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Leveraging machine learning in precision medicine to unveil organochlorine pesticides as predictive biomarkers for thyroid dysfunction
Abstract Exposure to organochlorine pesticides (OCPs) poses significant health risks, including cancer, endocrine dysregulation, neurological disorders, and reproductive disruption. This study investigates the association between OCP exposure and thyroid disturbances using machine learning (ML) models. Blood samples were analyzed for the concentration of 16 OCPs and thyroid hormones (T3, T4, TSH) using traditional methods such as Logistic Regression and least absolute shrinkage and selection operator (LASSO) and more advanced ML models such as Random Forest (RF), Support Vector Machine (SVM), XGBoost, and Gradient Boosting Machine (GBM). High frequencies of OCPs, including Heptachlor, Heptachlor epoxide, γ-HCH, Aldrin, Endrin aldehyde, α-endosulfan, and Methoxychlor, were detected in over 70% of serum samples. The RF and GBM models achieved the highest accuracy at 90.91%, while XGBoost demonstrated a high ROC-AUC score of 94.02%. The SVM model also showed robust performance, whereas Logistic Regression exhibited weaker results. Our findings highlighted specific OCPs, such as Methoxychlor, p,p-DDT, Heptachlor, Endrin, and various HCH isomers, could impact thyroid function. The study supports a strong correlation between OCP exposure and thyroid dysfunction, demonstrating high accuracy in classifying thyroid status using ML models. Significant OCPs identified include p, p-DDT, Methoxychlor, Endrin, β-endosulfan, and Heptachlor, which are associated with thyroid dysfunction.
Additive modeling of zonal level crop production in Ethiopia
Detecting implicit biases of large language models with Bayesian hypothesis testing
Imaging and speciation of intracellular metallic implant debris using synchrotron-based X-ray fluorescence micro-spectroscopy: a study of two cases
A novel reindeer cyclone optimization algorithm (RCOA)
Abstract This paper introduces the Reindeer Cyclone Optimization Algorithm (RCOA), a novel metaheuristic optimization technique inspired by the survival behavior of reindeer during predator attacks in formation cyclonic storms. RCOA imitates the defense-centric cooperative behavior of reindeer, where individuals cluster together to withstand external threats. This behavior is analogous to the optimization process where exploration (global search for exploring new areas) and exploitation (local refinement to copy or learn from neighbor in cyclonic form) are carefully balanced. The algorithm has been extensively evaluated against 14 unimodal and multimodal benchmark functions and 4 real-world complex optimization problems. RCOA demonstrates a moderate improvement of around 5–12% over other algorithms such as PSO, DE, COA and GSA on unimodal functions. On multimodal functions, RCOA shows more competitive performance, especially in terms of stability, with an improvement of around 10–15% in accuracy and consistency compared to WDO and PSO. The algorithm is evaluated using the CEC’17 benchmark suite with 50 dimensions and compared against different well-established optimization algorithms, including WOA, PSO, GSA, and DE. Experimental results demonstrate that RCOA outperforms existing methods on multiple test functions by achieving superior convergence speed and solution accuracy. The Wilcoxon Signed-Rank test confirms the statistical significance of RCOA’s performance, indicating its robustness and reliability in handling diverse optimization landscapes. The findings suggest that RCOA is a competitive optimization method suitable for a wide range of real-world applications.
Comprehensive performance and optimization of micro textured slipper pair of axial piston pumps
Abstract The present study aims to fully exploit the potential of micro-texturing to increase the operational efficiency of piston pump slipper pairs and expand the repertoire of discretely distributed pit micro-textures. Four micro-textures, each featuring unique opening and pit configurations, have been proposed. These micro-textures are applied to the working surface of the axial piston pump slipper to optimize friction and wear characteristics, mitigate heat generation during operation, and decrease leakage within the slipper pair. The influence of micro-texturing on the lubricant oil film of the slipper surface was evaluated using computational fluid dynamics (CFD) and experimental methodologies. The research employed response surface methodology to examine the impact of the position distribution and shape size parameters of the micro-textures on the bearing pressure, friction coefficient, temperature, and oil film leakage between slipper pairs, followed by multi-objective and multi-parameter optimization. The results indicate that the implementation of micro-textures substantially enhances the operational performance of the slipper surface. A texture design that integrates an elliptical opening with an elliptical offset parabolic pit body situated at the innermost two-ring support band of the slipper is recommended. Compared with the original prototype, this optimized design yields a 19.69% increase in the bearing pressure and a 21.08% reduction in the friction coefficient, coupled with a 14.20% decrease in the average temperature and a 14.03% reduction in oil film leakage. This research provides a valuable reference for the design of a wider array of pit micro-texture geometries and offers theoretical support for the performance enhancement of axial piston pumps.
A prevalence study focusing on hospitalized COPD related pulmonary hypertension
Development and validation of a novel endoplasmic reticulum stress-related lncRNA signature in laryngeal squamous cell carcinoma
Image dehazing algorithm based on light-value weighted allocation and multi-layer restricted perception
Robot assisted stereotactic surgery improves hematoma evacuation in intracerebral hemorrhage compared to frame based method
Abstract Intracerebral hemorrhage (ICH) requires prompt hematoma evacuation to mitigate poor outcomes. This study compares robot-assisted stereotactic surgery with traditional frame-based methods for ICH evacuation. A retrospective analysis of 131 patients (45 robot-assisted, 86 frame-based) undergoing surgery within 72 h of supratentorial basal ganglia hemorrhage was conducted. Propensity score matching balanced baseline characteristics between 40 patients per group. Results showed robot-assisted surgery achieved a significantly higher median hematoma evacuation rate (78.7% vs. 66.2%) and shorter median hospital stay (12 vs. 15 days) compared to frame-based surgery, with no significant differences in residual hematoma volume, surgical time, postoperative complications, or short-term functional outcomes. While robot-assisted techniques enhance evacuation efficiency and reduce hospitalization without increasing risks, their long-term neurological benefits require further investigation. These findings highlight the potential of robotic assistance as a safe and effective minimally invasive approach for ICH management.
Modulation of biofilm growth by shear and fluctuations in turbulent environments
Chitosan hydrogels loaded with Cu3SnS4 NSs for the treatment of second-degree burn wounds
CMTNet: a hybrid CNN-transformer network for UAV-based hyperspectral crop classification in precision agriculture
Knowledge, attitudes, and practices regarding exercise management among patients with primary hypertension: a cross-sectional study
CaMK2A/CREB pathway activation is associated with enhanced mitophagy and neuronal apoptosis in diabetic retinopathy
Identifying and validating PLAU as a potential prognostic biomarker for PDAC
A case study of optimal design and techno-economic analysis of an islanded AC microgrid
Abstract Microgrids (MGs) are essential in the distribution system by utilizing widely dispersed generation sources. Due to their economical and environmentally friendly attributes, Islanded AC MGs are commonly used to supply electricity to isolated locations independent of the primary grid. This study focuses on optimizing the configuration of an islanded AC MG to meet the electrical requirements of an international school in the New Administrative Capital, New Cairo, Egypt. Hybrid Optimization of Multiple Energy Resources (HOMER) software is employed to obtain the optimal size of the sources in the MG by minimizing the Levelized Cost of Energy (LCOE) and Total Net Present Cost (TNPC). According to the HOMER simulation results, a 200 kW PV system, a 180-kW wind turbine, a 50 kW FC, a 50 kW electrolyzer, a 50 kg hydrogen tank, a 180 kW DG, and a 686-kWh lead-acid battery form the optimal configuration of the islanded AC MG. The results reveal the contribution of each energy component to meeting the electricity demand, yielding an LCOE of $0.153/kWh and a TNPC of $1,775,300.00. The dynamic performance of the islanded microgrid is examined, introducing a Model Reference Adaptive Control based PI controller (MRAC-PI) to enhance transient response across all operational conditions. A comparative analysis is performed against traditional PI-PSO and PI-WOA controllers under load variations and changing weather conditions. The results indicate that the proposed control strategy effectively maintains system frequency and voltage amid various disturbances, improves dynamic performance, and achieves a balanced power generation and load demand. Additionally, the proposed controller demonstrates superior dynamic response, featuring reduced overshoot, undershoot, ITAE, and settling time compared to the others.