Search Results (32,145)

Background/Objectives: Palpitations are among the most common reasons for cardiology referrals. Despite widespread use of ambulatory cardiac monitoring, contemporary data from the Middle East are scarce. Extended Holter monitoring provides detailed evaluation of arrhythmia burden, autonomic regulation, and symptom–rhythm correlation in routine clinical practice. Methods: We conducted a prospective observational study of consecutive patients presenting with palpitations who underwent 24–96 h ambulatory Holter monitoring at a cardiology outpatient clinic in Saudi Arabia in 2025. Demographic and clinical characteristics, comorbidities, medication use, echocardiographic parameters, heart rate variability (HRV), ventricular and supraventricular ectopy, tachyarrhythmias, and symptom diary activations were systematically evaluated. Results: Among 251 patients (mean age 41.9 ± 16.4 years; 35.5% male), Holter monitoring showed excellent recording quality (mean analyzable time 98.7 ± 9.5%). Premature ventricular contractions (PVCs) were detected in 53.4% of patients, but burden was low (median 0.0%, IQR 0–0.1%), with only 4.4% exceeding 10%. Atrial premature contractions (APCs) were common (92.0%), though usually low-burden (median burden 0.0%, IQR 0–0.1%); atrial fibrillation and supraventricular tachycardia were rare (0.8% each). Symptom diary activation occurred in 116 patients (46.2%), with 996 events; most (87.9%) correlated with sinus tachycardia, while only 8.6% correlated with PVCs and 2.6% with APCs. In the remaining 53.8% of patients, no symptom–rhythm correlation was documented during monitoring. Heart rate variability showed expected age-related changes. Conclusions: In this predominantly young cohort, Holter monitoring revealed frequent low-burden atrial and ventricular ectopy, whereas clinically significant tachyarrhythmias were uncommon. Holter monitoring up to 96 h provided a diagnostic yield in approximately 50% of patients and should be considered a first-line screening tool. Patients without diagnostic findings may require prolonged monitoring using external or implantable devices. Full article

The rapid translocation of pesticide and metal residues in the environment and their entry into the food chain pose a significant risk to human health. Given the high global consumption of honey, quality control emphasizes the need for continuous monitoring and risk assessment. To evaluate contamination levels in honey from northern Croatia, a region with intensive agricultural land use, 38 comb honey and 22 extracted honey samples were collected by purposive one-time sampling in June 2023. These samples were analyzed for 190 pesticides using liquid chromatography–tandem mass spectrometry (LC-MS/MS) and gas chromatography–tandem mass spectrometry (GC-MS/MS), and for 17 trace metal(loid)s using inductively coupled plasma mass spectrometry (ICP-MS). The highest detection frequencies were observed for fipronil-sulfone, trifloxystrobin, and coumaphos in comb honey, and for N-(2,4-dimethylphenyl)-formamide (DMF) and N-(2,4-dimethylphenyl)-N′-methylformamidine (DPMF) in extracted honey. Glyphosate was the only pesticide to exceed the European Union (EU) maximum residue level (MRL) of 0.05 mg/kg in three honey samples. Elemental analysis quantified most target metals, with aluminum (Al), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni) and zinc (Zn) being the most abundant, while silver (Ag), arsenic (As), and selenium (Se) were not detected in this study. None of the samples contained lead (Pb) above the regulatory limit for honey established in the EU (0.1 mg/kg). To ensure food safety, further efforts are required to assess the health risks associated with exposure to these contaminants through consumption of the evaluated food. Full article

Background: Magnesium regulates neuronal excitability, NMDA receptor activity, and cerebrovascular tone. Dysmagnesemia is common in patients with acute brain injury (>65%), yet large randomized trials of magnesium neuroprotection have been neutral despite strong physiological rationale and consistent observational associations with outcomes. A key limitation may be diagnostic misclassification: the total serum magnesium poorly reflects the biologically active ionized fraction and may misclassify magnesium status in 20–85% of ICU patients during critical illness. Purpose: This narrative review synthesizes current evidence on magnesium physiology, measurement limitations, and clinical implications in neurocritical care. Overview: We discuss the mechanisms of magnesium depletion, outline the conceptual “two-hit” model (chronic deficiency plus acute ICU losses), and highlight the potential value of ionized magnesium for improved patient evaluation. Emerging syndrome-specific data suggest that magnesium disturbances are associated with prognostic signals. Improved phenotyping may help explain prior trial neutrality and support stratified approaches to magnesium monitoring and repletion. Future studies should evaluate magnesium-guided strategies and phenotype-driven trials to clarify the therapeutic role of magnesium in neurocritical care. Full article

Blood pressure is a central marker of cardiovascular risk, but continuous monitoring remains difficult because cuff-based measurements are intermittent and uncomfortable. Photoplethysmography (PPG) is already ubiquitous in wearables and can, in principle, enable cuffless blood pressure estimation from a single optical signal. However, many deep learning approaches that perform well in floating-point are impractical for microcontroller-class devices, where memory budgets, latency, and integer-only arithmetic constrain what can be deployed. A key open question is which neural architectures retain accuracy after full-integer quantization, rather than only under desktop inference. Here, we show an end-to-end, microcontroller-oriented evaluation framework that benchmarks multiple 1D convolutional models for cuffless systolic and diastolic pressure estimation from single-channel PPG, jointly optimizing estimation error, model footprint, and quantization robustness. We find that floating-point accuracy alone is a poor predictor of deployability: some lightweight CNNs exhibit substantial performance drift after INT8 conversion, whereas a compact residual 1D CNN preserves its predictions with near-identical error statistics after integer quantization. We then deploy the selected integer-only model on an STM32N6 microcontroller using an industrial toolchain and confirm that on-device inference maintains low bias and limited error dispersion while meeting real-time constraints for continuous operation. These results highlight architecture-dependent quantization stability as a critical design dimension for sensor-edge intelligence and support the feasibility of fully on-device cuffless blood pressure monitoring without multimodal sensing or cloud processing. Full article

Drought in Angola is a recurrent and cyclical natural phenomenon that poses significant environmental, economic, and social challenges, affecting water resources, agriculture, ecosystems, livestock, and vulnerable communities. This study investigates the temporal evolution and spatial behavior of drought in the provinces of Cunene, Huila, and Namibe over the period 1980–2024. Drought conditions were assessed using the Standardized Precipitation Index (SPI) and the Standard Precipitation–Evapotranspiration Index (SPEI) at multiple time scales. Trends were evaluated using the Modified Mann–Kendall test and Sen’s slope estimator, while drought intensity was analyzed using run theory. The results reveal a clear intensification of drought conditions in the last decade, characterized by an increase in frequency and intensity, particularly after 2010. Extreme drought events were identified in the early 1980s, the mid-1990s, and more recently in 2019 and 2021. Despite some regional variability, the three provinces exhibit consistent temporal patterns, with drought events generally occurring simultaneously over the study period. These findings highlight the increasing pressure on water and environmental systems and underscore the need for improved drought monitoring and forecasting approaches to support more effective adaptation and decision-making. Full article

The detection and monitoring of asbestos–cement roofing remain a critical public health and environmental challenge, especially in urban and suburban areas where asbestos-containing materials are still widespread due to their extensive use in the 20th century. Although hyperspectral and high-resolution multispectral remote sensing have proven effective for mapping asbestos–cement roofs, many existing approaches rely on proprietary software, limiting transparency, reproducibility, and large-scale adoption. This study presents a fully reproducible, cost-free Python-based workflow for the detection and temporal monitoring of asbestos–cement roofing using high-resolution multispectral WorldView-3 imagery. The workflow integrates atmospheric correction (using the Py6S radiative transfer model), spatial preprocessing, supervised pixel-based classification, postprocessing, and building-level aggregation within an open framework. A Maximum Likelihood Classifier is applied to VNIR and SWIR data using empirically defined roof typologies to enhance class separability. Pixel-level results are aggregated to the building scale through adaptive thresholding enabling the translation of spectral classifications into meaningful building-level information. Tested over the city of Mantua (Italy), the approach achieved reliable classification performance and enabled multi-temporal comparison to identify changes potentially due to roof remediation. Evaluation metrics (precision, recall, and F1-score) highlight the importance of carefully choosing the building-level threshold. By relying exclusively on open-source tools, the workflow enhances transparency, reproducibility, and scalability for long-term monitoring. Full article

Mangrove represent vital coastal ecosystems that contribute to shoreline stabilization, ecological balance, and environmental management. Nevertheless, the precise delineation of mangrove regions using remote sensing data is often impeded by spectral similarities with intertidal mudflats and aquatic features, alongside the irregular spatial patterns and intricate margins of mangrove stands. This research utilizes high-resolution Gaofen-6 (GF-6) satellite observations as the foundational data to develop Triplet Axial High-Resolution Network (TAHRNet), a semantic segmentation architecture derived from the High-Resolution Network with Object-Contextual Representations (HRNet-OCR) framework for mangrove identification. The model integrates a Triplet Attention module to facilitate cross-dimensional feature dependencies and an improved Multi-Head Sequential Axial Attention mechanism to capture long-range spatial context while maintaining structural consistency. Based on evaluations using the test dataset, TAHRNet yielded a Mean Intersection over Union (MIoU) of 92.01% and a Overall Accuracy of 96.38%. Relative to U-Net and SegFormer, the proposed approach showed MIoU improvements of 5.25% and 1.88%, with corresponding Accuracy gains of 2.68% and 0.94%. Further application to coastal mapping in Zhanjiang produced results that align with manual visual interpretation. These findings suggest that TAHRNet is a viable tool for mangrove extraction and can provide technical support for coastal monitoring and ecological analysis. Full article

To support low-cost, non-destructive crop growth monitoring, this study systematically compared different vegetation indices, voxel sizes, and camera angles using a point cloud voxelization approach combined with a vegetation index weighted canopy volume index (CVM_VI) to assess aboveground biomass (AGB) in winter oilseed rape (Brassica napus L.). Field experiments were conducted from 2021 to 2024 at the Yangma Experimental Base of the Chengdu Academy of Agricultural and Forestry Sciences. Red, green, blue (RGB) imagery of oilseed rape was acquired using an unmanned aerial vehicle (UAV) during the following five key growth stages: seedling, bolting, flowering, podding, and maturity. Collected images were processed to generate point clouds, which were subsequently voxelized at four resolutions (0.03, 0.05, 0.07, and 0.1 m). CVM_VI was constructed by integrating vegetation indices (VIs) derived from the RGB data and the voxelized canopy structural information. Regression models were established between the CVM_VI values and field-measured AGB to estimate biomass. Model performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and relative error (RE). There were strong correlations (r > 0.80) between the estimated and measured AGB across all voxelization treatments throughout the growth period. Among the 20 VIs tested, regression methods based on the blue green ratio index (BGI), color intensity index, blue red ratio index, vegetative index, and green red ratio index consistently showed superior estimation performance across three consecutive years, demonstrating their good applicability for estimating AGB in oilseed rape under varying agronomic conditions (different varieties, densities, and sowing dates). The cubic regression model CVM_BGI performed best under a 45° UAV camera angle, with the highest R² and lowest RMSE and RE (2021–2022: R² = 0.864, RMSE = 2414.18 kg/ha, RE = 14.8%; 2022–2023: R² = 0.754, RMSE = 2550.53 kg/ha, RE = 14.9%; 2023–2024: R² = 0.863, RMSE = 1953.61 kg/ha, RE = 22.9%). Since the estimation performance showed negligible differences among voxel sizes, and the 0.1–m voxel offered the smallest data volume and shortest analysis time, the CVM_BGI model with a 0.1–m voxel was selected as the preferred approach, providing a practical balance between estimation performance and processing demand. These findings highlight the application potential of point cloud voxelization technology for crop biomass estimation. This study proposes a novel, non-destructive, and efficient framework for estimating field crop AGB using low-cost UAV RGB imagery, facilitating the wider adoption of UAV technology in practical agricultural production. Full article

The rapid deterioration of road infrastructure requires accurate and efficient methods for detecting pavement distresses. Unmanned Aerial Vehicles (UAVs) have emerged as a reliable alternative to conventional inspection techniques, enabling high-resolution data acquisition and improved operational safety. This study investigates the application of the Smart Oblique Capture (SOC) technique for pavement inspection through a systematic calibration of UAV flight parameters, including Ground Sample Distance (GSD), frontal and lateral overlap, camera tilt angle, and flight pattern. A structured experimental campaign was conducted, comprising 135 parameter combinations evaluated across three independent scenarios, resulting in a total of 405 UAV flights. The analysis focused on assessing the impact of these parameters on the visual quality of two-dimensional pavement reconstructions and processing efficiency. The results show that a configuration consisting of a 0.5 cm/pixel GSD, 70% frontal overlap, 80% lateral overlap, and a 70° camera tilt angle achieves the best balance between reconstruction quality and computational cost. Furthermore, the findings indicate that Smart Oblique Capture does not provide a statistically significant improvement in reconstruction quality for linear infrastructure compared to conventional oblique configurations, despite requiring a higher number of images and longer processing times. Overall, the results demonstrate that flight parameter calibration plays a more critical role than the adoption of advanced acquisition strategies such as Smart Oblique Capture. This study provides practical and reproducible guidelines for UAV-based pavement inspection, supporting efficient data acquisition while minimizing redundant information and unnecessary computational costs in infrastructure monitoring workflows. Full article

Cement stabilized macadam (CSM) bases are prone to cracking and void damage under long-term traffic loading and environmental actions, which accelerates structural deterioration. Although grouting is an effective method for treating such concealed defects, laboratory-based evaluation of repair effectiveness remains limited. In this study, field-cored CSM specimens were recombined in a cylindrical mold to simulate four void conditions (1/4, 2/4, 3/4, and 4/4), and repaired using an inorganic cementitious composite grouting material based on ultra-fine cement and high-belite sulphoaluminate cement (HBSAC), and modified with ethylene-vinyl acetate (EVA) latex, wollastonite (WO) whiskers, and polyvinyl alcohol (PVA) fibers. The repair effectiveness was evaluated through ultrasonic testing, capacitance measurement, uniaxial compression with acoustic emission (AE) monitoring, and computed tomography (CT). The results show that the longitudinal wave velocity of all repaired groups increases continuously with curing time, with a maximum increase of 21.98% at 28 days. The normalized capacitance response exhibits clear time- and layer-dependent variation, with the 4/4 group showing the most pronounced spatial heterogeneity. In the uniaxial compression tests, the peak load increases from 181 kN in the control group to 201–286 kN in the repaired groups, while the tensile-related AE event proportion increases from 77.35% in the 1/4 group to 89.38% in the 4/4 group. CT analysis shows that the proportion of micropores smaller than 1 mm³ increases from 66.3% to 82.7%, whereas the proportion of pores larger than 100 mm³ decreases from 46.5% to 21.6% after repair. These results demonstrate that the composite grouting material provides effective filling, structural reconstruction, and mechanical enhancement for void-damaged CSM, and that the proposed multi-source characterization framework is suitable for evaluating grouting repair performance. Full article

Background: Sleep bruxism involves repetitive jaw-muscle activity, including teeth clenching, grinding, or mandibular bracing. Despite the growing interest in botulinum toxin type A (BTX-A) as a therapeutic intervention for bruxism, evidence remains limited, particularly regarding studies using portable electromyography (EMG) monitoring devices. This study evaluated the effects of BTX-A injections into the masseter muscle on the reduction of bruxism activity, as measured using the portable electromyographic Holter Bruxoff system. Methods: Adult patients with diagnosed sleep bruxism were monitored for two nights using the Bruxoff device to record masseter EMG activity, respiratory rate, and heart rate. After receiving standardized bilateral masseter BTX-A injections, participants underwent the same monitoring protocol 40 days later. Statistical analyses compared pre- and post-treatment values, and effect sizes were calculated. Results: Ten participants (60% women; mean age 47.6 ± 4.4 years) completed the study. The Bruxism Index showed a marked reduction, dropping from 12.2 ± 1.32 at baseline to 7.4 ± 1.35 after 40 days, a statistically significant change (t (9) = 10.23, p < 0.001; Cohen’s d = 3.25). Average heart rate also decreased significantly, from 64.4 ± 2.99 to 62.6 ± 2.63 (t (9) = 2.86, p = 0.018; Cohen’s d = 0.91). However, the respiratory rate measurement remains stable. Conclusions: BTX-A injections into the masseter muscles produced a marked reduction in sleep-related bruxism activity as measured by portable EMG. These findings support BTX-A as a promising and effective treatment option for sleep bruxism. Full article

This study investigates the impact of oil prices and exchange rates on the manufacturing output of small and medium-sized enterprises (SMEs) in Kazakhstan using data from the period 2000 to 2023, within the framework of the ARDL model. In the Kazakhstani economy, approximately 60% of SMEs operate in the wholesale and retail trade sectors, a factor that has been taken into consideration in interpreting the effects of macroeconomic variables on SME output. The results of the long-run analysis reveal that the exchange rate has a significant and strong positive effect on SME manufacturing output. Although oil prices do not directly exert a statistically significant influence on production output, the study identifies an indirect effect of oil revenues on SME output via the exchange rate channel. In the short-run findings, both exchange rates and oil prices are found to have significant effects on production output; in particular, oil prices exhibit a positive impact in the short term, which partially reverses in subsequent periods. The error correction term indicates a rapid adjustment back to equilibrium in the long run. These results highlight the high sensitivity of SME production performance in Kazakhstan to exchange rate fluctuations and underscore the indirect influence of oil prices through exchange rate movements. The study recommends enhancing the financial resilience of SMEs, minimizing exchange rate risks, and closely monitoring changes in energy prices. Furthermore, it suggests the development of policies aimed at promoting SMEs’ involvement in foreign currency-generating activities, as well as protecting enterprises in the wholesale and retail sectors against price volatility. In this context, the study makes a valuable contribution by providing a comprehensive evaluation of the effects of macroeconomic variables on SME manufacturing output. Full article

Knowledge about the condition of electrical equipment in energy networks is of great importance to network operators. Partial discharges are a key parameter for evaluating the health of the insulation. While a quantifiable PD measurement for offline tests is state of the art, it is costly and labour-intensive. It, therefore, makes sense to carry out permanent monitoring during operation. At the medium-voltage level in the European interconnected grid, comprehensive monitoring of PD is not implemented. This study presents a novel sensor concept that is used to detect PD in medium-voltage switchgear and cables: the so-called Magnetic Flux Concentrator Sensor (MFCS). It is an inductive sensor concept with high sensitivity in the frequency range of a few MHz, like well-established High-Frequency Current Transformers (HFCTs) but with better magnetic saturation properties in specific use cases. The highly permeable ferrite core of the MFCS is unconventionally shaped, resulting in a higher-saturation field strength. Therefore, this sensor is not driven into saturation by the operating currents of typical MV power cables. Using the MFCS and conventional HFCT in a suitable combination enables direction-selective PD detection. This work presents the sensor concept and the method for directional detection of the PD location, as analysed and evaluated theoretically and practically with laboratory experiments. Full article

Background/Objectives: B-cell maturation antigen (BCMA)-directed immunotherapies, including chimeric antigen receptor T-cell (CAR-T) therapies and bispecific antibodies (BsAbs), have improved clinical outcomes in multiple myeloma. However, cytokine release syndrome (CRS) remains a major safety concern, and comparative real-world evidence across BCMA-directed agents remains limited. This study aimed to evaluate and compare CRS reporting patterns associated with BCMA-targeted CAR-T and BsAb therapies using the FDA Adverse Event Reporting System (FAERS) data and to identify predictors of CRS reporting using machine learning-based approaches. Methods: A pharmacovigilance analysis was conducted using FAERS reports from 2021 Q1 to 2025 Q3. Disproportionality analyses were performed using the reporting odds ratio (ROR), proportional reporting ratio (PRR), and information component (IC), and signals were considered present when predefined thresholds were met. Multivariable logistic regression was applied to estimate adjusted odds ratios (aORs) for CRS reporting while adjusting for demographic and reporting characteristics. Machine learning models, including XGBoost, LightGBM, and random forest were developed to predict CRS reporting. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Results: Among 4046 reports included in the final dataset, CAR-T therapies showed higher CRS reporting odds than BsAbs (aOR: 2.55, 95% CI: 2.16–3.01). Disproportionality analyses identified significant CRS signals for CAR-T therapies across all indices, whereas BsAbs did not meet signal detection thresholds. At the agent level, idecabtagene vicleucel was the only agent meeting all predefined signal detection criteria and exhibited the strongest reporting pattern in multivariable analysis (aOR: 6.96, 95% CI: 5.53–8.75). Among the evaluated models, LightGBM achieved the highest predictive test AUROC (0.762). SHAP analysis identified idecabtagene vicleucel, United States region, and reporting year as the most influential predictors of CRS reporting. Conclusions: CAR-T therapies, particularly idecabtagene vicleucel, exhibited higher CRS reporting odds than BsAbs, with substantial agent-level heterogeneity observed across BCMA-directed immunotherapies. Integrating pharmacovigilance and machine learning approaches may facilitate more individualized safety monitoring by identifying agent-specific differences in CRS risk among BCMA-targeted therapies. Full article

This study has investigated the occurrence characteristics and population damage of weeds in double-cropping direct-seeded rice fields in Hunan, and has identified efficient and safe pre- and post-emergence herbicides to enhance resistance management. Field trials were conducted at two representative sites (Yiyang and Changsha) in Hunan in 2024~2025. Weed community composition and emergence patterns were systematically monitored. The inhibitory effects of weed infestations on rice growth and yield were quantified. The biological activity and field efficacy of various herbicide classes against barnyardgrass (Echinochloa crus-galli) were evaluated via greenhouse bioassays and field trials. Weed emergence lasted 3–48 days after sowing (DAS) with three distinct peaks. Grasses emerged earliest and dominated the community, with barnyardgrass peaking at 13–17 DAS (≈50% of total weeds), followed by broadleaves at 20 DAS (≈40%) and sedges at 25 DAS (<20%). Weed infestation drastically suppressed rice height (max 19% reduction) and tillering (max 50% reduction), with mixed-weed and grass-dominated plots causing the severest yield losses (92.0% and 90.5%, respectively), versus only 18.0% in broadleaf-dominated plots. Greenhouse bioassays showed that oxaziclomefone had the highest intrinsic activity against barnyardgrass (GR₉₀ = 17.70 g ai ha⁻¹). In pre-emergence applications in field trials, pretilachlor (900 g ai ha⁻¹) and mefenacet (147.6 g ai ha⁻¹) provided >96.8% control at 20 and 40 days after treatment (DAT), while oxaziclomefone (66 g ai ha⁻¹) achieved 88.2% control at 20 DAT. For post-emergence herbicides, Profoxydim showed the highest intrinsic activity (GR₉₀ = 33.01 g ai ha⁻¹), followed by feproxydim (GR₉₀ = 33.45 g ai ha⁻¹) and flusulfinam (GR₉₀ = 64.55 g ai ha⁻¹). In field trials, flusulfinam provided 100% control with superior crop safety at 20 and 40 DAT, while Florpyrauxifen-benzyl, feproxydim, and metamifop reached >93% efficacy. In conclusion, weed emergence in Hunan direct-seeded rice follows a three-peak pattern, with barnyardgrass being the most destructive species. An integrated strategy combining pretilachlor (pre-emergence) and flusulfinam (post-emergence), rotated with florpyrauxifen-benzyl and feproxydim, is recommended for effective barnyardgrass management and resistance mitigation. Full article