Search Results (1,918)

Robotic search of people in human-centered environments, including healthcare settings, is challenging, as autonomous robots need to locate people without complete or any prior knowledge of their schedules, plans, or locations. Furthermore, robots need to be able to adapt to real-time events that can influence a person’s plan in an environment. In this paper, we present MLLM-Search, a novel zero-shot person search architecture that leverages multimodal large language models (MLLM) to address the mobile robot problem of searching for a person under event-driven scenarios with varying user schedules. Our approach introduces a novel visual prompting method to provide robots with spatial understanding of the environment by generating a spatially grounded waypoint map, representing navigable waypoints using a topological graph and regions by semantic labels. This is incorporated into an MLLM with a region planner that selects the next search region based on the semantic relevance to the search scenario and a waypoint planner that generates a search path by considering the semantically relevant objects and the local spatial context through our unique spatial chain-of-thought prompting approach. Extensive 3D photorealistic experiments were conducted to validate the performance of MLLM-Search in searching for a person with a changing schedule in different environments. An ablation study was also conducted to validate the main design choices of MLLM-Search. Furthermore, a comparison study with state-of-the-art search methods demonstrated that MLLM-Search outperforms existing methods with respect to search efficiency. Real-world experiments with a mobile robot in a multi-room floor of a building showed that MLLM-Search was able to generalize to new and unseen environments. Full article

The value realization of ecological products is an important part of rural and agricultural development. As a significant force for protecting traditional agricultural systems and promoting rural revitalization, agricultural heritage systems (AHSs) have formed diverse value realization paths of ecological products in the process of dynamic protection and adaptive management. Through theoretical research, this article analyzed the characteristics of ecological products in AHS sites (EPAHSSs) and summarized the framework of value realization paths of EPAHSSs. Then, the Wannian Rice Culture System in China was selected as a case for conducting empirical research. The results showed that EPAHSSs exhibit obvious uniqueness in terms of climate environment, germplasm resources, farming and breeding models, and cultural heritage. The value realization paths of EPAHSSs mainly include industrial development support, such as the extension of agricultural industrial chains and the development of tourism, as well as fiscal transfer payments. The case analysis results indicated that Wannian County contains a rich variety of ecological products and developed a value realization pathway mainly based on the integration of industries and supplemented by fiscal transfer payments during the process of protection and development. However, further optimization is needed to promote the development of tourism and other paths. This study not only contributes to the sustainable development of the Wannian Rice Culture System, but the proposed framework is also applicable to other heritage systems and similar regions. Full article

Light-chain (AL) amyloidosis is a rare clonal plasma cell disorder that, if left untreated, carries a high risk of organ damage and mortality. Due to the rarity of the disease and the vulnerability of affected organ systems, treatment requires significant caution and nuance. As a plasma cell dyscrasia, AL amyloidosis treatment regimens are often adapted from those used for related disorders, particularly multiple myeloma. Despite substantial progress in research and drug development, optimal treatment strategies for relapsed/refractory (RR) AL amyloidosis remain unclear, and no FDA-approved therapies currently exist for this setting. B-cell maturation antigen (BCMA) has emerged as a promising immunotherapy target, with associated drug classes including antibody–drug conjugates, bispecific antibodies, and CAR-T cell therapies. These therapies have been extensively studied in relapsed/refractory multiple myeloma (RRMM) and are now being explored in the context of RR AL amyloidosis. This review summarizes the current literature on the efficacy and tolerability of BCMA-directed therapies in AL amyloidosis, with a particular emphasis on CAR-T cell therapy and offers comparisons to outcomes observed in RRMM. Full article

Introduction: The gut microbiota is vital for human health, and its modulation through dietary and pharmaceutical compounds has gained increasing attention. Among gut microbes, Akkermansia muciniphila has been extensively researched due to its role in maintaining intestinal barrier integrity, regulating energy metabolism, and influencing inflammatory responses. Subject: To analyze and synthesize the available scientific evidence on the influence of various bioactive compounds, including prebiotics, polyphenols, antioxidants, and pharmaceutical agents, on the abundance and activity of A. muciniphila, considering underlying mechanisms, microbial context, and its therapeutic potential for improving metabolic and intestinal health. Methods: A systematic literature review was conducted in accordance with the PRISMA 2020 guidelines. Databases such as PubMed, ScienceDirect, Scopus, Web of Science, SciFinder-n, and Google Scholar were searched for publications from 2004 to 2025. Experimental studies in animal models or humans that evaluated the impact of bioactive compounds on the abundance or activity of A. muciniphila were prioritized. The selection process was managed using the Covidence platform. Results: A total of 78 studies were included in the qualitative synthesis. This review compiles and analyzes experimental evidence on the interaction between A. muciniphila and various bioactive compounds, including prebiotics, antioxidants, flavonoids, and selected pharmaceutical agents. Factors such as the chemical structure of the compounds, microbial environment, underlying mechanisms, production of short-chain fatty acids (SCFAs), and mucin interactions were considered. Compounds such as resistant starch type 2, GOS, 2′-fucosyllactose, quercetin, resveratrol, metformin, and dapagliflozin showed beneficial effects on A. muciniphila through direct or indirect pathways. Discussion: Variability across studies reflects the influence of multiple variables, including compound type, dose, intervention duration, experimental models, and analytical methods. These differences emphasize the need for a contextualized approach when designing microbiota-based interventions. Conclusions: A. muciniphila emerges as a promising therapeutic target for managing metabolic and inflammatory diseases. Further mechanistic and clinical studies are necessary to validate its role and to support the development of personalized microbiota-based treatment interventions. Full article

In response to growing environmental pressures and material constraints, circular economy principles are gaining traction across manufacturing sectors. However, most existing frameworks emphasize design and supply chain considerations, with limited focus on how circularity can be operationalized within internal manufacturing systems. This paper proposes a conceptual model that embeds circular operations at the core of production strategy. Grounded in circular economy theory, operations management, and socio-technical systems thinking, the model identifies four key operational pillars: circular input management, looping process and waste valorization, product-life extension, and reverse logistics. These are supported by enabling factors—digital infrastructure, organizational culture, and leadership—and mediated by operational flexibility, which facilitates adaptive, closed-loop performance. The model aims to align internal processes with long-term sustainability outcomes, specifically resource efficiency and operational resilience. Practical implications are outlined for resource-intensive industries such as automotive, electronics, and FMCG, along with a readiness assessment framework for guiding implementation. This study offers a pathway for future empirical research and policy development by integrating circular logic into the structural and behavioral dimensions of operations. The model contributes to advancing the Sustainable Development Goals (SDGs), particularly SDG 9 and SDG 12, by positioning circularity as a regenerative operational strategy rather than a peripheral initiative. Full article

The livestock sector is a significant contributor to the economy and rural livelihoods, but it is exposed to high risk across the supply chain, which is detrimental and needs to be addressed for sustainable development. Therefore, this study aimed to identify the major risk mitigation strategies (RMSs) and associated factors that affect their adoption. This study conducted a comprehensive literature review to identify the eight major RMSs and prioritized them through an analytical hierarchical process (AHP). Thereafter, a multivariate probit (MVP) model was developed to identify the factors affecting the adoption of major RMSs. The primary RMSs are livestock insurance, vaccination of livestock, and advisory/extension services. Further, the multivariate probit regression analysis shows that ‘age’, ‘social category’, ‘economic status’, ‘educational level’, ‘income level’, ‘the total number of animals’, and ‘perceived risk of foot and mouth disease’ are significant factors that influence the adoption of RMSs. This study’s findings will be useful for livestock supply chain partners to mitigate the risks along the livestock supply chain. This research will also help policymakers to develop policies/plans for incorporating these RMSs by considering the influencing associated factors. Full article

The construction sector significantly contributes to global environmental degradation through intensive resource extraction, high energy consumption, and substantial waste generation. Addressing this unsustainable trajectory requires integrated approaches that simultaneously improve operational efficiency and material circularity. Lean Construction (LC) and Circular Economy (CE) offer complementary frameworks for enhancing process performance and reducing environmental impacts. However, their combined implementation remains underdeveloped and fragmented. This study conducts a systematic literature review (SLR) of 18 peer-reviewed articles published between 2010 and 2025, selected using PRISMA 2020 guidelines and sourced from Scopus and Web of Science databases. A mixed-method approach combines bibliometric mapping and qualitative content analysis to investigate how LC and CE are jointly operationalized in construction contexts. The findings reveal that LC improves cost, time, and workflow reliability, while CE enables reuse, modularity, and lifecycle extension. Integration is further supported by digital tools—such as Building Information Modelling (BIM), Design for Manufacture and Assembly (DfMA), and digital twins—which enhance traceability and flow optimization. Nonetheless, persistent barriers—including supply chain fragmentation, lack of standards, and regulatory gaps—continue to constrain widespread adoption. This review identifies six strategic enablers for LC-CE integration: crossdisciplinary competencies, collaborative governance, interoperable digital systems, standardized indicators, incentive-based regulation, and pilot demonstrator projects. By consolidating fragmented evidence, the study provides a structured research agenda and practical insights to guide the transition toward more circular, efficient, and sustainable construction practices. Full article

A novel unified progressive hybrid censoring is introduced to combine both progressive and hybrid censoring plans to allow flexible test termination either after a prespecified number of failures or at a fixed time. This work develops both frequentist and Bayesian inferential procedures for estimating the parameters, reliability, and hazard rates of the inverted Nadarajah–Haghighi lifespan model when a sample is produced from such a censoring plan. Maximum likelihood estimators are obtained through the Newton–Raphson iterative technique. The delta method, based on the Fisher information matrix, is utilized to build the asymptotic confidence intervals for each unknown quantity. In the Bayesian methodology, Markov chain Monte Carlo techniques with independent gamma priors are implemented to generate posterior summaries and credible intervals, addressing computational intractability through the Metropolis—Hastings algorithm. Extensive Monte Carlo simulations compare the efficiency and utility of frequentist and Bayesian estimates across multiple censoring designs, highlighting the superiority of Bayesian inference using informative prior information. Two real-world applications utilizing rare minerals from gold and diamond durability studies are examined to demonstrate the adaptability of the proposed estimators to the analysis of rare events in precious materials science. By applying four different optimality criteria to multiple competing plans, an analysis of various progressive censoring strategies that yield the best performance is conducted. The proposed censoring framework is effectively applied to real-world datasets involving diamonds and gold, demonstrating its practical utility in modeling the reliability and failure behavior of rare and high-value minerals. Full article

The extensive diversity of volatile organic compounds, along with their minor structural variations, presents significant challenges in the development of chemosensory-based biosensors. Previously, we generated sensor cells expressing insect odorant receptors (ORs) in Sf21 cells, demonstrating their potential as cell-based odorant sensor elements. However, it remains unclear whether the selectivity of cells expressing ORs in vitro for diverse compounds aligns with the receptor’s in vivo performance, aside from the response to target compounds. To address this, we assessed the ligand responses of sensor cells expressing ORs from Drosophila melanogaster using a high-throughput calcium imaging system. Our results demonstrate that in vitro receptor responses exhibit ligand selectivity comparable to in vivo conditions across different chemical categories. Broadly tuned OR-expressing sensor cells (Or13a, Or47a, and Or98a) displayed differential affinities, whereas the narrowly tuned Or56a-expressing sensor cells selectively responded to geosmin. Moreover, cell responses varied with subtle differences in chemical structure, including carbon chain length and functional group positioning. These findings provide valuable insights into insect OR–ligand interactions in vitro, demonstrating that receptor selectivity in sensor cells closely mirrors in vivo conditions. In addition to this consistency, our results highlight the subtle ligand differentiation capabilities of sensor cells enabling fluorescence-based visualization of receptor–ligand interactions. Full article

Propionate is a short-chain fatty acid (SCFA) produced by gut microbiota through the fermentation of dietary fibers. Among the SCFAs, butyrate stands out and has been extensively studied for its beneficial effects; however, propionate has received less attention despite its relevant roles in immune modulation, metabolism, and mucosal homeostasis. This narrative review focuses on propionate’s effects on metabolism, inflammation, microbiota, and gastrointestinal diseases. Propionate acts as a signalling molecule through FFAR2/FFAR3 receptors and modulates immunity, energy metabolism, and gut–brain communication. It has beneficial effects in metabolic disorders, inflammatory bowel disease (IBD), and alcohol-related liver disease (ALD). However, excessive accumulation is linked to neurotoxicity, autism spectrum disorder (ASD), and mitochondrial dysfunction. Its effects are dose-dependent and tissue-specific, with both protective and harmful potentials depending on the context. Propionate use requires a personalized approach, considering the pathological context, host microbiota composition, and appropriate dosage to avoid adverse effects. Full article

(1) The formation pathways and underlying mechanisms of health behaviors among young and middle-aged adults with chronic diseases under a temporal perspective remain underexplored. Based on Temporal Self-Regulation Theory and its subsequent extensions, this study aimed to investigate the time perspective among Chinese young and middle-aged adults with chronic diseases and analyzed the relationships between time perspective, illness perception, health behavior motivation, and health behaviors, thereby providing valuable empirical evidence for health behavior modification in this population. (2) This study was a cross-sectional survey conducted from March to April 2025. Structural Equation Modeling was employed to investigate the relationships among the variables. (3) This study enrolled a total of 391 participants with high levels of future and past-positive perspectives. Time perspective, directly related to health behaviors, namely past-negative, present-impulsive, and present-fatalistic perspectives, exerted negative associations with health behaviors, respectively, whereas future and past-positive perspectives showed positive associations with health behavior. Analysis of the mediating effect found that illness perception served as a mediator between the time perspective of past-negative, present-impulsive, and present-fatalistic perspectives, and health behaviors. And health behavior motivation acted as a mediator in the relationship between past-negative, present-impulsive, and past-positive perspectives, and health behaviors. Furthermore, illness perception and health behavior motivation formed a chain mediation pathway between time perspective and health behaviors. (4) Time perspective was associated with health behaviors, not only exerting directed effects but also manifesting a double-edged sword effect through illness perception and health behavior motivation. Subsequent interventions targeting health behaviors among young and middle-aged populations in China should incorporate considerations of the temporal perspective’s uniqueness and its intricate mechanisms of action on health behaviors. Full article

Ultra-high-molecular-weight polyethylene (UHMWPE) is a pivotal material in engineering and biomedical applications due to its exceptional mechanical strength, wear resistance, and impact performance. However, its extreme melt viscosity, caused by extensive chain entanglements, severely limits processability via conventional melt-processing techniques. Recent advances in catalytic synthesis have enabled the production of disentangled UHMWPE (dis-UHMWPE), which exhibits enhanced processability while retaining superior mechanical properties. Notably, heterogeneous catalytic systems, utilizing supported fluorinated bis (phenoxy-imine) titanium (FI) catalysts, polyhedral oligomeric silsesquioxanes (POSS)-modified Z-N catalysts, and other novel catalysts, have emerged as promising solutions, combining structural control with industrial feasibility. Moreover, optimizing polymerization conditions further enhances chain disentanglement while maintaining ultra-high molecular weights. These systems utilize nanoscale supports and ligand engineering to spatially isolate active sites, tailor the chain propagation/crystallization kinetics, and suppress interchain entanglement during polymerization. Furthermore, characterization techniques such as melt rheology and differential scanning calorimetry (DSC) provide critical insights into chain entanglement, revealing distinct reorganization kinetics and bimodal melting behavior in dis-UHMWPE. This development of hybrid catalytic systems opens up new avenues for solid-state processing and industrial-scale production. This review highlights recent advances concerning interaction between catalyst design, polymerization control, and material performance, ultimately unlocking the full potential of UHMWPE for next-generation applications. Full article

The swift expansion of China’s automotive manufacturing industry has spurred a constant rise in the demand for automotive parts production and distribution, making the optimization of distribution routes in complex environments a crucial research topic. Efficiently optimizing these routes not only boosts production efficiency and cuts costs for automotive manufacturers but also enhances supply chain management and advances sustainable development. This study focuses on the optimization of automotive parts distribution routes under a multi-manufacturer collaboration framework. An optimization model is proposed to minimize the total operational costs within a joint distribution system, incorporating an improved Ant Colony Optimization (ACO) algorithm to formulate an effective solution approach. The model considers complex factors such as dynamic demand, time-window constraints, and periodic distribution. A PIVNS algorithm integrating a virtual distribution center with an enhanced variable neighborhood search is designed to efficiently address the problem. The efficacy of the proposed model and algorithm is substantiated through extensive experiments grounded in real-world case studies. The results confirm the high computational efficiency of the proposed approach in solving large-scale problems, which significantly reduces distribution costs while improving overall supply chain performance. Specifically, the PIVNS algorithm achieves an average travel distance of 2020.85 km, an average runtime of 112.25 s, a total transportation cost of CNY 12,497.99, and a loading rate of 86.775%. These findings collectively highlight the advantages of the proposed method in enhancing efficiency, reducing costs, and optimizing resource utilization. Overall, this study provides valuable insights for logistics optimization in automotive manufacturing and offers a significant reference for future research and practical applications in the field. Full article

We present a pipeline for synthetic simplification of text in French that combines large language models with structured semantic guidance. Our approach enhances data generation by integrating contextual knowledge from Wikipedia and Vikidia articles and injecting symbolic control through lightweight knowledge graphs. To construct document-level representations, we implement a progressive summarization process that incrementally builds running summaries and extracts key ideas. Simplifications are generated iteratively and assessed using semantic comparisons between input and output graphs, enabling targeted regeneration when critical information is lost. Our system is implemented using LangChain’s orchestration framework, allowing modular and extensible coordination of LLM components. Evaluation shows that context-aware prompting and semantic feedback improve simplification quality across successive iterations. Full article

The rapid growth in large-scale distributed video conferencing, remote education, and real-time broadcasting poses significant challenges to traditional centralized streaming systems, particularly regarding scalability, cost, and reliability under high concurrency. Centralized approaches often encounter bottlenecks, increased bandwidth expenses, and diminished fault tolerance. This paper proposes a novel decentralized real-time broadcasting system employing a peer-to-peer (P2P) chain topology based on IPv6 networking and the Secure Reliable Transport (SRT) protocol. By exploiting the global addressing capability of IPv6, our solution simplifies direct node interconnections, effectively eliminating complexities associated with Network Address Translation (NAT). Furthermore, we introduce an innovative chain-relay transmission method combined with distributed node management strategies, substantially reducing reliance on central servers and minimizing deployment complexity. Leveraging SRT’s low-latency UDP transmission, packet retransmission, congestion control, and AES-128/256 encryption, the proposed system ensures robust security and high video stream quality across wide-area networks. Additionally, a WebSocket-based real-time fault detection algorithm coupled with a rapid fallback self-healing mechanism is developed, enabling millisecond-level fault detection and swift restoration of disrupted links. Extensive performance evaluations using Video Multi-Resolution Fidelity (VMRF) metrics across geographically diverse and heterogeneous environments confirm significant performance gains. Specifically, our approach achieves substantial improvements in latency, video quality stability, and fault tolerance over existing P2P methods, along with over tenfold enhancements in frame rates compared with conventional RTMP-based solutions, thereby demonstrating its efficacy, scalability, and cost-effectiveness for real-time video streaming applications. Full article