Search Results (3,495)

Indigenous chickens are raised in various rural areas in large quantities throughout Sudan. They must be transported over various distances to centralized slaughterhouses or for other purposes. In this study, we examined indigenous chicken farmers’ perceptions of chicken welfare during transportation. A total of 160 indigenous chickens (80 control + 80 transported with their owners) participated in this study. Our findings revealed that 69% and 88% of the farmers indicated that they were not knowledgeable about animal rights and animal welfare, respectively. The majority of the farmers (86%) reported that they were unaware of animal protection laws. Furthermore, the transported chickens showed a significantly long tonic immobility duration (p < 0.05) compared to the control chickens. Moreover, low pecking behavior was significant (p < 0.05) in transported chickens compared to control, particularly on day one of the experiment. In addition, the mean values of glucose, TWBCs, monocytes, basophils, eosinophils, H/L ratio, Hb, MCHC, and PLT were significantly higher (p < 0.05) in transported chickens compared to the controls. In addition, TNF-a, IL-1β, IL-2, IL-4, IFN-γ, IL-17, as well as ROS, MDA, cortisol, glucose, and total cholesterol were significantly higher (p < 0.05) in transportation chickens compared to control, while CAT, GSH, ATP, and SOD were significantly lower (p < 0.05) in transportation chickens compared to control. We conclude that the traditional transportation of indigenous Sudanese chickens affected their welfare, and this was associated with farmers’ low perceptions of chicken welfare, and stress-induced blood profile changes. Full article

Rapid population aging in developing countries has intensified demand for accessible nursing home services, yet spatial disparities in service distribution remain insufficiently examined in secondary cities. This study investigates spatial distribution and multi-dimensional accessibility of nursing homes in Kunming, China, using comprehensive spatial analytical methods to inform sustainable urban development. We analyzed 205 nursing homes with 47,600 beds, evaluating spatial distribution patterns, economic accessibility, and spatial accessibility across different transportation modes. Our analysis reveals a pronounced monocentric pattern with nursing resources concentrated within central urban districts, creating a “primary core-multiple satellite” structure and spatial mismatch between service supply and older adult population needs. A distinct institutional dichotomy exists between publicly and privately operated facilities, establishing a dual-track system with different accessibility implications for social equity. Economic accessibility analysis demonstrates significant barriers in central urban and tourism-oriented districts dominated by higher-priced private facilities, where minimum prices frequently exceed average monthly pension. Spatial accessibility remains inadequate across all transportation modes, with only 24.3% of communities achieving normal or higher accessibility via private car, 21.5% via public bus, and merely 13.9% via walking. These limitations primarily stem from insufficient service capacity (34 beds per 1000 older adults) relative to demographic needs rather than transportation constraints. We recommend three sustainable interventions: implementing demand-based planning mechanisms, establishing progressive pricing policies, and developing older adult-friendly transportation networks. This framework supports sustainable urbanization by promoting spatial equity and efficient resource allocation, providing valuable insights for secondary cities pursuing sustainable development goals. Full article

Decarbonizing air transport is a major challenge in the global energy transition since electrification is not yet feasible. Sustainable aviation fuel (SAF) is a promising solution because it can reduce CO₂ emissions without major infrastructure changes. This study proposes a decentralized model for producing SAF in Spain through the gasification of residual lignocellulosic biomass followed by a refinement process using Fischer–Tropsch (FT) synthesis. The model uses underexploited agricultural residues such as cereal straw, vine pruning, and olive pruning, converting them into syngas in medium-scale facilities situated near biomass sources. The syngas is then transported to a central upgrading unit to produce SAF compliant with ASTM D7566 standards. The following two configurations were evaluated: one with a single gasification plant and upgrading unit and another with three gasification plants supplying one central FT facility. Energy yields, capital and operational expenditures (CAPEX and OPEX), logistic costs, and the levelized cost of fuel (LCOF) were assessed. Under a conservative scenario using one-third of the available certain types of biomass from three regions of Spain, annual SAF production could reach 517.6 million liters, with unit costs ranging from 1.63 to 1.24 EUR/L and up to 47,060 tonnes of CO₂ emissions avoided per year. The findings support the model’s technical and economic viability and its alignment with circular economy principles and climate policy goals. This approach offers a scalable and replicable pathway for decarbonizing the aviation sector using local renewable resources. Full article

One practical approach to reduce GHG emissions is to shift from driving to modes with lower emissions, such as cycling. One key component of supporting cycling is the quality and quantity of cycling infrastructure. This study analyzes the relationship between the quality (or comfort) and quantity of bicycle infrastructure, the likelihood of cycling, and the emissions. The first objective of this study is to analyze the influence of various variables on cycling choice using an interpretable ensemble learning approach. Second, a scenario-based analysis is applied to examine the influence of various policy scenarios (related to cycling infrastructure) on the transportation life cycle GHG emissions. Using origin–destination survey data from Montreal and Laval, Canada, policy modelling results suggest that without current cycling infrastructure, cycling mode share would be 5.3% less, driving mode share would be 4% higher, and GHG emissions would be 10.2% higher among all trips of a reasonable cycling distance starting from home. Then, policy scenarios modelling for this subset of trips suggests that improving the quality of bikeways, increasing their quantity, and reducing the trip distances by 25% can reduce the GHG emissions by 3.9%, 6.6%, and 29.3%, and increase the number of cycling trips by 8.1%, 14%, and 24.4%, respectively. Full article

This study explores the potential for reducing CO₂ emissions in China’s express freight sector by promoting a modal shift from air and road transport to high-speed rail (HSR) through the implementation of a carbon tax policy. A bi-level optimization model is employed to analyze the decision-making processes of three key stakeholders: the government, HSR operators, and shippers. The government aims to maximize consumer surplus while reducing CO₂ emissions through a carbon tax policy; HSR operators seek to maximize transportation profit; and shippers select the most efficient transportation mode based on cost and service considerations. A solution algorithm combining particle swarm optimization, the CPLEX solver, and a custom convergence procedure is designed to solve the bi-level programming model and determine the optimal carbon tax rate. The findings from the Beijing–Shanghai corridor case study indicate that a well-designed carbon tax policy, when integrated with robust HSR services, can effectively encourage a modal shift towards HSR. The extent of emission reduction is influenced by both the capacity of HSR infrastructure and the stringency of the carbon tax policy. This research highlights the importance of addressing asymmetries in transportation mode preferences and market demands. The integration of carbon tax policies with HSR services not only mitigates emissions but also promotes greater symmetry and efficiency within the transportation network. Full article

Mesoscale eddies have a significant influence on primary productivity and upper-ocean variability, particularly in stratified and monsoon-driven basins like the Bay of Bengal (BoB). This study analyzes mesoscale eddies in the BoB from January 2010 to March 2020 using post-processed and gridded daily sea surface height anomaly (SLA) data from the Copernicus Marine Environment Monitoring Service. We used a hybrid detection method combining the Okubo–Weiss parameter and SLA contour analysis to identify 1880 anticyclonic and 1972 cyclonic eddies. Cyclonic eddies were mainly found in the western BoB along the east Indian coast, while anticyclonic eddies were less frequent in this area. Analysis of eddy lifespans revealed that short-lived (1-week) eddies were nearly equally distributed between anticyclonic (48.81%) and cyclonic (51.19%) types. However, for longer-lived eddies, cyclonic eddies became more prevalent, comprising 83.33% of 30-week eddies. A notable, consistent eddy presence was observed east of Sri Lanka, influencing the East India Coastal Current. Most eddies (91%) propagated west/southwestward along the western slope of the Andaman Archipelago, likely influenced by ocean currents and coastal topography, with concentrations in the Andaman Sea and central BoB. These patterns suggest significant interactions between eddies, coastal upwelling zones, and boundary currents, impacting nutrient transport and marine ecosystem productivity. This study contributes valuable insights into the dynamics of ocean circulation and the impacts of eddies, which can inform fisheries management strategies, advance climate resilience measures, expand scientific knowledge, and guide policies related to conservation and sustainable resource utilization. Full article

In recent years, self-autonomous intelligent transportation applications such as drones and autonomous vehicles have seen rapid development and deployment across various countries. Within the domain of artificial intelligence, self-autonomous agents are defined as software entities capable of independently operating drones in an intelligent transport system (ITS) without human intervention. The integration of these agents into autonomous vehicles and their deployment across distributed cloud networks have increased significantly. These systems, which include drones, ground vehicles, and aircraft, are used to perform a wide range of tasks such as delivering passengers and packages within defined operational boundaries. Despite their growing utility, practical implementations face significant challenges stemming from the heterogeneity of network resources, as well as persistent issues related to security, privacy, and processing costs. To overcome these challenges, this study proposes a novel blockchain-enabled self-autonomous intelligent transport system designed for drone workflow applications. The proposed system architecture is based on a remote method invocation (RMI) client–server model and incorporates a serverless computing framework to manage processing costs. Termed the self-autonomous blockchain-enabled cost-efficient system (SBECES), the framework integrates a client and system agent mechanism governed by Q-learning and deep-learning-based policies. Furthermore, it incorporates a blockchain-based hash validation and fault-tolerant (HVFT) mechanism to ensure data integrity and operational reliability. A deep reinforcement learning (DRL)-enabled adaptive scheduler is utilized to manage drone workflow execution while meeting quality of service (QoS) constraints, including deadlines, cost-efficiency, and security. The overarching objective of this research is to minimize the total processing costs that comprise execution, communication, and security overheads, while maximizing operational rewards and ensuring the timely execution of drone-based tasks. Experimental results demonstrate that the proposed system achieves a 30% reduction in processing costs and a 29% improvement in security and privacy compared to existing state-of-the-art solutions. Full article

Achieving decoupling between tourism economic growth and tourism carbon emissions is of paramount importance. This study innovatively integrates the geographically weighted regression (GWR) model—a tool for analyzing spatial heterogeneity—into the Tapio decoupling framework to examine the dynamic decoupling relationship between tourism growth and carbon emissions. It further investigates the driving factors behind decoupling evolution, their interactions, and precisely characterizes the mechanisms, directions, pathways, and intensities of these drivers. Key findings reveal an M-shaped fluctuation trend in tourism carbon emissions within the study area, with significant variations in emission shares across different tourism sectors and transportation modes. Spatially, carbon emissions exhibit heterogeneity and negative autocorrelation, where inter-regional disparities diminish while intra-regional disparities intensify. The tourism economic system in the Yangtze River Economic Belt (YREB) transitioned through weak decoupling, expansive negative decoupling, and strong decoupling states, eventually stabilizing at weak decoupling. Regional decoupling states varied markedly, suggesting that some areas require exploration of new low-carbon development paradigms. For sustainable tourism development, policy-makers should prioritize the decoupling relationship between tourism emissions and economic growth. Region-specific policies must be formulated to facilitate low-carbon transitions, promote industrial upgrading, and enhance inter-regional collaboration—ultimately advancing sustainable tourism under carbon neutrality goals. Full article

Obesity has emerged as one of the most significant public health challenges of the 21st century, with its prevalence increasing at an alarming rate globally. While individual factors such as diet and physical inactivity are well-known contributors, the built environment, particularly housing, plays a critical yet understudied role in shaping obesity-related behaviors. This study examines the multilayered relationship between housing and obesity, focusing on built and neighborhood environment, affordability, and the social environment. Poor housing quality, such as overcrowding and inadequate ventilation, can potentially lead to chronic stress and sedentary behaviors, while housing design influences physical activity through characteristics such as design features and outdoor spaces. Housing location affects access to amenities such as parks and healthy food options, with disparities in access contributing to obesity in low-income areas. Similarly, neighborhood walkability, influenced by infrastructure and land use, encourages active transportation and recreation. Housing affordability also impacts dietary choices and access to recreational facilities, particularly for low-income families. Moreover, the social environment within housing communities can foster or hinder healthy behaviors through social networks and community engagement. This study emphasizes the need for health-conscious urban planning and policies that address these housing-related factors to combat obesity and promote healthier lifestyles. By integrating these Obesity–Housing Nexus, policymakers can create environments that support physical activity, healthy eating, as well as overall health and well-being. Full article

Tropospheric ozone (O₃), a secondary pollutant of mounting global concern, emerges from complex, nonlinear photochemical reactions involving nitrogen oxides (NO_x) and volatile organic compounds (VOCs) under dynamically evolving meteorological conditions. Accurately characterizing and effectively regulating O₃ formation necessitates not only precise and multi-dimensional precursor observations but also modeling frameworks that are structurally coherent, chemically interpretable, and sensitive to regime variability. Despite significant technological progress, current research remains markedly fragmented: observational platforms often operate in isolation with limited vertical and spatial interoperability, while modeling paradigms—ranging from mechanistic chemical transport models (CTMs) to data-driven machine learning approaches—frequently trade interpretability for predictive performance and struggle to capture regime transitions across heterogeneous environments. This review provides a dual-perspective synthesis of recent advances and enduring challenges in the VOC–O₃ research landscape. We first establish a typology of ground-based, airborne, and satellite-based VOC monitoring systems, evaluating their capabilities, limitations, and roles within a vertically structured sensing architecture. We then examine the evolution of O₃ modeling strategies, from empirical and semi-mechanistic models to hybrid frameworks that integrate physical knowledge with algorithmic flexibility. By diagnosing the structural decoupling between observation and inference, we identify key methodological bottlenecks and advocate for a system-level redesign of the VOC–O₃ research paradigm. Finally, we propose a forward-looking framework for next-generation atmospheric governance—one that fuses cross-platform sensing, regime-aware modeling, and policy-relevant diagnostics into an integrated, adaptive, and chemically robust decision-support system. Full article

This article provides an overview of current hydrogen technologies used in road transport, with particular emphasis on their potential for decarbonizing the mobility sector. The author analyzes both fuel cells and hydrogen combustion in internal combustion engines as two competing approaches to using hydrogen as a fuel. He points out that although fuel cells offer higher efficiency, hydrogen combustion technologies can be implemented more quickly because of their compatibility with existing drive systems. The article emphasizes the importance of hydrogen’s source—so-called green hydrogen produced from renewable energy sources has the greatest ecological potential. Issues related to the storage, distribution, and safety of hydrogen use in transport are also analyzed. The author also presents the current state of refueling infrastructure and forecasts for its development in selected countries until 2030. He points to the need to harmonize legal regulations and to support the development of hydrogen technologies at the national and international levels. He also highlights the need to integrate the energy and transport sectors to effectively utilize hydrogen as an energy carrier. The article presents a comprehensive analysis of technologies, policies, and markets, identifying hydrogen as a key link in the energy transition. In conclusion, the author emphasizes that the future of hydrogen transport depends not only on technical innovations, but above all on coherent strategic actions and infrastructure investments. Full article

As China’s urbanization deepens, the spatial structure of residential areas and land use patterns has undergone profound transformations, with the differentiation of housing prices emerging as a key indicator of urban spatial dynamics and socioeconomic stratification. This study examines the spatial and temporal evolution of residential housing prices in Qingdao’s main urban area over a 20-year period, using data from three representative years (2003, 2013, and 2023) to capture key stages of change. It employs Local Indicators of Spatial Association (LISA) spatial and temporal path and leap analyses, as well as Geographically and Temporally Weighted Regression (GTWR) modeling. The results show that Qingdao’s housing price patterns exhibit distinct spatiotemporal heterogeneity, characterized by multi-level transitions, leapfrog dynamics and strong spatial dependence. The urban center and coastal zones demonstrate positive synergistic growth, while some inland and peripheral areas show negative spatial coupling. Evident is the spatial restructuring from a monocentric to a polycentric pattern, driven by shifts in industrial layout, policy incentives, and transportation infrastructure. Key driving factors, such as community attributes, locational conditions, and amenity support, show differentiated impacts across regions and over time. Business agglomeration and educational resources are primary positive drivers in central districts, whereas natural environments and commercial density play a more complex role in peripheral areas. These findings provide empirical evidence to inform our understanding of housing market dynamics and offer insights into urban planning and the design of equitable policies in transitional urban systems. Full article

In recent years, the evolution of cyber-attacks has exposed critical vulnerabilities in conventional defense mechanisms, particularly across national infrastructure systems such as power, transportation, and finance. Attackers are increasingly deploying persistent and sophisticated techniques to exfiltrate or manipulate sensitive data, surpassing static defense methods that depend on known vulnerabilities. This growing threat landscape underscores the urgent need for more advanced and adaptive defensive strategies to counter continuously evolving attack vectors. To address this challenge, this paper proposes a novel reinforcement learning-based optimization framework integrated with a Dynamic Heterogeneous Redundancy (DHR) architecture. Our approach uniquely utilizes reinforcement learning for the dynamic scheduling of encryption-layer configurations within the DHR framework, enabling adaptive adjustment of defense policies based on system status and threat progression. We evaluate the proposed system in a simulated adversarial environment, where reinforcement learning continuously adjusts encryption strategies and defense behaviors in response to evolving attack patterns and operational dynamics. Experimental results demonstrate that our method achieves a higher defense success rate while maintaining lower defense costs, thereby enhancing system resilience against cyber threats and improving the efficiency of defensive resource allocation. Full article

Air pollution poses growing challenges to public health and urban mobility in Southeast Asia. This study investigates how air quality crises affect travel mode choices in Chiang Rai, Thailand, a secondary city experiencing seasonal PM2.5 smog episodes. A structured online survey was conducted with 406 respondents, collecting paired data on travel behaviors during non-air quality crisis (N-AQC) and air quality crisis (AQC) periods. Using a multinomial logit model (MNL), key socioeconomic and trip-related variables were analyzed to estimate mode choice probabilities. The results reveal significant behavioral shifts during an air quality crisis, with private car usage increasing from 30.30% to 34.70% and motorcycle usage decreasing from 50.20% to 42.90%. Multinomial logit models attained correct classification rates of 67.5% and 63.8%, with pseudo R² values exceeding 0.50 for both periods. These findings highlight how environmental stress alters travel behavior, especially among younger and low-income populations. The study contributes new insights from a Southeast Asian urban context, emphasizing the need for adaptive transport policies, protective infrastructure, and equity-focused interventions to promote sustainable mobility during an environmental crisis. Full article

The shift toward sustainable aviation has accelerated research into hybrid electric aircraft, particularly in the context of regional air mobility. To support this transition, we introduce the Soft Fixed Route Hybrid Electric Aircraft Charging Problem with Variable Speed (S-FRHACP-VS), a novel optimization problem for managing hybrid electric aircraft operations that considers variable speed. The objective is to minimize total costs by determining charging strategies, refueling decisions, hybridization ratios, and speed decisions while adhering to a soft schedule. This paper introduces an iterative variable-based fixation heuristic, named Iterative Two-Stage Mixed-Integer Programming Heuristic (ITS-MIP-H), that alternatively optimizes speed and hybridization ratios while considering the soft schedule constraints, nonlinear charging, and nonlinear energy consumption functions. In addition, a metaheuristic genetic algorithm is proposed as an alternative optimization approach. Experiments on ten realistic flight instances demonstrate that optimizing speed leads to an average cost reduction of 7.64% compared to the best non-speed-optimized model, with reductions of up to 18.64% compared to an all-fuel-based heuristic. Although genetic algorithm provides a viable alternative that performs better than the best non-speed-optimized model, the proposed iterative variable-based fixation heuristic approach consistently outperforms the metaheuristic, achieving the best solutions within seconds. These results provide new insights into the integration of hybrid electric aircraft within transportation networks, contributing to advancements in aircraft routing optimization, energy-efficient operations, and sustainable aviation policy development. Full article