Search Results (1,906)

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

In this study, we tested a flexible polyurethane (PU) foam, synthesized from bio-based components, for the removal of petroleum-derived fuels from water samples. The PU was synthesized via the prepolymer method through the reaction of PEG 400 with L-lysine ethyl ester diisocyanate (L-LDI), followed by chain extension with 2,5-bis(hydroxymethyl)furan (BHMF), a renewable platform molecule derived from carbohydrates. Freshwater and seawater samples were artificially contaminated with commercial diesel, gasoline, and kerosene. Batch adsorption experiments revealed that the total sorption capacity (S, g/g) of the PU was slightly higher for diesel in both water types, with values of 67 g/g in freshwater and 70 g/g in seawater. Sorption kinetic analysis indicated that the process follows a pseudo-second-order kinetic model, suggesting strong chemical interactions. Equilibrium data were fitted using Langmuir and Freundlich isotherm models, with the best fit achieved by the Langmuir model, supporting a monolayer adsorption mechanism on homogeneous surfaces. The PU foam can be regenerated up to 50 times by centrifugation, maintaining excellent performance. This study demonstrates a promising application of this sustainable and bio-based polyurethane foam for environmental remediation. Full article

As electric multiple unit (EMU) operations and maintenance demands have expanded, spare parts supply chain management has become increasingly crucial. This study emphasizes the supply challenges of EMU spare parts, including inadequate minimum inventory levels and prolonged response times. Redesigning the OEM–railway bureau vendor-managed inventory (VMI) model contract incentive and penalty system is the key goal. Connecting the spare parts supply system with its characteristics yields a game theory model. This study analyzes and compares the equilibrium strategies and profits of supply chain members under different mechanisms for managing critical spare parts. The findings demonstrate that mechanism contracts can enhance supply chain performance in a Pareto-improving manner. An in-depth analysis of downtime loss costs, procurement challenges, and order losses reveals their effects on supply chain coordination and profit allocation, providing railway bureaus and OEMs with a theoretical framework for supply chain decision-making. This study offers theoretical justification and a framework for decision-making on cooperation between OEMs and railroad bureaus in the management of spare parts supply chains, particularly for extensive EMU operations. Full article

This study systematically investigates the impact of hydrolytic degradation on the crystallization kinetics and morphology of poly(butylene succinate-co-adipate) (PBSA). Gel Permeation Chromatography (GPC) confirmed extensive chain scission, significantly reducing the polymer’s weight-average molecular weight (M_w from ~103,000 to ~16,000 g/mol) and broadening its polydispersity index (PDI from ~2 to 7 after 64 days). Differential scanning calorimetry (DSC) analysis revealed that hydrolytic degradation dramatically accelerated crystallization rates, reducing crystallization time roughly 10-fold (e.g., from ~3000 s to ~300 s), and crystallinity increased from 34% to 63%. Multiple melting peaks suggested the presence of lamellae with varying thicknesses, consistent with the Gibbs–Thomson equation. Isothermal crystallization kinetics were evaluated using the Avrami equation (with n ≈ 3), reciprocal half-time of crystallization, and a novel inflection point slope method, all confirming accelerated crystallization; for instance, the slope increased from 0.00517 to 0.05203. Polarized optical microscopy (POM) revealed evolving spherulite morphologies, including hexagonal and flower-like dendritic spherulites with diamond-shape ends, while wide-angle X-ray diffraction (WAXD) showed a crystallization range shift to higher temperatures (e.g., from 72–61 °C to 82–71 °C) and a 14% increase in crystallite diameter, aligning with increased melting point and lamellar thickness and overall increased crystallinity. Full article

The development of interoperable and reusable information models is a key challenge for digitalization in manufacturing domains with heterogeneous and complex process chains. Ensuring seamless data exchange requires the standardization of both data syntax and semantics, while maintaining compatibility with existing industry standards. This paper presents a methodology for deriving standardizable and generalizable OPC UA information models tailored to domains with high process variability and interdisciplinary requirements. The methodology integrates system analysis, parameter mapping, and the development of modular submodels, supported by expert input and validation. It emphasizes the reuse and extension of existing OPC UA Companion Specifications to reduce complexity, avoid redundancy, and enable long-term standardization. The approach is exemplified by its application to battery cell production, an emerging manufacturing domain combining process and mechanical engineering with continuous and discrete processes. Its high degree of heterogeneity and lack of domain-specific standards pose significant challenges for model development. Through iterative expert workshops and structured model validation, a dedicated and transferable OPC UA framework is created. The resulting layered model structure combines a cross-industry standard with newly developed, process-aware model elements. This enables both broad applicability and the depth required for complex production environments, while supporting use cases such as traceability, regulatory reporting (e.g., EU Battery Passport), and process optimization. The resulting model improves interoperability, transparency, and data integration, offering a scalable blueprint for other complex manufacturing sectors. Full article

The symmetry of the interparticle interaction potential (IIP) plays a critical role in determining the thermodynamic and transport properties of solids. This study investigates the isolated effect of IIP asymmetry on thermalization. Asymmetry and nonlinearity are typically intertwined. To isolate the effect of asymmetry, we introduce a one-dimensional asymmetric harmonic (AH) model whose IIP possesses asymmetry but no nonlinearity, evidenced by energy-independent vibrational frequencies. Extensive numerical simulations confirm a power-law relationship between thermalization time (T_eq) and perturbation strength for the AH chain, revealing an exponent larger than the previously observed inverse-square law in the thermodynamic limit. Upon adding symmetric quartic nonlinearity into the AH model, we systematically study thermalization under combined asymmetry and nonlinearity. Matthiessen’s rule provides a good estimate of T_eq in this case. Our results demonstrate that asymmetry plays a distinct role in enhancing higher-order effects and governing relaxation dynamics. Full article

Antimicrobial resistance (AMR) poses a growing threat to global health, and its spread through the food chain is gaining increasing attention. While AMR in food of animal origin has been extensively studied, less is known about its prevalence in plant-based foods, particularly fresh and ready-to-eat (RTE) vegetables. This study investigated the occurrence of antimicrobial-resistant bacteria in fresh and RTE vegetables. Isolates were subjected to antimicrobial susceptibility testing and molecular analyses for the characterization of antimicrobial resistance genes (ARGs). A significant proportion of samples were found to harbor antimicrobial-resistant bacteria, including multidrug-resistant strains. Several ARGs, including those encoding extended-spectrum β-lactamases (ESBLs) and resistance to critically important antimicrobials, were detected. The findings point to environmental contamination—potentially originating from wastewater reuse and agricultural practices—as a likely contributor to AMR dissemination in vegetables. The presence of antimicrobial-resistant bacteria and ARGs in fresh produce raises concerns about food safety and public health. The current regulatory framework lacks specific criteria for monitoring AMR in vegetables, highlighting the urgent need for surveillance programs and risk mitigation strategies. This study contributes to a better understanding of AMR in the plant-based food sector and supports the implementation of a One Health approach to address this issue. Full article

Clinically significant antimicrobial-resistant bacteria and resistance genes are increasingly being reported in wildlife. In this study, 127 splenic samples from red foxes (Vulpes vulpes) from northern and central Italy were analysed for the presence of resistance genes against antimicrobials such as tetracycline, sulphonamide, β-lactam, and colistin, which were previously extensively used in human and veterinary management of bacterial diseases. One or more antimicrobial resistance genes were detected in 78 (61%) of 127 splenic samples. Polymerase chain reaction positivity was revealed for 13 genes—tet(A), tet(B), tet(K), tet(L), tet(M), tet(O), tetA(P), tet(Q), tet(S), tet(X), sul1, sul2, and bla_TEM-1—out of the 21 tested genes. Our results, corroborated by reports in the literature, confirm the potential role of the red fox as a sentinel for antimicrobial-resistant bacteria in contaminated environments and suggest that detecting resistance genes in biological samples by a culture-independent method might be an effective tool for the epidemiological study of antimicrobial resistance in wildlife. Full article

This study investigates a stochastic production planning problem with regime-switching parameters, inspired by economic cycles impacting production and inventory costs. The model considers types of goods and employs a Markov chain to capture probabilistic regime transitions, coupled with a multidimensional Brownian motion representing stochastic demand dynamics. The production and inventory cost optimization problem is formulated as a quadratic cost functional, with the solution characterized by a regime-dependent system of elliptic partial differential equations (PDEs). Numerical solutions to the PDE system are computed using a monotone iteration algorithm, enabling quantitative analysis. Sensitivity analysis and model risk evaluation illustrate the effects of regime-dependent volatility, holding costs, and discount factors, revealing the conservative bias of regime-switching models when compared to static alternatives. Practical implications include optimizing production strategies under fluctuating economic conditions and exploring future extensions such as correlated Brownian dynamics, non-quadratic cost functions, and geometric inventory frameworks. In contrast to earlier studies that imposed static or overly simplified regime-switching assumptions, our work presents a fully integrated framework—combining optimal control theory, a regime-dependent system of elliptic PDEs, and comprehensive numerical and sensitivity analyses—to more accurately capture the complex stochastic dynamics of production planning and thereby deliver enhanced, actionable insights for modern manufacturing environments. Full article

Polypropylene (PP), with its good physical, thermal, and mechanical properties and excellent processing capabilities, has become one of the most used synthetic polymers. It is known that the overall properties of semicrystalline polymers, including PP, are governed by morphology, which is influenced by the crystallization behavior of the polymer under specific conditions. The most important industrial PP remains the isotactic one, and it has been studied extensively for its polymorphic characteristics and crystallization behavior for over half a century. Due to its regular chain structure, isotactic polypropylene (iPP) belongs to the group of polymers with a high tendency for crystallization. The rapid quenching of molten iPP fails to produce a completely amorphous polymer but leads to an intermediate crystalline order. On the other hand, slow cooling yields a material with high crystalline content. The processing conditions that occur in practice and industry are between these two extremes and, in some cases, are even very close. Therefore, the study of limits in processability and the impact of extreme preparation conditions on morphology, structure, thermal, and mechanical properties fills a gap in the current understanding of how the processing conditions of iPP can be used to design the desired properties for specific applications and is in the focus of this research. The first set of samples (Q samples) was obtained by rapid quenching, while the second was prepared by very slow cooling from the melt to room temperature (SC samples). Testing of samples was performed by optical microscopy (OM), scanning electron microscopy (SEM), wide-angle X-ray diffraction (WAXD), Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), dynamic dielectric spectroscopy (DDS), and mechanical measurements. Characterization revealed that slowly cooled samples exhibited a significantly higher degree of crystallinity and larger crystallites (χ ≥ 55% and L₍₁₁₀₎ ≈ 20 nm), compared to quenched samples (χ < 30%, L₍₁₁₀₎ ≤ 3 nm). Mechanical testing showed a drastic contrast: quenched samples exhibited elongation at break > 500%, while slowly cooled samples broke below 15%, reflecting their brittle behavior. For the first time, DDS is applied to investigate molecular mobility differences between processing-dependent structural forms, specifically the mesomorphic (smectic) and α-monoclinic forms. In slowly cooled samples, α relaxation exhibited both enhanced intensity and an upward temperature shift, indicating stronger structural constraints due to a much higher crystalline phase content and significantly larger crystallite size, respectively. These findings provide novel insights into the structure–property–processing relationship, which is crucial for industrial applications. Full article

In the shift toward sustainable and resource-efficient manufacturing, Artificial Intelligence (AI) is playing a transformative role in overcoming the limitations of traditional production scheduling methods. This study, based on a Systematic Literature Review (SLR), explores how AI techniques enhance Advanced Planning and Scheduling (APS) systems, particularly under finite-capacity constraints. Traditional scheduling models often overlook real-time resource limitations, leading to inefficiencies in complex and dynamic production environments. AI, with its capabilities in data fusion, pattern recognition, and adaptive learning, enables the development of intelligent, flexible scheduling solutions. The integration of metaheuristic algorithms—especially Ant Colony Optimization (ACO) and hybrid models like GA-ACO—further improves optimization performance by offering high-quality, near-optimal solutions without requiring extensive structural modeling. These AI-powered APS systems enhance scheduling accuracy, reduce lead times, improve resource utilization, and enable the proactive identification of production bottlenecks. Especially relevant in high-variability sectors like fashion, these approaches support Industry 5.0 goals by enabling agile, sustainable, and human-centered manufacturing systems. The findings have been highlighted in a structured framework for AI-based APS systems supported by metaheuristics that compares the Industry 4.0 and Industry 5.0 perspectives. The study offers valuable implications for both academia and industry: academics can gain a synthesized understanding of emerging trends, while practitioners are provided with actionable insights for deploying intelligent planning systems that align with sustainability goals and operational efficiency in modern supply chains. Full article

Background/Objectives: The gut microbiota (GM) is pivotal for immune regulation, metabolism, and neurodevelopment. Infants undergoing surgery for congenital gastrointestinal anomalies are especially prone to microbial imbalances, with a paucity of beneficial bacteria (e.g., Bifidobacteria and Bacteroides) and diminished short-chain fatty acid production. Dysbiosis has been associated with severe complications, including necrotizing enterocolitis, sepsis, and feeding intolerance. This narrative review aims to critically examine strategies for microbiota modulation in this high-risk cohort. Methods: An extensive literature analysis was performed to compare the evolution of GM in healthy neonates versus those requiring gastrointestinal surgery, synthetizing strategies to maintain eubiosis, such as early nutritional interventions—particularly the use of human milk—along with antibiotic management and supplementary treatments including probiotics, prebiotics, postbiotics, and lactoferrin. Emerging techniques in metagenomic and metabolomic analysis were also evaluated for their potential to elucidate microbial dynamics in these patients. Results: Neonates undergoing gastrointestinal surgery exhibit significant alterations in microbial communities, characterized by reduced levels of eubiotic bacteria and an overrepresentation of opportunistic pathogens. Early initiation of enteral feeding with human milk and careful antibiotic stewardship are linked to improved microbial balance. Adjunctive therapies, such as the administration of probiotics and lactoferrin, show potential in enhancing gut barrier function and immune modulation, although confirmation through larger-scale studies remains necessary. Conclusions: Modulating the GM emerges as a promising strategy to ameliorate outcome in neonates with congenital gastrointestinal surgical conditions. Future research should focus on the development of standardized therapeutic protocols and the execution of rigorous multicenter trials to validate the efficacy and safety of these interventions. Full article

In recent years, the world has seen a significant increase in extreme weather events, such as floods, hurricanes, and storms, which have caused extensive damage to infrastructure and communities. These events result from natural phenomena and human-induced factors, including climate change and natural climate variations. For instance, the floods in Europe in 2024 and the hurricanes in the United States have highlighted the vulnerability of urban and rural areas. These extreme events are often unpredictable and pose considerable challenges for spatial planning and risk management. This study explores an innovative approach that employs machine learning and Markov chains to enhance spatial planning and predict flood risk areas. By utilizing data such as weather records, land use and land cover (LULC) information, topographic LIDAR data, and advanced predictive models, the study aims to identify the most vulnerable areas and provide recommendations for risk mitigation. The results indicate that integrating these technologies can improve forecasting accuracy, thereby supporting more informed decisions in land management. Given the effects of climate change and the increasing frequency of extreme events, adopting advanced forecasting and planning tools is crucial for protecting communities and reducing economic and social damage. This method was applied to the Calopinace area, also known as the Calopinace River or Fiumara della Cartiera, which crosses Reggio Calabria and is notorious for its historical floods. It can serve as part of an early warning system, enabling alerts to be issued throughout the monitored area. Furthermore, it can be integrated into existing emergency protocols, thereby enhancing the effectiveness of disaster response. Future research could investigate incorporating additional data and AI techniques to improve accuracy. Full article