Search Results (2,047)

Understanding how atmospheric pollutants interact with soil pollution is essential for assessing long-term environmental and human health risks. This study compares concentrations of polycyclic aromatic hydrocarbons (PAHs) and potentially toxic elements (PTEs) in PM₁₀ and surface soil near air quality monitoring stations in Zagreb, Croatia. While previous work identified primary emission sources affecting PM₁₀ composition in the area, this study extends the analysis to investigate potential pollutant transfer and accumulation in soils. Multivariate statistical tools, including correlation analysis and principal component analysis (PCA), were employed to gain a deeper understanding of the sources and behavior of pollutants. Results reveal significant correlations between air and soil concentrations for several PTEs and PAHs, particularly when air pollutant data are averaged over extended periods (up to 6 months), indicating cumulative deposition effects. Σ11PAH concentrations in soils ranged from 1.2 to 524 µg/g, while mean BaP in PM₁₀ was 2.2 ng/m³ at traffic-affected stations. Strong positive air–soil correlations were found for Pb and Cu, whereas PAH associations strengthened at longer averaging windows (3–6 months), especially at 10 cm depth. Seasonal variations were observed, with stronger associations in autumn, reflecting intensified emissions and atmospheric conditions that facilitate pollutant transfer. PCA identified similar pollutant groupings in both air and soil matrices, suggesting familiar sources such as traffic emissions, industrial activities, and residential heating. The integrated PCA approach, which jointly analyzed air and soil pollutants, showed coherent behaviour for heavier PAHs and several PTEs (e.g., Pb, Cu), as well as divergence in more volatile or mobile species (e.g., Flu, Zn). Spatial differences among monitoring sites show localized influences on pollutant accumulation. Furthermore, this work demonstrates the value of coordinated air–soil monitoring in urban environments and provides an understanding of pollutant distributions across different components of the environment. Full article

Alanine dosimetry based on Electron Paramagnetic Resonance (EPR) spectroscopy has been a reliable reference and transfer dosimetry method in high-dose applications, valued for its high precision, accuracy and long-term stability. Additional characteristics, such as dose-rate independence up to 10${}^{10}$ Gy/s under electron beam (e-beam) irradiation, electron energy independence and tissue equivalence, position alanine EPR as a promising candidate to address dosimetric challenges arising in e-beam Flash Radiotherapy (RT), where radiation energy is delivered at Ultra-High Dose-Rates (UHDR) ≥ 40 Gy/s. At such dose-rates, reliable real-time monitoring dosimeters such as ionization chambers in conventional RT, suffer from ion recombination, compromising accuracy in dose determination. Several studies are currently focused on developing real-time beam monitoring systems dedicated specifically for e-beam Flash RT. This creates a need for standardized reference dosimetry methods to validate beam parameters determined by these systems under investigation. This review provides an overview of the potential and limitations of the alanine EPR dosimetry method for control, validation and verification of e-beam Flash RT beam parameters at doses less than 10 Gy, where the method has shown low sensitivity and increased uncertainty. It further discusses strategies to optimize alanine EPR measurements to enhance sensitivity and accuracy at these dose levels. Improved measurement procedures will ensure reliable and accurate e-beam Flash RT accelerator commissioning, performance checks, patient safety and treatment efficacy across all therapeutic dose ranges. Full article

The integration of Internet of Things (IoT) technologies into public healthcare enables continuous monitoring and sustainable health management. However, conventional frameworks often depend on transmitting and storing raw personal data on centralized servers, posing challenges related to privacy, security, ethical compliance, and long-term sustainability. This study proposes a privacy-preserving framework that avoids the exposure of true health-related data. Sensor nodes encrypt collected measurements and collaborate with a secure computation core to evaluate health indicators under homomorphic encryption, maintaining confidentiality. For example, the system can determine whether a patient’s heart rate within a monitoring window falls inside clinically recommended thresholds, while the framework remains general enough to support a wide range of encrypted computations. A compliance verification client generates zero-knowledge range proofs, allowing external parties to verify whether health indicators meet predefined conditions without accessing actual values. Simulation results confirm the correctness of encrypted computation, controllability of threshold-based compliance judgments, and resistance to inference attacks. The proposed framework provides a practical solution for secure, auditable, and sustainable real-time health assessment in IoT-enabled public healthcare systems. Full article

This paper develops a Natural Language Processing (NLP) pipeline to quantify the hawkish–dovish stance in the Federal Reserve’s semiannual Monetary Policy Reports (MPRs). The goal is to transform long-form central-bank text into reproducible stance scores and interpretable policy signals for research and monitoring. The corpus comprises 26 MPRs (26 February 2013 to 20 June 2025). PDFs are parsed and segmented and chunks are embedded, indexed with FAISS, retrieved via LangChain, and scored by GPT-4o on a continuous scale from −2 (dovish) to +2 (hawkish). Reliability is assessed with a four-dimension validation suite: (i) semantic consistency using cosine-similarity separation, (ii) numerical consistency against theory-implied correlation ranges (e.g., Taylor-rule logic), (iii) bootstrap stability of reported metrics, and (iv) content-quality diagnostics. Results show a predominant Neutral distribution (50.0%), with Dovish (26.9%) and Hawkish (23.1%). The average stance is near zero (≈0.019) with volatility σ ≈ 0.866, and the latest window exhibits a hawkish drift of ~+0.8 points. The Numerical Consistency Score is 0.800, and the integrated validation score is 0.796, indicating publication-grade robustness. We conclude that an embedding-based, agentic RAG approach with GPT-4o yields a scalable, auditable measure of FED communication; limitations include biannual frequency and prompt/model sensitivity, but the framework is suitable for policy tracking and empirical applications. Full article

In oil well environments, pressure sensors are often challenged by electromagnetic interference, temperature drift, and corrosive fluids, which reduce their stability and service life. To improve long-term reliability under these conditions, we developed a fiber optic Fabry–Perot (FP) cavity pressure sensor that employs an Inconel 718 diaphragm to provide both high mechanical strength and corrosion resistance. An integrated fiber Bragg grating (FBG) was included to monitor temperature simultaneously, allowing temperature–pressure cross-sensitivity to be decoupled. The sensor was fabricated and tested over a temperature range of 20–100 °C and a pressure range of 0–60 MPa. Experimental characterization showed that the FP cavity length shifted linearly with pressure, with a sensitivity of 377 nm/MPa, while the FBG demonstrated a temperature sensitivity of 0.012 nm/°C. After temperature compensation, the overall pressure measurement accuracy reached 0.5% of the full operating pressure range (0–60 MPa). These results confirm that the combined FP–FBG sensing approach maintained stable performance in harsh downhole conditions, making it suitable for pressure monitoring in shallow and medium-depth reservoirs. The proposed design offers a practical route to extend the operational lifetime of optical sensors in oilfield applications. Full article

The coastal Oder/Szczecin Lagoon is subject to multiple external changes, particularly the reduction in external nutrient loads and the impacts of climate change, including rising temperatures and more frequent heatwaves. By combining monitoring data covering the past 40 years with 3D ecosystem modelling, we assess changes in phytoplankton abundance and diversity across different temporal scales, ranging from long-term trends to the short-term effects. Despite strong reductions in external nutrient loads, neither the average annual phytoplankton biomass nor the long-term species composition changed significantly, although extreme summer blooms appear to have decreased. In summer, cyanobacteria, usually dominated by Microcystis, can reach a relative biovolume of up to 90%. Bacillariophyceae (diatoms) contribute up to 72% of the annual relative biovolume and dominate in spring. Both interannual and short-term variability in phytoplankton biomass and composition are pronounced. Heat- and coldwaves show no consistent immediate effects; however, results suggest that cyanobacteria, particularly Microcystis, benefit from hot summers. In contrast, diatoms appear less responsive to temperature, although they tend to contribute more in colder years, with distinct shifts in species composition observed between hot and cold springs. Model simulations indicate that a 1.5 °C increase in air temperature would, via elevated water temperatures, raise average monthly phytoplankton biomass by 4% in July and by 9% in August, further promoting cyanobacteria growth. Full article

To address the challenge that MOSFET temperature in motor controllers is influenced by multiple factors, exhibits strong temporal dependence, and involves complex feature interactions, this study proposes a temperature prediction model that integrates Temporal Convolutional Networks (TCNs) and the Informer architecture in parallel, enhanced with a cross-attention mechanism. The model leverages TCNs to capture local temporal patterns, while the Informer extracts long-range dependencies, and cross-attention strengthens feature interactions across channels to improve predictive accuracy. A dataset was constructed based on measured MOSFET temperatures under various operating conditions, with input features including voltage, load current, switching frequency, and multiple ambient temperatures. Experimental evaluation shows that the proposed method achieves a mean absolute error of 0.2521 °C, a root mean square error of 0.3641 °C, and an R² of 0.9638 on the test set, outperforming benchmark models such as Times-Net, Informer, and LSTM. These results demonstrate the effectiveness of the proposed approach in reducing prediction errors and enhancing generalization, providing a reliable tool for real-time thermal monitoring of motor controllers. Full article

Accurate and real-time monitoring of low-frequency electromagnetic field (EMF) is essential in power and industrial environments, yet most conventional approaches still suffer from limited spatial coverage, manual operation, and insufficient digitization. To address these challenges, this paper proposes an intelligent EMF monitoring system that integrates six-axis magnetic field sensing, temperature compensation, vector synthesis, Sub-1 GHz wireless communication, and real-time data visualization. The system supports simultaneous measurement of both AC and DC magnetic fields across the 30 Hz–100 kHz range, with specific optimization for power-frequency conditions (50/60 Hz). Designed with modular integration and low power consumption, it is suitable for portable deployment in field scenarios. Comprehensive laboratory and substation tests demonstrate high accuracy, with maximum measurement errors of 1.17% under zero-field and 1.42% under applied-field conditions—well below the ±5% tolerance defined by international standards. Wireless performance tests further confirm stable long-distance communication, achieving ranges of up to 5 km without significant transmission errors, while overall system measurement error reached as low as 0.015%. These results verify the system’s robustness, fidelity, and compliance with international safety standards. Overall, the proposed platform provides a practical and scalable solution for intelligent EMF monitoring, offering strong potential for deployment in industrial environments and infrastructure-critical applications. Full article

Neuroaxonal damage underlies permanent disability in various neurological conditions, both neuroautoimmune and neurodegenerative. It is crucial to accurately quantify and monitor axonal injury using biomarkers to evaluate disease progression and treatment effectiveness and offer prognostic insights. Neurofilaments (NFs), and especially neurofilament light chain (NfL), show promise for this purpose, as their levels increase with neuroaxonal damage in both cerebrospinal fluid and blood, independent of specific causal pathways. Recent advances in ultrasensitive immunoassays enable the reliable detection of NFs in blood, transforming them from research tools into clinically applicable measures. In multiple sclerosis (MS), serum NfL correlates with disease activity, treatment response, and long-term disability, and may complement MRI in monitoring subclinical progression. In MS, NfL is primarily emerging as a marker of disease activity and treatment response; in amyotrophic lateral sclerosis (ALS), it has progressed further, being integrated into clinical trials as a pharmacodynamic endpoint and considered by regulatory agencies as a drug development tool. Additionally, NFs are increasingly being investigated in Alzheimer’s disease, frontotemporal dementia, and other neurodegenerative disorders, though their disease specificity is limited. Ongoing challenges include older and novel assay harmonization, normative range interpretation, biological and analytical variability, and integration with other molecular and imaging biomarkers. This critical narrative review synthesizes the existing literature on NFs as diagnostic, prognostic, predictive, and pharmacodynamic biomarkers and discusses their role in therapeutic development and precision medicine in neuroautoimmune and neurodegenerative diseases. Full article

The sustainability of Arctic ecosystems that are extremely vulnerable is contingent upon the state of cryosoils. Understanding the principles of ecosystem stability in permafrost conditions, particularly under external natural or human-induced influences, necessitates an examination of the thermal and moisture regimes of the seasonally thawed soil layer. The study concentrated on the variability in the soil’s thermophysical properties in Central Siberia’s permafrost zone (the northern part of Krasnoyarsk Region, Taimyr, Russia). In the industrially affected area of interest, we evaluated and contrasted the differences in the thermophysical properties of soils between two opposing types of landscapes. On the one hand, these are soils that are characteristic of the natural landscape of flat shrub tundra, with a well-developed moss–lichen cover. An alternative is the soils in the landscape, which have exhibited significant degradation in the vegetation cover due to both natural and human-induced factors. The heat-insulating properties of background areas are controlled by the layer of moss and shrubs, while its disturbance determines the excessive heating of the soil at depth. In comparison to the background soil characteristics, degradation of on-ground vegetation causes the active layer depth of the soils to double and the temperature gradient to decrease. With respect to depth, we examine the changes in soil temperature and heat flow dynamics (q, W/m²). The ranges of thermal conductivity (λ, W/(m∙K)) were assessed using field-measured temperature profiles and heat flux values in the soil layers. The background soil was discovered to have lower thermal conductivity values, which are typical of organic matter, in comparison to the soil of the transformed landscape. Thermal diffusivity coefficients for soil layers were calculated using long-term temperature monitoring data. It is shown that it is possible to use an adjusted model of the thermal conductivity coefficient to reconstruct the dynamics of moisture content from temperature dynamics data. A satisfactory agreement is shown when the estimated ($W_{calc}$, %) and observed ($W_{exp}$, %) moisture content values in the soil layer are compared. The findings will be employed to regulate the effects on landscapes in order to implement sustainable nature management in the region, thereby preventing the significant degradation of ecosystems and the concomitant risks to human well-being. Full article

Background: Glenoid radiolucenct lines (gRLL) and glenoid component subsidence (gSC) after anatomic total shoulder arthroplasty (aTSA) have traditionally been linked to implant loosening and functional decline. However, their impact on long-term clinical outcomes remains unclear. This study aimed to evaluate whether gRLL and gSC are associated with inferior clinical or functional results in patients without revision surgery. Methods: In this retrospective study, 52 aTSA cases (2008–2015) were analyzed with a minimum of five years of clinical and radiographic follow-up. Based on final imaging, patients were categorized according to the presence and extent of gRLL and gSC. Clinical outcomes included the Constant-Murley Score, DASH, VAS for pain, and range of motion (ROM). Radiographic parameters included the critical shoulder angle (CSA), acromiohumeral distance (AHD), lateral offset (LO), humeral head-stem index (HSI), and cranial humeral head decentration (DC). Group comparisons were conducted between: (1) ≤2 vs. 3 gRLL zones, (2) 0 vs. 1 zone, (3) 0 vs. 3 zones, (4) gSC vs. no gSC, and (5) DC vs. no DC. Results: Demographics and baseline characteristics were comparable across groups. Functional scores (Constant, DASH), pain (VAS), and ROM were largely similar. Patients with extensive gRLL showed reduced external rotation (p = 0.01), but the difference remained below the MCID. Similarly, gSC was associated with lower forward elevation (p = 0.04) and external rotation (p = 0.03), both below MCID thresholds. No significant differences were observed for DC. Conclusions: Neither extensive gRLL nor gSC significantly impaired long-term clinical or functional outcomes. As these radiographic changes can occur in the absence of symptoms, regular radiographic monitoring is essential, and revision decisions should be made individually in cases of progressive bone loss. Full article

Background/Objectives: Cerebral palsy (CP) is one of the most common causes of permanent motor disability in children, and its consequences for upper limb function have a significant impact on the patient’s independence and quality of life. Virtual reality is attracting increasing interest as a modern, engaging and effective method of physiotherapy for children with cerebral palsy. This systematic literature review aimed to synthesize current scientific data on the impact of virtual-reality-based physiotherapy on upper limb function in children with cerebral palsy. Methods: The review was conducted in accordance with PRISMA 2020 guidelines. PubMed, Science Direct, Scopus, Web of Science, Research Gate and Google Scholar databases were searched for studies published between 2010 and 2025. Six original studies meeting the following criteria were included in the analysis: virtual reality therapy, population of children with cerebral palsy, physiotherapy goals related to the upper limb and availability of full text. Results: All included studies demonstrated a positive impact of virtual reality on at least one functional parameter of the upper limb, including range of motion, muscle strength, coordination and manual precision. Task-oriented training, immersive virtual reality environments and home-based therapy supported by remote monitoring proved to be the most effective. The effects were particularly noticeable in children with moderate impairment at GMFCS I–III. Conclusions: Virtual reality represents a safe and promising technology to support upper limb physiotherapy in children with cerebral palsy. It can be used both in clinical and home settings, contributing to increased exercise intensity and motivation. Further long-term studies using high-quality methodology are needed to determine the sustainability of the effects and their impact on everyday living. Full article

This study presents the deployment of Machine Learning (ML) models on low-cost edge devices (ESP32) for real-time water quality prediction in tilapia aquaculture. A compact monitoring and control system was developed with low-cost sensors to capture key environmental parameters under field conditions in Northern Thailand. Three ML models—Multiple Linear Regression (MLR), Decision Tree Regression (DTR), and Random Forest Regression (RFR)—were evaluated. RFR achieved the highest accuracy (R² > 0.80), while MLR, with moderate performance (R² ≈ 0.65–0.72), was identified as the most practical choice for ESP32 deployment due to its computational efficiency and offline operability. The system integrates sensing, prediction, and actuation, enabling autonomous regulation of dissolved oxygen and pH without constant cloud connectivity. Field validation demonstrated the system’s ability to maintain DO within biologically safe ranges and stabilize pH within an hour, supporting fish health and reducing production risks. These findings underline the potential of Edge AIoT as a scalable solution for small-scale aquaculture in resource-limited contexts. Future work will expand seasonal data coverage, explore federated learning approaches, and include economic assessments to ensure long-term robustness and sustainability. Full article

Open-pit mining often induces geological hazards such as slope instability, surface subsidence, and ground fissures. To support sustainable mine operations and safety, high-resolution monitoring and mechanism-based interpretation are essential tools for early warning, risk management, and compliant reclamation. This study focuses on the Baorixile open-pit coal mine in Inner Mongolia, China, where 48 Sentinel-1 images acquired between 3 March 2017 and 23 April 2021 were processed using the Small-Baseline Subset and Distributed-Scatterer Interferometric Synthetic Aperture Radar (SBAS-DS-InSAR) technique to obtain dense and reliable time-series deformation. Furthermore, a Trend–Periodic–Residual Subspace-Constrained Regression (TPRSCR) method was developed to decompose the deformation signals into long-term trends, seasonal and annual components, and residual anomalies. By introducing Distributed-Scatterer (DS) phase optimization, the monitoring density in low-coherence regions increased from 1055 to 338,555 points (approximately 321-fold increase). Deformation measurements at common points showed high consistency ($R^2$ = 0.97, regression slope = 0.88; mean rate difference = −0.093 mm/yr, standard deviation = 3.28 mm/yr), confirming the reliability of the results. Two major deformation zones were identified: one linked to ground compaction caused by transportation activities, and the other associated with minor subsidence from pre-mining site preparation. In addition, the deformation field exhibits a superimposed pattern of persistent subsidence and pronounced seasonality. TPRSCR results indicate that long-term trend rates range from −14.03 to 14.22 mm/yr, with a maximum periodic amplitude of 40 mm. Compared with the Seasonal-Trend decomposition using LOESS (STL), TPRSCR effectively suppressed “periodic leakage into trend” and reduced RMSEs of total, trend, and periodic components by 48.96%, 93.33%, and 89.71%, respectively. Correlation analysis with meteorological data revealed that periodic deformation is strongly controlled by precipitation and temperature, with an approximately 34-day lag relative to the temperature cycle. The proposed “monitoring–decomposition–interpretation” framework turns InSAR-derived deformation into sustainability indicators that enhance deformation characterization and guide early warning, targeted upkeep, climate-aware drainage, and reclamation. These metrics reduce downtime and resource-intensive repairs and inform integrated risk management in open-pit mining. Full article

This research introduces a hybrid framework that integrates stochastic modeling and machine learning for predicting postprandial glucose levels in individuals with Type 1 Diabetes (T1D). The primary aim is to enhance the accuracy of glucose predictions by merging a biophysical Glucose–Insulin–Meal (GIM) model with advanced machine learning techniques. This framework is tailored to utilize the Kaggle BRIST1D dataset, which comprises real-world data from continuous glucose monitoring (CGM), insulin administration, and meal intake records. The methodology employs the GIM model as a physiological prior to generate simulated glucose and insulin trajectories, which are then utilized as input features for the machine learning (ML) component. For this component, the study leverages the Light Gradient Boosting Machine (LightGBM) due to its efficiency and strong performance with tabular data, while Long Short-Term Memory (LSTM) networks are applied to capture temporal dependencies. Additionally, Bayesian regression is integrated to assess prediction uncertainty. A key advancement of this research is the transition from a deterministic GIM formulation to a stochastic differential equation (SDE) framework, which allows the model to represent the probabilistic range of physiological responses and improves uncertainty management when working with real-world data. The findings reveal that this hybrid methodology enhances both the precision and applicability of glucose predictions by integrating the physiological insights of Glucose Interaction Models (GIM) with the flexibility of data-driven machine learning techniques to accommodate real-world variability. This innovative framework facilitates the creation of robust, transparent, and personalized decision-support systems aimed at improving diabetes management. Full article