Search Results (279)

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 recent years, China has experienced growing impacts from extreme weather events, emphasizing the importance of understanding regional atmospheric moisture dynamics, particularly Precipitable Water Vapor (PWV), to support sustainable environmental and urban planning. This study utilizes ten years (2013–2022) of Global Navigation Satellite System (GNSS) observations in typical cities in eastern China and proposes a comprehensive multiscale frequency-domain analysis framework that integrates the Fourier transform, Bayesian spectral estimation, and wavelet decomposition to extract the dominant PWV periodicities. Time-series analysis reveals an overall increasing trend in PWV across most regions, with notably declining trends in Beijing, Wuhan, and southern Taiwan, primarily attributed to groundwater depletion, rapid urban expansion, and ENSO-related anomalies, respectively. Frequency-domain results indicate distinct latitudinal and coastal–inland differences in the PWV periodicities. Inland stations (Beijing, Changchun, and Wuhan) display annual signals alongside weaker semi-annual components, while coastal stations (Shanghai, Kinmen County, Hong Kong, and Taiwan) mainly exhibit annual cycles. High-latitude stations show stronger seasonal and monthly fluctuations, mid-latitude stations present moderate-scale changes, and low-latitude regions display more diverse medium- and short-term fluctuations. In the short-term frequency domain, GNSS stations in most regions demonstrate significant PWV periodic variations over 0.5 days, 1 day, or both timescales, except for Changchun, where weak diurnal patterns are attributed to local topography and reduced solar radiation. Furthermore, ERA5-derived vertical temperature profiles are incorporated to reveal the thermodynamic mechanisms driving these variations, underscoring region-specific controls on surface evaporation and atmospheric moisture capacity. These findings offer novel insights into how human-induced environmental changes modulate the behavior of atmospheric water vapor. Full article

Groundwater at petroleum-contaminated sites typically exhibits elevated dissolved inorganic carbon (DIC) levels due to hydrocarbon biodegradation; however, our prior field investigations revealed an enigmatic DIC depletion anomaly that starkly contradicts this global pattern and points to an unrecognized carbon sink. In a breakthrough demonstration, this study provides the first experimental confirmation that sulfur-oxidizing bacteria (SOB) drive substantial carbon sequestration via a coupled sulfur oxidation autotrophic assimilation process. Through integrated hydrochemical monitoring and 16S rRNA sequencing in an enrichment culture system, we captured the complete DIC transformation trajectory: heterotrophic acetate degradation initially increased DIC to 370 mg/L, but subsequent autotrophic assimilation by SOB dramatically reduced DIC to 270 mg/L, yielding a net consumption of 85 mg/L. The distinctive pH dynamics (initial alkalization followed by acidification) further corroborated microbial regulation of carbon cycling. Critically, Pseudomonas stutzeri and P. alcaliphila were identified as the dominant carbon-fixing agents. These findings definitively establish that chemolithoautotrophic SOB convert DIC into organic carbon through a “sulfur oxidation-carbon fixation” coupling mechanism, overturning the conventional paradigm of petroleum-contaminated sites as perpetual carbon sources. The study fundamentally redefines natural attenuation frameworks by introducing microbial carbon sink potential as an essential assessment metric for environmental sustainability. Full article

Wetlands are critical components of the global carbon cycle, yet their carbon sink dynamics under hydrological fluctuations remain insufficiently understood. This study employed the Carnegie-Ames-Stanford Approach (CASA) model to estimate the net ecosystem productivity (NEP) of the Dongting Lake wetland and explored the spatiotemporal dynamics and driving mechanisms of carbon sinks from 2000 to 2022, utilizing the Theil-Sen median trend, Mann-Kendall test, and attribution based on the differentiating equation (ADE). Results showed that (1) the annual mean spatial NEP was 50.24 g C/m²/a, which first increased and then decreased, with an overall trend of −1.5 g C/m²/a. The carbon sink was strongest in spring, declined in summer, and shifted to a carbon source in autumn and winter. (2) Climate variability and human activities contributed +2.17 and −3.73 g C/m²/a to NEP, respectively. Human activities were the primary driver of carbon sink degradation (74.30%), whereas climate change mainly promoted carbon sequestration (25.70%). However, from 2000–2011 to 2011–2022, climate change shifted from enhancing to limiting carbon sequestration, mainly due to the transition from water storage and lake reclamation to ecological restoration policies and intensified climate anomalies. (3) NEP was negatively correlated with precipitation and water level. Land use adjustments, such as forest expansion and conversion of cropland and reed to sedge, alongside maintaining growing season water levels between 24.06~26.44 m, are recommended to sustain and enhance wetland carbon sinks. Despite inherent uncertainties in model parameterization and the lack of sufficient in situ flux validation, these findings could provide valuable scientific insights for wetland carbon management and policy-making. Full article

Climate change intensifies hydrological cycles, leading to an increased variability in terrestrial water storage anomalies (TWSAs) and a heightened drought risk. Understanding the spatiotemporal dynamics of TWSAs and their driving factors is crucial for sustainable water management. While previous studies have primarily attributed TWSAs to regional factors, this study employs wavelet coherence, partial correlation analysis, and multiple linear regression to comprehensively analyze TWSA dynamics and their drivers in the Hengduan Mountains (HDM) region from 2003 to 2022, incorporating both regional and global influences. Additionally, dry–wet variations were quantified using the GRACE-based Drought Severity Index (GRACE-DSI). Key findings include the following: The annual mean TWSA showed a non-significant decreasing trend (−2.83 mm/y, p > 0.05), accompanied by increased interannual variability. Notably, approximately 36.22% of the pixels in the western HDM region exhibited a significantly decreasing trend. The Nujiang River Basin (NRB) (−17.17 mm/y, p < 0.01) and the Lancang (−17.17 mm/y, p < 0.01) River Basin experienced the most pronounced declines. Regional factors—particularly precipitation (PRE)—drove TWSA in 59% of the HDM region, followed by potential evapotranspiration (PET, 28%) and vegetation dynamics (13%). Among global factors, the North Atlantic Oscillation showed a weak correlation with TWSAs (r = −0.19), indirectly affecting it via winter PET (r = −0.56, p < 0.05). The decline in TWSAs corresponds to an elevated drought risk, notably in the NRB, which recorded the largest GRACE-DSI decline (slope = −0.011, p < 0.05). This study links TWSAs to climate drivers and drought risk, offering a framework for improving water resource management and drought preparedness in climate-sensitive mountain regions. Full article

As a carrier of civilization, cultural heritage reflects the dynamic relationship between humans and their environment within specific historical contexts. During the Ming and Qing Dynasties (1368–1912 CE), the Yangtze River Basin was one of the most prominent regions for economic and cultural activities in ancient China. The cultural heritage of this period was characterized by its dense distribution and continuous evolution. Considering the applicability bias of modern data in historical interpretation, this study selected four characteristic variables: population density, agricultural productivity, technological level, and temperature anomaly. A hierarchical Bayesian model was constructed and change points were detected to quantitatively analyze the driving mechanisms behind the spatiotemporal distribution of cultural heritage. The results show the following: (1) The distribution of cultural heritage exhibited a multipolar trend by the mid-period in both Dynasties, with high-density areas contracting in the later period. (2) Agricultural productivity consistently had a significant positive impact, while population density also had a significant positive impact, except during the mid-Ming period. (3) The cultural calibration terms, which account for observational differences resulting from the interaction between cultural systems and environmental variables, exhibited slight variations. (4) The change point for population density was 364.83 people/km², and for agricultural productivity it was 2.86 × 10⁹ kJ/km². This study confirms that the differentiation in the spatiotemporal distribution of cultural heritage is driven by the synergistic effects of population and resources. This provides a new perspective for researching human–land relations in a cross-cultural context. Full article

This study examines the roles of air mass advection and solar radiation in shaping daily air temperature anomalies in Warsaw, Poland, from 2008 to 2023. It integrates solar radiation data, HYSPLIT back-trajectories, air temperature measurements, and machine learning methods, which are key atmospheric factors contributing to temperature anomalies in different seasons. Radiation dominates during warm seasons, while advection-related geographic factors are more influential during winter. Increased solar radiation is observed across all seasons during high-positive temperature anomalies (exceeding two standard deviations). In contrast, cold anomalies in summer are accompanied by strong negative solar radiation anomalies (−136.3 W/m²), while winter cold events may still coincide with positive radiation anomalies (25.7 W/m²). Very slow circulation over Central Europe, which occurs twice as often in summer as in winter, leads to positive temperature (1.3 °C) and negative radiation (−2.1 W/m²) anomalies in summer and to negative temperature (−1.9 °C) anomalies and slightly positive radiation (0.3 W/m²) anomalies in winter. The seasonal variability in the spatial origin of air masses reflects shifts in synoptic-scale circulation patterns. These findings highlight the importance of considering the combined influence of radiative and advective processes in driving temperature extremes and their seasonal dynamics in mid-latitude climates. Full article

Autonomous vehicles (AVs) rely on a heterogeneous sensor suite of RGB cameras, LiDAR, GPS/IMU, and emerging event-based dynamic vision sensors (DVS) to perceive and navigate complex environments. However, these sensors can be deceived by realistic cyberattacks, undermining safety. In this work, we systematically implement seven attack vectors in the CARLA simulator—salt and pepper noise, event flooding, depth map tampering, LiDAR phantom injection, GPS spoofing, denial of service, and steering bias control—and measure their impact on a state-of-the-art end-to-end driving agent. We then equip each sensor with tailored defenses (e.g., adaptive median filtering for RGB and spatial clustering for DVS) and integrate a unsupervised anomaly detector (EfficientAD from anomalib) trained exclusively on benign data. Our detector achieves clear separation between normal and attacked conditions (mean RGB anomaly scores of 0.00 vs. 0.38; DVS: 0.61 vs. 0.76), yielding over 95% detection accuracy with fewer than 5% false positives. Defense evaluations reveal that GPS spoofing is fully mitigated, whereas RGB- and depth-based attacks still induce 30–45% trajectory drift despite filtering. Notably, our research-focused evaluation of DVS sensors suggests potential intrinsic resilience advantages in high-dynamic-range scenarios, though their asynchronous output necessitates carefully tuned thresholds. These findings underscore the critical role of multi-modal anomaly detection and demonstrate that DVS sensors exhibit greater intrinsic resilience in high-dynamic-range scenarios, suggesting their potential to enhance AV cybersecurity when integrated with conventional sensors. Full article

The On-Board Diagnostics II (OBD-II) system, driven by a wide range of embedded sensors, has revolutionized the automotive industry by enabling real-time monitoring of key vehicle parameters such as engine load, vehicle speed, throttle position, and diagnostic trouble codes. Concurrently, recent advancements in machine learning (ML) have further expanded the capabilities of OBD-II applications, unlocking advanced, intelligent, and data-centric functionalities that significantly surpass those of conventional methodologies. This paper presents a comprehensive investigation into ML-based applications that leverage OBD-II sensor data, aiming to enhance sustainability, operational efficiency, safety, and security in modern vehicular systems. To this end, a diverse set of ML approaches is examined, encompassing supervised, unsupervised, reinforcement learning (RL), deep learning (DL), and hybrid models intended to support advanced driving analytics tasks such as fuel optimization, emission control, driver behavior analysis, anomaly detection, cybersecurity, road perception, and driving support. Furthermore, this paper synthesizes recent research contributions and practical implementations, identifies prevailing challenges, and outlines prospective research directions. Full article

Odontogenesis, the development of teeth, is a complex, multistage process that unfolds from early embryogenesis through tooth eruption and maturation. It serves as a classical model of organogenesis due to the intricate reciprocal interactions between cranial neural crest-derived mesenchyme and oral epithelium. This narrative review synthesizes current scientific knowledge on human tooth development, tracing the journey from the embryological origins in the first branchial arch to the formation of a fully functional tooth and its supporting structures. Key morphogenetic stages—bud, cap, bell, apposition, and root formation—are described in detail, highlighting the cellular events and histological features characterizing each stage. We discuss the molecular and cellular regulatory networks that orchestrate odontogenesis, including the conserved signaling pathways (Wnt, BMP, FGF, SHH, EDA) and transcription factors (e.g., PAX9, MSX1/2, PITX2) that drive tissue patterning and cell differentiation. The coordinated development of supporting periodontal tissues (cementum, periodontal ligament, alveolar bone, gingiva) is also examined as an integral part of tooth organogenesis. Finally, developmental anomalies (such as variations in tooth number, size, and form) and the fate of residual embryonic epithelial cells are reviewed to underscore the clinical significance of developmental processes. Understanding the normal course of odontogenesis provides crucial insight into congenital dental disorders and lays a foundation for advances in regenerative dental medicine. Full article

Aberrant or non-standard operations by ship drivers are a leading cause of water traffic accidents, making the development of real-time and reliable behavior detection systems critically important. However, the environment within a ship’s bridge is significantly more complex than typical scenarios, such as vehicle driving or general security monitoring, which results in poor performance when applying generic algorithms. In such settings, both the accuracy and efficiency of existing methods are notably limited. To address these challenges, this paper proposes a cross-modal behavioral intelligence framework designed specifically for a ship’s bridge, integrating multi-target tracking, behavior recognition, and feature object association. The framework employs ByteTrack, a high-performance multi-object tracker that maintains stable tracking even when subject to occlusions or motion blur through its novel association mechanism, using both high and low confidence detection boxes, for multi-driver tracking. Combined with an improved Temporal Shift Module (TSM) algorithm for behavior recognition, which effectively resolves issues concerning target association and action ambiguity in complex environments, the proposed framework achieves a Top-1 accuracy of 82.1%, based on the SCA dataset. Furthermore, the method incorporates a multi-modal decision optimization strategy, based on spatiotemporal correlation rules, leveraging YOLOv7-e6 for simultaneous personnel and small object detection, and introduces the Accuracy of Focused Anomaly Recognition (AFAR) metric to enhance the anomaly detection performance. This approach improves the anomaly detection rate, up to 81.37%, with an overall accuracy of 80.66%, significantly outperforming single-modality solutions. Full article

The Western Hemisphere (WH) circulation pattern was discovered in recent years through Self-Organizing Maps (SOMs) clustering of the Northern Hemisphere 500 hPa geopotential height during winter. For example, the extremely cold wave that occurred in North America during 2013–14 is associated with WH circulation anomalies. We discussed the extremely cold weather conditions within the WH pattern during the winter season from 1979 to 2023. The variations of cold air in North America during the WH pattern have been demonstrated using the NCEP/NCAR reanalysis datasets. By defining WH events and North American extremely cold events, we have identified a connection between the two. In extremely cold events, linear winds are the key factor driving the temperature drop, as determined by calculating temperature advection. The ridge in the Gulf of Alaska serves as an early signal for this cold weather. The WH circulation anomaly triggers an anomalous ridge in the Gulf of Alaska region, leading to trough anomalies downstream over North America. This results in the southward movement of cold air from the polar regions, causing cooling in the mid-to-northern parts of North America. With the maintenance of the stationary wave in the North Pacific (NP), the anomalous trough over North America can be deepened, driving cold air into the continent. Influenced by the low pressure over Greenland and the storm track, the cold anomalies are concentrated in the central and northern parts of North America. This cold air situation persists for approximately two weeks. The high-level patterns of the WH pattern in both the 500 hPa height and the troposphere level have been identified using SOM. This cold weather is primarily a tropospheric phenomenon with limited correlation to stratospheric activities. Full article

Hypoxia in river estuaries poses significant ecological and water safety risks, yet long-term high-frequency monitoring data for comprehensive analysis remain scarce. This study investigates hypoxia dynamics in the Shenzhen River Estuary (southern China) using two-year high-frequency monitoring data. A hybrid anomaly detection method integrating wavelet analysis and temporal information entropy was developed to identify hypoxia events. The drivers of hypoxia were also identified with correlation coefficients and transfer entropy (TE). The results reveal frequent spring–summer hypoxia. Turbidity and total nitrogen (TN) exhibited significant negative correlations and time-lagged effects on dissolved oxygen (DO), where TE reaches a peak of 0.05 with lags of 36 and 72 h, respectively. Wastewater treatment plant (WWTP) loads, particularly suspended solids (SSs), showed a linear negative correlation with estuarine DO. Notably, the 2022 data showed minimal correlations (except SSs) due to high baseline pollution, whereas the post-remediation 2023 data revealed stronger linear linkages (especially r = −0.81 for SSs). The proposed “high-frequency localization–low-frequency assessment” detection method demonstrated robust accuracy in identifying hypoxia events, and mechanistic analysis corroborated the time-lagged pollutant impacts. These findings advance hypoxia identification frameworks and highlight the critical role of Turbidity and SSs in driving estuarine oxygen depletion, offering actionable insights for adaptive water quality management. Full article

The South China block (SCB) is characterized by complex tectonics, large-scale lithospheric deformation, and extensive mineralization in its southeastern region. However, the geodynamic processes and mechanisms driving mineralization remain controversial, partly due to the lack of information on its fine crustal structure. The resolution of crustal seismic tomography is relatively low due to the uneven distribution of local earthquakes in South China. In this study, we conduct a joint inversion of Bouguer gravity and seismic travel-time data to investigate the detailed 3-D P-wave velocity (Vp) structure of the crust beneath the SCB. Our results show the following: (1) strong lateral heterogeneities exist in the crust, which reflect the surface geology and tectonics well; (2) the Vp patterns at different depths beneath the Yangtze block are almost consistent, but those beneath the Cathaysia block vary significantly, which might be related to the lithosphere thinning in the Mesozoic; (3) decoupling between the upper crust and the lower crust occurs at ~20 km depth beneath the eastern SCB; (4) the Vp patterns vary beneath different metallogenic belts; and (5) distinct low-Vp anomalies exist in the lower crust beneath mineral deposits. These results suggest that the deep mineralization is closely associated with the lithospheric thinning and upwelling thermal flow in the Mesozoic beneath the eastern SCB. Our Vp tomographic result also strongly supports the viewpoint that the mineralization mechanism varies for different metallogenic belts. Full article

High-risk pregnancies (HRPs) constitute a significant global health issue due to their strong association with increased maternal and neonatal morbidity and mortality. Although pregnancy is generally characterized by positive expectations, the presence of maternal comorbidities, gestational complications, or adverse socioeconomic and environmental conditions can markedly elevate the probability of unfavorable outcomes. HRPs contribute disproportionately to complications such as preterm birth, fetal growth restriction, low birth weight, and congenital anomalies, which are key determinants of neonatal mortality and long-term developmental and health challenges. A broad spectrum of risk factors as well as insufficient prenatal care, underscores the complex nature of HRPs. These conditions necessitate a multidisciplinary management approach encompassing early risk identification, continuous monitoring, and individualized interventions. The neonatal prognosis in such contexts is strongly influenced by gestational age at delivery, birth weight, the standard of neonatal care, and the underlying etiological factors driving preterm or complicated deliveries. Preventive strategies including comprehensive prenatal screening, systematic antenatal follow-up, and timely referral to specialized perinatal care centers are essential for reducing the burden of HRPs. Furthermore, addressing social determinants of health—such as low socioeconomic status and limited access to healthcare—is critical for optimizing maternal and neonatal outcomes. This review consolidates current evidence on the epidemiology, etiological factors, and clinical implications of high-risk pregnancies, emphasizing the necessity of an integrative, preventive, and multidisciplinary framework to mitigate adverse neonatal outcomes and improve long-term health trajectories. Full article