Structural sensitivity of a tri-trophic food chain model in a parameter plane.

Biological models are important in describing species interaction, disease spread, and environmental processes. One key aspect in improving the predictive capability of these models is deciding which parametrization is used to formulate the mathematical model. Considering two distinct functions with similar shapes and the same qualitative properties in a model can lead to markedly different model predictions.

Multi-stage bidirectional informed-RRT * plant protection UAV path planning method based on A * algorithm domain guidance.

Traditional path planning algorithms often face problems such as local optimum traps and low monitoring efficiency in agricultural UAV operations, making it difficult to meet the operational requirements of complex environments in modern precision agriculture. Therefore, there is an urgent need to develop an intelligent path planning algorithm. To address this issue, this study proposes an improved Informed-RRT* path planning algorithm guided by domain-partitioned A* algorithm.

Optimized node-level capsule graph neural network for subject-independent emotion recognition from EEG signals.

Subject-independent emotion detection using EEG (Electroencephalography) using Vibrational Mode Decomposition and deep learning is made possible by the scarcity of labelled EEG datasets encompassing a variety of emotions. Labelled EEG data collection over a wide range of emotional states from a broad and varied population is challenging and resource-intensive. As a result, models trained on small or biased datasets may fail to generalize well to unknown individuals or emotional states, resulting in lower accuracy and robustness in real-world applications.

Multiscale detection of power quality disturbances and cyber intrusions in smart grids using NSCT and frequency band scalograms.

This paper presents a novel multiscale signal processing framework for power quality disturbance (PQD) and cyber intrusion detection in smart grids, combining Non-Subsampled Contourlet Transform (NSCT), Split Augmented Lagrangian Shrinkage Algorithm (SALSA), and Morphological Component Analysis (MCA). A key innovation lies in an adaptive weighting mechanism within NSCT's directional sub bands, enabling dynamic energy redistribution and enhanced representation of both low-frequency anomalies (e.g.

Photocatalyst-free synthesis of quinazolinones from -aminobenzamides and rongalite under visible light.

A photocatalyst-free method has been developed for the synthesis of quinazolinones from -aminobenzamides and rongalite as a C synthon under visible light for the first time. This transformation proceeds using atmospheric oxygen as the oxidant, involving the generation of a superoxide radical anion (O˙), as supported by EPR analysis. Additionally, the formation of HO was confirmed by UV-Vis spectrophotometric analysis.

Brain Tumor Detection Based on Hybrid Convolutional Adaptive Neuro Fuzzy Inference System Using MRI Image.

The abnormal or irregular growth of cells in regions of the human body that affects surrounding tissues is termed a tumor. Brain tumors are among the most dangerous and life-threatening types of tumors, arising from the abnormal growth of cells within the brain. However, existing detection methods often suffer from limitations, such as poor noise handling in MRI images, inaccurate segmentation, and low generalization across varying datasets.

The Role of Phytosterol Derivatives in Inhibiting LuxS-Mediated Quorum Sensing and Biofilm Formation in Vibrio parahaemolyticus.

Vibrio parahaemolyticus, a halophilic bacterium of the Vibrionaceae family, is a notable opportunistic pathogen that affects aquatic organisms, such as shrimp and fish. The LuxS enzyme, a Zn-dependent metalloenzyme, governs the synthesis of autoinducer-2 (AI-2), a conserved quorum-sensing molecule that modulates gene expression related to virulence in Vibrio species and Escherichia coli. This study aimed to investigate the inhibitory potential of marine algae-derived bioactive compounds against the LuxS/AI-2 quorum-sensing system in Vibrio parahaemolyticus.

Novel Triazole-Linked Indole Derivatives as Potent Soluble Epoxy Hydrolase Inhibitors With Promising Anticancer Activity.

A novel series of triazole-linked indole derivatives was designed, synthesized, and evaluated as soluble epoxide hydrolase inhibitors (sEHIs) for their potential anticancer activity. These compounds exhibit strong binding affinity within the hydrophobic pockets of sEH, with compounds 9a and 9b emerging as the most potent inhibitors, achieving IC₅₀ values of 0.270 ± 0.

Harnessing Infrared Radiation: Transforming Vegetable Oil Quality, Drying Kinetics, and Bioactive Potential.

Infrared (IR) radiation, a versatile and efficient heating method, has garnered significant attention in the food and oil processing industries because of its unique heat transfer mechanism and broad range of applications. IR is applied in drying, peeling, decontamination, enzyme inactivation, and roasting, underscoring its potential for energy-efficient, high-quality processing. Its application to oil-bearing materials improves moisture removal rates and supports the preservation of critical quality parameters.

Probabilistic machine learning-based forecasting of wind speed uncertainty using adaptive kernel density estimation.

Short-term wind speed forecasting is essential for enhancing the efficiency and dependability of wind renewable energy installations. Although often used, conventional point predictions generated by machine learning techniques frequently fail to accurately capture the natural uncertainty associated with wind speed variation. Modeling this type of uncertainty is crucial for providing credible information as the level of uncertainty increases.

A machine learning approach for detecting WPA3 downgrade attacks in next-generation Wi-Fi systems.

This paper presents a hybrid adaptive approach based on machine learning (ML) for classifying incoming traffic, feature selection and thresholding, aimed at enhancing downgrade attack detection in Wi-Fi Protected Access 3 (WPA3) networks. The fast proliferation of WPA3 is regarded critical for securing modern Wi-Fi systems, which have become integral to 5G and Beyond (5G&B) Radio Access Networks (RAN) architecture. However, the wireless communication channel remains inherently susceptible to downgrade attacks, where adversaries intentionally cause networks to revert from WPA3 to WPA2, with the malicious intent of exploiting known security flaws.

Effect of different light-curing modes and curing times on degree of conversion and microhardness of three different bulk-fill composites: an in vitro study.

Objectives: This in vitro study aims to compare the effect of different light curing modes and curing times on the degree of conversion (DC) and microhardness (MH) of three different bulk fill composites at two different depths.

Materials And Methods: A total of 224 cylindrical specimens (4 mm height, 6 mm diameter) were prepared using four composites: SDR Plus, Ever X Posterior (EXP), Beautifil Bulk Restorative (BBR), and Tetric N Ceram (TNC). Each material group (n = 56) was subdivided into four curing subgroups based on light-curing mode (Low Power or High Power) and exposure time (20-30 s).

Drug Repurposing Investigation for Combating Ebola Virus Disease: Database Mining, Docking Calculations, Molecular Dynamics, and Density Functional Theory Study.

Ebola virus (EBOV), one of the deadliest diseases, is responsible for infecting individuals with hemorrhagic fever syndrome, which remains an ongoing worldwide health concern. The extremely deadly nature and virulence of EBOV illness illuminate the imperative need to evolve effective curative agents. Viral protien (VP35) acts as an Achilles heel for EBOV reproduction and also interacts with numerous human proteins, which leads to impairing the immune system.

Metabarcoding-Based Seasonal Assessment of Airborne Microbial Communities in PM₁₀ Samples from a Semi-Urban Region in Tamil Nadu, India.

Airborne microbial communities show marked seasonal variability, with implications for both environmental processes and public health. In this study, metagenomic sequencing was applied to characterize airborne microbiota across four distinct seasons in India-winter (Sw), summer (Ss), southwest monsoon (Ssw), and northeast monsoon (Sne). Distinct shifts in dominant bacterial taxa were observed.

Optimized hierarchical CLSTM model for sentiment classification of tweets using boosted killer whale predation strategy.

Opinion mining is more challenging than it was before because of all the user-generated material on social media. People use Twitter (X) to gather opinions on products, advancements, and laws. Sentiment Analysis (SA) examines people's thoughts, feelings, and views on numerous topics.

Personalized health monitoring using explainable AI: bridging trust in predictive healthcare.

AI has propelled the potential for moving toward personalized health and early prediction of diseases. Unfortunately, a significant limitation of many of these deep learning models is that they are not interpretable, restricting their clinical utility and undermining trust by clinicians. However, all existing methods are non-informative because they report generic or post-hoc explanations, and few or none support patient-specific, accurate, individualized patient-level explanations.

A transcriptomics-based analysis provides insights into the sex-dependent reproductive toxicity of DEHP in zebrafish (Danio rerio).

Di-(2-ethylhexyl) phthalate (DEHP) threatens human and wildlife health seriously, yet sex-dependent reproductive toxicity remains unclear. Therefore, in this study, we exposed zebrafish to various concentrations (0.01, 0.

Leveraging advanced ensemble learning techniques for methane uptake prediction in metal organic frameworks.

Energy and environmental policy agencies have been looking for suitable adsorbent materials to promote the use of adsorbed natural gas (ANG). Various candidate adsorbent materials have been developed and tested for methane adsorption. Metal-Organic Frameworks (MOFs) have shown a promising performance in methane adsorption and are of particular interest due to their power in adsorption and separation of gases, chemical tunability, ease of synthesis, and high surface area.

A fused weighted federated learning-based adaptive approach for early-stage drug prediction.

Early accurate drug prediction is crucial in clinical decision support, where privacy of the patient data is a paramount importance. In this study, we introduce a fused weighted adaptive federated learning (FWAFL) framework to achieve joint training among distributed healthcare institutions without requiring raw data sharing. The method employs local model updates and client-level adaptive weighting to enhance generalization and performance while preserving data privacy.

Mining of the Streptome Database for Hunting Putative Candidates to Allosterically Inhibit the Dengue Virus (Serotype 2) RdRp.

In the last few decades, the dengue virus, a prevalent flavivirus, has demonstrated various epidemiological, economic, and health impacts around the world. Dengue virus serotype 2 (DENV2) plays a vital role in dengue-associated mortality. The RNA-dependent RNA polymerase (RdRp) of DENV2 is a charming druggable target owing to its crucial function in viral reproduction.

Aramid Fiber-Reinforced Plastics (AFRPs) in Aerospace: A Review of Recent Advancements and Future Perspectives.

This review examines the application of aramid fiber-reinforced plastics (AFRPs) in the aerospace industry, highlighting their significance in enhancing aircraft performance. Aramid fibers, such as Kevlar and Twaron, have emerged as key materials due to their exceptional tensile strength, low density, and thermal stability. However, challenges persist in manufacturing, durability, and multifunctionality.

EndoNet: A Multiscale Deep Learning Framework for Multiple Gastrointestinal Disease Classification via Endoscopic Images.

Gastrointestinal (GI) disorders present significant healthcare challenges, requiring rapid, accurate, and effective diagnostic methods to improve treatment outcomes and prevent complications. Wireless capsule endoscopy (WCE) is an effective tool for diagnosing GI abnormalities; however, precisely identifying diverse lesions with similar visual patterns remains difficult. Many existing computer-aided diagnostic (CAD) systems rely on manually crafted features or single deep learning (DL) models, which often fail to capture the complex and varied characteristics of GI diseases.

A multi-strategy enhanced secretary bird optimization algorithm for high-precision inverse kinematics in robotic arms.

The assembly of pyrotechnic grain demands high precision and stability in robotic arm motion control due to the small shell apertures and stringent assembly accuracy requirements. Inverse kinematics is a core technology in robotic arm motion control. This paper constructs a robotic arm inverse kinematics model by reformulating the inverse kinematics problem as a constrained optimization problem and employs a multi-strategy improved Secretary Bird Optimization Algorithm (ISBOA) to achieve high-precision solutions.

Triboelectric self-powered soft robotics: paving the way towards a sustainable future.

The integration of triboelectric nanogenerators (TENGs) into soft robotic systems marks a significant advancement toward autonomous, self-powered, and environmentally responsive machines. TENGs offer lightweight, flexible structures capable of efficiently converting mechanical energy into electricity, supporting both on-board power generation and active sensing. This review provides a comprehensive overview of recent progress in TENG-powered soft robotics, emphasizing developments in actuation, sensing, locomotion, and intelligent interaction.

Comparative Investigations on Ni-Doped and ZnTe Monolayers for SF Decomposition Characteristic Gases Based on Density Functional Theory.

When SF decomposition is caused by partial discharges, characteristic gases such as HS, SO, SOF, and SOF are generated in high-voltage equipment, which poses a great threat to the regular operation of power equipment. Based on density functional theory, the adsorption and sensing characteristics of these four gases on a ZnTe monolayer and Ni-ZnTe are investigated in terms of adsorption energy, charge transfer, differential charge density, microscopic state density, desorption time, and sensitivity in this paper. Through comparative analysis, the doping of transition metal significantly opens the band gap and ensures that the desorption times of HS (538 s at 298 K), SO (1.