Search Results (8,412)

The siphon effect between airports has amplified the polarization in passenger throughput, undermining the balanced development and sustainability of airport clusters. The airport siphon effect occurs when one airport attracts a disproportionate share of passengers, concentrating traffic at the expense of others, which affects the overall resilience of the entire airport cluster. To address this issue, this study proposes a siphon index, expands the range of ground transportation options for passengers, and establishes a zero-siphon model to assess the impact of siphoning on the resiliency of airport clusters. Using this framework, four major airport clusters in China were selected as research subjects, with regional aviation accessibility serving as a measure of resilience. The results showed that among the four airport clusters, the siphon effect is most pronounced in the Guangzhou region. To explore the implications of this effect further, three airport disruption scenarios were simulated to assess the resilience of the Pearl River Delta airport cluster. The results indicated that the intensity and timing of disruptive events significantly affect airport cluster resilience, with hub airports being particularly sensitive. This study analyzes the risks associated with excessive route concentration, providing policymakers with critical insights to enhance the sustainability, equity, and resilience of airport clusters. The proposed strategies facilitate coordinated infrastructure development, optimized air–ground intermodal connectivity, and risk mitigation. These measures contribute to building more sustainable and adaptive aviation networks in rapidly urbanizing regions. Full article

The rapid urbanization process has accelerated the degradation of ecosystem services (ESs) in peri-urban rural areas of the Yangtze River Delta (YRD), leading to increasing landscape ecological risks (LERs). Establishing a scientifically grounded landscape ecological risk assessment (LERA) system and corresponding control strategies is therefore imperative. Using rural areas of Jiangning District, Nanjing as a case study, this research proposes an optimized dual-dimensional coupling assessment framework that integrates ecosystem service value (ESV) and ecological risk probability. The spatiotemporal evolution of LER in 2000, 2010, and 2020 and its key driving factors were further studied by using spatial autocorrelation analysis and geodetector methods. The results show the following: (1) From 2000 to 2020, cultivated land remained dominant, but its proportion decreased by 10.87%, while construction land increased by 26.52%, with minimal changes in other land use types. (2) The total ESV increased by CNY 1.67 × 10⁹, with regulating services accounting for over 82%, among which water bodies contributed the most. (3) LER showed an overall increasing trend, with medium- to highest-risk areas expanding by 55.37%, lowest-risk areas increasing by 10.10%, and lower-risk areas decreasing by 65.48%. (4) Key driving factors include landscape vulnerability, vegetation coverage, and ecological land connectivity, with the influence of distance to road becoming increasingly significant. This study reveals the spatiotemporal evolution characteristics of LER in typical peri-urban villages. Based on the LERA results, combined with terrain features and ecological pressure intensity, the study area was divided into three ecological management zones: ecological conservation, ecological restoration, and ecological enhancement. Corresponding zoning strategies were proposed to guide rural ecological governance and support regional sustainable development. Full article

The exponential proliferation of the Internet of Things (IoT) and optical IoT (O-IoT) has introduced substantial challenges concerning computational capacity and energy efficiency. IoT devices generate vast volumes of aggregated data and require intensive processing, often resulting in elevated latency and excessive energy consumption. Task offloading has emerged as a viable solution; however, many existing strategies fail to adequately optimize both latency and energy usage. This paper proposes a novel task-offloading approach based on deep Q-network (DQN) learning, designed to intelligently and dynamically balance these critical metrics. The proposed framework continuously refines real-time task offloading decisions by leveraging the adaptive learning capabilities of DQN, thereby substantially reducing latency and energy consumption. To further enhance system performance, the framework incorporates optical networks into the IoT–fog–cloud architecture, capitalizing on their high-bandwidth and low-latency characteristics. This integration facilitates more efficient distribution and processing of tasks, particularly in data-intensive IoT applications. Additionally, we present a comparative analysis between the proposed DQN algorithm and the optimal strategy. Through extensive simulations, we demonstrate the superior effectiveness of the proposed DQN framework across various IoT and O-IoT scenarios compared to the BAT and DJA approaches, achieving improvements in energy consumption and latency of 35%, 50%, 30%, and 40%, respectively. These findings underscore the significance of selecting an appropriate offloading strategy tailored to the specific requirements of IoT and O-IoT applications, particularly with regard to environmental stability and performance demands. Full article

Hydrogen production is increasingly vital for global decarbonization but remains a water- and energy-intensive process, especially in arid regions. Despite growing attention to its climate benefits, limited research has addressed the environmental impacts of water sourcing. This study employs a life cycle assessment (LCA) approach to evaluate three water supply strategies for hydrogen production: (1) seawater desalination without brine treatment (BT), (2) desalination with partial BT, and (3) freshwater purification. Scenarios are modeled for the United Arab Emirates (UAE), Australia, and Spain, representing diverse electricity mixes and water stress conditions. Both electrolysis and steam methane reforming (SMR) are evaluated as hydrogen production methods. Results show that desalination scenarios contribute substantially to human health and ecosystem impacts due to high energy use and brine discharge. Although partial BT aims to reduce direct marine discharge impacts, its substantial energy demand can offset these benefits by increasing other environmental burdens, such as marine eutrophication, especially in regions reliant on carbon-intensive electricity grids. Freshwater scenarios offer lower environmental impact overall but raise water availability concerns. Across all regions, feedwater for SMR shows nearly 50% lower impacts than for electrolysis. This study focuses solely on the environmental impacts associated with water sourcing and treatment for hydrogen production, excluding the downstream impacts of the hydrogen generation process itself. This study highlights the trade-offs between water sourcing, brine treatment, and freshwater purification for hydrogen production, offering insights for optimizing sustainable hydrogen systems in water-stressed regions. Full article

This study proposes an intelligent patrol planning framework that leverages reinforcement learning, spatiotemporal crime forecasting, and simulated sensor telemetry to optimize autonomous vehicle (AV) navigation in urban environments. Crime incidents from Washington DC (2024–2025) and Seattle (2008–2024) are modeled as a dynamic spatiotemporal graph, capturing the evolving intensity and distribution of criminal activity across neighborhoods and time windows. The agent’s state space incorporates synthetic AV sensor inputs—including fuel level, visual anomaly detection, and threat signals—to reflect real-world operational constraints. We evaluate and compare three learning strategies: Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Proximal Policy Optimization (PPO). Experimental results show that DDQN outperforms DQN in convergence speed and reward accumulation, while PPO demonstrates greater adaptability in sensor-rich, high-noise conditions. Real-map simulations and hourly risk heatmaps validate the effectiveness of our approach, highlighting its potential to inform scalable, data-driven patrol strategies in next-generation smart cities. Full article

Introduction: Carbapenem-resistant Klebsiella pneumoniae (CRKP) represents a critical public health threat due to its rapid nosocomial dissemination, limited therapeutic options, and elevated mortality rates. This study aimed to characterize the epidemiology, carbapenemase profiles, and antimicrobial susceptibility patterns of CRKP isolates, as well as the clinical features and outcomes observed in infected or colonized patients. Materials and Methods: We conducted a retrospective analysis of clinical and microbiological data from patients with CRKP infections or colonization admitted between January 2023 and January 2024. Descriptive statistics were used to assess prevalence, resistance patterns, and patient outcomes. Two binary logistic regression models were applied to identify independent predictors of sepsis and in-hospital mortality. Results: Among 89 CRKP isolates, 45 underwent carbapenemase typing. More than half were metallo-β-lactamase (MBL) producers, with 44.4% co-harbouring NDM and OXA-48-like enzymes. Surgical intervention was associated with a significantly lower risk of sepsis (p < 0.01) and in-hospital mortality (p = 0.045), whereas intensive care unit (ICU) stay was a strong predictor of both outcomes. ICU admission conferred a 10-fold higher risk of sepsis (95%Cl 2.4–41.0) and a 40.8-fold higher risk of in-hospital death (95% Cl 3.5–473.3). Limitations: This single-center retrospective study included a limited number of isolates in certain groups. Additionally, cefiderocol (FDC) susceptibility was assessed by disk diffusion rather than by the broth microdilution method. Conclusions: Our study underscores the increasing prevalence of metallo-beta-lactamase-producing CRKP, particularly strains harbouring dual carbapenemases. Timely recognition of high-risk patients, combined with the implementation of targeted infection control measures and the integration of novel therapeutic options, is crucial to optimize clinical management and reduce mortality associated with CRKP. Full article

Flocculants are indispensable in water and wastewater treatment, enabling the aggregation and removal of suspended particles, colloids, and emulsions. However, the conventional development and application of flocculants rely heavily on empirical methods, which are time-consuming, resource-intensive, and environmentally problematic due to issues such as sludge production and chemical residues. Recent advances in machine learning (ML) have opened transformative avenues for the design, optimization, and intelligent application of flocculants. This review systematically examines the integration of ML into flocculant research, covering algorithmic approaches, data-driven structure–property modeling, high-throughput formulation screening, and smart process control. ML models—including random forests, neural networks, and Gaussian processes—have successfully predicted flocculation performance, guided synthesis optimization, and enabled real-time dosing control. Applications extend to both synthetic and bioflocculants, with ML facilitating strain engineering, fermentation yield prediction, and polymer degradability assessments. Furthermore, the convergence of ML with IoT, digital twins, and life cycle assessment tools has accelerated the transition toward sustainable, adaptive, and low-impact treatment technologies. Despite its potential, challenges remain in data standardization, model interpretability, and real-world implementation. This review concludes by outlining strategic pathways for future research, including the development of open datasets, hybrid physics–ML frameworks, and interdisciplinary collaborations. By leveraging ML, the next generation of flocculant systems can be more effective, environmentally benign, and intelligently controlled, contributing to global water sustainability goals. Full article

One of the fundamental goals of contemporary mobility is to optimize transport processes in urban areas. The solution in this area seems to be the implementation of the idea of sustainable transport systems based on the Smart City concept. The article presents a case study—an assessment of the possibilities of changing mobility habits based on the idea of sustainable urban transport, taking into account the criterion of energy consumption of individual means of transport. The analyses are based on a comparison of selected means of transport occurring in the urban environment according to several key parameters for the optimization and efficiency of transport processes, i.e., cost, time, travel comfort, and impact on the natural environment, while simultaneously linking them to the criterion of energy consumption of individual means of transport. The analyzed parameters currently constitute the most important group of challenges in the area of shaping and planning optimal and sustainable urban transport. The presented research was used to indicate the connections between various areas of optimization of the transport process and the energy efficiency of individual modes of transport. Analyses have shown that the least time-consuming process of urban mobility is associated with the highest level of CO₂ emissions and, at the same time, the highest level of energy efficiency. However, combining public transport with other means of transport can meet most of the transport expectations of city residents, also in terms of energy optimization. The research results presented in the article can contribute to the creation of a strategy for the development of the transport network based on the postulates of increasing the optimization and efficiency of individual means of transport in urban areas. At the same time, recognizing the criterion of energy intensity of means of transport as leading in the development of sustainable urban mobility. Thus, confirming the important role of existing transport systems in the process of shaping and planning sustainable urban mobility in accordance with the idea of Smart City. Full article

Background: Nutrition is of paramount importance during early development, since suboptimal growth in this period of life is linked to adverse long- and mid-term outcomes. This is particularly relevant for preterm infants, who fail to thrive during the first weeks of life and develop extrauterine growth restriction (EUGR). This group of premature babies represents an interesting population to investigate using a metabolomic approach to optimize nutritional intake. Aims: To analyse and compare the urinary metabolomic pattern at birth of preterm infants with and without growth restriction at 36 weeks of postmenstrual age or at discharge, searching for putative markers of growth failure. Methods: We enrolled preterm infants between 23 and 32 weeks of gestational age (GA) and/or with a birth weight <1500 g, admitted to the Neonatal Intensive Care Unit (NICU) at the Department of Women’s and Children’s Health of Padova University Hospital. We collected urinary samples within 48 h of life and performed untargeted metabolomic analysis using mass spectrometry. Results: Sixteen EUGR infants were matched with sixteen non-EUGR controls. The EUGR group showed lower levels of L-cystathionine, kynurenic acid, L-carnosine, N-acetylglutamine, xanthurenic acid, aspartylglucosamine, DL5-hydroxylysine-hydrocloride, homocitrulline, and L-aminoadipic acid, suggesting a lower anti-inflammatory and antioxidant status with respect to the non-EUGR group. Conclusions: Metabolomic analysis suggests a basal predisposition to growth restriction, the identification of which could be useful for tailoring nutritional approaches. Full article

Background/Objectives: Neck pain caused by myofascial pain syndrome (MPS) is a highly prevalent musculoskeletal condition. Repetitive peripheral magnetic stimulation (rPMS) is a promising treatment option; however, its therapeutic effect and optimal treatment frequency remain unclear. This study aimed to investigate the therapeutic effect and duration of effect of rPMS in patients with MPS of the neck. Methods: In this randomized, sham-controlled, crossover trial, 27 patients with neck MPS and baseline visual analog scale (VAS) scores ≥ 40 were enrolled. The mean age was 43.8 ± 9.1 years, and 63% were female. Participants were randomly assigned to receive either an initial rPMS treatment (a 10 min session delivering 3900 pulses at 5–10 Hz) or sham stimulation. After 7 days, groups crossed over. Pain intensity (VAS), disability (Neck Disability Index; NDI), and analgesic use were recorded daily for seven consecutive days. A linear mixed-effects model was used for analysis. Results: At baseline, the VAS and NDI scores were 61.8 ± 10.5 and 26.0 ± 6.3, respectively. rPMS produced a significantly greater reduction in both VAS and NDI scores, with the greatest differences observed on Day 4: the differences were −24.1 points in VAS and −8.5 points in NDI compared to the sham group. There was no significant difference in analgesic use between the two groups. Conclusions: A single rPMS session provides short-term improvement in pain and disability in neck MPS. Based on the observed therapeutic window, more frequent sessions (e.g., twice weekly) may provide sustained benefit and should be explored in future studies. Full article

Monitoring pH and acidity during yoghurt fermentation is essential for product quality and process efficiency. Conventional measurement methods, however, are invasive and labour-intensive. This study developed artificial neural network (ANN) models to predict pH and titratable acidity during yoghurt fermentation using CIELAB colour parameters (L, a*, b*). Reconstituted milk powder with 12% total solids was prepared with varying protein levels (4.2–4.8%), inoculum concentrations (1–3%), and fermentation temperatures (36–44 °C). Data were collected every 10 min until pH 4.6 was reached. Forty models were trained for each output variable, using 90% of the data for training and 10% for validation. The first two phases of the fermentation process were clearly distinguishable, lasting between 4.5 and 7 h and exceeding 0.6% lactic acid in all treatments evaluated. The best pH model used two hidden layers with 28 neurons (R² = 0.969; RMSE = 0.007), while the optimal acidity model had four hidden layers with 32 neurons (R² = 0.868; RMSE = 0.002). The strong correlation between colour and physicochemical changes confirms the feasibility of this non-destructive approach. Integrating ANN models and colourimetry offers a practical solution for real-time monitoring, helping improve process control in industrial yoghurt production. Full article

Electric buses (EBs) are increasingly adopted for their environmental and operational benefits, yet their real-world efficiency is influenced by climate, route characteristics, and auxiliary energy demands. While most existing research identifies winter as the most energy-intensive season due to cabin heating and reduced battery performance, this study presents a contrasting perspective based on a three-year longitudinal analysis of the LYMMO fleet in Orlando, Florida—a subtropical U.S. region. The findings reveal that summer is the most energy-intensive season, primarily due to sustained HVAC usage driven by high ambient temperatures—a seasonal pattern rarely reported in the current literature and a key regional contribution. Additionally, idling time exceeds driving time across all seasons, with HVAC usage during idling emerging as the dominant contributor to total energy consumption. To mitigate these inefficiencies, a proxy-based HVAC energy estimation method and an optimization model were developed, incorporating ambient temperature and peak passenger load. This approach achieved up to 24% energy savings without compromising thermal comfort. Results validated through non-parametric statistical testing support operational strategies such as idling reduction, HVAC control, and seasonally adaptive scheduling, offering practical pathways to improve EB efficiency in warm-weather transit systems. Full article

Background and Purpose: Stratification based on specific image biomarkers applicable in clinical settings could help optimize treatment outcomes for recurrent non-small cell lung cancer patients. For this purpose, we aimed to determine the clinical impact of positive delta changes (any difference above zero > 0) between baseline [¹⁸F]FDG PET/CT metrics before the first treatment course and reirradiation. Material/Methods: Forty-seven patients who underwent thoracic reirradiation with curative intent at our institute between 2013 and 2021 met the inclusion criteria. All patients had histologically verified NSCLC, ECOG (Eastern Cooperative Oncology Group) ≤ 2, and underwent [¹⁸F]FDG PET/CT for initial staging and re-staging before primary radiotherapy and reirradiation, respectively. The time interval between radiation treatments was at least nine months. Quantitative metabolic volume and intensity parameters were measured before first irradiation and before reirradiation, and the difference above zero (>0; delta change) between them was statistically correlated to locoregional control (LRC), progression-free survival (PFS), and overall survival (OS). Results: Patients were followed for a median time of 33 months after reirradiation. The median OS was 21.8 months (95%-CI: 16.3–27.3), the median PFS was 12 months (95%-CI: 6.7–17.3), and the median LRC was 13 months (95%-CI: 9.0–17.0). Multivariate analysis revealed that the delta changes in SULpeak, SUVmax, and SULmax of the lymph nodes significantly impacted OS (SULpeak p = 0.017; SUVmax p = 0.006; SULmax p = 0.006), PFS (SULpeak p = 0.010; SUVmax p = 0.009; SULmax p = 0.009), and LRC (SULpeak p < 0.001; SUVmax p = 0.003; SULmax p = 0.003). Conclusions: Delta changes in SULpeak, SUVmax, and SULmax of the metastatic lymph nodes significantly impacted all clinical endpoints (OS, PFS and LRC) in recurrent NSCLC patients treated with reirradiation. Hence, these imaging biomarkers could be helpful with regard to patient selection in this challenging clinical situation. Full article

Compared with biocompatibility, osteoconductivity, and mechanical properties, the poor injectability of calcium phosphate bone cements (CPCs) is always ignored, which actually hinders the development of CPC clinical transfer in minimally invasive orthopedic surgeries. Moreover, currently, CPC preparation in the clinic is labor-intensive and requires well-trained technicists, which might also result in the unstable quality of CPCs. In this work, we focused on three research objectives: (i) introducing a standardized preparation method for CPCs; (ii) studying the effects of preparation parameters on CPC injectability and compressive strength; and (iii) studying the injecting condition effects on CPC injectability, aiming to overcome CPCs’ disadvantages in minimally invasive surgeries. Firstly, two strategies, named “variable mixing barrel control (VMBC)” and the “nested blade–baffle stirring rod (NBBSR)”, were proposed in this study to solve the problems in the preparation of CPCs, which involved blending CPC powder and an agent to generate a paste, by enhancing the mixing performance and mimicking human manual stirring actions. Secondly, although the grinding parameter could significantly generate differences in the microstructure of CPCs, the compressive strength remained relatively stable. However, it was found to significantly affect the injectability of CPCs, leading to the inefficient injection of CPCs. Finally, the effects of syringe design, dimensions, and injecting conditions on CPC injectability were studied, and the results showed that the optimization of these factors enables the injection of CPCs, which has otherwise always been infeasible to implement in minimally invasive orthopedic surgeries. Full article

The current paper presents a comprehensive intelligent system designed to optimize the performance of a barrier eddy current separator (BECS), comprising a conveyor belt, a vibration feeder, and a magnetic drum. This system was trained and validated on real-world industrial data gathered directly from the working separator under 81 different operational scenarios. The intelligent models were used to recommend optimal settings for drum speed, belt speed, vibration intensity, and drum angle, thereby maximizing separation quality and minimizing energy consumption. the smart separation module utilizes YOLOv11n-seg and achieves a mean average precision (mAP) of 0.838 across 7163 industrial instances from aluminum, copper, and plastic materials. For shape classification (sharp vs. smooth), the model reached 91.8% accuracy across 1105 annotated samples. Furthermore, the thermal monitoring unit can detect iron contamination by analyzing temperature anomalies. Scenarios with iron showed a maximum temperature increase of over 20 °C compared to clean materials, with a detection response time of under 2.5 s. The architecture integrates a Digital Twin using Azure Digital Twins to virtually mirror the system, enabling real-time tracking, behavior simulation, and remote updates. A full connection with the PLC has been implemented, allowing the AI-driven system to adjust physical parameters autonomously. This combination of AI, IoT, and digital twin technologies delivers a reliable and scalable solution for enhanced separation quality, improved operational safety, and predictive maintenance in industrial recycling environments. Full article