Search Results (140)

In 2023, there were approximately 32 million refugees globally. Nine out of the ten countries with the highest origins of refugees were in the Global South; conversely, only three of the ten countries hosting the highest numbers of refugees were in the Global North. In this study, we introduce the conceptual framework of a global division of responsibility sharing to describe how functions of Global North countries as permanent “resettlement” countries and Global South countries as perpetual countries of “asylum” and “transit” constitute unequal burdens with unequal protections for refugees. We illustrate—theoretically and empirically—how the structural positions of state actors in a global network introduce and reify a global division in refugee flows. Empirically, we test and develop this framework with network analysis of refugee flows to countries of asylum from 1990 to 2015 in addition to employing data on monetary donations to the United Nations High Commissioner for Refugees (UNHCR) from 2017 to 2021. We (1) provide evidence of the structure and role of intermediary countries in refugee flows and (2) examine how UNHCR monetary aid conditions intermediary countries’ role of routing and transit. We illustrate how network constraints and monetary donations affect and constitute a global division in the management of historic and contemporary international refugee flows and explore the consequences of this global division for refugees’ access to resources and social and human rights. Full article

Plastic waste is currently a major concern in Romania due to the annual increase in quantities generated from anthropogenic and industrial activities, especially from end-of-life vehicles (ELVs), and the need to reduce environmental impact. This study investigates an alternative valorization route for polypropylene (PP) and polyethylene (PE) plastic waste through microwave-assisted pyrolysis, aiming to maximize conversion into gaseous products, particularly hydrogen-rich gas. A monomode microwave reactor was employed, using layered configurations of plastic feedstock, silicon carbide as a microwave susceptor, and activated carbon as a catalyst. The influence of catalyst loading, reactor configuration, and plastic type was assessed through systematic experiments. Results showed that technical-grade PP, under optimal conditions, yielded up to 81.4 wt.% gas with a hydrogen concentration of 45.2 vol.% and a hydrogen efficiency of 44.8 g/g. In contrast, PE and mixed PP + PE waste displayed lower hydrogen performance, particularly when containing inorganic fillers. For all types of plastics studied, the gaseous fractions obtained have a high calorific value (46,941–55,087 kJ/kg) and at the same time low specific CO₂ emissions (4.4–6.1 × 10⁻⁵ kg CO₂/kJ), which makes these fuels very efficient and have a low carbon footprint. Comparative tests using conventional heating revealed significantly lower hydrogen yields (4.77 vs. 19.7 mmol/g plastic). These findings highlight the potential of microwave-assisted pyrolysis as an efficient method for transforming ELV-derived plastic waste into energy carriers, offering a pathway toward low-carbon, resource-efficient waste management. Full article

To address the need for intelligent scheduling and model integration under spatiotemporal variability and uncertainty in water systems, this study proposes a hybrid correction method for pump characteristic curves that integrates data-driven techniques with an affine modeling framework. Steady-state data are extracted through adaptive filtering and statistical testing, and representative operating conditions are identified via unsupervised clustering. An affine transformation is then applied to the factory-provided characteristic equation, followed by parameter optimization using the clustered dataset. Using the Hongze Pump Station along the eastern route of the South-to-North Water Diversion Project as a case study, the method reduced the mean blade angle prediction error from 1.73° to 0.51°, and the efficiency prediction error from 7.32% to 1.30%. The results demonstrate improved model accuracy under real-world conditions and highlight the method’s potential to support more robust and adaptive hydrodynamic scheduling models, contributing to the advancement of sustainable and smart water resource management. Full article

Accurate decision making under uncertainty for transport problems often requires predicting unknown parameters from contextual information. Traditional two-stage frameworks separate prediction and optimization, which can lead to suboptimal decisions, as minimizing prediction error does not necessarily minimize decision loss. To address this limitation, inspired by the smart predict-then-optimize framework, we introduce a novel tunable piecewise linear loss function (PLLF). Rather than directly incorporating decision loss into the learning objective based on specific problem, PLLF serves as a general feedback mechanism that guides the prediction model based on the structure and sensitivity of the downstream optimization task. This design enables the training process to prioritize predictions that are more decision-relevant. We further develop a heuristic parameter search strategy that adapts PLLF using validation data, enhancing its generalizability across different data settings. We test our method with a binary route selection task—the simplest setting to isolate and assess the impact of our modeling approach on decision quality. Experiments across multiple machine learning models demonstrate consistent improvements in decision quality, with neural networks showing the most significant gains—improving decision outcomes in 36 out of 45 cases. These results highlight the potential of our framework to enhance decision-making processes that rely on predictive insights in transportation systems, particularly in routing, scheduling, and resource allocation problems where uncertainty plays a critical role. Overall, our approach offers a practical and scalable solution for integrating prediction and optimization in real-world transport applications. Full article

Efficient and scalable inference is essential for deploying large-scale generative models across diverse hardware platforms, especially in real-time or resource-constrained scenarios. To address this, we propose a novel unified and resource-aware inference optimization framework that uniquely integrates three complementary techniques: sensitivity-aware mixed-precision quantization, heterogeneous sparse attention for reducing attention complexity, and capacity-aware dynamic expert routing for input-adaptive computation. This framework distinctively achieves fine-grained adaptivity by dynamically adjusting computation paths based on token complexity and hardware conditions, offering substantial performance gains and execution flexibility across diverse platforms, including edge devices like Jetson Orin. Implemented using PyTorch 1.13 and ONNX Runtime, our framework demonstrates significant reductions in inference latency and memory usage, alongside substantial throughput improvements in language and image generation tasks, outperforming existing baselines even under constrained GPU environments. Qualitative analyses reveal its fine-grained adaptivity, while robustness tests confirm stable behavior under resource fluctuation and input noise, offering an interpretable optimization approach suitable for heterogeneous deployments. Future work will explore reinforcement-based routing and multimodal inference. Full article

The need for renewable alternatives to petroleum-based polymers is growing in response to environmental concerns and resource depletion. Polyimides (PIs), which are traditionally synthesized from petroleum-derived monomers, raise sustainability issues. In this work, renewable 2,5-furandicarboxylic acid (FDCA) was employed as a sustainable feedstock to synthesize a bio-based diamine monomer, N,N′-bis(4-aminophenyl)furan-2,5-dicarboxamide (FPA). Subsequently, FPA was polymerized with various aromatic dianhydrides through thermal imidization, yielding four distinct bio-based polyimide (FPA-PI) films. The resulting films exhibited exceptional thermal stability, with 5% weight loss temperatures exceeding 425 °C and char yields ranging from 54% to 60%. Mechanical characterization revealed high elastic moduli (2.14–3.20 GPa), moderate tensile strengths (50–99 MPa), and favorable aging resistance. Gas permeation tests demonstrated promising CO₂/N₂ separation performance, with FPA-DODDA achieving superior CO₂/N₂ selectivity (27.721) compared to commercial films such as Matrimid^®, polysulfone, and polycarbonate, while FPA-BPFLDA exhibited enhanced CO₂ permeability (P(CO₂) = 2.526 Barrer), surpassing that of Torlon^®. The CO₂/N₂ separation performance of these FPA-PI films is governed synergistically by size-sieving effects and solution-diffusion mechanisms. This work not only introduces a novel synthetic route for bio-based polymers but also highlights the potential of replacing conventional petroleum-based materials with renewable alternatives in high-temperature and gas separation applications, thereby advancing environmental sustainability. Full article

Demand-responsive transport (DRT) provides flexible ride-sharing by dynamically adjusting routes based on real-time user demand, making it suitable for complex urban mobility needs. This study proposes a modular simulation framework based on the DEVS (Discrete Event System Specification) formalism and introduces an “express service” strategy that enables direct trips without intermediate stops. The framework supports scenario-based analysis using key performance indicators (KPIs) and allows for flexible testing of operational strategies. Two experiments were conducted: the first validated the simulation model under varying demand and fleet conditions; and the second assessed the impact of the express service. Results showed that express passengers experienced significantly shorter waiting and riding times, while standard passenger service remained stable. The strategy also improved operational efficiency under constrained resources. This study contributes to a configurable simulation platform for evaluating differentiated DRT services and provides practical insights for adaptive service planning, especially in urban settings where tiered mobility solutions are increasingly needed. Full article

The brown seaweed Rugulopteryx okamurae is a major invasive species in Europe, menacing local ecosystems. The challenge lies in assessing application routes for this biomass, testing different extraction technologies (overnight agitation, mechanical homogenization, pH-shift, ionic liquid-, and ultrasound-assisted extractions) and parameters. There was a higher yield in the extracts homogenized with 70% ethanol, especially with 1:20, w/v, biomass–solvent ratio, than in aqueous extracts. As to overnight agitation, 70% ethanol produced results (24.5–28.3%) similar to those found in the homogenized extracts. However, in the former, the best biomass–solvent proportion was 1:10, w/v. Mineral matter yield presented specific patterns, reaching 59.6 ± 1.1% (70% ethanol) and 82.3 ± 0.1% (water). The highest total polyphenol level was attained in the 70% ethanol, 1:20, w/v, extract, 310.7 ± 22.1 mg GAE/100 g dw seaweed. This extract had a higher FRAP/ABTS. The extract attained with overnight agitation, 70% ethanol, 1:10, w/v, had 48% COX-2 inhibition as anti-inflammatory activity. Besides showing the potential of R. okamurae for pharmacological purposes, especially in the antioxidant and anti-inflammatory area, this study enabled us to rank technologies and conditions for the utilization of this abundant biomass resource by the industry. Full article

Background/Objectives: Access to healthy and affordable food remains a challenge for under-resourced communities due to uneven food distribution and the need for reliable transportation. This study developed and evaluated an interactive Geographic Information System (GIS)-based food asset map for a low-income community in Windham, Connecticut to improve awareness of food resources and expand opportunities for fresh food access. Methods: Using the human-centered design (HCD) framework and the Asset-Based Community Development (ABCD) model, the map integrates food locations, transportation routes, and assistance eligibility. Internal pilot testing (n = 8) identified usability issues, leading to updates such as mobile compatibility and user guides. Usability testing (n = 74) assessed navigation performance and user feedback through task-based evaluations and surveys. Categorical map usability, sociodemographic, diet, and health characteristics were tested for participants with food security (yes/no) or digital literacy (passed/failed). Results: Food-secure participants showed higher usability success than food-insecure individuals (p < 0.05), while those relying on food assistance faced greater challenges (p < 0.05). Individuals rating their diet as “very good/excellent” were most likely to pass the map usability testing (p < 0.05), whereas younger, college-educated, employed participants and those with vehicles trended toward passing (p < 0.1). Participants generally reported the map easy to navigate, especially those with food security. Conclusions: The asset map promotes food resource awareness and addresses barriers such as limited public transportation information. Additional efforts are needed to support food-insecure users in utilizing digital food access resources. This study contributes to initiatives to improve food access, digital inclusion, and community engagement in under-resourced communities. Full article

Large language models (LLMs), such as GPT-4.0 and Gemini, have achieved excellent performance on natural-language tasks, and they also show high expectations for logical reasoning. In the realm of print circuit board (PCB) routing, complex routing scenarios still rely on manual routing performed by seasoned engineers, which consumes significant human resources and time. This paper proposes an approach using few-shot and chain-of-thought training LLMs to tackle this issue, enabling LLMs to assist engineers in design tasks with a small number of samples. We tested the performance of LLMs in different routing scenarios with a few examples, validating the applicability of this method. Furthermore, we explored fine-tuning techniques to enhance the effectiveness of the few-shot learning approach, to overcome the limitation of scarce real-world PCB cases, and we employed code synthetic cases to fine-tune the model in place of actual PCB scenarios, ultimately improving the LLMs’ capability to manage intricate routing tasks. The results validate the feasibility and effectiveness of this method, offering a promising avenue for reducing the manual burden in PCB design. Full article

Acid mine drainage (AMD) generated during the exploitation and utilization of mineral resources poses a severe environmental problem globally within the mining industry. The Xiaomixi Stream in Ziyang County, Shaanxi Province, is a primary tributary of the Han River, which is surrounded by historically concentrated mining areas for stone coal and vanadium ores. Rainwater erosion of abandoned mine tunnels and waste rock piles has led to the leaching of acidic substances and heavy metals, which then enter the Haoping River and its tributaries through surface runoff. This results in acidic water, posing a significant threat to the water quality of the South-to-North Water Diversion Middle Route within the Han River basin. According to this study’s investigation, Xiaomixi’s acidic water exhibits yellow and white precipitates upstream and downstream of the river, respectively. These precipitates stem from the oxidation of iron-bearing minerals and aluminum-bearing minerals. The precipitation process is controlled by factors such as the pH and temperature, exhibiting seasonal variations. Taking the Xiaomixi Stream in Ziyang County, Shaanxi Province, as the study area, this paper conducts field investigations, systematic sampling of water bodies and river sediments, testing for iron and aluminum pollutants in water, and micro-area observations using field emission scanning electron microscopy (FESEM) on sediments, along with analyzing the iron and aluminum content. The deposition is analyzed using handheld X-ray fluorescence (XRF) analyzers, X-ray diffraction (XRD), and visible–near-infrared spectroscopy data, and a geochemical model is established using PHREEQC software. This paper summarizes the migration and transformation mechanisms of iron and aluminum pollutants in acidic water and proposes appropriate prevention and control measures. Full article

Toxoplasma gondii, an obligate intracellular parasite, is a globally prevalent pathogen capable of infecting a wide range of warm-blooded animals, including humans. Ocular toxoplasmosis (OT), a severe manifestation of T. gondii infection, can lead to potentially blinding complications. This comprehensive review delves into the current understanding of T. gondii biology, exploring its complex life cycle, diverse transmission routes, and strain diversity. This article provides an in-depth analysis of the clinical manifestations of OT, which can result from both congenital and acquired infections, presenting a spectrum of signs and symptoms. The review examines various diagnostic strategies employed for OT, including clinical examination, multimodal imaging techniques such as fundus fluorescein angiography (FFA), indocyanine green angiography (ICGA), optical coherence tomography (OCT), and optical coherence tomography angiography (OCTA), as well as laboratory tests including serology and molecular methods. Despite extensive research, the specific mechanisms underlying ocular involvement in T. gondii infection remain elusive, and current diagnostic options have limitations. Moreover, the treatment of active and recurrent OT remains a challenge. While existing therapies, such as antimicrobial agents and immunosuppressants, can control active infections, they do not offer a definitive cure or completely prevent recurrence. The clinical endpoints for the management of active and recurrent OT are also not yet well-established, and the available treatment methods carry the potential for adverse effects. This article highlights the need for future research to elucidate the pathogenesis of OT, investigate genetic factors influencing susceptibility to infection, and develop more sensitive and specific diagnostic tools. Enhancing global surveillance, implementing robust prevention strategies, and fostering multidisciplinary collaborations will be crucial in reducing the burden of OT and improving patient outcomes. This comprehensive review aims to provide a valuable resource for clinicians, researchers, and policymakers, contributing to a better understanding of T. gondii infection and its impact on ocular health. Full article

This research presents a novel hybrid approach, which combines the White Shark Optimizer (WSO) metaheuristic algorithm with chaotic maps integrated into the binarization process. Inspired by the predatory behavior of white sharks, WSO has shown great potential to navigate complex search spaces for optimization tasks. On the other hand, chaotic maps are nonlinear dynamical systems that generate pseudo-random sequences, allowing for better solution diversification and avoiding local optima. By hybridizing WSO and chaotic maps through adaptive binarization rules, the complementary strengths of both approaches are leveraged to obtain high-quality solutions. We have solved the Set Covering Problem (SCP), a well-known NP-hard combinatorial optimization challenge with real-world applications in several domains, and experimental results indicate that LOG and TENT chaotic maps are better after statistical testing. This hybrid approach could have practical applications in telecommunication network optimization, transportation route planning, and resource-constrained allocation. Full article

This study investigates the optimization of a low-cost IoT-based weather station designed for disaster monitoring, focusing on minimizing power consumption. The system architecture includes application, middleware, communication, and sensor layers, with solar power as the primary energy source. A novel task scheduling algorithm was developed to reduce power usage by efficiently managing the sensing and data transmission periods. Experiments compared the energy consumption of polling and deep sleep techniques, revealing that deep sleep is more energy-efficient (4.73% at 15 s time intervals and 16.45% at 150 s time intervals). Current consumption was analyzed across different test scenarios, confirming that efficient task scheduling significantly reduces power consumption. The energy consumption models were developed to quantify power usage during the sensing and transmission phases. This study concludes that the proposed system, utilizing affordable hardware and solar power, is an effective and sustainable solution for disaster monitoring. Despite using non-low-power devices, the results demonstrate the importance of adaptive task scheduling in extending the operational life of IoT devices. Future work will focus on implementing dynamic scheduling and low-power routing algorithms to enhance system functionality in resource-constrained environments. Full article

Cost reduction and staff retention are important optimization objectives in home healthcare (HHC) systems. Home healthcare operators need to balance their objectives by optimizing resource use, service delivery and profits. Minimizing total travel distances to control costs is a common routing problem objective while minimizing total finishing time differences is a scheduling objective whose purpose is to enhance staff satisfaction. To optimize routing and scheduling, we propose mixed integer linear programming with a bi-objective function, which is a subset of the vehicle routing problem with time windows (VRPTWs). VRPTWs is a known NP-hard problem, and optimal solutions are very hard to obtain in practice. Metaheuristics offer an alternative solution to this type of problem. Our metaheuristic uses the simulated annealing algorithm and weighted sum approach to convert the problems to single-objective problems and is equipped with operators including swapping, moving, path exchange and ruin and recreate. The results show, firstly, that the algorithm can effectively find the Pareto front, and secondly, that minimizing total finishing time differences to balance the number of jobs per caretaker is an efficient way to tackle HHC scheduling. A statistical test shows that the algorithm can obtain the Pareto front with a lower number of weighted sum problems. Full article