Search Results (42)

Background: The waist–calf circumference ratio (WCR) is an index that combines waist and calf circumference measurements, offering a potentially effective method for evaluating the imbalance between abdominal fat and leg muscle mass in older adults. Objective: To assess the association between WCR and indicators of body composition, muscle strength, and physical performance in community-dwelling older women. Methods: This was a cross-sectional study involving 133 older women (≥65 years) from an urban-marginal community in Guayaquil, Ecuador. The WCR was categorized into quartiles (Q1: 2.07–2.57; Q2: 2.58–2.75; Q3: 2.76–3.05; Q4: 3.06–4.76). Body indicators included fat-free mass (FFM), skeletal muscle mass (SMM), appendicular muscle mass (ASM), appendicular muscle mass index (ASMI), visceral fat (VF), fat mass (FM), and fat mass index (FMI). Handgrip strength (HGS) and the Short Physical Performance Battery test (SPPB) score were used to assess muscle strength and function, respectively. Results: The median age of the participants was 75 [IQR: 65–82] years. The mean WCR was 2.92 ± 0.93. Statistically significant associations were found between WCR and VF (p < 0.001), WCR and SMM (p = 0.039), and WCR and ASM (p = 0.016). Regarding muscle function, WCR was associated with HGS (p = 0.025) and SPPB score (p = 0.029). Conclusions: A significant association was observed between WCR and body composition, and muscle strength and function in older women. Full article

Background: Fundamental movement skills (FMS)—including muscular strength, endurance, and mobility—are linked to better health-related quality of life and higher physical activity in children. Rural children often score lower on FMS tests than urban peers due to resource limitations rather than ability. Thus, increasing access to FMS activities in under-resourced rural areas is essential. The primary objective was to test the feasibility of Hoosier Strength in a rural middle school sample, and the secondary objective was to observe the preliminary changes in FMS-related outcomes pre- to post-intervention and at follow-up. The exploratory objective was to explore how participants responded to different coaches on the Hoosier Strength coaching team (i.e., gender, coaching style during activities). Methods: This study used a Hybrid Type 3 design to evaluate feasibility and FMS outcomes, integrating qualitative and quantitative data. The four-week intervention included a test group (n = 24; 14 females, 10 males; mean age: females 12.4 ± 0.5 years, males 12.7 ± 0.4 years) and a control group (n = 12; 8 females, 4 males; mean age: females 12.9 ± 0.3 years, males 12.7 ± 0.3 years). Data analysis included descriptive statistics for feasibility indicators (Acceptability of Intervention Measures [AIM], Intervention Appropriateness Measure [IAM], and Feasibility of Intervention Measure [FIM]), linear regression for mobility and muscular endurance changes, t-tests for psychological need satisfaction and frustration, and regression analysis for squat knowledge and post-intervention confidence. Results: (1) There was high feasibility across the 4-week Hoosier Strength intervention and at follow-up; (2) there were no statistically significant changes in squat performance; (3) participants’ confidence in their ability to squat at the end of the intervention was significantly predicted by their squat knowledge at baseline; and (4) participants prioritized leadership and team management over tactical analysis, highlighting a preference for coaches who foster teamwork. Conclusions: The findings offer a transparent approach for evaluating the feasibility and preliminary outcomes of the Hoosier Strength intervention in an under-resourced rural middle school, thereby encouraging further investigation into strength training interventions in rural schools. Full article

Achieving a sustainable energy future is the cornerstone of global efforts to combat environmental degradation and align with corporate social responsibility (CSR) objectives. This study examines the complex relationship between energy consumption, carbon emissions, and the moderating influence of foreign direct investment (FDI) in the E-7 economies of Brazil, China, India, Indonesia, Mexico, Russia, and Türkiye from 2000 to 2022. Employing advanced panel data methodologies, including continuously updated fully modified (Cup-FM) and continuously updated bias-corrected (Cup-BC) techniques, we explored the long-term dynamics of energy use, urbanization, human capital, and FDI. Our findings reveal persistent cointegration among these variables, with energy consumption, urbanization, and human capital significantly contributing to CO₂ emissions. However, FDI has emerged as a critical mitigating factor, exhibiting a negative correlation with carbon emissions and moderating the emission-enhancing effects of urbanization and human capital. These results underscore the dual role of FDI as both an engine of economic growth and a catalyst for environmental sustainability. This study advocates for prioritizing green FDI inflows, particularly in renewable energy infrastructure, to harmonize economic development with global sustainability targets. By integrating CSR strategies with energy transition policies, this study provides actionable insights for policymakers and corporate leaders to foster sustainable development in rapidly industrializing economies. These findings contribute to the broader discourse on sustainable development, emphasizing the need for strategic investments and policy frameworks to achieve a low-carbon future. Full article

The low-light environments under urban viaducts significantly hinder plant growth and development. An in-depth study of the plasticity response mechanisms and survival strategies of plants in these conditions is crucial for selecting appropriate species. This study examined how light intensity affects leaf plasticity in four plants (Ophiopogon japonicus, Pittosporum tobira, Euonymus japonicus, and Ligustrum sinense) under two representative urban viaducts and how they respond to changes in light intensity in Zhengzhou City. The leaf morphology, physiological photosynthesis, and chlorophyll (Chl) fluorescence parameters were analyzed at three light intensities (one natural full-light and two viaduct-shaded low-light environments.): CK (full light), T1 (21.29%–25.99%), and T2 (5.16%–8.20%). The results showed that (1) with decreasing light intensity, most plants showed reductions in leaf thickness (LT), palisade and spongy tissue thickness (PT, ST), net photosynthetic rate (P_n), stomatal conductance (G_s), and F_v/F_m and F_v′/F_m′, while leaf area, Chl content, and malondialdehyde (MDA) content increase, with antioxidant enzyme activity also rising. The photosynthetic indicators of O. japonicus first increased and then decreased. (2) The overall plasticity of the plants ranked from high to low as follows: O. japonicus > E. japonicus > P. tobira > L. sinense. O. japonicus showed the strongest adaptability through comprehensive photosynthetic physiology and antioxidant mechanisms, with a wide light tolerance range. E. japonicus relied more on adjustments in photosynthetic and anatomical structures, as well as leaf area. P. tobira improved light tolerance by modifying leaf area, epidermal structure, and physiological traits. L. sinense had the lowest adaptability, relying on limited antioxidant enzymes and leaf thickness adjustments. (3) In conclusion, plant plasticity is primarily reflected through photosynthetic and physiological traits. High plasticity in these parameters is key for plants to adapt to thrive in dynamic low-light environments. Therefore, when greening viaduct-shaded areas, it is crucial to consider the light environment and the light adaptability range of different plant species. Plants with high photosynthetic and physiological plasticity should be selected to ensure the optimal growth and development of plants in shaded areas. Full article

This study investigates the influence of human capital and natural resource productivity on achieving sustainable cities and society (SDG-11) within the European Union (EU) while also considering the contribution of renewable energy (RE). This research analyzes data from the European Union between 2011 and 2020 by deploying the first-difference generalized method of moments (FM-GMM) model to distinguish between two different effects of the human capital variable—a low effect (negative influence) and a high effect (positive influence). The analysis has identified an optimal threshold value of 1.867 for the human capital index (HCI) score in the context of European Union countries. This threshold value represents a critical point at which the effect of human capital on achieving SDG-11, which aims to make cities and human settlements inclusive, safe, resilient, and sustainable, undergoes a significant shift. The impact of renewable energy consumption on SDG-11 exhibits a non-linear pattern. There is a negative relationship at lower levels of renewable energy adoption (below a certain threshold), with renewable energy negatively impacting SDG-11 progress at a 1% significance level. However, the relationship becomes significantly positive once renewable energy consumption surpasses this threshold. This non-linearity suggests that achieving mass renewable energy adoption is crucial to unlocking its full potential in promoting the sustainable urban development goals captured by SDG-11. The results also demonstrate a positive effect on natural resource productivity both before and after exceeding a specific threshold, although the magnitude of this effect varies. This robust evidence underscores the necessity for targeted policies in the European Union to enhance human capital, increase renewable energy adoption, and boost natural resource productivity, thereby securing sustainable funding mechanisms for SDG-11. Full article

The success of establishing new trees in cities and their subsequent growth depend, among others, on the proper selection of tree species which can easily tolerate the post-planting stress. In the spring of 2023, young Italian alder (Alnus cordata (Loisel.) Duby) and common lime (Tilia × europaea L. ‘Pallida’) trees were planted in a street of heavy traffic in Warsaw. In the summer of 2023, leaf samples were collected during the growing season for chlorophyll a fluorescence measurements and chemical analyses. Additionally, the autumn phenological phases were monitored. Chlorophyll a fluorescence measurements revealed higher values of F_v/F_m, density of reaction centers per cross-section, and electron transport chain efficiency between photosystems II and I, as well as lower energy dissipation rate per active reaction center of photosystem II in A. cordata. Moreover, A. cordata revealed higher chlorophyll a, chlorophyll b, and carotenoid content. The flavonoid and proline content in both species was the highest by the end of July and then decreased. In T. × europea ‘Pallida’, the contents of these stress biomarkers increased in the late growing season. Our results showed that T. × europaea ‘Pallida’ is less resistant to post-planting stress in urban conditions, while A. cordata showed higher resistance to variable weather conditions, high photosynthetic efficiency, and long foliage lifespan. Full article

Climate constraints impose greenhouse gas emissions mitigation, and passenger cars have considerable contributions to contribute to this. To improve the engine efficiency of vehicles equipped with conventional powertrains, many technologies are available but with limited individual contribution. The experimental assessment of some technology regarding fuel economy measurement results is sometimes lower than test uncertainties. This study proposes a methodology to compensate the fuel economy for two test uncertainties: vehicle speed variations and battery recharging. The proposed method can be applied when investigating the effects of different vehicle design changes, including engine power cell design. In this work, the proposed method is demonstrated on the test of two oils: one 5W40, the other 5W20, both without FM. Applying the proposed methodology to experimental results, the expected higher influence of oil viscosity on urban conditions could be observed, and the experimental results presented a much better correlation with the vehicle numerical simulation. Applying the proposed compensation, fuel savings of using the 5W20 in comparison to the 5W40 oil was 3.5% under urban conditions and 2.0% on highways. Full article

Data-driven digital transformation is becoming increasingly relevant. Building information modelling (BIM) and geographic information systems (GIS) are two technologies specific to the construction industry. The two approaches are different, but complementary. In this article, BIM–GIS integration is approached from some of the most relevant aspects, such as standardization or level of detail, and a comparison between both approaches is presented with the aim of improving the operation and maintenance of urban infrastructure. By means of the Madrid Calle 30 ring road as a case study, the integration of the BIM model of the road in a GIS scenario using the IFC and SLPK formats is shown. The information is stored in an external database, which allows updates without modifying the 3D model and facilitates the inclusion of real-time data. The study highlights the challenges of interoperability between BIM and GIS, as well as the need for open standards and software tools that enable a wider implementation in the FM of this type of infrastructure. Full article

Within the current scenario of cropland use and forest surface loss, there is a need for the implementation of viable urban farming systems, e.g., indoor vertical farming (VF). Light management is fundamental in VF, although responses to light spectra are often species-specific. As the interest of consumers and farmers towards baby-leaf vegetables has recently increased, this study aimed at assessing the most effective red:blue (RB) ratio for enhanced baby-leaf production of kale (Brassica oleracea). Within an ebb-and-flow system, increasing RB ratios (RB₃, RB₅, RB₇ and RB₉) were tested, sharing a photoperiod of 16 h day⁻¹ and a light intensity of 215 μmol m⁻² s⁻¹. A larger yield was obtained for plants under RB_5, featuring an intermediate B fraction compared to other treatments, with plants displaying more expanded and thinner leaves. Also, for lighting energy and cultivated surface use efficiency, RB₅ was the most effective treatment, performing up to 57 g FW kWh⁻¹ and 54 kg FW m⁻² y⁻¹, respectively. From multispectral data, a tendency of reduced F_v/F_m and F_q′/F_m′ was observed as the RB ratio increased, while the chlorophyll index was enhanced under RB ≥ 7. This study highlighted the light recipe with an RB ratio of 5 as the most effective lighting mixture for optimal baby-leaf kale production in terms of balanced growth, resource use efficiency and yield. Full article

The Pearl River Estuary (PRE) is one of the world’s most urbanized subtropical coastal systems. It presents a typical environmental gradient suitable for studying estuarine phytoplankton communities’ dynamics and photosynthetic physiology. In September 2018, the maximum photochemical quantum yield (Fv/Fm) of phytoplankton in different salinity habitats of PRE (oceanic, estuarine, and freshwater zones) was studied, revealing a complex correlation with the environment. Fv/Fm of phytoplankton ranged from 0.16 to 0.45, with taxa in the upper Lingdingyang found to be more stressed. Community composition and structure were analyzed using 18S rRNA, accompanied by a pigment analysis utilized as a supplementary method. Nonmetric multidimensional scaling analysis indicated differences in the phytoplankton spatial distribution along the estuarine gradients. Specificity-occupancy plots identified different specialist taxa for each salinity habitat. Dinophyta and Haptophyta were the predominant taxa in oceanic areas, while Chlorophyta and Cryptophyta dominated freshwater. Bacillariophyta prevailed across all salinity gradients. Canonical correlation analysis and Mantel tests revealed that temperature, salinity, and elevated nutrient levels (i.e., NO₃⁻-N, PO₄³⁻-P, and SiO₃²⁻-Si) associated with anthropogenic activities significantly influenced the heterogeneity of community structure. The spatial distribution of phytoplankton, along with in situ photosynthetic characteristics, serves as a foundational basis to access estuarine primary productivity, as well as community function and ecosystem health. Full article

Creeping bentgrass (Agrostis stolonifera) is an excellent cool-season turfgrass that is widely used in urban gardening, landscaping, and golf turf. Triennial field experiments from 2017 to 2019 were conducted to investigate effects of the foliar application of chitosan (CTS), γ-aminobutyric acid (GABA), or sodium chloride (NaCl) on mitigating summer bentgrass decline (SBD) and exploring the CTS, GABA, or NaCl regulatory mechanism of tolerance to summer heat stress associated with changes in chlorophyll (Chl) loss and photosynthetic capacity, osmotic adjustment (OA), oxidative damage, and cell membrane stability. The findings demonstrated that persistent ambient high temperatures above 30 °C during the summer months of 2017, 2018, and 2019 significantly reduced the turf quality (TQ), Chl content, photochemical efficiency of PSII (Fv/Fm and PI_ABS), leaf relative water content, and osmotic potential (OP) but significantly increased electrolyte leakage (EL) and the accumulations of free proline, water-soluble carbohydrate (WSC), hydrogen peroxide (H₂O₂), and malondialdehyde (MDA). The foliar application of CTS, GABA, or NaCl could significantly alleviate SBD, as reflected by improved TQ and delayed Chl loss during hot summer months. Heat-induced declines in Fv/Fm, PI_ABS, the net photosynthetic rate (Pn), the transpiration rate (Tr), and water use efficiency (WUE) could be significantly mitigated by the exogenous application of CTS, GABA, or NaCl. In addition, the foliar application of CTS, GABA, or NaCl also significantly improved the accumulations of free proline and WSC but reduced the EL, OP, and H₂O₂ content and the MDA content in leaves of creeping bentgrass in favor of water and redox homeostasis in summer. Based on the comprehensive evaluation of the subordinate function value analysis (SFVA), the CTS had the best effect on the mitigation of SBD, followed by GABA and NaCl in 2017, 2018, and 2019. The current study indicates that the foliar application of an appropriate dose of GABA, CTS, or NaCl provides a cost-effective strategy for mitigating SBD. Full article

The objective of this study is to examine the impact of climate and technology on forest efficiency (FE) in China’s provinces from 2002 to 2020. First, the study used SBM-data envelopment analysis (SBM-DEA) to estimate Chinese provinces’ FE using multidimensional forest inputs and outputs. The climate influence is assessed using temperature, precipitation, sunlight hours, and carbon dioxide levels in the second phase. A climate index was created using principal component analysis (PCA) for a complete estimation. In addition to prior research, we analyze the technology impact through two technological indicators: (i) research and development, and (ii) investment in forests. Furthermore, we explore the non-linear influence of economic development on both FE and climate quality. The regression study by CupFM and CupBC found that temperature and precipitation increase FE, whereas sunlight hours and carbon emissions decrease it. The positive association observed between Climate Index1, and the negative relationship noted for Climate Index2, suggests that forests positively influence climate conditions, signifying that an improvement in FE leads to an improvement in climate quality. Technology boosts forest productivity and climatic quality. The environmental Kuznets curve shows an inverted U-shape relationship between economic development and FE. Similarly, climate and economic development have an inverted U-shaped EKC relationship. Urbanization reduces FE due to human growth and activity. Our findings are important for forest management, climate change, and sustainable development policymakers and scholars. Full article

In this paper, the urban signal propagation characteristics based on the location of blind sources are investigated. To address the issue of blind electromagnetic radiation sources in complex urban environments, intelligent methods for propagation channel path classification, and model determination are brought forth based on field test data. The intelligent classification method distinguishes between the Line-of-Sight (LoS) path channel and a direct path, the LoS multipath channel with a direct path and other multiple paths, and the Non-Line-of-Sight (NLoS) multipath channel without a direct path from the source to the test point. The modeling aspect determines the model type to which the received signal belongs based on the statistical model derived from the tested data of a specific source. A validation measurement system was constructed for the FM broadcasting band, and validation campaigns were conducted in the city of Wuhan. The process and analysis of the data using this method demonstrate the accurate distinction of the different propagation path channels and models and involve the construction of a statistical model for the FM band in Wuhan’s urban area. Full article

The outbreak of the COVID-19 pandemic has resulted in reduced opportunities for children to engage in fundamental motor skills [FMS]. This prolonged inactivity and restriction of play can have serious consequences for children’s physical and mental health. The purpose of this study was to explore teaching strategies during the pandemic, whether there were differences in children’s motor development, and the differences in the implementation of physical movement courses before and during the pandemic from the perspective of preschool teachers. This study was a retrospective study using an internet survey, and participants comprised 2337 preschool teachers. The statistical methodology of this study included descriptive statistics, the dependent t-test, and the independent t-test. The results showed that regardless of the time, frequency, activity intensity, and frequency of outdoor courses, the results from before the pandemic was better than those taken during the pandemic. Only the “frequency of implementing physical movement courses indoors every week” had not been affected by the pandemic. This study also obtained the performance of “children’s fitness”, “overall performance of physical movement ability”, “stability movement skills”, “locomotor movement skills”, and “manipulative movement skills”. All were better before the pandemic than during the pandemic. During the COVID-19 pandemic, mixed-age classes performed better than same-age classes in terms of frequency, time, intensity, outdoor course implementation, and physical fitness. Public schools performed better than private schools in terms of frequency, time, intensity, outdoor course implementation, and fundamental motor skills performance. Private schools implemented physical movement courses indoors every week, which was more than public schools. Excepting the frequency of implementing physical movement courses indoors every week, fewer than schools with five classes performed better than those who had more than schools with six classes. Finally, rural schools were better than urban schools in the implementation of outdoor courses and fundamental motor skills performance. Therefore, we suggest that in response to the pandemic, teachers should further improve their professionalism and use diversified teaching methods, and guide students to be willing to learn and improve their skill performance. Full article

Building type prediction is a critical task for urban planning and population estimation. The growing availability of multi-source data presents rich semantic information for building type prediction. However, existing residential building prediction methods have problems with feature extraction and fusion from multi-type data and multi-level interactions between features. To overcome these limitations, we propose a deep learning approach that takes both the internal and external characteristics of buildings into consideration for residential building prediction. The internal features are the shape characteristics of buildings, and the external features include location features and semantic features. The location features include the proximity of the buildings to the nearest road and areas of interest (AOI), and the semantic features are mainly threefold: spatial co-location patterns of points of interest (POI), nighttime light, and land use information of the buildings. A deep learning model, DeepFM, with multi-type features embedded, was deployed to train and predict building types. Comparative and ablation experiments using OpenStreetMap and the nighttime light dataset were carried out. The results showed that our model had significantly higher classification performance compared with other models, and the F1 score of our model was 0.9444. It testified that the external semantic features of the building significantly enhanced the predicted performance. Moreover, our model showed good performance in the transfer learning between different regions. This research not only significantly enhances the accuracy of residential building identification but also offers valuable insights and ideas for related studies. Full article