Search Results (54,123)

Background/Objectives: Poisoning represents a significant global public health challenge, particularly with its complex manifestations in adult populations. Understanding regional epidemiology through digital health systems is crucial for developing evidence-based prevention and management strategies. This nationwide study analyzes hospital-based toxicology teleconsultation data from the Toxicology Consultation Service-Saudi Medical Appointments and Referrals Center (TCS-SMARC) platform to characterize the epidemiological patterns, clinical features, and outcomes of adult poisoning cases across Saudi regions. Methods: We conducted a retrospective cross-sectional analysis of 6427 adult poisoning cases where hospitals sought teleconsultation from the Saudi Toxicology Consultation Service (TCS) from January to December 2023. Descriptive statistics were used to analyze poisoning rates by demographic characteristics, agents responsible for the poisoning, clinical presentations, and management decisions. Population-adjusted rates were calculated using the national census data. Associations between variables were analyzed using cross-tabulations and chi-square tests. Results: Young adults aged 18–35 years constituted most cases (58.67%), with the highest population-adjusted rates observed among those aged 18–24 (5.15 per 10,000). Medicine-related poisonings were the most common across all regions (50.04%), followed by bites and stings (15.31%). Regional analysis indicated relatively uniform poisoning rates across Business Units (BUs) (2.02–2.74 per 10,000). Most cases (87.44%) were asymptomatic, with 91.71% exhibiting normal Glasgow Coma Scale scores, although substance abuse cases had higher rate of severe manifestations (24.34%). Significant seasonal variations were observed (p < 0.001), with peak incidents occurring in the summer (29.25%). Management decisions primarily involved hospital observation (40.27%) and admission (30.34%), with agent-specific variations in care requirements (p < 0.001). Conclusions: This comprehensive analysis demonstrates the effectiveness of Saudi Arabia’s digital health infrastructure in capturing and managing nationwide poisoning data. The integrated digital platform enables real-time surveillance, standardized triage, enhanced access to specialized toxicology services, and coordinated management across diverse geographical contexts. Our findings inform evidence-based recommendations for targeted prevention strategies, particularly for young adults and medicine-related poisonings, while establishing a scalable model for digital health-enabled poisoning management. Full article

Background/Objectives: Ferroptosis, a type of programmed cell death, is mainly associated with disruptions in iron metabolism, imbalances in the amino acid antioxidant system, and the build-up of lipid peroxides. Triple-negative breast cancer (TNBC) has a dismal prognosis. Since activating ferroptosis can suppress breast cancer cell proliferation, it holds promise as a novel therapeutic target for breast cancer patients. Thus, the objective of this study was to clarify the mechanism of ferroptosis in TNBC, aiming to find new treatment strategies for TNBC patients. Methods: We screened out the differential genes related to ferroptosis in TNBC after GluOC treatment based on the whole-genome sequencing results. At the cellular level, we conducted explorations using techniques such as quantitative real-time polymerase chain reaction (qRT-PCR), Western blotting, fluorescence staining, and siRNA transfection. Moreover, we further verified the role of GluOC in inhibiting ferroptosis in TNBC through in vivo experiments using nude mice. Results: The results showed that GluOC enhanced glutathione expression levels by inducing SLC7A11 accumulation via the specific signaling pathway. Additionally, GluOC increased ATP production and tricarboxylic acid flux resistance to ferroptosis through SLC38A1. Overall, GluOC coordinately regulated SLC7A11 and SLC38A1 to inhibit ferroptosis in TNBC. Conclusions: This study elucidated the mechanism of GluOC in inhibiting ferroptosis in TNBC. The findings not only provided new insights into ferroptosis but also potentially offered new concepts for the development of future anticancer therapies, which may contribute to improving the treatment of TNBC patients. Full article

Long-term observations in wells make it possible to study changes in groundwater pressure/level during individual earthquakes (seismo-hydrogeodynamic effects—SHGEs) over a wide range of periods of their manifestation. Information on the morphological features and durations of the SHGEs together with data on earthquake parameters form the basis for creating the unique typifications of SHGEs for individual observation wells. With reliable verification, such SHGE typifications provide the practical use of well observation data to predict strong earthquakes and assess their impact on groundwater. During long-term (1996–2022) precision observations of pressure/water level variations in wells of the Petropavlovsk–Kamchatsky test site (Kamchatka Peninsula, northwest Pacific seismic belt), SHGE typifications describing the manifestations of various types of SHGEs at the earthquakes in ranges of magnitudes M_w = 5.0–9.1 and epicentral distances d_e = 80–14,600 km were developed. At the same time, the issue of verifying created SHGE typifications for individual wells in relation to the strongest and closest earthquakes, accompanied by noticeable tremors in the observation area, is relevant. On 3 April 2023, an earthquake, M_w = 6.6 (EQ), occurred at an epicentral distance d_e = 67–77 km from observation wells. Various changes in the groundwater pressure/level were recorded in the wells: oscillations and other short-term and long-term effects of seismic waves, coseismic jumps in water pressure caused by a change in the static stress state of water-bearing rocks during the formation of rupture in the earthquake source, and supposed hydrogeodynamic precursors. The EQ was used to verify the SHGE typifications for wells YuZ-5 and E-1 with the longest observation series of more than 25 years. In these wells, the seismo-hydrogeodynamic effects recorded during the EQ corresponded to the previously observed SHGE during the two strongest earthquakes with M_w = 7.2, d_e = 80 km and M_w = 7.8, d_e = 200 km. This correspondence is considered an example of the experimental verification of previously created SHGE typifications in individual wells in relation to the most powerful earthquakes in the wells’ area. Updated SHGE typifications for wells E-1 and YuZ-5 are presented, showing the patterns of water level/pressure changes in these wells depending on earthquake parameters and thereby increasing the practical significance of well observations for assessing earthquake consequences for groundwater, searching for hydrogeodynamic precursors and forecasting strong earthquakes. The features of the hydrogeodynamic precursor manifesting in the water level/pressure lowering with increased rates in well E-1 before earthquakes with M_w ≥ 5.0 at epicentral distances of up to 360 km are considered. A retrospective statistical analysis of the prognostic significance of this precursor showed that its use for earthquake forecasting increases the efficiency of predicting earthquakes with M_w ≥ 5.0 by 1.55 times and efficiency of predicting earthquakes with M_w ≥ 5.8 by 2.34 times compared to random guessing. This precursor was recorded during the 92 days before the EQ and was identified in real time with the issuance of an early prognostic conclusion on the possibility of a strong earthquake to the Kamchatka branch of the Russian Expert Council for Earthquake Forecasting. Full article

Environmental effects often trigger false alarms in vibration-based damage detection methods used for structural health monitoring (SHM). While conventional techniques like Principal Component Analysis (PCA) and cointegration have been somewhat effective in addressing this issue, challenges such as measurement noise, nonlinear behavior, and non-Gaussian data distribution continue to affect their performance. To address these limitations, a novel damage detection method combining Variational Mode Decomposition (VMD) and Dynamic Kernel Entropy Component Analysis (DKECA) is proposed. The proposed method initially uses the VMD technique to remove seasonal patterns and noise from the modal frequencies. Subsequently, a DKECA model is constructed based on a time-delay data matrix, and the principal components that maximize the Rényi entropy in the high-dimensional space are selected. Using these principal components, a damage detector developed from the statistic is used to determine damage indices for SHM. The effectiveness of the proposed method is verified through both a simulated 7-DOF model and real-world data from the Z24 bridge, with comparative studies highlighting its advantages over existing techniques. Full article

Soil environmental monitoring is crucial for ensuring the sustainability and productivity of agriculture. This study aims to develop a wireless soil monitoring system that utilizes Narrowband Internet of Things (NB-IoT), solar energy, and Global Positioning System (GPS) technologies to address the issues of high labor demand, high costs, and delayed feedback in traditional soil monitoring methods. This system can collect soil temperature, humidity, and meteorological data in real time, transmit them to a cloud platform for analysis and visualization, and predict future soil data. It employs multiple learning algorithms to build models and uses the Tree-structured Parzen Estimator (TPE) algorithm for hyperparameter optimization. Field stability experiments were conducted on the system, and the performance of the soil moisture prediction model was evaluated. During the 84-day stability experiment, the system operated stably for 80 days, with a data collection success rate of 95.87%. In the performance evaluation of the soil moisture model, the GBDT model achieved a coefficient of determination (R²) of 0.9838 on the validation set and a root-mean-square error (RMSE) of 0.0013, with an RMSE of 0.0013 on the test set as well. The experimental results demonstrate that the system is stable and reliable, featuring low power consumption, wide coverage, and high accuracy. It can effectively predict soil moisture, providing timely and accurate support for irrigation and farming decisions. Full article

The civil engineering sector operates within a complex ecosystem of stakeholders, requiring efficient management and maintenance of structural and infrastructural assets. In this context, there is an increasing need for robust tools to track critical events (e.g., alerts, unusual behaviors) and support decision-making processes related to maintenance and interventions. At the same time, ensuring secure and prompt payments is essential for timely and effective responses. This paper investigated the potential of smart contracts, integrated with blockchain technology, to automate and optimize asset management and maintenance processes. The proposed framework examines how these technologies can enhance operational efficiency, security, and event traceability, providing a structured approach for both routine operations and emergency interventions. Although smart contracts have been widely applied in the construction phase of infrastructure projects, their use in long-term asset management remains largely unexplored. As a conceptual study, this work does not present a quantitative analysis but instead lays the groundwork for future research and real-world applications of blockchain-based smart contracts in infrastructure management and safety procedures. Full article

Enhancing the dynamic response of Flux-Switching Permanent Magnet Synchronous Machines (FSPMSMs) is crucial for high-performance applications such as electric vehicles, renewable energy systems, and industrial automation. Conventional Proportional Integral (PI) controllers within model predictive current control (MPCC) frameworks often struggle to meet the demands of rapid transient response and precise speed tracking, particularly under dynamic operating conditions. To address these challenges, this paper presents a hybrid control strategy that integrates Sliding Mode Control (SMC) into the speed loop of MPCC, aiming to significantly improve the dynamic response and control robustness of FSPMSMs. The feasibility and effectiveness of the proposed approach are validated through high-fidelity real-time simulations using OPAL-RT Technologies’ OP5707XG simulator. Two control schemes are compared: MPCC with a PI controller in the speed loop (MPCC-PI) and MPCC with SMC in the speed loop (MPCC-SMC). Testing was conducted under various operating scenarios, including starting tests, load variations, speed ramping, and speed reversals. The results demonstrate that the MPCC-SMC strategy achieves superior dynamic performance, faster settling times, smoother transitions, and enhanced steady-state precision compared to the MPCC-PI scheme. The comparative results confirm that the MPCC-SMC method outperforms conventional MPCC strategies, making it a compelling solution for advanced motor drive applications requiring enhanced dynamic control. Full article

Inverse synthetic aperture ladar (ISAL) systems combine laser coherent detection technology with inverse synthetic aperture imaging methods, offering advantages such as compact size, long detection range, and high resolution. The traditional optical delay line technique is widely used in frequency-modulated continuous wave (FMCW) ISAL imaging systems, but its flexibility is limited, posing challenges for high-precision signal processing. Additionally, frequency modulation errors, atmospheric disturbances, and other errors inevitably affect image quality. Therefore, this paper proposes a signal processing method based on digital delay for FMCW ISAL, aiming to achieve the high-resolution imaging of targets across several kilometers. Firstly, the paper introduces the FMCW ISAL system. By introducing digital delay technology, it enables the flexible and real-time adjustment of reference signal delay. Next, to address the frequency offset issue caused by the introduction of digital delay technology, a preprocessing method for unified frequency shift correction is proposed to ensure signal consistency. Then, a set of internal calibration signal datasets is generated based on digital delay technology. Following this, a frequency modulation error iteration estimation method based on gradient descent is introduced. Without the need for target echo signals, the method accurately estimates the frequency modulation phase errors of both the transmitted and reference signals using only the internal calibration signals. Finally, this paper effectively decomposes the motion of the target, derives the echo model for the FMCW ISAL system, and constructs compensation functions to eliminate the intra-pulse Doppler shift and the residual video phase (RVP). Additionally, the Phase Gradient Autofocus (PGA) algorithm is used after two-dimensional imaging to eliminate the impact of atmospheric disturbances. We conducted two sets of experiments on point targets and surface targets to verify the effectiveness of error compensation in improving imaging quality. The results show that the two-dimensional resolution of point targets was optimized to 3 cm (range) × 0.6 cm (azimuth), while the resolution and entropy of the surface targets were both improved by 0.1. These results demonstrate that the proposed method effectively enhances target imaging quality and provides a new technical approach for high-precision signal processing in FMCW ISAL imaging. Full article

In manufacturing automation, accurately determining the optimal tool replacement timing is critical yet challenging. Tool condition monitoring (TCM) has been widely studied to address this issue. Cutting force is a key parameter for evaluating tool wear, but conventional force sensors are costly and difficult to implement. This study proposes a cost-effective alternative by estimating cutting forces using spindle vibration data through a long short-term memory (LSTM)-based machine learning model. First, the correlation between cutting force and tool wear is analyzed to emphasize the need for accurate force estimation. Then, vibration data collected from the spindle are used to train an LSTM model, which is effective for time-series data processing. The model is trained with vibration signals from various machining positions, with structured time-series datasets improving performance and generalization. Experimental results show that the developed model accurately estimates cutting forces using short segments of vibration data from a single tool revolution. Additionally, the observed relationship between cutting force and tool wear remains consistent across different machining conditions. This study validates real-time cutting force estimation via spindle vibration monitoring and suggests its potential for tool wear prediction. The proposed method offers a practical, low-cost solution for improving tool condition monitoring in automated machining. Full article

Plant pathogen zoospores play a vital role in the transmission of several significant plant diseases, with their early detection being important for effective pathogen management. Current methods for pathogen detection involve labour-intensive specimen collection and laboratory testing, lacking real-time feedback capabilities. Methods that can be deployed in the field and remotely addressed are required. In this study, we have developed an innovative zoospore-sensing device by combining a microfluidic sampling system with a microfluidic cytometer and incorporating a chemotactic response as a means to selectively detect motile spores. Spores of Phytophthora cactorum were guided to swim up a detection channel following a gradient of attractant. They were then detected by a transient change in impedance when they passed between a pair of electrodes. Single-zoospore detection was demonstrated with signal-to-noise ratios of ~17 when a carrying flow was used and ~5.9 when the zoospores were induced to swim into the channel following the gradient of the attractants. This work provides an innovative solution for the selective, sensitive and real-time detection of motile zoospores. It has great potential to be further developed into a portable, remotely addressable, low-cost sensing system, offering an important tool for field pathogen real-time detection applications. Full article

Surface plasmon resonance (SPR) sensing technology has been widely utilized in fields such as biomedicine, food safety, and drug screening for real-time, rapid, and label-free detection of biomolecular interactions. However, conventional SPR sensing methods find it difficult to provide the necessary sensitivity and stability when detection applications go toward ultra-low concentrations and tiny molecular weight analytes. Here, we present a high-sensitivity Goos–Hänchen shift sensing based on SPR and beam displacement amplification technology (BDAT). The incorporation of BDAT significantly amplifies the magnitude of GH shift with remarkable stability, enhancing the sensing sensitivity by an order of magnitude. The sensor achieves a sensitivity of 3.62 × 10⁴ μm/RIU and a minimum detection limit of 3.10 × 10⁻⁵ RIU. Furthermore, both theoretical and experimental results demonstrate that GH shift sensing offers superior performance compared with traditional intensity-based SPR, particularly for low-concentration solutions. The BDAT approach amplifies GH shifts by at least 12 times, significantly improving sensitivity. With the use of SPR and BDAT, we are able to generate a large GH shift, which makes it easier to detect low concentrations and offers a wide range of possible uses in clinical diagnostics and biomedicine. Full article

Anthocyanin synthetase (ANS), a key enzyme in the final step of the anthocyanin synthesis pathway, catalyzes the conversion of leucoanthocyanidins to anthocyanins. In this study, an ANS structural protein (TRINITY_DN18024_c0_g1) was found to be associated with anthocyanin accumulation in Acer pseudosieboldianum leaves, named ApsANS. Real-time quantitative fluorescence PCR analysis revealed that the expression of ApsANS was significantly higher in red-leaved (variant) than green-leaved (wild-type) strains, which was consistent with the transcriptome data. The UPLC results showed that the cyanidin metabolites may be the key substance influencing the final color formation of Acer pseudosieboldianum. The ApsANS gene was cloned and analyzed through bioinformatics analysis. ApsANS has a total length of 1371 bp, and it encodes 360 amino acids. Analysis of the structural domain of the ApsANS protein revealed that ApsANS contains a PcbC functional domain. Protein secondary structure predictions indicate that α-helix, irregularly coiled, and extended chains are the major building blocks. Subcellular localization predicted that ApsANS might be localized in the nucleus. The phylogenetic tree revealed that ApsANS is relatively closely related to ApANS in Acer palmatum. The prediction of miRNA showed that the ApsANS gene is regulated by miR6200. This study provides a theoretical reference for further analyzing the regulatory mechanism of leaf color formation in Acer pseudosieboldianum. Full article

Ensuring the reliability and safety of electrical power systems requires the efficient detection of defects in high-voltage transmission line insulators, which play a critical role in electrical isolation and mechanical support. Environmental factors often lead to insulator defects, highlighting the need for accurate detection methods. This paper proposes an enhanced defect detection approach based on a lightweight neural network derived from the YOLOv11n architecture. Key innovations include a redesigned C3k2 module that incorporates multidimensional dynamic convolutions (ODConv) for improved feature extraction, the introduction of Slimneck to reduce model complexity and computational cost, and the application of the WIoU loss function to optimize anchor box handling and to accelerate convergence. Experimental results demonstrate that the proposed method outperforms existing models like YOLOv8 and YOLOv10 in precision, recall, and mean average precision (mAP), while maintaining low computational complexity. This approach provides a promising solution for real-time, high-accuracy insulator defect detection, enhancing the safety and reliability of power transmission systems. Full article

Making use of the well-known renormalization group (RG) scale dependences of the gravitational couplings in the framework of the two-parameter Einstein–Hilbert (EH) theory of gravity, the single scalar field-driven cosmological inflation is discussed in a spatially homogeneous, isotropic, and flat model universe. The inflaton field is represented by a one-component real, non-self-interacting, massive scalar field minimally coupled to gravity. Cases without and with the incorporation of the RG scaling of the inflaton mass are compared with each other and with the corresponding classical case. It is shown that the quantum improvement drastically alters the timing of the slow-roll inflation with the desirable number $\mathcal{N}\approx 60$ e-foldings, as compared with the classical case. Furthermore, accounting for the RG flow of the inflaton mass has an enormous effect on the timing of the desirable slow roll, too. Although providing the desirable slow-roll inflation, none of the versions of the investigated quantum-improved toy models provide a realistic value of the amplitude of the scalar perturbations. Full article

Determining the appropriate position of a positive pressure ventilator, where it exhibits the highest efficiency (measured by the achieved volumetric flow rate), can influence the success of rescue operations conducted by fire protection units. The aim of this article is to evaluate the possibility of using LES (Large Eddy Simulation) analyses to verify the positioning parameters of positive pressure ventilators in numerical simulation conditions, without the need for time-consuming experiments. The article presents a comparative analysis of full-scale experimental studies (conducted on a test setup to assess the velocity profile of the air jet in open flow) and CFD numerical analyses. The analysis confirmed the convergence of the flow rate parameter entering the surface of the door opening model installed on the test setup. Depending on the distance of the ventilator position (1–7 m), a convergence degree ranging from 1.6% to 3.8% was achieved for the volumetric flow rate. This publication demonstrates that the LES model is a suitable tool for effectively determining the working positions of positive pressure ventilators, as defined in real working conditions (open flow). The analysis may serve as a helpful tool for manufacturers of mobile ventilators, who can use the method for the technological testing of their units without conducting time-consuming experiments. Full article