Robust fault detection and classification in power transmission lines via ensemble machine learning models.

Transmission lines are vital for delivering electricity over long distances, yet they face reliability challenges due to faults that can disrupt power supply and pose safety risks. This research introduces a novel approach for fault detection and classification by analyzing voltage and current patterns across transmission line phases. Leveraging a comprehensive dataset of diverse fault scenarios, various machine learning algorithms-including Random Forest (RF), K-Nearest Neighbors (KNN), and Long Short-Term Memory (LSTM) networks-are evaluated.

Taxifolin Protects Against 5-Fluorouracil-Induced Cardiotoxicity in Mice Through Mitigating Oxidative Stress, Inflammation, and Apoptosis: Possible Involvement of Sirt1/Nrf2/HO-1 Signaling.

Although 5-fluorouracil (5-FU) is widely utilized in cancer treatment, its side effects, including cardiotoxicity, limit its use. Taxifolin (TAX) is a bioactive anti-inflammatory and antioxidant flavonoid. This study aimed to elucidate the protective effect of TAX against 5-FU-induced cardiac injury in male mice.

Cancer-specific mortality after radical prostatectomy versus radiotherapy in incidental prostate cancer.

Introduction: To test for cancer specific mortality (CSM) differences after either radical prostatectomy (RP) or radiotherapy (RT) in incidental prostate cancer (IPCa) patients.

Patients And Methods: Within the Surveillance, Epidemiology, and End Results (SEER) database (2004-2015), IPCa patients were identified. Cumulative incidence plots as well as competing risks regression (CRR) models were fitted to address CSM after adjustment for other-cause mortality (OCM).

Advanced microgrid optimization using price-elastic demand response and greedy rat swarm optimization for economic and environmental efficiency.

In this paper, a comprehensive energy management framework for microgrids that incorporates price-based demand response programs (DRPs) and leverages an advanced optimization method-Greedy Rat Swarm Optimizer (GRSO) is proposed. The primary objective is to minimize the generation cost and environmental impact of microgrid systems by effectively scheduling distributed energy resources (DERs), including renewable energy sources (RES) such as solar and wind, alongside fossil-fuel-based generators. Four distinct demand response models-exponential, hyperbolic, logarithmic, and critical peak pricing (CPP)-are developed, each reflecting a different price elasticity of demand.

Energy feature extraction and visualization of voltage sags using wavelet packet analysis for enhanced power quality monitoring.

Power quality (PQ) disturbances, such as voltage sags, are significant issues that can lead to damage in electrical equipment and system downtime. Detecting and classifying these disturbances accurately is essential for maintaining reliable power systems. This paper introduces a novel approach to voltage sag analysis by employing wavelet packet analysis combined with energy-based feature extraction to enhance PQ monitoring.

Optimal parameter identification of photovoltaic systems based on enhanced differential evolution optimization technique.

Identifying the parameters of a solar photovoltaic (PV) model optimally, is necessary for simulation, performance assessment, and design verification. However, precise PV cell modelling is critical for design due to many critical factors, such as inherent nonlinearity, existing complexity, and a wide range of model parameters. Although different researchers have recently proposed several effective techniques for solar PV system parameter identification, it is still an interesting challenge for researchers to enhance the accuracy of the PV system modelling.

Length of Hospital Stay and Its Predictions Among Patients With Exacerbations of Chronic Respiratory Diseases.

Background: Chronic respiratory disorders such as asthma and chronic obstructive pulmonary disease (COPD) may deteriorate into acute exacerbations requiring hospitalization. Assessing the predictors of prolonged hospital stays could help identify potential interventions to reduce the burden on patients and healthcare systems.

Aim: This study aimed to identify the risk factors attributed to prolonged hospital stays among patients admitted with acute exacerbations of chronic respiratory disorders in Jordan.

Deciphering the Anti-Diabetic Potential of Gymnema Sylvestre Using Integrated Computer-Aided Drug Design and Network Pharmacology.

This study explores novel therapeutic avenues for diabetes, a global health concern marked by elevated blood glucose levels. We investigated the anti-diabetic potential of Gymnema Sylvestre's bioactive compounds, including Gymnemic acid I, Stigmasterol, Deacylgymnemic acid, Beta-Amyrin acetate, Longispinogenin, Gymnemic acid II, Gymnemic acid, Gymnemic acid X, Gymnemaside VI, Phytic acid and Gymnemic acid X. Employing network pharmacology, molecular docking and molecular dynamics (MD), we elucidated the potential mechanism of action.

Exploring the Anticancer Effect of on Colorectal Cancer: Insights From Eight Colorectal Cancer Cell Lines.

Colorectal cancer (CRC) is a prevalent and deadly disease, necessitating the exploration of novel therapeutic strategies. Traditional chemotherapy often encounters drug resistance and adverse side effects, highlighting the need for alternative approaches. , a plant rich in phytochemical constituents, was investigated for its potential as an anticancer agent against colorectal cancer (CRC).

Non-Caucasian Race/Ethnicity Predisposes to Unfavorable Stage and Grade at Upper Tract Urothelial Carcinoma Diagnosis.

Objective: To test whether race/ethnicity affects stage or grade distribution at upper tract urothelial carcinoma (UTUC) diagnosis.

Methods: Within the Surveillance, Epidemiology, and End Results (SEER) database 2004-2020, UTUC patients were identified. Multivariable logistic regression models tested for the association between race/ethnicity and stage as well as grade at diagnosis according to renal pelvis vs.

Advanced control parameter optimization in DC motors and liquid level systems.

In recent times, there has been notable progress in control systems across various industrial domains, necessitating effective management of dynamic systems for optimal functionality. A crucial research focus has emerged in optimizing control parameters to augment controller performance. Among the plethora of optimization algorithms, the mountain gazelle optimizer (MGO) stands out for its capacity to emulate the agile movements and behavioral strategies observed in mountain gazelles.

'Applications of machine learning in liposomal formulation and development'.

Machine learning (ML) has emerged as a transformative tool in drug delivery, particularly in the design and optimization of liposomal formulations. This review focuses on the intersection of ML and liposomal technology, highlighting how advanced algorithms are accelerating formulation processes, predicting key parameters, and enabling personalized therapies. ML-driven approaches are restructuring formulation development by optimizing liposome size, stability, and encapsulation efficiency while refining drug release profiles.

Wind energy assessment and hybrid micro-grid optimization for selected regions of Saudi Arabia.

This study investigates the optimization of wind energy integration in hybrid micro grids (MGs) to address the rising demand for renewable energy, particularly in regions with limited wind potential. A comprehensive assessment of wind energy potential was conducted, and optimal sizing of standalone MGs incorporating photovoltaic (PV) systems, wind turbines (WT), and battery storage (BS) systems was performed for six regions in the Kingdom Saudi Arabia. Wind resource analysis utilizing the Weibull distribution function shows that all regions exhibited Class 1 wind energy characteristics, with average annual wind power densities ranging from 36.

An approach for load frequency control enhancement in two-area hydro-wind power systems using LSTM + GA-PID controller with augmented lagrangian methods.

This paper proposes an advanced Load Frequency Control (LFC) strategy for two-area hydro-wind power systems, using a hybrid Long Short-Term Memory (LSTM) neural network combined with a Genetic Algorithm-optimized PID (GA-PID) controller. Traditional PID controllers, while extensively used, often face limitations in handling the nonlinearities and uncertainties inherent in interconnected power systems, leading to slower settling time and higher overshoot during load disturbances. The LSTM + GA-PID controller mitigates these issues by utilizing LSTM's capacity to learn from historical data by using gradient descent to forecast the future disturbances, while the GA optimizes the PID parameters in real time, ensuring dynamic adaptability and improved control precision.

Bioactive potency of extracts from and with silver nanoparticles against cancer cell lines and pathogenic bacteria.

Silver nanoparticles (AgNPs) are spherical particles with a number of specific and unique physical (such as surface plasmon resonance, high electrical conductivity and thermal stability) as well as chemical (including antimicrobial activity, catalytic efficiency and the ability to form conjugates with biomolecules) properties. These properties allow AgNPs to exhibit desired interactions with the biological system and make them prospective candidates for use in antibacterial and anticancer activities. AgNPs have a quenching capacity, which produces reactive oxygen species and disrupts cellular processes (such as reducing the function of the mitochondria, damaging the cell membrane, inhibiting DNA replication and altering protein synthesis).

Impact of AI and big data analytics on healthcare outcomes: An empirical study in Jordanian healthcare institutions.

Artificial intelligence (AI) and big data analytics are transforming healthcare globally and in Jordan. This study investigates the effects of AI and big data analytics on healthcare outcomes in Jordanian healthcare institutions. A comprehensive model is proposed to understand the antecedents of healthcare outcomes, including the impact of perceived ease of use, perceived usefulness, and organizational capabilities.

Disseminated Intravascular Coagulation in Pediatric Acute Leukemia: Prevalence, Laboratory Features, and Prognostic Significance of ISTH Score.

Introduction: Disseminated intravascular coagulation (DIC) is associated with acute leukemia. DIC prevalence and clinical consequences are complex and varies across acute leukemia subtypes. The International Society of Thrombosis and Hemostasis (ISTH) scoring system is used for the detection of overt DIC.

Critical Care Therapy After Cytoreductive Nephrectomy for Metastatic Kidney Cancer.

Objectives: To examine critical care therapy rates after cytoreductive nephrectomy in metastatic kidney cancer patients.

Design, Setting, And Patients: Relying on the National Inpatient Sample (2000-2019), we addressed critical care therapy use (total parenteral nutrition, invasive mechanical ventilation, renal replacement therapy, percutaneous endoscopic gastrostomy tube insertion, and tracheostomy) and in-hospital mortality in surgically treated metastatic kidney cancer patients. Estimated annual percentage changes and multivariable logistic regression models were fitted.

Novel acridone derivatives as potential P2Y receptor inhibitors: integrating computational modeling and experimental analysis.

The combination of clopidogrel and acetylsalicylic acid is the standard treatment for atherosclerotic cardiovascular disease. Nonetheless, there is a pressing need for more potent P2Y receptor inhibitors with quicker onset, especially for early intervention in acute myocardial infarction. Integrating computational modeling, i.

An Intelligent Approach for Early and Accurate Predication of Cardiac Disease Using Hybrid Artificial Intelligence Techniques.

This study proposes a new hybrid machine learning (ML) model for the early and accurate diagnosis of heart disease. The proposed model is a combination of two powerful ensemble ML models, namely ExtraTreeClassifier (ETC) and XGBoost (XGB), resulting in a hybrid model named ETCXGB. At first, all the features of the utilized heart disease dataset were given as input to the ETC model, which processed it by extracting the predicted probabilities and produced an output.

Use of inpatient palliative care in metastatic testicular cancer patients undergoing critical care therapy: insights from the national inpatient sample.

To test for rates of inpatient palliative care (IPC) in metastatic testicular cancer patients receiving critical care therapy (CCT). Within the Nationwide Inpatient Sample (NIS) database (2008-2019), we tabulated IPC rates in metastatic testicular cancer patients receiving CCT, namely invasive mechanical ventilation (IMV), percutaneous endoscopic gastrostomy tube (PEG), dialysis for acute kidney failure (AKF), total parenteral nutrition (TPN) or tracheostomy. Univariable and multivariable logistic regression models addressing IPC were fitted.

Improving Thermal and Light Stability of Black Grape Anthocyanins Using Cobalt Complexation.

Anthocyanins (ANCs) are water-soluble pigments with antioxidant properties, offering potential as alternatives to synthetic food colorants. However, their stability is compromised by factors such as pH, temperature, and light exposure. Previous research demonstrated improved pH stability in black grape ANCs through cobalt ion (Co) complexation.

Significance of Marangoni convection in ethylene glycol base hybrid nanofluid flow with viscous dissipation through a porous medium.

The current research deals with analytical analysis of Marangoni convection on ethylene glycol base hybrid nanofluid two-dimension flow with viscous dissipation through a porous medium, which have some important application in mechanical, civil, electronics, and chemical engineering. Two types of nanoparticles one is sliver and other is graphene oxide and ethylene glycol is used as base fluid in this research work. The authors applied appropriate transformations to convert a collection of dimension form of nonlinear partial differential equations to dimensionless form of nonlinear ordinary differential equations.

Novel transfer learning based bone fracture detection using radiographic images.

A bone fracture is a medical condition characterized by a partial or complete break in the continuity of the bone. Fractures are primarily caused by injuries and accidents, affecting millions of people worldwide. The healing process for a fracture can take anywhere from one month to one year, leading to significant economic and psychological challenges for patients.

Nurses' professional quality of life and job satisfaction in Jordan.

Background: Professional quality of life has received widespread concern in nursing over the last few years. Nurses with a high professional quality of life enthusiastically approach their work and provide excellent patient care. On the other hand, poor professional quality of life may affect nurses' quality of care, resulting in job dissatisfaction and jeopardizing patient outcomes.