Search Results (913)

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

This literature review provides an overview of the food safety and toxicological characteristics of various heavy metals and metalloids and the public health significance of their occurrence in food. Metals also occur as natural components of the environment, but they can enter food of animal origin and the human body primarily due to anthropogenic (industrial, agricultural, transport-related) activities. The persistent heavy metals (e.g., Hg, Pb, Cd) found in the environment are not biodegradable, can accumulate, and can enter the bodies of higher animals and subsequently, humans, where they are metabolized into various compounds with differing toxicity. Thus, due to their environmental contamination, they can accumulate in living organisms and their presence in the food chain is of great concern for human health. Regulations of the European Community in force lay down maximum levels for a limited number of metals, and the types of regulated foodstuffs of animal origin are also narrower than in the past, e.g., wild game animals and eggs are not included. The regulation of game meat (including offal) deserves consideration, given that it is in close interaction with the environmental condition of a given area and serves as indicator of it. Full article

The mental and physical health of college students, especially in urban environments like Shanghai, is crucial given the high academic and urban stressors, which were intensified by the COVID-19 lockdown. Prior research has shown gender differences in health impacts during public health crises, with females often more vulnerable to mental health issues. Objective: This study aimed to comprehensively assess the physical and psychological health of Chinese college students post-lockdown, focusing on the relationship between stress, anxiety, depression, sleep patterns, and physical health, with a particular emphasis on gender differences. Methods: We conducted a cross-sectional study involving 116 students in Shanghai, utilizing psychological scales (HAMA, IPAQ, PSQI, SDS, FS 14, PSS, SF-36) and physical fitness tests (resting heart rate, blood pressure, hand grip, forced vital capacity, standing long jump, sit-and-reach, one-minute sit-up test and the one-minute squat test, single-leg stand test with eyes closed), to analyze health and behavior during the pandemic lockdown. All students have undergone the same life habits during the pandemic. Results: The HAMA scores indicated no significant levels of physical or mental anxiety. The PSS results (42.45 ± 8.93) reflected a high overall stress level. Furthermore, the PSQI scores (5.4 ± 2.91) suggested that the participants experienced mild insomnia. The IPAQ scores indicated higher levels of job-related activity (1261.49 ± 2144.58), transportation activity (1253.65 ± 987.57), walking intensity (1580.78 ± 1412.20), and moderate-intensity activity (1353.03 ± 1675.27) among college students following the lockdown. Hand grip strength (right) (p = 0.001), sit-and-reach test (p = 0.001), standing long jump (p = 0.001), and HAMA total score (p = 0.033) showed significant differences between males and females. Three principal components were identified in males: HAMA, FS14, and PSQI, explaining a total variance of 70.473%. Similarly, three principal components were extracted in females: HAMA, PSQI, and FS14, explaining a total variance of 69.100%. Conclusions: Our study underscores the complex interplay between physical activity (PA), mental health, and quality of life, emphasizing the need for gender-specific interventions. The persistent high stress, poor sleep quality, and reduced PA levels call for a reorganized teaching schedule to enhance student well-being without increasing academic pressure. Full article

The proliferation of digital public service platforms and the expansion of e-government initiatives have significantly increased the volume and diversity of citizen-generated feedback. This trend emphasizes the need for classification systems that are not only tailored to specific administrative domains but also robust to the linguistic, contextual, and structural variability inherent in user-submitted content. This study investigates the comparative effectiveness of large language models (LLMs) alongside instruction-tuned embedding models in the task of categorizing public transportation complaints. LLMs were tested using a few-shot inference, where classification is guided by a small set of in-context examples. Embedding models were assessed under three paradigms: label-only zero-shot classification, instruction-based classification, and supervised fine-tuning. Results indicate that fine-tuned embeddings can achieve or exceed the accuracy of LLMs, reaching up to 90 percent, while offering significant reductions in inference latency and computational overhead. E5 embeddings showed consistent generalization across unseen categories and input shifts, whereas BGE-M3 demonstrated measurable gains when adapted to task-specific distributions. Instruction-based classification produced lower accuracy for both models, highlighting the limitations of prompt conditioning in isolation. These findings position multilingual embedding models as a viable alternative to LLMs for classification at scale in data-intensive public sector environments. Full article

According to the World Health Organization (WHO), indoor air quality (IAQ) is becoming a serious global concern due to its significant impact on human health. However, not all relevant health parameters are currently regulated. For example, particle number concentration (PNC) and its associated carbonaceous species, such as black carbon (BC), which are classified as carcinogenic by the International Agency for Research on Cancer (IARC), are not currently regulated. Compared with IAQ studies in other types of buildings, studies focusing on IAQ in hospitals or other healthcare facilities are scarce. Therefore, this study aims to evaluate the impact of these outdoor pollutants, among others, on the indoor environment of a hospital under different atmospheric conditions. To identify the seasonal influence, two different periods of two consecutive seasons (summer 2020 and winter 2021) were selected for the measurements. Regulated pollutants (NO, NO₂, O₃, PM₁₀, and PM_2.5) and nonregulated pollutants (PM₁, PNC, and equivalent BC (eBC)) in outdoor air were simultaneously measured indoor and outdoor. This study also investigated the impact of indoor activities on indoor air quality. In the absence of indoor activities, outdoor sources significantly contribute to indoor traffic-related pollutants. Indoor and outdoor (I-O) measurements showed similar behavior, but indoor concentrations were lower, with peak levels delayed by up to two hours. Seasonal variations in indoor/outdoor ($\mathrm{I}/\mathrm{O}$) ratios were lower for particles than for associated gaseous pollutants. Particle infiltration depended on particle size, with it being higher the smaller the particle size. Indoor activities also significantly affected indoor pollutants. PM_x (especially PM₁₀ and PM_2.5) concentrations were mainly modulated by walking-induced particle resuspension. Vertical eBC profiles indicated a relatively well-mixed environment. Ventilation through open windows rapidly altered indoor air quality. Outdoor-dominant pollutants (PNC, eBC, and NO_X) had $\mathrm{I}/\mathrm{O}$ ratios ≥ 1. Staying in the room with an open window had a synergistic effect, increasing the $\mathrm{I}/\mathrm{O}$ ratios for all pollutants. Higher I/O ratios were associated with turbulent outdoor conditions in both unoccupied and occupied conditions. Statistically significant differences were observed between stable (TKE ≤ 1 m² s⁻²) and unstable (TKE > 1 m² s⁻²) conditions, except for NO₂ in summer. This finding was particularly significant when the wind direction was westerly or easterly during unstable conditions. The results of this study highlight the importance of understanding the behavior of indoor particulate matter and related pollutants. These pollutants are highly variable, and knowledge about them is crucial for determining their health effects, particularly in public buildings such as hospitals, where information on IAQ is often limited. More measurement data is particularly important for further research into I-O transport mechanisms, which are essential for developing preventive measures and improving IAQ. Full article

Modern software solutions have a multiplicative effect on enhancing quality of life across various urban sectors, including the environment, education, public health, security, transportation, time efficiency, employment, and other key aspects of city living. This article addresses a specific issue concerning the organisation of leisure activities for both local residents and tourists, using the Chechen Republic as a case study. In response, the study aimed to develop a digital solution to address this challenge, with potential for integration into the Republic’s unified digital ecosystem. By employing system analysis methods, the authors identified the key objects and stakeholders involved in the problem domain. They also defined the software product’s functionality and classified user categories. Using Unified Modelling Language methods, a use case diagram was developed to illustrate the conceptual operation of the system. Furthermore, object-oriented design methods were applied to create a user interface prototype for the software product. As a result, a digital service was developed that enables users to create personalised leisure routes, taking into account individual goals, time constraints, traffic conditions, and the real-time status of urban infrastructure. The resulting software solution is both customisable and scalable. The article also presents selected examples of project development. Full article

Accurately estimating the energy consumption of unmanned aerial vehicles (UAVs) in real-world delivery scenarios remains a critical challenge, particularly when UAVs operate in complex urban environments and are coupled with public transportation systems. Most existing models rely on oversimplified assumptions or static mission profiles, limiting their applicability to realistic, scalable drone-based logistics. In this paper, we propose a physically-grounded and scenario-aware energy sizing methodology for UAVs operating as part of a last-mile delivery system integrated with a city’s bus network. The model incorporates detailed physical dynamics—including lift, drag, thrust, and payload variations—and considers real-time mission constraints such as delivery execution windows and infrastructure interactions. To enhance the realism of the energy estimation, we integrate computational fluid dynamics (CFD) simulations that quantify the impact of surrounding structures and moving buses on UAV thrust efficiency. Four mission scenarios of increasing complexity are defined to evaluate the effects of delivery delays, obstacle-induced aerodynamic perturbations, and early return strategies on energy consumption. The methodology is applied to a real-world transport network in Belfort, France, using a graph-based digital twin. Results show that environmental and operational constraints can lead to up to 16% additional energy consumption compared to idealized mission models. The proposed framework provides a robust foundation for UAV battery sizing, mission planning, and sustainable integration of aerial delivery into multimodal urban transport systems. Full article

This study investigates public perceptions of sustainable mobility within university environments, which are important trip generation hubs with the potential to influence and disseminate sustainable mobility behaviors. Using sentiment analysis on 120,236 tweets from São Paulo, Rio de Janeiro, Lisbon, and Porto, tweets were classified into positive, neutral, and negative sentiments to assess perceptions across transport modes. It was hypothesized that universities would exhibit more positive sentiment toward active and public transport modes compared to perceptions of these modes within the broader city environment. Results show that active modes and public transport consistently receive higher positive sentiment rates than individual motorized modes, and, considering the analyzed contexts, universities demonstrate either similar (São Paulo) or more positive perceptions compared to the overall sentiment observed in the city (Rio de Janeiro, Lisbon, and Porto). Chi-square tests confirmed significant associations between transport mode and sentiment distribution. An exploratory analysis using topic modeling revealed that perceptions around bicycle use are linked to themes of safety, cycling infrastructure, and bike sharing. The findings highlight opportunities to promote sustainable mobility in universities by leveraging user sentiment while acknowledging limitations such as demographic bias in social media data and potential misclassification. This study advances data-driven methods to support targeted strategies for increasing active and public transport in university settings. Full article

The exponential growth of video data across domains such as surveillance, transportation, and healthcare has raised critical challenges in scalability, real-time processing, and privacy preservation. While existing studies have addressed individual aspects of Video Big Data Analytics (VBDA), an integrated, up-to-date perspective remains limited. This paper presents a comprehensive survey of system architectures and enabling technologies in VBDA. It categorizes system architectures into four primary types as follows: centralized, cloud-based infrastructures, edge computing, and hybrid cloud–edge. It also analyzes key enabling technologies, including real-time streaming, scalable distributed processing, intelligent AI models, and advanced storage for managing large-scale multimodal video data. In addition, the study provides a functional taxonomy of core video processing tasks, including object detection, anomaly recognition, and semantic retrieval, and maps these tasks to real-world applications. Based on the survey findings, the paper proposes ViMindXAI, a hybrid AI-driven platform that combines edge and cloud orchestration, adaptive storage, and privacy-aware learning to support scalable and trustworthy video analytics. Our analysis in this survey highlights emerging trends such as the shift toward hybrid cloud–edge architectures, the growing importance of explainable AI and federated learning, and the urgent need for secure and efficient video data management. These findings highlight key directions for designing next-generation VBDA platforms that enhance real-time, data-driven decision-making in domains such as public safety, transportation, and healthcare. These platforms facilitate timely insights, rapid response, and regulatory alignment through scalable and explainable analytics. This work provides a robust conceptual foundation for future research on adaptive and efficient decision-support systems in video-intensive environments. Full article

Tiny Machine Learning (TinyML) extends edge AI capabilities to resource-constrained devices, offering a promising solution for real-time, low-power intelligence in smart cities. This review systematically analyzes 66 peer-reviewed studies from 2019 to 2024, covering applications across urban mobility, environmental monitoring, public safety, waste management, and infrastructure health. We examine hardware platforms and machine learning models, with particular attention to power-efficient deployment and data privacy. We review the approaches employed in published studies for deploying machine learning models on resource-constrained hardware, emphasizing the most commonly used communication technologies—while noting the limited uptake of low-power options such as Low Power Wide Area Networks (LPWANs). We also discuss hardware–software co-design strategies that enable sustainable operation. Furthermore, we evaluate the alignment of these deployments with the United Nations Sustainable Development Goals (SDGs), highlighting both their contributions and existing gaps in current practices. This review identifies recurring technical patterns, methodological challenges, and underexplored opportunities, particularly in the areas of hardware provisioning, usage of inherent privacy benefits in relevant applications, communication technologies, and dataset practices, offering a roadmap for future TinyML research and deployment in smart urban systems. Among the 66 studies examined, 29 focused on mobility and transportation, 17 on public safety, 10 on environmental sensing, 6 on waste management, and 4 on infrastructure monitoring. TinyML was deployed on constrained microcontrollers in 32 studies, while 36 used optimized models for resource-limited environments. Energy harvesting, primarily solar, was featured in 6 studies, and low-power communication networks were used in 5. Public datasets were used in 27 studies, custom datasets in 24, and the remainder relied on hybrid or simulated data. Only one study explicitly referenced SDGs, and 13 studies considered privacy in their system design. Full article

Air quality forecasts play a crucial role in informing the public about atmospheric pollutant levels that pose risks to human health and the environment. The accuracy of these forecasts strongly depends on the quality and resolution of the input data used in the modelling process. At HungaroMet, the Hungarian Meteorological Service, the CHIMERE chemical transport model is used to provide two-day air quality forecasts for the territory of Hungary. This study compares two configurations of the CHIMERE model: the current operational setup, which uses climatological averages from the LMDz-INCA database for boundary conditions, and a test configuration that incorporates real-time boundary conditions from the CAMS global forecast. The primary objective of this work was to assess how the use of real-time versus climatological boundary conditions affects modelled concentrations of key pollutants, including NO₂, O₃, PM₁₀, and PM_2.5. The model results were evaluated against observational data from the Hungarian Air Quality Monitoring Network using a range of statistical metrics. The results indicate that the use of real-time boundary conditions, particularly for aerosol-type pollutants, improves the accuracy of PM₁₀ forecasts. This improvement is most significant under meteorological conditions that favour the long-range transport of particulate matter, such as during Saharan dust or wildfire episodes. These findings highlight the importance of incorporating dynamic, up-to-date boundary data, especially for particulate matter forecasting—given the increasing frequency of transboundary dust events. Full article

The integration of shared autonomous vehicles into historic urban areas presents both opportunities and challenges. In heritage-rich environments like very old Asian (such as Suzhou old town, which serves as a use case example) or European (especially Mediterranean coastal cities) areas—characterized by narrow alleys, dense development, and sensitive cultural landscapes—shared autonomous vehicle adoption raises critical spatial and social questions. This study employs a qualitative, user-centered approach based on the ripple model to examine residents’ perceptions across four dimensions: residential patterns, parking land use, regional accessibility, and street-level infrastructure. Semi-structured interviews with 27 participants reveal five key findings: (1) public trust depends on transparent decision-making and safety guarantees; (2) shared autonomous vehicles may reshape generational residential clustering; (3) the short-term parking demand remains stable, but the long-term reuse of space is feasible; (4) shared autonomous vehicles could enhance accessibility in historic cores; (5) transport systems may evolve toward intelligent, human-centered designs. Based on these insights, the study proposes three strategies: (1) transparent risk assessment using explainable artificial intelligence and digital twins; (2) polycentric development to diversify land use; (3) hierarchical street retrofitting to balance mobility and preservation. While this study is limited by its qualitative scope and absence of simulation, it offers a framework for culturally sensitive, small-scale interventions supporting sustainable mobility transitions in historic urban contexts. Full article

This paper analyzes the factors affecting the willingness to pay of urban households concerned with efficient waste management in Bangladesh. The multistage random sampling approach selected 1400 families from seven major cities in Bangladesh. This study addresses the socioeconomic and environmental factors that influence urban households’ willingness to pay for improved waste management services in Bangladesh. This study uniquely contributes to the literature by providing a large-scale empirical analysis of 1470 households using a logit model, revealing income, education, and environmental awareness as key predictors of WTP. Detailed survey data from respondents were then analyzed using a logit model based on the contingent valuation method. Indeed, the logit model showed that six variables (education, monthly income, value of the asset, knowledge of environment, and climate change) had a statistically significant effect on the WTP of the households. The results show that 63% of respondents were willing to pay BDT 250 or more per month. The most influential factors driving this willingness to pay were income (OR = 1.35), education level (OR = 1.45), and environmental awareness (OR = 3.56). These variables all contribute positively towards WTP. The idea is that families have some socioeconomic characteristics, regardless of which they are ready to pay for a higher level of waste collection. It is recommended that government interference be affected through various approaches, as listed below: support for public–private sector undertaking and disposal, an extensive cleaning campaign, decentralized management, cutting waste transport costs, and privatization of some waste management systems. These could be used to develop solutions to better waste management systems and improve public health. Full article

Agricultural production waste (APW) is characterized by pollution, increasing volume, spatial dispersion, and temporal and spatial variability in its generation. The improper handling of APW poses a growing risk to the environment and public health. This paper focuses on the planning of APW recycling networks, primarily analyzing the selection of temporary storage sites and treatment facilities, as well as vehicle scheduling and route optimization. First, to minimize the required number of temporary storage sites, a set coverage model was established, and an immune algorithm was used to derive preliminary site selection results. Subsequently, the analytic hierarchy process and fuzzy comprehensive evaluation method were employed to refine and determine the optimal site selection results for recycling treatment facilities. Second, based on the characteristics of APW, with the minimization of recycling transportation costs as the optimization objective, an ant colony algorithm was used to establish a corresponding vehicle scheduling route optimization model, yielding the optimal solution for recycling vehicle scheduling and transportation route optimization. This study not only improved the recycling efficiency of APW but also effectively reduced the recycling costs of APW. Full article

Rapid urbanization and climate change pose significant challenges to air quality in arid metropolitan areas, with critical implications for public health and sustainable development. This study projects the evolution of air pollution in Riyadh, Saudi Arabia, through 2070 using an integrated modeling approach that combines CMIP6 climate projections with localized air quality data. We analyzed daily concentrations of major pollutants (SO₂, NO₂) across 15 strategically selected monitoring stations representing diverse urban environments, including traffic corridors, residential areas, healthcare facilities, and semi-natural zones. Climate data from two Earth System Models (CNRM-ESM2-1 and MPI-ESM1.2) were bias-corrected and integrated with historical pollution measurements (2000–2015) using hierarchical Bayesian statistical modeling under SSP2-4.5 and SSP5-8.5 emission scenarios. Our results revealed substantial deterioration in air quality, with projected increases of 80–130% for SO₂ and 45–55% for NO₂ concentrations by 2070 under high-emission scenarios. Spatial analysis demonstrated pronounced pollution gradients, with traffic corridors (Eastern Ring Road, Northern Ring Road, Southern Ring Road) and densely urbanized areas (King Fahad Road, Makkah Road) experiencing the most severe increases, exceeding WHO guidelines by factors of 2–3. Even semi-natural areas showed significant increases in pollution due to regional transport effects. The hierarchical Bayesian framework effectively quantified uncertainties while revealing consistent degradation trends across both climate models, with the MPI-ESM1.2 model showing a greater sensitivity to anthropogenic forcing. Future concentrations are projected to reach up to 70 μg m⁻³ for SO₂ and exceed 100 μg m⁻³ for NO₂ in heavily trafficked areas by 2070, representing 2–3 times the Traffic corridors showed concentration increases of 21–24% compared to historical baselines, with some stations (R5, R13, and R14) recording projected levels above 4.0 ppb for SO₂ under the SSP5-8.5 scenario. These findings highlight the urgent need for comprehensive emission reduction strategies, accelerated renewable energy transition, and reformed urban planning approaches in rapidly developing arid cities. Full article