Search Results (2,187)

Vegetation dynamics are complexly influenced by multiple factors such as climate, human activities, and topography. In recent years, the frequency, intensity, and diversity of human activities have increased, placing substantial pressure on the growth of vegetation. Arid and semi-arid regions are particularly sensitive to climate change, and climate change and large-scale ecological restoration have led to significant changes in the dynamic of dryland vegetation. However, few studies have explored the nonlinear relationships between these factors and vegetation dynamic. In this study, we integrated trend analysis (using the Mann–Kendall test and Theil–Sen estimation) and machine learning algorithms (XGBoost-SHAP model) based on long time-series remote sensing data from 2001 to 2020 to quantify the nonlinear response patterns and threshold effects of bioclimatic variables, topographic features, soil attributes, and anthropogenic factors on vegetation dynamic. The results revealed the following key findings: (1) The kNDVI in the study area showed an overall significant increasing trend (p < 0.01) during the observation period, of which 26.7% of the area showed a significant increase. (2) The water content index (Bio 23, 19.6%), the change in land use (15.2%), multi-year average precipitation (pre, 15.0%), population density (13.2%), and rainfall seasonality (Bio 15, 10.9%) were the key factors driving the dynamic change of vegetation, with the combined contribution of natural factors amounting to 64.3%. (3) Among the topographic factors, altitude had a more significant effect on vegetation dynamics, with higher altitude regions less likely to experience vegetation greening. Both natural and anthropogenic factors exhibited nonlinear responses and interactive effects, contributing to the observed dynamic trends. This study provides valuable insights into the driving mechanisms behind the condition of vegetation in arid and semi-arid regions of China and, by extension, in other arid regions globally. Full article

Wearable pressure sensors have emerged as vital tools in personalized monitoring, promising transformative advances in patient care and diagnostics. Nevertheless, conventional devices frequently suffer from limited sensitivity, inadequate flexibility, and concerns regarding biocompatibility. Herein, we introduce silk fibroin, a naturally occurring protein extracted from silkworm cocoons, as a promising material platform for next-generation wearable sensors. Owing to its remarkable biocompatibility, mechanical robustness, and structural tunability, silk fibroin serves as an ideal substrate for constructing capacitive pressure sensors tailored to medical applications. We engineered silk-derived capacitive architecture and evaluated its performance in real-time human motion and physiological signal detection. The resulting sensor exhibits a high sensitivity of 18.68 kPa⁻¹ over a broad operational range of 0 to 2.4 kPa, enabling accurate tracking of subtle pressures associated with pulse, respiration, and joint articulation. Under extreme loading conditions, our silk fibroin sensor demonstrated superior stability and accuracy compared to a commercial resistive counterpart (FlexiForce™ A401). These findings establish silk fibroin as a versatile, practical candidate for wearable pressure sensing and pave the way for advanced biocompatible devices in healthcare monitoring. Full article

In response to the stress commonly experienced by young people in high-pressure daily environments, a music-based stress-relief interactive system was developed by integrating music-assisted care with emotion-sensing technology. The design principles of the system were established through a literature review on stress, music listening, emotion detection, and interactive devices. A prototype was created accordingly and refined through interviews with four experts and eleven users participating in a preliminary experiment. The system is grounded in a four-stage guided imagery and music framework, along with a static activity model focused on relaxation-based stress management. Emotion detection was achieved using a wearable EEG device (NeuroSky’s MindWave Mobile device) and a two-dimensional emotion model, and the emotional states were translated into visual representations using seasonal and weather metaphors. A formal experiment involving 52 users was conducted. The system was evaluated, and its effectiveness confirmed, through user interviews and questionnaire surveys, with statistical analysis conducted using SPSS 26 and AMOS 23. The findings reveal that: (1) integrating emotion sensing with music listening creates a novel and engaging interactive experience; (2) emotional states can be effectively visualized using nature-inspired metaphors, enhancing user immersion and understanding; and (3) the combination of music listening, guided imagery, and real-time emotional feedback successfully promotes emotional relaxation and increases self-awareness. Full article

Many commercial wearable sensor systems typically rely on a single continuous cardiorespiratory sensing modality, photoplethysmography (PPG), which suffers from inherent biases (i.e., differences in skin tone) and noise (e.g., motion and pressure artifacts). In this research, we present a wearable device that provides robust estimates of cardiorespiratory variables by combining three physiological signals from the upper arm: multiwavelength PPG, single-sided electrocardiography (SS-ECG), and bioimpedance plethysmography (BioZ), along with an inertial measurement unit (IMU) providing 3-axis accelerometry and gyroscope information. We evaluated the multimodal device on 16 subjects by its ability to estimate heart rate (HR) and breathing rate (BR) in the presence of various static and dynamic noise sources (e.g., skin tone and motion). We proposed a hierarchical approach that considers the subject’s skin tone and signal quality to select the optimal sensing modality for estimating HR and BR. Our results indicate that, when estimating HR, there is a trade-off between accuracy and robustness, with SS-ECG providing the highest accuracy (low mean absolute error; MAE) but low reliability (higher rates of sensor failure), and PPG/BioZ having lower accuracy but higher reliability. When estimating BR, we find that fusing estimates from multiple modalities via ensemble bagged tree regression outperforms single-modality estimates. These results indicate that multimodal approaches to cardiorespiratory monitoring can overcome the accuracy–robustness trade-off that occurs when using single-modality approaches. Full article

By embedding optical fiber sensors into fiber preforms and utilizing liquid molding processes such as resin transfer molding (RTM), intelligent composite materials with self-sensing capabilities can be fabricated. In the liquid molding process of these intelligent composites, the quality of the final product is highly dependent on the resin flow and impregnation effects. The embedding of optical fibers can affect the microscopic flow and impregnation behavior of the resin; therefore, it is necessary to investigate the specific impact of optical fiber embedding on the resin flow and impregnation of fiber bundles. Due to the difficulty of directly observing this process at the microscopic scale through experiments, numerical simulation has become a key method for studying this issue. This paper focuses on the resin micro-flow in RTM processes for intelligent composites with embedded optical fibers. Firstly, a steady-state analysis of the resin flow and impregnation process was conducted using COMSOL 6.0 obtaining the velocity and pressure field distribution characteristics under different optical fiber embedding conditions. Secondly, the dynamic process of resin flow and impregnation of fiber bundles at the microscopic scale was simulated using Fluent 2022R2. This study comprehensively analyzes the impact of different optical fiber embedding configurations on resin flow and impregnation characteristics, determining the impregnation time and porosity after impregnation under different optical fiber embedding scenarios. Additionally, this study reveals the mechanisms of pore formation and their distribution patterns. The research findings provide important theoretical guidance for optimizing the RTM molding process parameters for intelligent composite materials. Full article

Using LULC change detection analysis, it is possible to identify changes due to urbanization, deforestation, or a natural disaster in an area. As population growth and urbanization increase, real-time solutions for the effects of urbanization on land use are required to assess its implications for food security and livelihood. This study seeks to identify and quantify recent LULC changes in Limbe, Cameroon, and to measure rates of conversion between agricultural, forest, and urban lands between 1986 and 2020 using remote sensing and GIS. Also, there is a deficiency of research employing these data to evaluate the efficiency of LULC satellite data and a lack of awareness by local stakeholders regarding the impact on LULC change. The changes were identified in four classes utilizing maximum supervised classification in ENVI and ArcGIS environments. The classification result reveals that the 2020 image has the highest overall accuracy of 94.6 while the 2002 image has an overall accuracy of 89.2%. The overall gain for agriculture was approximately 4.6 km², urban had an overall gain of nearly 12.7 km², while the overall loss for forest was −16.9 km² during this period. Much of the land area previously occupied by forest is declining as pressures for urban areas and new settlements increase. This study’s findings have significant policy implications for sustainable land use and food security. It also provides a spatial method for monitoring LULC variations that can be used as a framework by stakeholders who are interested in environmentally conscious development and sustainable land use practices. Full article

The ecological environment is crucial for human survival and development. As ecological issues become more pressing, studying the spatiotemporal evolution of ecological quality (EQ) and its driving mechanisms is vital for sustainable development. This study, based on MODIS data from 2000 to 2022 and the Google Earth Engine platform, constructs a remote sensing ecological index for the Beibu Gulf Urban Agglomeration and analyzes its spatiotemporal evolution using Theil–Sen trend analysis, Hurst index (HI), and geographic detector. The results show the following: (1) From 2000 to 2010, EQ improved, particularly from 2005 to 2010, with a significant increase in areas of excellent and good quality due to national policies and climate improvements. From 2010 to 2015, EQ degraded, with a sharp reduction in areas of excellent quality, likely due to urban expansion and industrial pressures. After 2015, EQ rebounded with successful governance measures. (2) The HI analysis indicates that future changes will continue the past trend, especially in areas like southeastern Chongzuo and northwestern Fangchenggang, where governance efforts were effective. (3) EQ shows a positive spatial correlation, with high-quality areas in central Nanning and Fangchenggang, and low-quality areas in Nanning and Beihai. After 2015, both high–high and low–low clusters showed changes, likely due to ecological governance measures. (4) NDBSI (dryness) is the main driver of EQ changes (q = 0.806), with significant impacts from NDVI (vegetation coverage), LST (heat), and WET (humidity). Urban expansion’s increase in impervious surfaces (NDBSI rise) and vegetation loss (NDVI decline) have a synergistic effect (q = 0.856), significantly affecting EQ. Based on these findings, it is recommended to control construction land expansion, optimize land use structure, protect ecologically sensitive areas, and enhance climate adaptation strategies to ensure continuous improvement in EQ. Full article

The global food industry is facing growing pressure to enhance food safety, extend shelf life, minimize waste, and adopt environmentally sustainable packaging solution. Nanotechnology offers innovative ways to meet these demands by enabling the creation of smart and sustainable food packaging systems. Due to their unique properties, nanomaterials can significantly enhance the functional performance of packaging by boosting mechanical strength, barrier efficiency, antimicrobial activity, and responsiveness to environmental stimuli. This review provides a comprehensive overview of nanomaterials used as smart and sustainable food packaging, focusing on their role in active and intelligent packaging systems. By integrating nanomaterials like metal and metal oxide nanoparticles, carbon-based nanostructures, and nano-biopolymers, packaging can now perform real-time sensing, spoilage detection, and traceability. These systems improve food quality management and supply chain transparency while supporting global sustainability goals. The review also discusses potential risks related to nanomaterials’ migration, environmental impact, and consumer safety, as well as the current regulatory landscape and limitations in industrial scalability. Emphasis is placed on the importance of standardized safety assessments and eco-friendly design to support responsible innovation. Overall, nano-enabled smart packaging represents a promising strategy for advancing food safety and sustainability. Future developments will require collaboration across disciplines and robust regulatory frameworks to ensure the safe and practical application of nanotechnology in food systems. Full article

African hydrological systems are incredibly complex and highly sensitive to climate variability. This review synthesizes observational data, remote sensing, and climate modeling to understand the interactions between fluvial processes, water cycle dynamics, and anthropogenic pressures. Currently, these systems are experiencing accelerating warming (+0.3 °C/decade), leading to more intense hydrological extremes and regionally varied responses. For example, East Africa has shown reversed temperature–moisture correlations since the Holocene onset, while West African rivers demonstrate nonlinear runoff sensitivity (a threefold reduction per unit decline in rainfall). Land-use and land-cover changes (LULCC) are as impactful as climate change, with analysis from 1959–2014 revealing extensive conversion of primary non-forest land and a more than sixfold increase in the intensity of pastureland expansion by the early 21st century. Future projections, exemplified by studies in basins like Ethiopia’s Gilgel Gibe and Ghana’s Vea, indicate escalating aridity with significant reductions in surface runoff and groundwater recharge, increasing aquifer stress. These findings underscore the need for integrated adaptation strategies that leverage remote sensing, nature-based solutions, and transboundary governance to build resilient water futures across Africa’s diverse basins. Full article

Ultra-high-temperature and pressure downhole environments pose challenges for conventional electronic instruments to adapt to high-temperature formations, thereby restricting the application of downhole electronic tool technology in deep and ultra-deep wells. Given the aforementioned limitation of electronic inclination measurement systems, specifically their poor temperature resistance, this study proposes a novel shunt flow control method. This method employs a mechanical structure to overcome temperature constraints: gravitational torque generated by the mechanical structure is utilized to control valve opening and regulate flow rate. By converting sensed well inclination information into changes in flow rate, this approach enables the transformation of well inclination sensing and its associated signals. In this study, a kinetic analysis model of the shunt-regulating valve spool was established. Using computational fluid dynamics (CFD) simulations, the flow characteristics of the regulating spool were analyzed under varying valve openings. The structure of the flow control valve was optimized with the goal of maximizing internal flow. Finally, the reliability of the designed structure for well deviation sensing and flow control was verified using simulation experimental studies and theoretical analyses. Full article

Turbidity is a crucial parameter for assessing the ecological health of aquatic ecosystems, particularly in shallow tropical lakes that are subject to climatic variability and anthropogenic pressures. Lake Chapala, the largest freshwater body in Mexico, has experienced persistent turbidity and sediment influx since the 1970s, primarily due to upstream erosion and reduced water inflow. In this study, we utilized Landsat satellite imagery in conjunction with near-synchronous in situ reflectance measurements to monitor spatial and seasonal turbidity patterns between 2023 and 2025. The surface reflectance was radiometrically corrected and validated using spectroradiometer data collected across eight sampling sites in the eastern sector of the lake, the area where the highest rates of horizontal change in turbidity occur. Based on the relationship between near-infrared reflectance and field turbidity, second-order polynomial models were developed for spring, fall, and the composite annual model. The annual model demonstrated acceptable performance (R² = 0.72), effectively capturing the spatial variability and temporal dynamics of the average annual turbidity for the whole lake. Historical turbidity data (2000–2018) and a particular case study in 2016 were used as a reference for statistical validation, confirming the model’s applicability under varying hydrological conditions. Our findings underscore the utility of empirical remote-sensing models, supported by field validation, for cost-effective and scalable turbidity monitoring in dynamic tropical lakes with limited monitoring infrastructure. Full article

Total Knee Arthroplasty (TKA) is a well-established surgical intervention for the management of end-stage knee osteoarthritis. While the procedure is generally successful, postoperative rehabilitation remains a key determinant of long-term functional outcomes. Traditional rehabilitation protocols, particularly those requiring in-person clinical visits, often encounter limitations in accessibility, patient adherence, and personalization. In response, emerging sensor technologies have introduced innovative solutions to support and enhance recovery following TKA. This review provides a thematically organized synthesis of the current landscape and future directions of sensor-assisted rehabilitation in TKA. It examines four main categories of technologies: wearable sensors (e.g., IMUs, accelerometers, gyroscopes), smart implants, pressure-sensing systems, and mobile health (mHealth) platforms such as ReHub^® and BPMpathway. Evidence from recent randomized controlled trials and systematic reviews demonstrates their effectiveness in tracking mobility, monitoring range of motion (ROM), detecting gait anomalies, and delivering real-time feedback to both patients and clinicians. Despite these advances, several challenges persist, including measurement accuracy in unsupervised environments, the complexity of clinical data integration, and digital literacy gaps among older adults. Nevertheless, the integration of artificial intelligence (AI), predictive analytics, and remote rehabilitation tools is driving a shift toward more adaptive and individualized care models. This paper concludes that sensor-enhanced rehabilitation is no longer a future aspiration but an active transition toward a smarter, more accessible, and patient-centered paradigm in recovery after TKA. Full article

In this research, a single composite-type stretchable triboelectric nanogenerator (TENG) is proposed for efficient energy harvesting and handwriting recognition. The composite TENGs were fabricated by blending dielectric barium titanate (BT) and conductive carbon nanotubes (CNTs) in varying amounts into a styrene–butadiene rubber matrix. The energy harvesting efficiency depends on the type and amount of fillers, as well as their dispersion within the matrix. Stearic acid modification of BT enables near-nanoscale filler distribution, resulting in high energy conversion efficiencies. The composite achieved power efficiency, power density, charge efficiency, and charge density values of 1.127 nW/N, 8.258 mW/m³, 0.146 nC/N, and 1.072 mC/m³, respectively, under only 2% cyclic compressive strain at 0.85 Hz. The material performs better at low stress–strain ranges, exhibiting higher charge efficiency. The generated charge in the TENG composite is well correlated with the compressive stress, which provides a minimum activation pressure of 0.144 kPa, making it suitable for low-pressure sensing applications. A flat composite with dimensions of 0.02 × 6 × 5 cm³ can produce a power density of 26.04 W/m³, a charge density of 0.205 mC/m³, and an output voltage of 10 V from a single hand pat. The rubber composite also demonstrates high accuracy in handwriting recognition across different individuals, with clear differences in sensitivity curves. Repeated attempts by the same person show minimal deviation (<5%) in writing time. Additionally, the presence of reinforcing fillers enhances mechanical strength and durability, making the composite suitable for long-term cyclic energy harvesting and wearable sensor applications. Full article

The use of pressure-sensitive paints (PSPs), an optical oxygen sensing technique, to visualise and measure the surface pressure on vehicle models in wind tunnel testing is becoming increasingly prevalent. Porphyrins have long been the standard luminophore for PSP formulations, with the majority employing the red-emitting platinum(II)-5,10,15,20-tetrakis-(2,3,4,5,6-pentafluorphenyl)-porphyrin. nIR-emitting luminophores, such as Pt(II) tetraphenyl tetrabenzoporphyrins, possess distinct advantages over visible emitting luminophores. In particular, they have wider spectrally useful ‘windows’, facilitating the insertion of a secondary visible emitting temperature-sensitive luminophore to be used for internal calibration without spectral crosstalk that detrimentally impacts PSP performance. In this work, we explore the effect of changing the loading quantity of an nIR-emitting para-CF₃ Pt(II) benzoporphyrin luminophore on the performance of PSP formulations. An optimal luminophore loading of 1.28% wt/wt benzoporphyrin luminophore to polystyrene binder was identified, resulting in a low temperature sensitivity at 100 kPa of 0.61%/K and a large pressure sensitivity at 293 K of 0.740%/kPa. These strong performance metrics, for a polystyrene-based PSP, demonstrate the efficacy of benzoporphyrin luminophores as an attractive luminophore option for the development of a new generation of high-performance PSP formulations that outperform current commercially available ones. Full article

Accurate monitoring of animal behaviors enables improved management in precision livestock farming (PLF), supporting critical applications including health assessment, estrus detection, parturition monitoring, and feed intake estimation. Although both contact and non-contact sensing modalities are utilized, wearable devices with embedded sensors (e.g., accelerometers, pressure sensors) offer unique advantages through continuous data streams that enhance behavioral traceability. Focusing specifically on contact sensing techniques, this review examines sensor characteristics and data acquisition challenges, methodologies for processing behavioral data and implementing identification algorithms, industrial applications enabled by recognition outcomes, and prevailing challenges with emerging research opportunities. Current behavior classification relies predominantly on traditional machine learning or deep learning approaches with high-frequency data acquisition. The fundamental limitation restricting advancement in this field is the difficulty in maintaining high-fidelity recognition performance at reduced acquisition rates, particularly for integrated multi-behavior identification. Considering that the computational demands and limited adaptability to complex field environments remain significant constraints, Tiny Machine Learning (Tiny ML) could present opportunities to guide future research toward practical, scalable behavioral monitoring solutions. In addition, algorithm development for functional applications post behavior recognition may represent a critical future research direction. Full article