Search Results (905)

With the deployment and application of the Fifth-Generation (5G) mobile communication technologies and the ongoing research and development of the Sixth-Generation (6G) mobile communication technologies, the space–air–ground–sea integrated network has become the core development vision for future communications. As aerial nodes, Unmanned Aerial Vehicles (UAVs) can be applied in a wide range of scenarios, including emergency rescue, surveying and mapping, environmental monitoring, and communication coverage enhancement. In terms of communication coverage enhancement, the space–air–ground integrated network, with UAVs as a key component, can provide seamless communication coverage for the full-domain three-dimensional space such as remote areas, deserts, and oceans. Benefiting from advantages such as low cost and high flexibility, UAVs have become a critical research focus, and the one-hop Base Station (BS)–relay UAV–slave UAV architecture for communication coverage enhancement has emerged as an important development direction. However, the high mobility and wide coverage characteristics of UAVs also pose significant synchronization challenges. Aiming at the uplink synchronization problem on the sidelink between slave UAVs and the relay UAV, a two-step random-access scheme based on Asynchronous Non-Orthogonal Multiple Access (A-NOMA) is designed to mitigate the Doppler Frequency Offset (DFO), improve access efficiency, reduce resource consumption, and accommodate the asynchrony among different users. This scheme leverages the existing preamble sequences of the Physical Random Access Channel (PRACH) and realizes DFO estimation in combination with the pairing index. On this basis, a Successive Interference Cancellation (SIC) algorithm based on DFO and phase compensation is designed to complete the demodulation of user data. For the downlink synchronization problem on the sidelink between slave UAVs and the relay UAV, the frequency offset estimation performance is improved by redesigning the resource allocation scheme of the Sidelink Synchronization Signal Block (S-SSB). Meanwhile, considering the energy constraint of UAVs, a downsampling-based detection scheme is designed to reduce UAV power consumption, and a full-link algorithm is developed to support the practical implementation of the proposed scheme. Full article

Artificial sweeteners have emerged as contaminants of increasing concern due to their widespread consumption, environmental persistence, and resistance to conventional wastewater treatment. This review provides an integrated assessment of global research trends and the environmental behavior of major artificial sweeteners, including sucralose, acesulfame potassium, saccharin, and aspartame. Bibliometric analysis of SCOPUS-indexed publications reveals rapid growth in research since 2010, with key themes focusing on environmental occurrence, treatment technologies, and ecotoxicological effects. These compounds are frequently detected in wastewater effluents, surface waters, groundwater, and even drinking water systems, driven by their high solubility and limited biodegradability. Their persistence raises concerns regarding ecological impacts, including potential alterations to microbial communities and aquatic organisms. In addition, emerging evidence suggests potential human health implications, including gut microbiota disruption, metabolic effects, and risks associated with chronic low-dose exposure, although these remain poorly understood. The performance of existing treatment technologies, including biological processes, adsorption, advanced oxidation, and membrane filtration, is critically evaluated, highlighting limitations in complete removal and in the formation of transformation products. Future research should prioritize sustainable treatment strategies, comprehensive risk assessment, and improved monitoring frameworks to better address both environmental and human health risks associated with artificial sweeteners. Full article

In millimeter wave V2I communication systems, accurate beam prediction is crucial for optimizing network performance and improving signal transmission efficiency. Traditional beam prediction methods mainly rely on single-modal data, which often fails to capture the comprehensive environmental information required for high accuracy prediction. In contrast, multi-modal approaches leverage complementary information from different data sources and offer a more promising solution. However, many existing fusion methods primarily depend on real-time sensory inputs and do not fully exploit stable environmental features in V2I scenarios, limiting the effective use of each modality. To address these limitations, this paper proposes a environment-aware proactive beam prediction method based on a multi-modal prior mask map (MMPMM), which integrates offline mapping with an online beam prediction network. Specifically, the method fuses information from images, point clouds, positions, and the MMPMM to predict the optimal beam index. The MMPMM provides channel-related prior information by extracting static V2I scene features offline without incurring any additional online measurement overhead. Experimental results on real-world datasets demonstrate that the proposed method achieves a Top-3 beam prediction accuracy of up to 71.23% while maintaining stable performance under the evaluated dynamic and degraded conditions, demonstrating its effectiveness in the considered scenarios. Full article

Microbial bio-inoculants have been proposed as management tools to enhance crop performance under variable environmental conditions; however, their effectiveness is often influenced by site-specific factors. This study evaluated the effects of bio-inoculation on seed germination and seedling vigor of pea under osmotic stress induced by polyethylene glycol (PEG 6000), and its interaction with two fertilization levels (75% and 100% of the recommended dose) under field conditions in the Amazonas region of Peru. Under laboratory conditions, germination percentage remained high across all treatments (93.3–100%) and was not affected by bio-inoculation or osmotic potential; however, osmotic stress altered germination dynamics, increasing mean germination time from 1.85–2.09 days at 0 MPa to 2.26–2.43 days at −0.8 MPa, while germination synchrony and seedling vigor decreased as stress increased. The seedling vigor index reached maximum values at −0.2 MPa (4.47–5.29) and declined at −0.8 MPa (1.50–2.00), and multivariate analyses showed that variation in germination responses was mainly associated with germination timing and vigor rather than seed viability. Under field conditions, no significant effects of fertilization level, microbial bio-inoculation, or their interaction were detected on agronomic traits or yield, although variability between locations was observed; plant height ranged from 38.5–46.3 cm in Lamud and from 100.6–108.3 cm in Molinopampa, while grain yield varied from 698–1846 kg/ha and 8771–9919 kg/ha, respectively. Overall, environmental conditions exerted a stronger influence than microbial bio-inoculation on germination dynamics and field productivity, while the findings provide practical guidance for improving pea production with bio-inoculants and optimized fertilization. Full article

Soil health is essential for food security, ecosystem stability, and sustainable development, yet its spatial heterogeneity and driving mechanisms remain insufficiently understood at regional scales. This study investigates soil health in Heilongjiang Province, China. A Soil Health Index (SHI) was constructed using eight indicators covering physical, chemical, and biological properties based on multi-source datasets at 1 km spatial resolution. A random forest (RF) model was applied to identify key environmental drivers, and Moran’s I and Getis–Ord Gi* statistics were used to analyze spatial clustering. The results showed that SHI values ranged from 0.19 to 0.70, with a mean of 0.45. The RF model achieved strong performance (R² = 0.6666, RMSE = 0.03184, MAE = 0.02372), significantly outperforming linear regression (R² ≈ 0.17). Significant spatial clustering was observed, where “hotspots” refer to statistically significant clusters of high SHI values, and “coldspots” indicate clusters of low SHI values based on Getis–Ord Gi* analysis. Climate factors (temperature and precipitation) and elevation were the dominant drivers. Significant spatial clustering was observed, with clear hotspot and coldspot patterns. These findings provide spatial evidence for sustainable land-use planning and zonal soil management. However, the analysis is limited by data resolution and model interpretability, which may affect the representation of fine-scale variability. Full article

The valorization of agro-industrial residues for solid biofuel production represents a sustainable strategy to meet energy demand, reduce open-field burning, and mitigate environmental impacts. This study aimed to assess the feasibility of producing high-quality briquettes from agro-industrial residues such as oregano stems, pecan shells, and peanut shells sourced from a semi-arid region of northern Mexico. Raw materials were obtained from local industries, processed, and characterized through proximate analysis and determination of higher heating value (HHV). Briquettes were manufactured under various compaction pressures and temperatures without the use of binders, and their physical and energy properties were evaluated according to international standards. Results indicated that all briquette types met Class A quality standards, with moisture contents between 6 and 9%, ash contents below 6%, and HHVs ranging from 18.9 to 21.0 MJ kg⁻¹. Pecan shell briquettes exhibited the highest particle density (1.18 g cm⁻³), while peanut shell briquettes demonstrated superior mechanical performance and the highest Impact Resistance Index (97% and 200, respectively). Oregano stem briquettes showed lower densities but maintained satisfactory energy properties (19.5 MJ kg⁻¹). Beyond its energy potential, this valorization approach contributes to local economic development, reduces environmental pollution, and decreases dependence on firewood in rural communities. Full article

High-intensity interval training (HIIT) has emerged as a time-efficient exercise strategy with potential benefits for older adults. However, evidence regarding its effects on functional fitness, body composition, and quality of life in older women remains limited. This randomized controlled trial included community-dwelling older women allocated to a HIIT group or a control group. The intervention consisted of a 65-week HIIT program (3 sessions/week), while the control group maintained usual activities. Functional fitness was assessed using standardized field-based tests, body composition was evaluated by bioelectrical impedance analysis, and quality of life was measured using the WHOQOL-BREF questionnaire. Pre- and post-intervention assessments were performed under standardized conditions. Data were analyzed using mixed ANOVA models, with significance set at p < 0.05. Compared with the control group, the HIIT group significantly improved aerobic capacity (2MST: +25.4 vs. −19.6 repetitions; p < 0.001), lower-limb strength (30s CST: +4.8 vs. −2.6 repetitions; p < 0.001), and mobility (TUG: −0.3 vs. +0.4 s; p < 0.001). Body composition improved with reductions in body fat percentage (−1.8% vs. +1.9%; p < 0.001) and visceral fat index (−0.6 vs. +0.3; p < 0.001), alongside increased total body water (+2.3% vs. −1.8%; p < 0.001). Quality of life improved significantly in physical, psychological, and environmental domains (p < 0.001). HIIT was associated with improvements in functional fitness, body composition, and quality of life, with no major adverse events reported. These findings support the use of HIIT as a practical intervention to enhance health and functional independence in aging populations. Full article

The rapid growth of avocado production in Mexico has intensified concerns about balancing economic competitiveness with environmental sustainability, especially regarding water use and production intensification. This study introduces the Integrated Competitiveness and Sustainability Composite Index for Avocado (ICSCA), a multidimensional indicator that evaluates the structural performance of avocado production systems across Mexican states. The index combines environmental efficiency, economic productivity, and socio-productive stability, using state level data from 2014–2024. The ICSCA was calculated and examined through principal component analysis, regression models, and spatial visualization. Results show strong heterogeneity among producing regions. States like Michoacan and Jalisco achieve high productivity and economic output but exert significant pressure on water resources, while others maintain more balanced sustainability profiles with moderate productivity; furthermore, spatial patterns reveal clear regional gradients in sustainability performance. Robust tests, including out of sample temporal validation, expanding window validation, and sliding window analysis, demonstrate high predictive consistency across analysis periods. Overall, the ICSCA provides a robust tool for assessing the interactions between productivity, environmental efficiency, and social structural stability, supporting evidence-based policy design and regional planning for more sustainable avocado production. Full article

Interlayer bond strength is critical for ensuring the safety and durability of 3D-printed concrete (3DPC) structures. However, there remains a lack of real-time prediction methods addressing interlayer performance under the combined effects of interval time and environmental factors during the in situ printing process. To address this issue, this study conducted experiments considering various printing interval times and environmental conditions, incorporating monitoring of dielectric constant and water evaporation, alongside interlayer splitting tensile tests. By integrating the SHAP interpretability algorithm with nonlinear regression analysis, the results indicate that the printing interval time is the dominant factor inducing interlayer strength decay (with a contribution rate of 68.6%), while relative humidity emerges as the primary environmental variable (with a contribution rate of 21.3%). Mechanism analysis reveals that prolonged printing intervals intensify the hydration of the lower deposited layer, leading to reduced interfacial moisture content and loss of plasticity. Furthermore, environmental evaporation significantly regulates this process, with high-humidity environments notably mitigating the moisture loss and strength reduction caused by time delays. Based on the correlation mechanism between moisture and strength, a dimensionless general prediction model for 3DPC interlayer strength was established, incorporating printing interval time and an evaporation index (goodness of fit, R² = 0.96). Consequently, a digital twin quality inversion scheme based on companion specimen monitoring and printing timestamps was proposed. This study quantifies the intrinsic relationships among printing interval time, environmental conditions, and interlayer strength, offering a novel approach for determining the construction window and achieving non-destructive quality prediction for 3DPC in complex environments. Full article

Ensuring uninterrupted access to essential medicines in public healthcare systems is a persistent challenge with clinical, economic, and environmental implications. Oncology services are particularly vulnerable to stockouts, which compromise therapeutic continuity and increase reliance on urgent procurement with high carbon and waste footprints. This study proposes a risk-informed, data-driven framework for pharmaceutical inventory governance in a high-complexity public oncology hospital in Chile, aligning with sustainability goals and green supply chain principles. Using operational data from 2023–2024, we integrate descriptive analytics, ABC–XYZ segmentation, and a continuous-review (s, Q) policy extended through a Logistic Risk Index (LRI) that consolidates demand variability, supply performance, and clinical-economic criticality. Empirical analysis reveals strong expenditure concentration in AX/AY segments and significant misalignment between institutional and analytically derived parameters. A Monte Carlo simulation N = 1000 runs per scenario) compares baseline, adjusted, and fully risk-informed policies under stochastic demand and lead-time conditions. Results show that the risk-informed configuration reduces stockout exposure by up to 46%, improves fill rates (93.1% → 96.4%), and shortens replenishment delays, while maintaining total logistic cost stability. Critically, urgent orders decrease from 27.4 to 14.8 per year, avoiding an estimated 630 kg CO₂ emissions and 25 kg of packaging waste annually. These findings demonstrate that resilience, efficiency, and sustainability are not competing objectives but can be jointly achieved through integrated analytics and governance. The proposed approach offers a scalable blueprint for public health systems seeking to transition from reactive inventory management toward anticipatory, transparent, and sustainability-oriented decision-making, contributing to SDG 3 (health and well-being) and SDG 12 (responsible consumption and production). Full article

Titanium dioxide (TiO₂) is a versatile material that exhibits a high refractive index, strong light-scattering capability, effective UV-absorption, wide band gap semiconductor behavior (3.0–3.2 eV), and excellent chemical stability. Owing to this unique combination of properties, TiO₂ is widely used in applications such as cosmetic and healthcare products, architectural and automotive coatings, and photocatalytic degradation of environmental pollutants. In photocatalytic applications, the crystal structure, phase composition and electronic properties of TiO₂ play a critical role in determining its performance. In the present study, TiO₂ nanotubes were synthesized by anodization of Ti° coatings deposited via a semi-industrial arc-PVD process. A post-anodization heat treatment was carried out at 430 °C for 1 h to promote the formation of the anatase phase within the TiO₂ nanotube structures. The structural characterization of the synthesized film was performed using X-ray diffraction (XRD) and Rietveld refinement. This methodology enabled the identification of the formed oxide phases, structure, and crystalline, confirming the formation of mixed oxides in the coating. To address the difficulty of refinement of these crystalline phases, the Le Bail method was applied. This refinement strategy allowed the identification of the crystalline phases that are present in the Ti_xO_y coating, including a hexagonal structure characteristic of α-Ti (space group P63/mmc, No. 194), the tetragonal anatase TiO₂ (space group I41/amd, No. 141) phase, and the trigonal Ti₂O₃ phase (space group R-3/c No. 167). Key crystallographic parameters such as lattice constants, bond lengths and angles, crystallite sizes, unit cell distortion and electron density were systematically evaluated for each phase. In addition, the Wyckoff positions and interatomic distances of the constitutive atoms were calculated, providing a comprehensive description of the TiO₂+Ti₂O₃/Ti° crystallographic system. The topographic and surface oxidation states were recorded by using profilometry and X-ray photoelectron spectroscopy, respectively. Full article

High-spatiotemporal-resolution precipitation data are essential for studies on regional hydrology, meteorology, and ecological conservation. Because the Qaidam Basin is a data-scarce region with a few ground stations and coarse-resolution remote sensing products, its utility in regional research is constrained. Therefore, high-resolution precipitation data are urgently needed. Here, longitude, latitude, the normalized difference vegetation index (NDVI), the digital elevation model (DEM), daytime and nighttime land surface temperature, slope, and aspect were selected as environmental variables. Four machine learning methods, Artificial Neural Network (ANN), Cubist, Random Forest (RF), and Support Vector Machine (SVM), were used to downscale Tropical Rainfall Measuring Mission (TRMM) precipitation data from 25 to 1 km in the Qaidam Basin and validated using ground observation stations. For annual downscaling, the accuracy ranked as Cubist > ANN > RF > SVM, and residual correction further improved performance. The Cubist model produced the best results, generating finer spatial patterns and reducing outliers in both annual and monthly products. Longitude, latitude, the DEM, and the NDVI were important contributors to the Cubist model. The resulting high-resolution dataset provides valuable support for hydrological and climate change research in the Qaidam Basin. Full article

Utilizing the InVEST coastal exposure model and multi-source geospatial data, this study evaluates coastal vulnerability to sea-level rise along a critical stretch of the North Coast of Central Java, Indonesia, specifically focusing on the Semarang, Demak, and Jepara regions. A Coastal Exposure Index (CEI) was constructed for 256.63 km of shoreline by integrating key environmental variables, including wave climate, high-resolution coastal topography, shoreline geomorphology, bathymetry, coastal habitat distribution, and observed sea-level rise trends-based satellite altimetry from AVISO. The CEI classified coastal segments into five risk categories from Very Low to Very High exposure. A comparative analysis was performed between a scenario incorporating coastal habitats and a scenario without habitats to determine the protective role of natural ecosystems. The results of the analysis show that the average sea-level rise in the study area is 4.3 mm/year. Moreover, the findings also show that the inclusion of coastal habitats significantly reduces extreme exposure levels. Without accounting for habitats, 22.8% of the coastline was classified as Very High exposure, whereas with habitats included this portion dropped to 1.8%. For example, in Jepara Regency the length of shoreline in Very High exposure class decreased from 53.7% (no habitat scenario) to 5.5% when habitats were considered. Overall, the presence of coastal ecosystems shifted large stretches of the coast to lower exposure classes. This study demonstrates that natural habitats have a critical influence on coastal exposure, substantially mitigating the vulnerability of North Java’s coastline to sea-level rise. Full article

Gross primary production (GPP) models are widely used to estimate carbon fluxes at local and global scales, and play a crucial role in understanding the dynamics of terrestrial carbon cycling. While numerous studies have compared the performance of various GPP models, most evaluations rely on in situ GPP derived from eddy covariance flux towers, which may be constrained by estimation uncertainties and limited spatial representativeness. In this study, we employed a three-dimensional (3D) simulation framework characterized by high accuracy and strong environmental controllability to evaluate the performance of GPP models of varying complexity (FvCB, MOD17, VPM, and MVPM) under different leaf area index (LAI) levels and environmental stress conditions. The results revealed significant differences among the models at both instantaneous and daily scales. Under high-temperature stress, the performance of VPM was most comparable to that of MOD17. FvCB and MOD17 exhibited strong consistency in their sensitivity to environmental variations, whereas MVPM generally produced lower GPP estimates but showed the highest responsiveness to environmental changes. The process-based FvCB model was the most sensitive to canopy structure and light distribution, and its resilience to environmental stress increased with LAI. These findings provide a novel methodological perspective for evaluating GPP models and offer important insights into the structural and mechanistic factors driving performance differences among the models. Full article

Background: Tuberculosis (TB) remains a major global health challenge, with Mycobacterium tuberculosis (M. tuberculosis) causing significant morbidity and mortality mainly in high-burden countries. Following exposure to M. tuberculosis, individuals may become infected, developing TB infection (TBI) through inhalation of the bacillus: this affects approximately one-fourth of the global population and serves as a critical reservoir for potential disease reactivation and transmission. The risk of being infected with M. tuberculosis is shaped by bacterial load of people with TB, contact patterns, environmental factors, and host susceptibility, particularly in high-risk congregate settings. Elucidating these determinants is instrumental for optimising TB prevention and control strategies. Methods: A preliminary PubMed search was conducted on 25 August 2024, using the keywords “latent tuberculosis infection,” “risk factors,” and “systematic review.” Targeted reviews were then performed in November 2024 to examine factors influencing progression from exposure to M. tuberculosis to TBI. Systematic reviews published between January 2000 and November 2024 were included. Results: The scoping review analysed eight systematic reviews, grouping findings into three key themes: (1) proximity and behavioural risk factors; (2) environmental risk factors; and (3) host immune vulnerabilities. Close contact with people with TB in crowded settings, such as dormitories, healthcare facilities, and prisons, was strongly associated with an elevated risk of TBI. Healthcare workers travelling from low- to high-incidence regions faced the highest risk due to frequent exposure to M. tuberculosis, while military personnel and general travellers had lower risks. Environmental exposures, including second-hand smoke and inadequate ventilation, further heightened susceptibility among children and adults. Host immune risk factors, such as advanced age, low body mass index, lack of BCG vaccination, and metabolic disorders such as diabetes, markedly increase susceptibility to TBI. The interplay between proximity, behavioural and environmental risk factors, and host immune vulnerabilities highlights the multifactorial nature of TBI risk. Conclusion: Effective TBI control demands a multifaceted approach, combining robust infection prevention and control measures, comorbidity management, and mitigation of behavioural risk factors like smoking. Tailored strategies are crucial for high-risk settings such as healthcare facilities and prisons. Multisectoral collaboration is essential to address key risk factors and protect vulnerable populations from progressing to TBI. Full article