Search Results (2,314)

Background/Objectives: Multiple sclerosis (MS) often leads to gait impairments, even in early stages, and can affect autonomy and quality of life. Traditional assessment methods, while widely used, have been criticized because they lack sensitivity to subtle gait changes. This scoping review aims to map the landscape of advanced gait analysis technologies—both wearable and non-wearable—and evaluate their application in detecting, characterizing, and monitoring possible gait dysfunction in individuals with MS. Methods: A systematic search was conducted across PubMed and Scopus databases for peer-reviewed studies published in the last decade. Inclusion criteria focused on original human research using technological tools for gait assessment in individuals with MS. Data from 113 eligible studies were extracted and categorized based on gait parameters, technologies used, study design, and clinical relevance. Results: Findings highlight a growing integration of advanced technologies such as inertial measurement units, 3D motion capture, pressure insoles, and smartphone-based tools. Studies primarily focused on spatiotemporal parameters, joint kinematics, gait variability, and coordination, with many reporting strong correlations to MS subtype, disability level, fatigue, fall risk, and cognitive load. Real-world and dual-task assessments emerged as key methodologies for detecting subtle motor and cognitive-motor impairments. Digital gait biomarkers, such as stride regularity, asymmetry, and dynamic stability demonstrated high potential for early detection and monitoring. Conclusions: Advanced gait analysis technologies can provide a multidimensional, sensitive, and ecologically valid approach to evaluating and detecting motor function in MS. Their clinical integration supports personalized rehabilitation, early diagnosis, and long-term disease monitoring. Future research should focus on standardizing metrics, validating digital biomarkers, and leveraging AI-driven analytics for real-time, patient-centered care. Full article

Coastal marine ecosystems are facing serious ecological risks from metals pollution, threatening biodiversity and human health. The main objective of this study is to evaluate the spatial distributions and ecological risks of dissolved cadmium (Cd), lead (Pb), and copper (Cu) in the Shandong Peninsula coastal areas, China. Two sampling campaigns were conducted at 21 sites in early spring 2025 to measure the concentrations of the three trace metals in the study area using an electrochemical detection system. The results revealed higher metals concentrations in nearshore areas (e.g., port entrances, aquaculture zones, and estuaries). Specifically, the Cd, Pb, and Cu concentrations in the study area ranged from 0 to 0.079 µg L⁻¹, 0.30 to 0.84 µg L⁻¹, and 2.19 to 4.79 µg L⁻¹, with average concentrations of 0.033, 0.55, and 3.18 µg L⁻¹, respectively. The contamination factors (C_f) of the three metals were below 1, indicating low pollution levels and thus meeting China’s Class I seawater quality standard. However, the ecological risk assessment, employing complementary methods, revealed varying interpretations: the risk quotient (RQ), based on species sensitivity distribution and predicted no-effect concentrations (PNECs), indicated low risks associated with Cd and Pb (RQ < 0.1) but a high risk for Cu (RQ > 1) at all sites, attributable to the exceedance of Cu’s protective threshold (0.46 µg L⁻¹), despite its low C_f. These findings highlight the need for continuous monitoring of Cu due to its high ecological impacts. In contrast, the Hakanson potential ecological risk index (ERI), which incorporates toxicity coefficients, suggested overall low risks (ERI < 150) for the combined metals; however, Cd contributed approximately 70% to the ERI due to its high toxicity coefficient, warranting attention despite the low individual Erⁱ values for Cd across the study area. This study provides valuable recent data on metals pollution dynamics in the Shandong Peninsula coastal areas, offering a scientific basis for developing marine pollution control policies and sustainable marine resource management. Full article

Under the context of global climate change, the frequent occurrence of extreme low-temperature events poses a severe challenge to plant distribution and ecosystem stability. The arid and semi-arid regions of Northwestern China, as a sensitive response area to global change, have proven to possess significant development potential with their unique desert vegetation systems. This study focuses on the ecological adaptability mechanisms of Nitraria tangutorum Bobr., a key species of the desert ecosystem in Northwestern China, and systematically analyzes the evolution patterns of its geographical distribution under the coupled effects of climate change and human activities through a MaxEnt model. The research conclusions are as follows: (i) This study constructs a Human Footprint-MaxEnt (HF-MaxEnt) coupling model. After incorporating human footprint variables, the AUC value of the model increases to 0.914 (from 0.888), demonstrating higher accuracy and reliability. (ii) After incorporating human footprint variables, the predicted area of the model decreases from 2,248,000 km² to 1,976,000 km², with the High Suitability experiencing a particularly sharp decline of up to 79.4%, highlighting the significant negative impact of human disturbance on Nitraria tangutorum. (iii) Under the current climate baseline period, solar radiation, precipitation during the wettest season, and mean temperature of the coldest month are the core driving factors for suitable areas of Nitraria tangutorum. (iv) Under future climate scenarios, the potential distribution area of Nitraria tangutorum is significantly positively correlated with carbon emission levels. Under the SSP370 and SSP585 emission pathways, the area of potential distribution reaches 172.24% and 161.3% of that in the current climate baseline period. (v) Under future climate scenarios, the distribution center of potential suitable areas for Nitraria tangutorum shows a dual migration characteristic of “west–south” and “high altitude”, and the mean temperature of the hottest month will become the core constraint factor in the future. This study provides theoretical support and data backing for the delineation of habitat protection areas, population restoration, resource management, and future development prospects for Nitraria tangutorum. Full article

The transition towards sustainable agri-food systems requires understanding factors influencing innovation adoption across agri-food companies. This systematic literature review, following PRISMA methodology, examines innovation types, their intended purposes, and adoption determinants among worldwide stakeholders. Data were extracted from Scopus and Web of Science databases using rigorous selection criteria, covering publications from January 2014 to January 2025. From 775 initial records, 80 publications were selected for quantitative analysis, of these 74 empirical studies included in qualitative analysis. Innovations were categorized based on ecological, economic, social, and institutional purposes, revealing ecological purpose innovations predominated. Subsequently, adoption factors were classified using the tripartite framework based on extrinsic, intrinsic, and intervening variables. Findings reveal developing regions (Sub-Saharan Africa and Asia) representing 65% of studies. Agriculture sector dominated research attention, with cereals as the most investigated value chain, reflecting their fundamental role in global food security and nutrition. Analysis demonstrates that adoption decisions result from complex interactions between external structural conditions, individual psychological factors, and support mechanisms. Results underscore the context-dependent nature of innovation adoption and the need for context-sensitive, multi-stakeholder approaches facilitating sustainable and digital food system transformations. Full article

Revealing the trade-offs, synergies, and driving mechanisms among land use functions is essential for mitigating conflicts between functions, optimizing territorial spatial patterns, and providing policy support for regional sustainable development. Taking the Central Yunnan Urban Agglomeration as a case study, this study adopts a grid-based evaluation unit and employs a multi-model fusion approach to systematically analyze the interaction mechanisms among land use functions. By integrating the Pearson correlation method and root mean square deviation (RMSD) model, the trade-off and synergy relationships and their spatiotemporal evolution were quantitatively assessed. The XGBoost–SHAP model and optimized parameter-based geographical detector (OPGD) were introduced to identify the nonlinear characteristics and interaction effects of influencing factors on land use function trade-offs and synergies. In addition, a geographically weighted regression (GWR) model was used to explore spatial heterogeneity in these effects. The results indicate that (1) from 2010 to 2020, the overall synergy between production and ecological functions (PF&EF) in the urban agglomeration was enhanced, while trade-offs between production and living functions (PF&LF) intensified, and the trade-off intensity between living and ecological functions (LF&EF) decreased. Significant spatial heterogeneity exists among land use function interactions: PF&EF and PF&LF trade-offs are concentrated in the central and eastern parts of the urban agglomeration, while LF&EF trade-offs are more scattered, mainly occurring in highly urbanized and ecologically sensitive areas; (2) the dominant factors influencing land use function trade-offs and synergies include precipitation, slope, land use intensity, elevation, NDVI, Shannon diversity index (SHDI), distance to county centers, and distance to expressways; (3) these dominant factors exhibit strong nonlinear effects and significant threshold responses in shaping trade-offs and synergies among land use functions; and that (4) compared with the OLS model, the GWR model demonstrated higher fitting accuracy. This reveals that the impacts of natural, socio-economic, and landscape pattern factors on land use function interactions are characterized by pronounced spatial heterogeneity. Full article

Background/Objectives: The ability to preserve cognitive health in aging populations increasingly relies on early detection and intervention in neurodegenerative processes. Spatial memory, a fundamental cognitive ability supporting navigation, environmental awareness, and daily independence, often deteriorates in the preclinical stages of neurodegenerative diseases. However, conventional assessment tools frequently lack ecological validity and fail to capture the multifaceted nature of spatial cognition in real-world contexts. This systematic review aims to examine the application of immersive technologies, specifically Immersive Virtual Reality (VR) and Mixed Reality (MR), in the evaluation and rehabilitation of spatial memory. Methods: Following PRISMA guidelines, a total of 42 peer-reviewed studies were selected from SCOPUS, Web of Science, and PubMed databases. We included original, peer-reviewed studies that assessed spatial memory or cognition using VR/MR in adults aged ≥50 or clinical populations at neurodegenerative risk and reported quantitative data or diagnostic validity. A narrative synthesis was performed to examine the most employed immersive tools, assessing their benefits, limitations, and practical challenges. Results: Findings indicate substantial variability in diagnostic sensitivity, ecological validity, and user engagement across platforms. Nevertheless, the evidence supports the potential of immersive environments as effective tools for the early detection of spatial disorientation and cognitive decline, particularly in at-risk populations such as individuals with Mild Cognitive Impairment and Alzheimer’s Disease. Conclusions: Immersive and semi-immersive VR technologies represent a promising advancement in spatial memory assessment and rehabilitation, offering scalable solutions for both clinical and home-based interventions in aging populations. Full article

Vegetation–heat flux feedbacks have a great influence on ecosystems, but the interaction between them is still unclear. This is particularly critical in ecologically fragile areas, where plant growth is especially sensitive to land–atmosphere interactions that help plants withstand environmental pressures. To the causal relationship between vegetation and heat flux under different topographies on the Tibetan Plateau, we improved the Granger causality model to handle nonstationary scenarios, enabling us to uncover previously unknown interaction patterns between unstable vegetation change and heat fluxes. Further sensitivity analysis was performed to assess the strength of causal influences. The results showed that the sensible heat (SH) and latent heat (LH) fluxes were increasing at rates of 0.28 W·m⁻²·decade⁻¹ and 0.105 W·m⁻²·decade⁻¹, respectively. The interaction between them on vegetation change depends on terrains, at low elevations below 3000 m and high elevations of 5000–6000 m, SH and LH jointly regulate vegetation growth of shady and gentle to moderate slopes, predominantly involving dense grasslands, but the influence of SH is stronger. While at middle elevations of 3000–5000 m and on steep slopes, LH and vegetation of all types interact to form an intensive local energy cycle. Conversely, vegetation change also influences heat flux. Below 6000 m (excluding the 2000–3000 m), vegetation only regulates LH, and this influence appears largely independent of terrain, contributing to energy redistribution and water cycle maintenance in these regions. These interactions suggest that vegetation plays a central role in shaping energy distribution on the plateau, maintaining the water cycle, and regulating climate in alpine regions by regulating heat flux. Full article

Surface subsidence caused by coal mining activities generates diverse wetland ecosystems. These newly formed wetlands exhibit distinct environmental characteristics due to variations in subsidence age, resulting in divergent biological communities. While species adapt to environmental changes through specific functional trait combinations, the response of aquatic community functional diversity to environmental gradients across chronosequences of mining subsidence wetlands remains unclear. This study investigated 13 coal mining subsidence wetlands (1–18 years) of macrozoobenthos in Jining, China. Through seasonal monitoring, we analyzed functional traits along with taxonomic and functional diversity patterns. Initial-stage wetlands were dominated by medium-sized (63.9%) and tegument-respiring taxa, whereas late-stage wetlands exhibited a shift toward large-sized (43.9%) and gill-respiring groups. Both species richness and functional richness declined over time, with taxonomic diversity demonstrating greater sensitivity to subsidence age. Seasonal community variability was more pronounced in initial-stage wetlands (1–4 years post-subsidence). Despite increasing habitat heterogeneity with subsidence age, functional redundancy maintains ecosystem stability. The shared origin and developmental trajectory of these wetlands may constrain functional divergence. Current research predominantly relies on traditional taxonomic metrics, whereas our findings emphasize functional trait analysis’s importance for ecosystem assessment, which provides a theoretical framework for ecological restoration and biodiversity conservation in post-subsidence wetlands. Full article

Understanding and monitoring the phenological phases of trees is essential for ecological research and climate change studies. In this work, we present a comprehensive evaluation of state-of-the-art convolutional neural networks (CNNs) and transformer architectures for the automated classification of the flowering phase of Tilia cordata Mill. (small-leaved lime) based on a large set of real-world images acquired under natural field conditions. The study introduces a novel, automated image quality filtering approach using an XGBoost classifier trained on diverse exposure and sharpness features to ensure robust input data for subsequent deep learning models. Seven modern neural network architectures, including VGG16, ResNet50, EfficientNetB3, MobileNetV3 Large, ConvNeXt Tiny, Vision Transformer (ViT-B/16), and Swin Transformer Tiny, were fine-tuned and evaluated under a rigorous cross-validation protocol. All models achieved excellent performance, with cross-validated F1-scores exceeding 0.97 and balanced accuracy up to 0.993. The best results were obtained for ResNet50 and ConvNeXt Tiny (F1-score: 0.9879 ± 0.0077 and 0.9860 ± 0.0073, balanced accuracy: 0.9922 ± 0.0054 and 0.9927 ± 0.0042, respectively), indicating outstanding sensitivity and specificity for both flowering and non-flowering classes. Classical CNNs (VGG16, ResNet50, and ConvNeXt Tiny) demonstrated slightly superior robustness compared to transformer-based models, though all architectures maintained high generalization and minimal variance across folds. The integrated quality assessment and classification pipeline enables scalable, high-throughput monitoring of flowering phases in natural environments. The proposed methodology is adaptable to other plant species and locations, supporting future ecological monitoring and climate studies. Our key contributions are as follows: (i) introducing an automated exposure-quality filtering stage for field imagery; (ii) publishing a curated, season-long dataset of Tilia cordata images; and (iii) providing the first systematic cross-validated benchmark that contrasts classical CNNs with transformer architectures for phenological phase recognition. Full article

Understanding the spatial distribution and socio-cultural perceptions of threatened raptors is essential for evidence-based conservation in biodiverse yet understudied regions such as Nigeria. This study combines species distribution modelling with community-based surveys to explore the ecological and human dimensions influencing raptor conservation. To investigate the influence of anthropogenic pressures on threatened raptors’ reporting rates, we modelled the relationship between the reporting rate (RR) and two key predictors: the Human Footprint Index and population density. Concurrently, 318 questionnaires were administered across multiple sites to assess public perceptions and attitudes toward raptors. Results indicate that there was a notable reduction in the RR of threatened raptor species with an increase in population density (Estimate = −0.085, SE = 0.028, t = −3.056, p = 0.002). In socio-cultural analyses, sentiment analysis revealed that more than 60% of respondents with higher knowledge of raptors often held more negative perceptions, typically associated with poultry predation and cultural beliefs. In contrast, individuals with limited knowledge frequently exhibited more positive (50%) attitudes. Interestingly, areas with high raptor abundance were associated with more negative community perceptions, suggesting that human–wildlife conflict plays a significant role in shaping attitudes. These findings highlight the complexity of human–raptor interactions and the need for conservation strategies that extend beyond formal protected areas. We advocate for an integrated approach that combines ecological modelling with culturally sensitive education and community-based interventions to foster coexistence and support raptor conservation in Nigeria and similar socio-ecological landscapes. Full article

The content and stoichiometric ratios of plant biogenic elements are key indicators for understanding plants’ ecological traits and their responses to environmental changes. However, it remains unclear how wetland herbaceous plants allocate these biogenic elements and how they relate to soil conditions. This study examines the variations in carbon (C), nitrogen (N), and phosphorus (P) stoichiometry across different organs and life forms, and their response to soil factors in Yellow River Delta wetlands. We analyzed the stoichiometric characteristics of 44 herbaceous species (17 annuals and 27 perennials) and their organs (leaves and stems). The results showed that annual plants show higher N and P but lower C content compared to perennials, indicating distinct life history strategies. In plant organs, leaves exhibited higher C, N, and P concentrations than stems, reflecting functional adaptation. Notably, random forest analysis identified stem C content as a key indicator for life history strategy differentiation. Furthermore, soil factors directly influenced organ-level stoichiometry but showed limited effects across life forms. The plants demonstrated P limitation with high sensitivity to soil P availability. This study provides new insights into organ-specific nutrient allocation strategies in wetland plants and offers valuable guidance for coastal wetland conservation. Full article

Climate change is reshaping plant reproductive processes, particularly at the vulnerable seed germination stage. This study examines the germination responses of four Actinidia species (A. rufa, A. latifolia, A. deliciosa, and A. setosa) under controlled experimental conditions, integrating empirical germination data with classifier modeling to predict species-specific responses under future climate scenarios. Unlike traditional species distribution models (SDMs), our classifier approach incorporates physiological dormancy mechanisms and key environmental cues such as chilling requirements, temperature fluctuations, and drought stress. Results reveal significant interspecific differences: A. rufa exhibited strong ecological plasticity, maintaining stable germination under warming and drought, while A. deliciosa displayed extreme sensitivity to warming, with germination dropping below 25% due to its strict chilling requirement. A. latifolia showed latitude-dependent vulnerability, with southern populations experiencing reduced germination under warming conditions, and A. setosa demonstrated complex dormancy patterns with higher germination at high elevations. The predictive accuracy of our models was validated against long-term field data, underscoring their robustness in forecasting climate-induced germination shifts. These findings highlight the need for targeted breeding programs to develop A. deliciosa cultivars with reduced chilling requirements and suggest A. rufa as a strong candidate for ecological restoration under future warming scenarios. By refining climate impact assessments through physiological modeling, this study provides valuable insights for kiwifruit conservation, agricultural adaptation, and broader plant-climate interactions under global warming. Full article

Forest soil greenhouse gas emissions play a critical role in global climate change. This review synthesizes the mechanisms of temperature change impacts on forest soil greenhouse gas (CO₂, CH₄, N₂O) emissions, the complex response patterns of ecosystems, and existing knowledge gaps in current research. We highlight several critical mechanisms, such as the high temperature sensitivity (Q₁₀) of methane (CH₄) and CO₂ emissions from high-latitude peatlands, and the dual effect of chronic nitrogen deposition, which can cause short-term stimulation but long-term suppression of soil CO₂ emissions. It emphasizes how climatic factors, soil characteristics, vegetation types, and anthropogenic disturbances (such as forest management and fire) regulate emission processes through multi-scale interactions. This review further summarizes the advancements and limitations of current research methodologies and points out future research directions. These include strengthening long-term multi-factor experiments, developing high-precision models that integrate microbial functional genomics and isotope tracing techniques, and exploring innovative emission reduction strategies. Ultimately, this synthesis aims to provide a scientific basis and key ecological threshold references for developing climate-resilient sustainable forest management practices and effective climate change mitigation policies. Full article

Continental ecosystems are crucial constituents of the worldwide carbon process, and their carbon source and sink processes are highly sensitive to human-induced climate change. However, the spatiotemporal changes and principal determinants of carbon source/sink in Shandong Province remain unclear. This study constructs six dominant control modes of carbon sources/sinks based on three carbon sink indicators (gross primary production (GPP), net primary production (NPP), and net ecosystem productivity (NEP)) and three carbon source indicators (autotrophic respiration (Ra), heterotrophic respiration (Rh), and total ecosystem respiration (Rs)), revealing the main control characteristics of the spatiotemporal dynamics of carbon source/sink in the continental ecosystems of Shandong Province. Additionally, the principal determinants of carbon sources and sinks are quantitatively analyzed using cloud models. The research findings are as follows: (1) From 2001 to 2020, the continental ecosystem of Shandong Province demonstrated a weak carbon sink overall, with both carbon sinks and sources showing fluctuating growth trends (growth rate: GPP, NEP, NPP, Rs, Ra, and Rh consist of 15.55, 6.14, 6.09, 9.59, 9.47, and 0.07 gCm⁻²a⁻¹). (2) The dominant control characteristics of carbon source/sink in Shandong Province exhibit significant spatial differentiation, which can be classified into absolute carbon sink cities (Jinan, Zibo, Rizhao, Jining, Liaocheng, Zaozhuang, Binzhou, Dezhou, Tai’an) and relative carbon source cities (Weifang, Yantai, Weihai, Linyi, Qingdao, Heze, and Dongying). GPP is the dominant control factor in carbon sink areas and is widely distributed across the province, while Rs and GPP are the dominant control factors in carbon source fields, focused on the eastern coastal and southwestern inland sites. (3) Landscape modification and rainfall are the main driving elements influencing the carbon sink and source variations in Shandong Province’s continental ecosystems. (4) The spatial differentiation of the driving factors of carbon producers and reservoirs is significant. In absolute carbon sink cities, land-use change and vegetation cover are the dominant factors for carbon sinks and sources, with significant changes in both range and spatial differentiation. In relative carbon source cities, land-use change is the leading factor for carbon source/sink, and the range of changes and spatial differentiation is most notable. The observations from this study supply scientific underpinnings and reference for enhancing carbon sequestration in continental ecosystems, urban ecological safety management, and achieving carbon neutrality goals. Full article

Understanding the reproductive ecology of birds and the factors influencing nest predation is essential for developing scientifically sound and effective bird conservation strategies. Certain pheasant species sensitive to environmental changes are vulnerable to threats and face survival pressures such as habitat destruction and human activities. However, research related to their reproductive ecology is lacking. Here for the first time we reported information on breeding biology of the Chinese francolin (Francolinus pintadeanus). This study was conducted during the breeding seasons of the Chinese francolin in 2021, 2023, 2024, and 2025, combining traditional survey and infrared camera technology to monitor its reproductive ecology and nest predators in the Datian and Bangxi Reserves and to identify its potential predators through artificial nest experiments. All nests were open-ground nests located at the roots of dwarf shrubs and grasses. Our findings revealed that the breeding season of the Chinese francolin was mainly in March–September, peaking in May; its clutch size was 4.09 ± 1.27 (N = 22), reproductive success was 27.27%, and 16 nests were failed; and all failed nests were predated, with abandoned nests accounting for 93.75% of the failed nests. In artificial nest experiments, the predation rates of Datian Reserve and Bangxi Reserve were 70.91% (N = 55) and 60.00% (N = 30), respectively, with no significant difference in predation rates between the fully covered and exposed groups (Datian: χ² = 0.258, p = 0.612; Bangxi: p = 0.710). Natural nest monitoring and artificial nest experiments on the Chinese francolin identified snakes and the small Indian civet (Viverricula indica) as the main predators in Datian Reserve, as well as the greater coucal (Centropus sinensis) and wild boar (Sus scrofa) as potential predators. In contrast, the main predators in Bangxi Reserve were snakes and rodents. These findings indicate differences in nest predator taxa between the two reserves. We recommend prioritizing the restoration of dwarf scrub vegetation and optimizing the habitat management strategy in these reserves to better protect the breeding habitats of pheasants while promoting long-term stability and continuation of their populations. Full article