Search Results (1,208)

With the rapid pace of urbanization, cities are increasingly facing severe challenges related to environmental pollution, ecological degradation, and climate change. Extreme climate events—such as heatwaves, droughts, heavy rainfall, and wildfires—have intensified public concern about sustainability, environmental protection, and low-carbon development. Ensuring environmental preservation while maintaining residents’ quality of life has become a central focus of urban governance. In this context, evaluating green indicators and predicting urban greenness is both necessary and urgent. This study incorporates international frameworks such as the EU Green City Index, the European Green Capital Award, and the United Nations Sustainable Development Goals to assess urban sustainability. The Extreme Gradient Boosting (XGBoost) algorithm is employed to predict the green level of cities and to develop multiple optimized models. Comparative analysis with traditional models demonstrates that XGBoost achieves superior performance, with an accuracy of 0.84 and an F1-score of 0.81. Case study findings identify “Greenhouse Gas Emissions per Person” and “Per Capita Emissions from Transport” as the most critical indicators. These results provide practical guidance for policymakers, suggesting that targeted regulations based on these key factors can effectively support emission reduction and urban sustainability goals. Full article

Various factors influence the formation and adjustment of network freight prices, including transportation costs, cargo characteristics, and policies and regulations. The interaction of these factors increases the difficulty of accurately predicting network freight prices through regressions or other machine learning models, especially when the amount and quality of training data are limited. This paper introduces large language models (LLMs) to predict network freight prices using their inherent prior knowledge. Different data sorting methods and serialization strategies are employed to construct the corpora of LLMs, which are then tested on multiple base models. A few-shot sample dataset is constructed to test the performance of models under insufficient information. The Chain of Thought (CoT) is employed to construct a corpus that demonstrates the reasoning process in freight price prediction. Cross entropy loss with LoRA fine-tuning and cosine annealing learning rate adjustment, and Mean Absolute Error (MAE) loss with full fine-tuning and OneCycle learning rate adjustment to train the models, respectively, are used. The experimental results demonstrate that LLMs are better than or competitive with the best comparison model. Tests on a few-shot dataset demonstrate that LLMs outperform most comparison models in performance. This method provides a new reference for predicting network freight prices. Full article

Rural roads are critical for connecting isolated communities to essential services such as education and health and administrative services, as well as production and market opportunities in low-income countries. More than 70% of movements of people and goods in Sub-Saharan Africa are heavily reliant on rural transport systems, using both motorised but mainly alternative means of transport. However, rural roads often suffer from poor construction due to the use of low-strength, in situ soils and limited financial resources, leading to premature failures and subsequent traffic disruptions with significant economic losses. This study investigates the use of rice husk ash (RHA), a waste byproduct from rice production, as a sustainable supplement to Ordinary Portland Cement (OPC) for soil stabilisation in order to increase durability and sustainability of rural roads, hence limit recurrent maintenance needs and associated transport costs and challenges. To conduct this study, soil samples collected from Mulungushi, Zambia, were treated with combinations of 6–10% OPC and 10–15% RHA by weight. Laboratory tests measured maximum dry density (MDD), optimum moisture content (OMC), and California Bearing Ratio (CBR) values; the main parameters assessed to ensure the quality of road construction soils. Results showed that while the MDD did not change significantly and varied between 1505 kg/m³ and 1519 kg/m³, the OMC increased hugely from 19.6% to as high as 26.2% after treatment with RHA. The CBR value improved significantly, with the 8% OPC + 10% RHA mixture achieving the highest resistance to deformation. These results suggest that RHA can enhance the durability and sustainability of rural roads and hence improve transport systems and subsequently improve socioeconomic factors in rural areas. Full article

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue in recent years, is lacking. The team proposes a traffic prediction method that leverages K-means clustering, long short-term memory (LSTM) neural network, and Fourier transform (FT) for long-term traffic prediction. The proposed method was evaluated on a real-world dataset from the U.S. Travel Monitoring Analysis System (TMAS) database, which enhances practical relevance and potential impact on transportation planning and management. The forecasting performance is evaluated with real-world traffic flow data in the state of California, in the western USA. Results show good forecasting accuracy on traffic trends and counts over a one-year period, capturing periodicity and variation. Full article

As an energy-intensive industry, the aviation sector’s carbon emissions have drawn significant attention. Against the backdrop of the “dual carbon” goals, how to enhance the carbon emission efficiency of airlines has become an urgent issue to be addressed for both industry development and low-carbon targets. This paper constructs an evaluation system for the carbon emission efficiency of airlines and uses the SBM-DDF model under the global production possibility set, combined with the bootstrap-DEA method, to calculate the efficiency values. On this basis, the fuzzy-set qualitative comparative analysis method is employed to analyze the synergistic effects of multiple influencing factors in three dimensions: economic benefits, transportation benefits, and energy consumption on improving carbon emission efficiency. The research findings reveal that, first, a single influencing factor does not constitute a necessary condition for achieving high carbon emission efficiency; second, there are four combinations that enhance carbon emission efficiency: “load volume-driven type”, “scale revenue-driven type”, “high ticket price + technology-driven type”, and “passenger and cargo synergy mixed type”. These discoveries are of great significance for promoting the construction of a carbon emission efficiency system by Chinese airlines and achieving high-quality development in the aviation industry. Full article

The Yangtze River Delta urban agglomeration, characterized by high population density, an advanced transportation system, and a concentration of industrial activity, is one of the regions severely affected by O₃ pollution in central and eastern China. Using data collected from 251 monitoring stations between 2015 and 2025, this paper analyzed the spatio-temporal variation of 8 h O₃ concentrations and instances of exceedance. On the basis of exploring the influence of meteorological factors on regional 8 h O₃ concentration, the potential source contribution areas of pollutants under the exceedance condition were investigated using the HYSPLIT model. The results indicate a rapid increase in the 8 h O₃ concentration at a rate of 0.91 ± 0.98 μg·m⁻³·a⁻¹, with the average number of days exceeding concentration standards reaching 41.05 in the Yangtze River Delta urban agglomeration. Spatially, the 8 h O₃ concentrations were higher in coastal areas and lower in inland regions, as well as elevated in plains compared to hilly terrains. This distribution was significantly distinct from the concentration growth trend characterized by higher levels in the northwest and lower levels in the southeast. Furthermore, it diverged from the spatial characteristics where exceedances primarily occurred in the heavily industrialized northeastern region and the lightly industrialized central region, indicating that the growth and exceedance of 8 h O₃ concentrations were influenced by disparate factors. Local human activities have intensified the emissions of ozone precursor substances, which could be the key driving factor for the significant increase in regional 8 h O₃ concentrations. In the context of high temperatures and low humidity, this has contributed to elevated levels of 8 h O₃ concentrations. When wind speeds were below 2.5 m·s⁻¹, the proportion of 8 h O₃ concentrations exceeding the standards was nearly 0 under almost calm wind conditions, and it showed an increasing trend with rising wind speeds, indicating that the potential precursor sources that caused high O₃ concentrations originated occasionally from inland regions, with very limited presence within the study area. This observation implies that the main cause of exceedances was the transport effect of pollution from outside the region. Therefore, it is recommended that the Yangtze River Delta urban agglomeration adopt economic and technological compensation mechanisms within and between regions to reduce the emission intensity of precursor substances in potential source areas, thereby effectively controlling O₃ concentrations and improving public living conditions and quality of life. Full article

Background: Considering the critical role of physical activity (PA) beginning in early childhood education and the demonstrated validity and reliability of contemporary technological measurement tools, this paper aimed to systematically review and analyze studies comparing PA levels in preschool-aged children during weekdays versus weekend days, using objective technological devices, and highlight what factors correlate with children’s PA. Methods: The search strategy was designed based on the PICOS framework. A systematic review was conducted using two databases (PubMed and Web of Science) to identify studies that included preschool children doing PA during weekdays, measured through technological devices, and compared to PA during weekend days. Study quality was evaluated using the MINORS scale. Results: From 1959 articles, 30 documents met the inclusion criteria, encompassing 32,251 preschool children. Conclusions: The results suggest that preschoolers were generally more active on weekdays than weekends, although it could depend more on contextual or individual factors than on the day of the week. In this sense, parental/maternal behavior (sedentary behavior, shared activities during weekend days, parents’ educational level, and parental/maternal screen time) influences children’s PA level, as well as other factors such as gender, morphology, motor competence level, the type of activity (indoor vs. outdoor), age, meeting PA guidelines, and the community transportation environment. Considering these factors, professionals working in preschools or kindergartens, as well as parents/mothers, should consider these factors to foster children’s PA level from early childhood, which could influence children’s lifespan. Full article

Ornamental fish shipped by road or rail may spend days in transit without food, leading to a reduction in somatic growth after transportation and during acclimatization. In the present study, a time-series (0, 2, 4, 6, 8, 10, and 12 days) experiment was conducted to investigate the growth recovery of male Siamese fighting fish (Betta splendens, 1.56 ± 0.02 g body weight, n = 15 per group) transported by road for two days. Biometric changes, nesting activity, skin pigmentation, digestive enzyme activity, muscle quality, and whole-body composition, were compared across all fish groups. The recovery in growth, as indicated by final body weight, increased with post-transportation time (p < 0.05), causing a significant reversal of weight loss with a proportionally stable condition factor from day 8 until the end of observation (p > 0.05). During this time period, the fish exhibited similar bubble-nest building activity to the control group that was not transported (p > 0.05). Color parameters, digestive enzyme activities, muscle quality, and whole-body composition of fish 8 days after shipping were comparable to the control fish group (p > 0.05). Our findings indicate that an 8-day recovery time is an appropriate protocol for Siamese fighting fish acclimatization following overland shipping. Full article

With the rapid advancement of urbanization, the increasing demand and insufficient supply of public service facilities for children have become urgent problems requiring resolution. This study employs the Shannon diversity index, the location entropy, spatial autocorrelation, and the Geographically Weighted Regression (GWR) to analyze the spatial distribution characteristics and influencing factors of children’s public service facilities in the central urban area of Shenyang. The findings of the study are as follows: (1) There are significant differences in the spatial distribution of children’s public service facilities. Higher quantity distribution and diversity index are observed in the core area and Hunnan District compared to the peripheral areas. The Gini coefficient of various facilities is below the fair threshold of 0.4, but 90.32% of the study units have location entropy values below 1, indicating a supply–demand imbalance. (2) The spatial distribution of various facilities exhibits significant clustering characteristics, with distinct differences between high-value and low-value cluster patterns. (3) The spatial distribution of facilities is shaped by four factors: population, transportation, economy, and environmental quality. Residential area density and commercial service facility density emerge as the primary positive drivers, whereas road density and average housing price act as the main negative inhibitors. (4) The mechanisms of influencing factors exhibit spatial heterogeneity. Positive driving factors exert significant effects on new urban areas and peripheral zones, while negative factors demonstrate pronounced inhibitory effects on old urban areas. Non-linear threshold effects are observed in factors such as subway station density and public transport station density. Full article

The quality and freshness of fruits and vegetables are critical factors in consumer acceptance and are significantly affected during transport and storage. This study aimed to evaluate the quality of greenhouse-grown tomatoes stored for 24 days by combining non-destructive image analysis, spectrophotometric assays (including total phenolic content and antioxidant and antiradical activity assessments), and attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy. Additionally, water activity, moisture content, total soluble solids, texture, and color were evaluated. Most physicochemical changes occurred between days 14 and 17, without major impact on overall fruit quality. A progressive transition in peel hue from orange to dark orange, and increased surface irregularity of their textural image were noted. Moreover, the combined use of instrumental and image analyses results via multivariate analysis allowed the clear discrimination of tomatoes according to storage days. In this sense, tomato samples were effectively classified by ATR-FTIR spectral bands, linked to carotenoids, phenolics, and polysaccharides. Machine learning (ML) models, including Random Forest and Gradient Boosting, were trained on image-derived features and accurately predicted shelf life and quality traits, achieving R² values exceeding 0.9. The findings demonstrate the effectiveness of combining imaging, spectroscopy, and ML for non-invasive tomato quality monitoring and support the development of predictive tools to improve postharvest handling and reduce food waste. Full article

The sharp decline in the population along the northeastern border poses a significant threat to the security of the region, the prosperity of border areas, and the stability of the social economy in our country. Effective management of human and land resources is crucial for the high-quality development of border areas. Taking Changbai County on the northeastern border as an example, based on multi-source data such as land use, the natural environment, climate conditions, transportation location, and social economy from 2000 to 2020, the land use transfer matrix, spatial kernel density, and PLUS model were used to analyze the spatio-temporal evolution characteristics of land use and explore simulation scenarios and optimization strategies under different planning concepts. This study reveals the following: (1) During the study period, the construction land continued to increase, but the growth rate slowed down, mainly transferred from cultivated land and forest land, and the spatial structure evolved from a single center to a double center, with the core always concentrated along the border. (2) The distance to the port (transportation location), night light (social economy), slope (natural environment), and average annual temperature (climate conditions) are the main driving factors for the change in construction land, and the PLUS model can effectively simulate the land use trend under population contraction. (3) In the reduction scenario, the construction land decreased by 1.67 km², the scale of Changbai Town slightly reduced, and the contraction around Malugou Town and Badagou Town was more significant. The study shows that the reduction scenario is more conducive to the population aggregation and industrial carrying capacity improvement of shrinking county towns, which is in line with the high-quality development needs of border areas in our country. Full article

Tourist satisfaction models typically assume that service performance dimensions carry the same weight for all travelers. Drawing on Bourdieu, we reconceptualize age, gender, and region of origin as demographic capital, durable resources that mediate how visitors decode service cues. Using a SERVPERF-based survey of 407 international travelers departing Quito (Ecuador), we test measurement invariance across six sociodemographic strata with multi-group confirmatory factor analysis. The four-factor SERVPERF core (Access, Lodging, Extra-hotel Services, Attractions) holds, yet partial metric invariance emerges: specific loadings flex with demographic capital. Gen-Z travelers penalize transport reliability and safety; female visitors reward cleanliness and empathy; and Latin American guests are the most critical of basic organization. These patterns expose a boundary condition for universalistic satisfaction models and elevate demographic capital from a descriptive tag to a structuring construct. Managerially, we translate the findings into segment-sensitive levers, visible security for youth and regional markets, gender-responsive facility upgrades, and dual eco-luxury versus digital-detox bundles for long-haul segments. By demonstrating when and how SERVPERF fractures across sociodemographic lines, this study intervenes in three theoretical conversations: (1) capital-based readings of consumption, (2) the search for boundary conditions in service-quality measurement, and (3) the shift from segmentation to capital-sensitive interpretation in emerging markets. The results position Ecuador as a critical case and provide a template for destinations facing similar performance–perception mismatches in the Global South. Full article

Due to the recent significant increase in the scale of both domestic and international cargo transportation, the transport sector is becoming an important factor in the country’s economic development. This implies the need to improve all links in the cargo transportation chain. A key role in it is played by logistics centers, which in their activities must meet both state (CO₂ emissions, reduction in road load, increase in transportation safety, etc.) and commercial (cargo transportation in the shortest time and at the lowest cost) requirements. The objective of the paper is freight transportation from China to European countries, reflecting issues of CO₂ emissions, reduction in road load, and increase in transportation safety. Transport operations from the manufacturer to the logistics center are especially important in this chain, since the efficiency of transportation largely depends on the decisions made by its employees. They select the appropriate types of transport (air, sea, rail, and road transport) and routes for a specific situation. In methodology, the analyzed problem can be presented as a dynamic multi-criteria decision model. It is assumed that the decision-maker—the manager responsible for planning transportation operations—is interested in achieving three basic goals: financial goal minimizing total delivery costs from factories to the logistics center, environmental goal minimizing the negative impact of supply chain operations on the environment, and high level of customer service goal minimizing delivery times from factories to the logistics center. The proposed methodology allows one to reduce the total carbon dioxide emission by 1.1 percent and the average duration of cargo transportation by 1.47 percent. On the other hand, the total cost of their delivery increases by 1.25 percent. By combining these, it is possible to create optimal transportation options, effectively use vehicles, reduce air pollution, and increase the quality of customer service. All this would significantly contribute to the country’s socio-economic development. It is proposed to solve this complex problem based on a dynamic multi-criteria model. In this paper, the problem of constructing a schedule of transport operations from factories to a logistics center is considered. The analyzed problem can be presented as a dynamic multi-criteria decision model. Linear programming and the AHP method were used to solve it. 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

Increasing the use of healthcare facilities has resulted in the growing production of biomedical waste, which poses health risks to users, health professionals, and the environment. The aim of this research is to study the correlation between governance in Togo’s public health facilities and the quality of biomedical waste management within these facilities. Methods: This was a cross-sectional, descriptive, and analytical study conducted from September to December 2024. It involved 264 public health facilities of all types in all health regions of Togo. Health facilities were selected using the simple random selection technique. Healthcare providers were selected using the reasoned choice technique. The statistical tests used were the chi-square test and logistic regression, which enabled proportions to be compared and confounding factors to be eliminated, respectively. Results: Multivariate analysis revealed a statistically significant association between the organization and training component of governance and the quality of biomedical waste management (BMWM) in health facilities (OR = 3.79; 95% CI [1.79–8.03]; p < 0.001). This relationship suggests that health facilities with functional infection prevention and control (ICP) or BMWM committees, trained staff at all levels (nursing, technical, and administrative), and dedicated waste management personnel are more likely to implement compliant waste management practices. Analyses of the data also revealed that, among the criteria for assessing the quality of biomedical waste management (BMWM), the most significant were sorting (OR = 1.482; 95% CI [1.286; 1.708]), quantification (OR = 2.026; 95% CI [1.491; 2.753]), transportation (OR = 1.403; 95% CI [1.187; 1.66]), and disposal infrastructure (OR = 1.604; 95% CI [1.298; 1.982]). The application of this grid shows that 17.8% of the health facilities surveyed had a score equal to or above 80% on all the criteria used to assess the quality of biomedical waste management, and they were therefore managing waste in an “acceptable” manner. The study highlights key findings in biomedical waste management practices, providing actionable insights for improving public health safety. Full article