Search Results (16,700)

Resveratrol is a promising polyphenolic bioactive compound found in peanuts. However, the distribution of cis- and trans-resveratrol and their glycosides varies significantly among cultivars, and their correlations with other quality traits remain unclear. In this study, Ultra-Performance Liquid Chromatography (UPLC) combined with high-throughput spectral analysis was employed to systematically evaluate 42 main cultivated peanut varieties from seven series across China’s three major production regions. The results indicated that trans-piceid (trans-resveratrol glycoside) was the predominant component, accounting for over 85% of the total content. Significant variation was observed in the total content of resveratrol and its glycosides among cultivars (4.61–88.79 mg/kg), with the Weihua series (represented by Weihua 23) exhibiting the strongest resveratrol enrichment ability. Multidimensional correlation analysis systematically revealed, for the first time, distinct association patterns: trans-piceid was positively correlated with sucrose, cis-resveratrol was positively correlated with fatty acids such as oleic acid, and trans-resveratrol showed a specific association with linoleic acid. Based on these findings, seven specialized high-resveratrol cultivars suitable for processing, including Weihua 23 and Kainong 301, were identified. Furthermore, a specific correlation system between “resveratrol and key quality indicators” was established. This study provides important theoretical support for the targeted breeding of novel functional peanut varieties that combine a high resveratrol content with traits such as high oleic acid or sugar content, thereby promoting the high-value industrial utilization of high-quality peanuts. Full article

Aconitum carmichaelii Debeaux has been a traditional medicinal resource in China for over two millennia. However, sustainable utilization and preservation strategies for A. carmichaelii require a thorough understanding of environmental factors influencing its distribution. An optimized MaxEnt model was constructed using the ENMeval package based on 185 quality-controlled occurrence records and 10 selected environmental variables (bioclimatic, edaphic, topographic, and anthropogenic). The optimized model demonstrated reliable predictive accuracy, with an area under curve (AUC) value of 0.896. Soil moisture (37.7% contribution), human footprint (HFP) (23.9%), and July solar radiation (11.1%) were the primary variables determining A. carmichaelii distribution. The suitable thresholds were defined as soil moisture > 87.34 mm, HFP > 10.69, and July solar radiation < 19,125.72 kJ m⁻² day⁻¹. At present, highly suitable habitat covers approximately 8.243 × 10⁵ km², predominantly located in the Sichuan Basin and surrounding regions, including Sichuan, Chongqing, Guizhou, and northeastern Yunnan. Future predictions under all Shared Socioeconomic Pathway (SSP) scenarios indicate a significant reduction in highly suitable habitat, with losses of 63.01% (2041–2060, SSP126), 62.62% (2041–2060, SSP245), 61.35% (2041–2060, SSP370), and 61.99% (2061–2080, SSP585). Habitat contraction mainly occurs toward higher altitudes and southwestern areas, with a maximum displacement distance of 50.56 km under the SSP585 scenario. This study enhances our understanding of environmental factors affecting the distribution of A. carmichaelii and offers guidance for its sustainable management and cultivation amid global climate change. Full article

Purpose: Pure three-dimensional (3D)-printed resin for denture base shows strength in comparison with the conventional heat-cured materials. The purpose of this study was to assess how physical and mechanical properties of 3D-printed denture base resins are affected by the addition of cerium zirconium oxide nanofibers (CeZrO₄ NFs), which have a unique combination of thermophysical and mechanical properties. Materials and Methods: The specimens were digitally created utilizing Microsoft Corporation’s 3D builder software through computer-aided design. To meet the test criteria for transverse strength, impact strength, hardness, radiopacity, and degree of conversion (DC), specimens were designed and printed with specific dimensions according to the relevant standards. The 3D-printed denture base resin was mixed with CeZrO₄ NFs (diameter: 300–800 nm, length: 2–10 µm) at weight percentages of 0.5, 1.0%, 1.5%, 2%, and 2.5%. The data were analyzed using Tukey’s post hoc test (α = 0.05) and ANOVA. Field emission scanning electron microscopy (FESEM) and energy dispersive X-ray spectroscopy (EDX) were used to evaluate surface morphologies of the composites and nanofibers, and the dispersion of the NFs within the resin matrix respectively. Results: The results demonstrated that compared with those of the control group, the average transverse strength, impact strength, and hardness values of the CeZrO₄ NF reinforcement groups significantly increased up to a nanofiller concentration of 1.5 wt.%., whereas those of the other reinforcement groups significantly decreased. For example, the impact strength significantly increased from 5.84 kJ/m² (0 wt.%) to the maximum value 8.76 kJ/m² at 1.0 wt.% CeZrO₄ NF. On the other hand, the Shore D hardness increased from 80.84 for the control group to the maximum value 83.27 at 1.5 wt.% CeZrO₄ NF. The radiopacity increased as the NF concentration increased. Although Fourier transform infrared (FTIR) spectroscopy analysis did not show any noticeable change in the chemical structure of the resin after incorporating the NFs, there was a notable improvement in the DC of the nanocomposites with NF concentrations of 0.5, 1.0 and 1.5 wt.%. Energy dispersive X-ray spectroscopy (EDX) and field emission scanning electron microscopy (FESEM) showed evidence of uniform distribution of the CeZrO₄ NFs in the 3D-printed specimens. Conclusions: The properties of the denture bases fabricated from 3D-printed resin were enhanced by the addition of 0.5%, 1 wt.% and 1.5 wt.% CeZrO₄-milled NFs, though the latter two concentrations produced the most significant results. Full article

Silk fibroin (SF) is an ideal biomaterial for next-generation clinical wound dressings due to its biocompatibility and tunable mechanical properties. Cell therapies for wound healing have explored using SF as the base for delivering beneficial cargo; however, retention is poor due to exudate “wash out.” To address concerns with the premature release of cargo from SF-fabricated wound dressings, we utilized amine-reactive chemistry to conjugate SF mats with azido-reactive dibenzocyclooctyne (DBCO) that can then attach complementary azido-tagged cargo through chemoselective immobilization. SF mats were made using electrospinning of a 1:1 SF/PCL solution and were then conjugated with N-Hydroxysuccinimide-dibenzocyclooctyne ester (DBCO). PBS soaking was used for control SF mats. SF mats were then imaged and characterized using the following metrics: pore size, fiber alignment, fiber distribution, fiber diameter, ultimate tensile strength, tangent modulus, proteolytic degradation, absorption, and retention. Successful DBCO conjugation of SF mats was confirmed through the presence of the Az-Cy5 dye while exhibiting no significant changes to the DBCO SF mats in any of the tested metrics compared to controls. Our results provide evidence that the amine chemistry responsible for the DBCO conjugation does not alter important SF mat properties. This confirms that DBCO augmentation paired with Az-Cy5 tags may be a viable approach for immobilizing different therapeutic cargoes to aid wound healing efforts. Full article

Thoracic aortopathies, including aneurysm and dissection, are complex vascular disorders characterized by structural alterations of the aortic wall that disrupt normal haemodynamics. Altered shear stress, turbulent flow, and endothelial dysfunction promote thrombus formation and modulate systemic hemostasis via platelet activation and the von Willebrand factor–ADAMTS13 axis. The Total Thrombus-Formation Analysis System (T-TAS) is a microfluidic, flow-dependent assay that quantitatively evaluates thrombus formation under physiological shear conditions. Although studied in various cardiovascular contexts, its application in aortopathies remains largely unexplored, and no prospective studies have validated its clinical utility. Integrating T-TAS with computational haemodynamic approaches, such as two-way fluid–structure interaction simulations, enables assessment of the interplay between blood flow, vessel wall mechanics, pulse wave propagation, and local shear patterns. Patient-specific modelling, including individualized flow profiles, pressure distributions, and wall properties, may enhance mechanistic insights. Genetic variants in Fibrillin-1 gene (FBN1), Transforming Growth Factor Beta Receptor 1/2 (TGFBR1/2), Actin Alpha 2 (ACTA 2), and Myosin Heavy Chain 11 (MYH11) further contribute to structural vascular heterogeneity and diverse systemic haemostatic phenotypes, highlighting the need for personalized assessment. T-TAS should currently be considered an exploratory research tool rather than a validated diagnostic or prognostic method. This narrative review proposes a hypothesis-generating framework integrating structural, haemodynamic, molecular, and functional perspectives. Combining flow-based thrombosis assays with advanced modelling may inform future translational studies, improve mechanistic understanding of thrombus formation, and support personalized risk stratification and management in patients with thoracic aortopathies. Full article

Background: In children with cerebral palsy, bony acetabular deficiencies are common and may be associated with progressive hip subluxation, abnormal joint loading, and ultimately hip dislocation. Hip reconstruction surgery is typically performed to prevent dislocation, and this includes acetabular reshaping using acetabuloplasty. The location of acetabular deficiency may vary among individuals; however, only radiographs are used for planning and intraoperative correction in many centers. Precise reconstruction and preop planning are necessary for the accurate correction of acetabular coverage. This study compares conventional hip reconstruction with a 3D-guided technique using individual preop 3D planning and 3D-printed guides during surgery to determine which method allows for a more accurate correction. We hypothesize that the patient-specific 3D planning leads to more precise anatomical correction of acetabular coverage compared to conventional freehand osteotomy. Methods: This study was registered in the German Clinical Trial Register (DRKS-ID: DRKS00031356) on 14 July 2023. In a randomized controlled trial, various imaging-based parameters were used to assess the bony anatomy preoperatively and postoperatively. Preoperative and 6-week postoperative computed tomography (CT) scans are part of routine clinical care. Additionally, an immediate postoperative CT scan was performed. One hip was operated on using individualized 3D preoperative planning, while the other hip was corrected using a conventional surgical approach. A standardized subtrochanteric osteotomy was performed for the varisation, derotation, and shortening of the proximal femur. This osteotomy was followed by acetabuloplasty under fluoroscopic control. For the 3D-planned operation, patient-specific cutting and repositioning guides were produced based on preoperative CT imaging. Patients with bilateral cerebral palsy (GMFCS levels I–V), aged 4–18 years, with an open triradiate growth plate and a migration index ≥ 40% in at least one hip were included. In a preliminary retrospective part, this project reproduces the existing three-dimensional acetabular index (3-DAI) and compares it with established radiographic methods to determine the utility and reliability of a reconstructed 3D CT measurement technique. A further component of the retrospective part is the creation of an age-adjusted database of typically developed hips and the development of a 3D head coverage index (3D-HCI) as a new 3D parameter to express acetabular coverage; therefore, it will be used as a secondary parameter and correlated to the 3DAI in the prospective part. Conclusions: Improved precision may have meaningful clinical implications for long-term joint congruency, load distribution, pain, and mobility outcomes. Full article

Measuring the service levels and spatial equity of urban parks constitutes a core research topic within the field of environmental justice. Against the backdrop of low-carbon urban transformation and sustainable development, this study constructs an ecological supply indicator calculation model for parks based on landscape ecology theory. Leveraging spatio-temporal big data such as Points of Interest (POI) and second-hand property transactions, it establishes a demand evaluation indicator system centered on human activity intensity. The study employs the Gini coefficient and location entropy to gauge the spatial equity of park supply–demand balance, utilizing the Z-score method to classify supply–demand matching types. An empirical case study is conducted in Shenzhen. Findings indicate that despite Shenzhen possessing abundant global-scale park resources, a Gini coefficient of 0.489 reveals significant deficiencies in the equitable provision of park services, with spatial distribution exhibiting pronounced social stratification. Specifically: (1) location entropy values exhibit an east-high, west-low spatial pattern; (2) areas with high location entropy are predominantly concentrated in Dapeng New District, rich in green space resources, where supply exceeds demand, creating an imbalance; and (3) areas with low locational entropy values are predominantly distributed in industrial clusters such as western Bao’an and western Longgang, exhibiting contradictory characteristics of low supply and high demand. Overall, the distribution of park and green space resources exhibits a polarized pattern. Full article

Non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related mortality globally. While multi-modal artificial intelligence (AI) models offer significant predictive potential, their translation into routine clinical practice is delayed by the “black box” nature of complex algorithms and the fragmentation of heterogeneous data. We present LANTERN-XGB, a hierarchical machine learning workflow designed to bridge this gap by generating interpretable “digital human avatars” for precision oncology. The methodology employs a multi-stage scalable tree boosting system (XGBoost) architecture utilizing shapley additive explanations (SHAP) for rigorous hierarchical feature selection, missing value management, and patient-specific decision support. The workflow was developed and benchmarked using a retrospective cohort of 437 patients with clinical N0 NSCLC, followed by validation on a prospective dataset (n = 100) and an independent external dataset (n = 100). The pipeline integrates diverse data modalities to predict occult lymph node metastasis (OLM). LANTERN-XGB identified a robust consensus signature driven by non-linear interactions among CT textural fragmentation, PET metabolic heterogeneity, tumor density distribution, and systemic clinical modulators. Exploratory transcriptomic pathway analysis (GSVA) revealed that high-risk predictions strongly correlate with systemic molecular dysregulation, such as the enrichment of immune-inflammatory signaling and metabolic stress pathways. The model achieved robust discrimination in external validation (AUC ≈ 0.77), performing comparably to state-of-the-art nomogram benchmarks. Crucially, the LANTERN-XGB framework demonstrated superior utility in handling diagnostic ambiguity; local force plots allowed for the correct reclassification of “borderline” prediction by visualizing feature interactions that standard linear models fail to capture. LANTERN-XGB provides a validated, open-source framework that successfully balances predictive power with clinical transparency. By empowering clinicians to visualize and verify the logic behind AI predictions, this workflow offers a pragmatic path for integrating reliable multi-modal avatars into daily medical decision-making. Full article

Extreme precipitation constitutes one of the most devastating climatic resulting from global climate change. Jilin Province, a significant commodities grain base in China by a temperate monsoon climate, is particularly susceptible to flood disasters caused by extreme precipitation, usually occurring from late July to early August. The 2010 flood impacted moreover 5.12 million individuals and resulted in direct economic damages amounting to 45.1 billion CNY. However, research on the spatiotemporal characteristics and future trends of extreme precipitation in Jilin Province is still quite inadequate. This study examined the spatiotemporal distribution and future forecasts of extreme precipitation utilizing daily meteorological data from 31 stations (1960–2019) and three CMIP6 models (CanESM5, MPI-ESM1-2-HR, FGOALS-g3) under SSP2-4.5 and SSP5-8.5 scenarios. Eleven extreme precipitation indices, as specified by the WMO, were analyzed utilizing linear regression, the Mann–Kendall test, wavelet analysis, and inverse distance weighting interpolation. The findings indicated that from 1960 to 2019, extreme precipitation demonstrated traits of “increased frequency and total amount, decreased intensity”, with a significant decline in CDD (−2.184 d·(10a)⁻¹, p < 0.001), a notable increase in PRCPTOT (1.493 mm·(10a)⁻¹, p < 0.05), and a significant reduction in SD II (−0.016 mm·d⁻¹·(10a)⁻¹, p < 0.01). The majority of indicators had a predominant cycle of 30 to 50 years. A significant northwest-to-southeast gradient characterized most indicators, with PRCPTOT varying from 327.5 mm in Baicheng to 824.3 mm in Tonghua. Future projections (2025–2100) suggested scenario-dependent intensification. Under SSP5-8.5, all three models forecast substantial increases in precipitation amount indices (PRCPTOT: 2.071–2.457 mm·(10a)⁻¹) and SD II (0.010–0.013 mm·d⁻¹·(10a)⁻¹), reversing the past downward trend in intensity. The anticipated alterations exhibited a northwest-to-southeast gradient, with PRCPTOT increases above 230 mm in the central and southeastern regions. These findings offer a scientific basis for flood management and climate change adaptation in Jilin Province and analogous areas. Full article

Metro train timetable rescheduling (TTR) is a critical task for ensuring the reliability of urban rail transit systems. However, with the increasing density of railway networks and the growing number of operational trains, TTR has evolved into a typical high-dimensional and large-scale optimization problem. Traditional mathematical programming and heuristic approaches often struggle with the “curse of dimensionality” and fail to provide real-time responses under stochastic disturbances. To address these challenges, this paper proposes a novel framework based on Multi-Agent Deep Reinforcement Learning (MADRL). Specifically, we model the TTR problem as a decentralized cooperative process and utilize the Multi-Agent Advantage Actor-Critic (MAA2C) algorithm to optimize train schedules dynamically. The proposed framework is implemented within the Flatland simulation environment, which allows for the representation of complex arbitrary topologies. We design a composite reward function that minimizes total delay deviation while maximizing passenger satisfaction, subject to constraints such as headway, operating time, and train capacity. Furthermore, to enhance the robustness of the model against high-dimensional state uncertainties, random disturbances following a negative exponential distribution are introduced during training. Experimental results across various scenarios—ranging from simple dual-track to complex random networks—demonstrate that the MAA2C-based approach significantly outperforms traditional baselines. It not only achieves faster convergence in small-scale scenarios but also demonstrates superior computational efficiency and scalability in large-scale environments, effectively minimizing passenger waiting times. This study validates the potential of MADRL in solving high-dimensional traffic control problems for intelligent transportation systems. Full article

Coral reefs in the South China Sea (SCS) are critical for regional marine biodiversity and ecosystem services but face escalating threats from climate change and anthropogenic stressors. However, a holistic evaluation of habitat suitability spanning the distinct environmental gradients from low-latitude deep-water atolls to high-latitude marginal reefs remains limited. This study utilized high-resolution remote sensing data and the MaxEnt (Maximum Entropy) model combined with Principal Component Analysis (PCA) to systematically map potential habitat suitability and elucidate the multi-scale environmental drivers shaping the realized niche of SCS corals. The results revealed significant spatial heterogeneity characterized by a distinct “High South, Low North” latitudinal gradient, with Unsuitable areas dominating 85.5% of the study region, followed by Marginally Suitable habitats at 5.0%, while the northern Nansha Islands were identified as the core distribution area with the highest suitability and continuity. Minimum Phosphate (Min. Phos.) concentration and Sea Surface Temperature (SST) were identified as the core environmental factors determining the spatial distribution of coral reefs in the South China Sea. The optimal environmental ranges were identified as: SST between 28.52 °C and 29.41 °C, water depth shallower than 34 m, extremely low phosphate (0–0.005 mmol/m³), and low cumulative thermal stress (DHW < 0.83 °C-weeks). Crucially, PCA further quantified two potential climate refugia: low-latitude thermal refugia in the southern Nansha Islands, characterized by high environmental stability, and high-latitude marginal refugia in the Beibu Gulf, which offer physical buffering against warming, while necessitating targeted efforts to mitigate the risks of habitat degradation and eutrophication driven by intensifying anthropogenic activities These findings challenge the traditional conservation view relying solely on high-latitude migration, advocating for a climate-resilient spatial planning strategy that prioritizes strict protection of southern biodiversity source banks while enhancing the connectivity of northern marginal stepping stones. Full article

The precise identification of minute, high-velocity entities within unconstrained visual fields represents a significant hurdle in computational perception. This difficulty primarily arises from the geometric degradation stemming from scale volatility, motion-induced asymmetry, and heterogeneous background clutter. To mitigate the critical deficit of high-fidelity benchmarks for dynamic micro-targets, we present Soccer-Wild. This comprehensive dataset is characterized by the extreme visual complexity of microscopic objects in diverse ecological settings. Built upon this empirical foundation, we introduce GOAL (Global Object Alignment for Localization). This novel computational paradigm is designed to enhance the weak features of tiny targets by integrating frequency-domain filtering, dynamic feature routing, and entropy-guided probabilistic modeling. The GOAL framework rigorously preserves spatial-structural equilibrium and information fidelity through three synergetic mechanisms: (1) Spectral Purification: We implement a Frequency-aware Spectral Gating approach that operates in the Fourier manifold, suppressing stochastic noise to accentuate the spectral signatures of the targets; (2) Geometric Adaptation: A Multi-Granularity Mixture of Experts (MG-MoE) is formulated with heterogeneous receptive fields to dynamically rectify anisotropic distortions caused by kinetic blurring. This adaptive routing ensures cross-state representation consistency; (3) Information Recovery: We propose Information-Guided Gaussian Distribution Estimation (IGDE), which utilizes information entropy to conceptualize target coordinates as radially symmetric probability densities. This facilitates the implicit recovery of latent signals typically discarded by rigid deterministic regression. Empirical validations on the Soccer-Wild and VisDrone2019 benchmarks reveal that the proposed methodology yields substantial gains in precision. Specifically, our model achieves 40.0% and 40.4% AP (Average Precision), respectively, establishing a new state-of-the-art for localizing highly dynamic, micro-scale objects. Full article

Background/Objectives: Obstetrics and gynecology healthcare represents a global health concern requiring coordinated, accessible services across diverse populations. The Saudi Medical Referrals Centre (MRC) functions as a comprehensive digital health surveillance and coordination platform managing nationwide obstetrics and gynecology (OB/GYN) services. This study characterizes national OB/GYN e-referral patterns coordinated through the MRC platform to describe subspecialty utilization and inform capacity planning, and examines temporal trends in referral direction over the study period. Methods: A retrospective descriptive analysis of the MRC’s digital coordination platform examined 39,526 OB/GYN referrals across Saudi Arabia’s healthcare system (2023–2024). Descriptive statistics, chi-square tests, and one-way ANOVA tests were used to analyze patient demographics, subspecialty distribution, referral types, bed requirements, acceptance rates, geographic patterns, and multivariable logistic regression examined temporal trends in referral direction. Results: The platform achieved 91.81% overall acceptance rates. Platform surveillance revealed referral request distribution by subspecialty: general OB/GYN (60.68%), obstetrics and fetal medicine (16.37%), and reproductive endocrinology and infertility (14.94%). Most referral requests were for outpatient care (71.35%), though obstetrics and fetal medicine demonstrated relatively high NICU utilization (55.62%). Urgent referral requests constituted 22.05% of cases. Internal referral odds increased 1.7% monthly over the study period (OR = 1.017; p < 0.001). Conclusions: This nationwide descriptive analysis of 39,526 OB/GYN e-referrals reveals distinct subspecialty-specific referral patterns, with high overall acceptance rates and predominantly internal referral coordination. These system-level findings provide a baseline for future studies within digital referral platforms. Full article

Sustainable groundwater management is essential for water security and human health protection. Fluoride contamination is a serious concern for the sustainable drinking water supply in many parts of Pakistan, including Balochistan, where arid climate conditions and geological formations support the enrichment of fluoride. The toxic nature of fluoride contamination has resulted in negative health impacts on the local population. Conventional geostatistical techniques are usually ineffective to delineate the nonlinear relationships that affect the distribution of fluoride. This study aims to develop a machine learning-driven spatial modelling framework for classifying the spatial distribution of fluoride contamination in groundwater across the study area. The model will help to understand the spatial variability of fluoride contamination and its controlling factors, essential for effective mitigation and early warning systems. Physiochemical elements were used as predictive features in this study, utilizing a unified feature importance framework combining hydrogeochemical analysis, spatial distribution assessment, and ensemble SHAP-based interpretation to identify consistent predictors. Model performance was evaluated using a nested cross-validation framework, followed by validation on an independent geology-informed spatial holdout test set to ensure realistic generalization. Among machine learning models, the Logistic Regression (LR), Support Vector Classifier (SVC), XGBoost (XGB), Decision Tree (DT), Gaussian Naïve Bayes (GNB), and K-Nearest Neighbours (KNN) were evaluated. Support Vector Classifier (SVC) demonstrated a high predictive performance. On the independent spatial holdout dataset, SVC achieved an overall accuracy of 0.75 and an area under the receiver operating characteristic curve (AUC) of 0.821. In addition to classification, a human health risk assessment was conducted using chronic daily intake (CDI) and hazard quotient (HQ) calculations for children and adults, identifying several high-risk water supply schemes. The prediction maps successfully delineated high-risk fluoride points across specific areas, offering a tool for sustainable groundwater management. This study helps to achieve a Sustainable Development Goal (Clean Water and Sanitation, SDG#6) and promotes long-term sustainable planning in water-stressed areas by integrating spatial machine learning mapping and health risk assessment. Full article

The deployment of Wireless Sensor Networks (WSNs) remains challenging and time consuming due to the manual commissioning, configuration, and maintenance of resource-constrained Internet of Things (IoT) devices. Achieving precise network-wide time synchronization in such systems further increases this deployment complexity. This paper presents a novel Real-Time Operating System (RTOS)-integrated time synchronization method that distributes an absolute Coordinated Universal Time (UTC) reference across the network using a single Global Navigation Satellite System (GNSS)-enabled host. The method extends the semantics of the RTOS tick count by directly linking it to a global time reference. Consequently, sensor nodes obtain a notion of UTC time and can execute time-critical tasks at precisely defined moments without requiring a dedicated Real-Time Clock (RTC) or GNSS module on each sensor node. This design reduces both hardware cost and overall system complexity. Experimental results obtained on custom-developed hardware running FreeRTOS demonstrate a task synchronization error below ±30 μs between the GNSS reference and a sensor node operating at a clock frequency of 32 MHz. Such precise network-wide synchronization enables more efficient channel utilization, reduces power consumption, and improves the accuracy of both local and coordinated task execution across multiple devices in WSNs. It therefore serves as a key enabler for self-deployable WSNs. Full article