while the retailer’s profit margin tends to be compressed, and order quantity exhibits rigidity in response to green investment. (4) The synergy between fairness concerns and green investment drives dynamic adjustments in channel strategies and the overall profit structure of the supply chain. This study not only reveals new equilibrium patterns under the interaction of multidimensional behavioral factors but also provides theoretical support for achieving both economic efficiency and sustainable development goals in supply chains. Based on these findings, it is recommended that managers optimize fairness incentives and green benefit-sharing mechanisms, improve information-sharing platforms, and promote collaborative upgrading of green supply chains to better integrate social responsibility with business performance. Full article

Viscosupplementation with intra-articular hyaluronic acid (HA) is a key therapeutic option for osteoarthritis (OA), yet the field is hampered by clinical controversies and an outdated classification of available products. This comprehensive review critically analyzes the current landscape, moving from a mechanical to a biological paradigm of HA’s mechanism of action. We argue that the traditional HA product classification based solely on molecular weight is insufficient, as it conflates chemically distinct products. Therefore, we propose a new, two-tiered classification framework: the primary distinction is based on chemical structure, separating linear (non-modified) HA from cross-linked (chemically modified) HA. Linear HA is then sub-classified by molecular weight (Low, Intermediate, and High), while cross-linked HA is defined as a separate category of hydrogels with a ultra-high effective molecular weight. Within this clearer framework, we analyze the central controversy between formulations, highlighting the pivotal emergence of high-concentration, high-molecular-weight (>2 million Dalton) linear HA. These formulations not only challenge the durability rationale for cross-linking by providing year-long efficacy but also possess a superior biological profile for chondroprotection, preserving chondrocyte viability and function. Furthermore, we explore the expanding frontier of combination therapies, where linear HA serves as the ideal physiological scaffold for agents like corticosteroids, PRP and other injectable orthobiologics such as bone marrow aspirate and stromal vascular fraction. Full article

In the contemporary international context, the preventive conservation of cultural relics has become a widespread consensus. “Risk management” has emerged as a pivotal research focus at the present stage. However, the preventive protection of cultural relics is confronted with deficiencies in risk assessment and prediction. There is an urgent requirement for research to present a comprehensive and in-depth overview of the frontier technologies applicable to the preventive protection of cultural relics, with a particular emphasis on risk prevention and control. Additionally, it is essential to delineate the prospects for future investigations and developments in this domain. Consequently, this study employs bibliometric methods, applying CiteSpace (6.3.R1) and Biblioshiny (4.3.0) to perform comprehensive visual and analytical examinations of 392 publications sourced from the Web of Science (WoS) database covering the period 2010 to 2024. The results obtained from the research are summarized as follows: First, it is evident that scholars originating from China, Italy, and Spain have exhibited preponderant publication frequencies, contributing the largest quantity of articles. Second, augmented reality, digital technology, and risk-based analysis have been identified as the cardinal research frontiers. These areas have attracted significant scholarly attention and are at the forefront of innovation and exploration within the discipline. Third, the “Journal of Culture Heritage” and “Heritage Science” have been empirically determined to be the most frequently cited periodical within this particular field of study. Moreover, over the past decade, under the impetus and influence of the concept of Intangible Cultural Heritage, virtual reality, digital protection, and 3D models have progressively evolved into the central and crucial topics that have pervaded and shaped the research agenda. Finally, with respect to future research trajectories, there will be a pronounced focus on interdisciplinary design. This will be accompanied by an escalation in the requisites and standards for preventive conservation. Specifically, the spotlight will be cast upon aspects such as the air quality within the preservation environment of cultural relics held in collections, the implementation and efficacy of environmental real-time monitoring systems, the utilization and interpretation of big data analysis and early warning mechanisms, as well as the comprehensive and in-depth risk analysis of cultural relics. These multifaceted investigations will be essential for advancing understanding and safeguarding of cultural heritage. These findings deepen our grasp of how risk management in cultural heritage conservation has progressed and transformed between 2010 and 2024. Furthermore, the study provides novel insights and directions for subsequent investigations into risk assessment methodologies for heritage collections. Full article

Under the twin imperatives of climate change mitigation and sustainable development, achieving a low-carbon transformation of power systems has become a national priority. To clarify this objective, China issued the Blue Book on the Development of New Power System, which comprehensively defines the guiding concepts and characteristic features of a new power system. In this study, natural language processing-based keyword extraction techniques were applied to the document, employing both the TF-IDF and TextRank algorithms to identify its high-frequency terms as characteristic keywords. These keywords were then used as topic queries in the Web of Science Core Collection, yielding 1568 relevant publications. CiteSpace was employed to perform a bibliometric analysis of these records, extracting research hotspots in the new power system domain and tracing their evolutionary trajectories. The analysis revealed that “renewable energy” appeared 247 times as the core high-frequency term, while “energy storage” exhibited both high frequency and high centrality, acting as a bridge across multiple subfields. This pattern suggests that research in the new power system field has evolved from a foundation in renewable energy and storage toward smart grids, market mechanisms, carbon capture, and artificial intelligence applications. Taken together, these results indicate that early research was primarily grounded in renewable energy and storage technologies, which provided the technical basis for subsequent exploration of smart grids and market mechanisms. In the more recent stage, under the dual-carbon policy and digital intelligence imperatives, research hotspots have further expanded toward carbon capture, utilization, and storage (CCUS) and artificial intelligence applications. Looking ahead, interdisciplinary studies focusing on intelligent dispatch and low-carbon transition are poised to emerge as the next major research frontier. Full article

This study explores how the integration of generative artificial intelligence, multi-objective evolutionary optimization, and reinforcement learning can enable sustainable and cost-effective decision-making in supply chain strategy. Using real-world retail demand data enriched with synthetic sustainability attributes, we trained a Variational Autoencoder (VAE) to generate plausible future demand scenarios. These were used to seed a Non-Dominated Sorting Genetic Algorithm (NSGA-II) aimed at identifying Pareto-optimal sourcing strategies that balance delivery cost and CO₂ emissions. The resulting Pareto frontier revealed favorable trade-offs, enabling up to 50% emission reductions for only a 10–15% cost increase. We further deployed a deep Q-learning (DQN) agent to dynamically manage weekly shipments under a selected balanced strategy. The reinforcement learning policy achieved an additional 10% emission reduction by adaptively switching between green and conventional transport modes in response to demand and carbon pricing. Importantly, the agent also demonstrated resilience during simulated supply disruptions by rerouting decisions in real time. This research contributes a novel AI-based decision architecture that combines generative modeling, evolutionary search, and adaptive control to support sustainability in complex and uncertain supply chains. Full article

Carbon nanotubes (CNTs) have remarkable mechanical, electrical, and thermal properties, making them highly attractive as foundational elements for advanced materials. However, translating their nanoscale potential into macroscale reliability and longevity requires a holistic design approach that integrates precise architectural control with robust damage mitigation strategies. This review presents a synergistic perspective on enhancing the durability of CNT-based systems by critically examining the interplay between molecular assembly, self-repair mechanisms, and the advanced characterization techniques required for their validation. We first establish how foundational architectural control—achieved through strategies like chemical functionalization, field-directed alignment, and dispersion—governs the ultimate performance of CNT materials. A significant focus is placed on advanced functionalization, such as fluorination, and its verification using high-powered spectroscopic tools, including X-ray photoelectron spectroscopy (XPS) and near-edge X-ray absorption fine structure (NEXAFS) spectroscopy. Subsequently, this manuscript delves into the mechanisms of self-repair, systematically analyzing both the intrinsic capacity of the carbon lattice to heal atomic-level defects and the extrinsic strategies that incorporate engineered healing agents into composites. This discussion is uniquely supplemented by an exploration of the experimental techniques, such as electron energy loss spectroscopy (EELS) and Auger electron spectroscopy (AES), that provide crucial evidence for irradiation-induced healing dynamics. Finally, we argue that a “characterization gap” has limited the field’s progress and highlight the critical role of techniques like in situ Raman spectroscopy for quantitatively monitoring healing efficiency at the molecular level. By identifying current challenges and future research frontiers, this review underscores that the creation of truly durable materials depends on an integrated understanding of how to build, repair, and precisely measure CNT-based systems. Full article

The complex pathological mechanisms of atherosclerosis (AS) involve lipid metabolism disorders, inflammatory responses, and plaque instability, resulting in significant challenges to effective clinical management. Current therapeutic approaches, such as statins and stent implantation, suffer from issues including single-target action, notable side effects, and the risk of restenosis. Nanoparticle-based drug delivery systems have demonstrated considerable promise by enabling the codelivery of multiple agents directly to atherosclerotic lesions, thereby improving therapeutic efficacy and minimizing systemic toxicity. Among various nanomaterials, organic nanoparticles have recently emerged as a research hotspot in the field of AS treatment due to their excellent biocompatibility, degradability, and potential for targeted modification. This review systematically summarizes the recent advances and emerging trends in the application of organic nanoparticles for AS treatment, employing bibliometric analysis to delineate research frontiers. We employed bibliometric tools to analyze 1999 articles on organic nanocarriers for AS therapy indexed in the Web of Science Core Collection. The analysis included co-occurrence and clustering techniques to explore influential keywords and key contributors. Temporal analysis was applied to identify emerging research hotspots and track the evolution of this field. The literature reveals three major current focal areas: (1) the development of engineered biomimetic organic nanoparticles; (2) the design of multifunctional polymer-based organic nanocarriers; and (3) the innovation of organic-coated stents. This article not only provides a comprehensive overview of cutting-edge organic nanotechnologies for AS therapy, but also critically discusses the challenges in clinical translation, offering insights into future directions for the development of safe, effective, and personalized nanomedicine strategies against AS. Full article

The environmental challenges posed by global warming have significantly increased the global pursuit of renewable and clean energy sources. Among these, solar energy stands out due to its abundance, renewability, low environmental impact, and favorable long-term economic viability. However, its intermittent nature and dependence on weather conditions hinder consistent and efficient utilization. To address these limitations, nanoparticle-enhanced phase change materials (NPCMs) have emerged as a promising solution for enhancing thermal energy storage in solar thermal systems. NPCMs incorporate superior-performance nanoparticles within traditional phase change material matrices, resulting in improved thermal conductivity, energy storage density, and phase change efficiency. This review systematically examines the recent advances in NPCMs for solar energy applications, covering their classification, structural characteristics, advantages, and limitations. It also explores in-depth analytical approaches, including mechanism-oriented analysis, simulation-based modelling, and algorithm-driven optimization, that explain the behavior of NPCMs at micro and macro scales. Furthermore, the techno-economic implications of NPCM integration are evaluated, with particular attention to cost-benefit analysis, policy incentives, and market growth potential, which collectively support broader adoption. Overall, the findings highlight NPCMs as a frontier in materials innovation and enabling technology for achieving low-carbon, environmentally responsible energy solutions, contributing significantly to global sustainable development goals. Full article

MRNA-based therapeutics and vaccines represent a rapidly expanding frontier in biomedical innovation, with lipid nanoparticles (LNPs) serving as a clinically validated delivery platform. This study explores critical quality attributes of LNP-mRNA formulations, with a particular focus on in vitro biological activity, a key quality attribute of vaccine activity and batch-to-batch consistency. We discuss the importance of optimizing both LNP components and mRNA structure, highlighting recent advances in formulation strategies. Furthermore, we examine the influence of factors such as cell-line selection, experimental design, storage conditions, and targeted cellular delivery on transduction efficiency. Our findings underscore the need for standardized in vitro assays and process-integrated monitoring to support the scalable development and regulatory assessment of mRNA-based therapies. Full article

Scene text understanding, serving as a cornerstone technology for autonomous navigation, document digitization, and accessibility tools, has witnessed a paradigm shift from traditional methods relying on handcrafted features and multi-stage processing pipelines to contemporary deep learning frameworks capable of learning hierarchical representations directly from raw image inputs. This survey distinctly categorizes modern scene text recognition (STR) methodologies into three principal paradigms: two-stage detection frameworks that employ region proposal networks for precise text localization, single-stage detectors designed to optimize computational efficiency, and specialized architectures tailored to handle arbitrarily shaped text through geometric-aware modeling techniques. Concurrently, an in-depth analysis of text recognition paradigms elucidates the evolutionary trajectory from connectionist temporal classification (CTC) and sequence-to-sequence models to transformer-based architectures, which excel in contextual modeling and demonstrate superior performance. In contrast to prior surveys, this work uniquely emphasizes several key differences and contributions. Firstly, it provides a comprehensive and systematic taxonomy of STR methods, explicitly highlighting the trade-offs between detection accuracy, computational efficiency, and geometric adaptability across different paradigms. Secondly, it delves into the nuances of text recognition, illustrating how transformer-based models have revolutionized the field by capturing long-range dependencies and contextual information, thereby addressing challenges in recognizing complex text layouts and multilingual scripts. Furthermore, the survey pioneers the exploration of critical research frontiers, such as multilingual text adaptation, enhancing model robustness against environmental variations (e.g., lighting conditions, occlusions), and devising data-efficient learning strategies to mitigate the dependency on large-scale annotated datasets. By synthesizing insights from technical advancements across 28 benchmark datasets and standardized evaluation protocols, this study offers researchers a holistic perspective on the current state-of-the-art, persistent challenges, and promising avenues for future research, with the ultimate goal of achieving human-level scene text comprehension. Full article

Background/Objectives: Despite advances in diagnosis and treatment, colorectal cancer (CRC) remains one of the most prevalent and challenging malignancies worldwide. The dysregulation of microRNAs (miRNAs) has emerged as a critical factor in CRC onset, progression, and therapeutic resistance. This study aims to provide an overview of global research trends on miRNAs in CRC, (i) identifying the most studied miRNAs, (ii) exploring under-investigated areas, and (iii) highlighting emerging themes and potential future directions. Methods: To assess the evolution of the global miRNA–CRC research trends, we conducted a bibliometric analysis of 828 CRC–miRNA-focused articles published between 2008 and 2024, sourced from the Scopus database. Bibliometric mapping was performed using the R/Bibliometrix package and by leveraging a customized Python-based pipeline, which is useful for extracting and validating miRNA identifiers (miRNA IDs) based on the miRBase database. This miRNA ID-related approach enabled us to systematically identify the most frequently studied miRNAs over time while highlighting underexplored miRNA. Results: The analysis revealed a substantial and accelerating publication growth rate, delineating three major phases in CRC–miRNA research. China emerged as the leading contributor in terms of the publication volume. miR-21, miR-34a, and miR-195-5p were among the most frequently studied miRNAs, underscoring their relevance to CRC biology and therapy. Keyword and citation analyses identified key thematic areas, such as cell proliferation, epithelial–mesenchymal transition, and chemoresistance, especially to oxaliplatin and 5-fluorouracil. Emerging research frontiers included ferroptosis, ceRNA networks, and exosome-mediated miRNA transport. An analysis of the collaborations indicated strong intra-national collaborations, with room for expanding international research networks. Conclusions: This study provides an in-depth bibliometric landscape of the CRC-related miRNA research by highlighting influential studies and journals while identifying gaps and underexplored topics. These insights offer valuable guidance for future translational and clinical research on this topic. Full article

The accelerating threat of antimicrobial resistance (AMR) demands transformative strategies that go beyond conventional antibiotic therapies. Nanoparticles (NPs) have emerged as versatile antimicrobial agents, offering a combination of physical, chemical, and immunological mechanisms to combat multidrug-resistant (MDR) pathogens. Their small size, surface tunability, and ability to disrupt microbial membranes, generate reactive oxygen species (ROS), and deliver antibiotics directly to infection sites position them as powerful tools for infection control. This narrative review explores the major classes, mechanisms of action, and biomedical applications of antimicrobial NPs—including their roles in wound healing, implant coatings, targeted drug delivery, inhalation-based therapies, and the treatment of intracellular infections. We also highlight the current landscape of clinical trials and evolving regulatory frameworks that govern the translation of these technologies into clinical practice. A distinctive feature of this review is its focus on the interplay between NPs and the human microbiota—an emerging frontier with significant implications for therapeutic efficacy and safety. Addressing this bidirectional interaction is essential for developing microbiota-informed, safe-by-design nanomedicines. Despite promising advances, challenges such as scalability, regulatory standardization, and long-term biosafety remain. With interdisciplinary collaboration and continued innovation, antimicrobial NPs could reshape the future of infectious disease treatment and help curb the growing tide of AMR. Full article

Efficient autonomous exploration in unknown obstacle cluttered environments with interior obstacles remains a challenging task for mobile robots. In this work, we present a novel exploration process for a non-holonomic agent exploring 2D spaces using onboard LiDAR sensing. The proposed method generates velocity commands based on the calculation of the solution of an elliptic Partial Differential Equation with Dirichlet boundary conditions. While solving Laplace’s equation yields collision-free motion towards the free space boundary, the agent may become trapped in regions distant from free frontiers, where the potential field becomes almost flat, and consequently the agent’s velocity nullifies as the gradient vanishes. To address this, we solve a Poisson equation, introducing a source point on the free explored boundary which is located at the closest point from the agent and attracts it towards unexplored regions. The source values are determined by an exponential function based on the shortest path of a Hybrid Visibility Graph, a graph that models the explored space and connects obstacle regions via minimum-length edges. The computational process we apply is based on the Walking on Sphere algorithm, a method that employs Brownian motion and Monte Carlo Integration and ensures efficient calculation. We validate the approach using a real-world platform; an AmigoBot equipped with a LiDAR sensor, controlled via a ROS-MATLAB interface. Experimental results demonstrate that the proposed method provides smooth and deadlock-free navigation in complex, cluttered environments, highlighting its potential for robust autonomous exploration in unknown indoor spaces. Full article

Robotics has emerged as a transformative discipline at the intersection of the engineering, computer science, and cognitive sciences. This state-of-the-art review explores the current trends, methodologies, and challenges in both robotics research and education. This paper presents a comprehensive review of the evolution of robotics, tracing its development from early automation to intelligent, autonomous systems. Key enabling technologies, such as Artificial Intelligence (AI), soft robotics, the Internet of Things (IoT), and swarm intelligence, are examined along with real-world applications in healthcare, manufacturing, agriculture, and sustainable smart cities. A central focus is placed on robotics education, where hands-on, interdisciplinary learning is reshaping curricula from K–12 to postgraduate levels. This paper analyzes instructional models including project-based learning, laboratory work, capstone design courses, and robotics competitions, highlighting their effectiveness in developing both technical and creative competencies. Widely adopted platforms such as the Robot Operating System (ROS) are briefly discussed in the context of their educational value and real-world alignment. Through case studies, institutional insights, and synthesis of academic and industry practices, this review underscores the vital role of robotics education in fostering innovation, systems thinking, and workforce readiness. The paper concludes by identifying the key challenges and future directions to guide researchers, educators, industry stakeholders, and policymakers in advancing robotics as both technological and educational frontiers. Full article

Nanoradiopharmaceuticals integrate nanotechnology with nuclear medicine to enhance the precision and effectiveness of radiopharmaceuticals used in diagnostic imaging and targeted therapies. Nanomaterials offer improved targeting capabilities and greater stability, helping to overcome several limitations. This review presents a comprehensive overview of the fundamental design principles, radiolabeling techniques, and biomedical applications of nanoradiopharmaceuticals, with a particular focus on their expanding role in precision oncology. It explores key areas, including single- and multi-modal imaging modalities (SPECT, PET), radionuclide therapies involving beta, alpha, and Auger emitters, and integrated theranostic systems. A diverse array of nanocarriers is examined, including liposomes, micelles, albumin nanoparticles, PLGA, dendrimers, and gold, iron oxide, and silica-based platforms, with an assessment of both preclinical and clinical research outcomes. Theranostic nanoplatforms, which integrate diagnostic and therapeutic functions within a single system, enable real-time monitoring and personalized dose optimization. Although some of these systems have progressed to clinical trials, several obstacles remain, including formulation stability, scalable manufacturing, regulatory compliance, and long-term safety considerations. In summary, nanoradiopharmaceuticals represent a promising frontier in personalized medicine, particularly in oncology. By combining diagnostic and therapeutic capabilities within a single nanosystem, they facilitate more individualized and adaptive treatment approaches. Continued innovation in formulation, radiochemistry, and regulatory harmonization will be crucial to their successful routine clinical use. Full article