Sustainable phytoprotection: a smart monitoring and recommendation framework using Puma Optimization for potato pathogen detection.

Ensuring sustainable and resilient agricultural systems in the face of intensifying crop disease threats requires intelligent, data-driven tools for early detection and intervention. This study proposes a novel hybrid framework for potato disease classification that integrates copula-based dependency modeling with a Restricted Boltzmann Machine (RBM), further enhanced through hyperparameter tuning using the biologically inspired Puma Optimization (PO) algorithm. The system is trained and evaluated on a real-world dataset derived from structured field experiments, comprising 52 instances and 42 agronomic, microbial, and ecological variables.

Condition monitoring and fault diagnosis of power transformer based on non-invasive measurement.

In modern power systems, it is crucial to monitor and detect internal faults in power transformers promptly and accurately to ensure reliability and prevent disruptions. Failure to identify these faults promptly can reduce the transformer's lifespan, cause system disconnection, and compromise network stability. This paper introduces an innovative method for the discrimination, classification, and localization of internal short-circuit faults in power transformers, with a focus on three types of winding faults: turn-to-turn fault, series short circuits, and shunt short circuits.

Ocotillo optimization-driven deep learning for bone marrow cytology classification.

Manual diagnosis of hematological cancers like leukemia through bone marrow smear analysis is labor-intensive, prone to errors, and highly dependent on expert knowledge. To overcome these limitations, this study introduces a comprehensive deep learning framework enhanced with the innovative bio-inspired Ocotillo Optimization Algorithm (OcOA), designed to improve the accuracy and efficiency of bone marrow cell classification. The contributions include developing a baseline Convolutional Neural Network (CNN) that achieves an initial accuracy of 86.

α‑MoO Nanobelts Boosted the Structural, Optical, Thermal, and Dielectric Properties of PEO/PVP Blends for Emerging Optoelectronic/Energy-Storage Applications.

This study presents a comprehensive investigation of the optical, thermal, structural, and electrical properties of poly-(ethylene oxide) (PEO)/polyvinylpyrrolidone (PVP) blends embedded with molybdenum trioxide nanobelts (MoO NBs). The nanocomposites were fabricated via solution casting with varying concentrations of MoO NBs to assess their influence on the polymer matrix. X-ray diffraction (XRD) analysis demonstrated the semicrystalline nature of PEO/PVP blends and revealed structural modifications induced by the nanofiller.

Exact wave structures with stochastic effects in birefringent optical fibers modeled by cubic-quintic-septic nonlinear Schrödinger equation.

Stochastic optical solitons are a fascinating phenomenon in nonlinear optics where soliton-like behavior emerges in systems affected by stochastic noise. This study establishes a fundamental framework for stochastic wave propagation in birefringent fibers through the cubic-quintic-septic nonlinear Schrödinger equation (NLSE). Our modified extended mapping technique yields exact analytical solutions (bright, dark, and singular solitons, periodic structures, and Weierstrass elliptic waves) that explicitly incorporate multiplicative noise and birefringent coupling.

Optical soliton perturbation with complex ginzburg-landau equation having multiplicative white noise and nine forms of self-phase modulation structures.

This paper investigates new optical soliton solutions to the complex Ginzburg-Landau equation in the presence of white noise, a fundamental model in nonlinear optics that describes soliton dynamics. The study focuses on nine distinct forms of self-phase modulation structures, each exhibiting unique nonlinear characteristics and dispersion properties. To derive the soliton solutions, the generalized -expansion approach is employed, which is known for its effectiveness in handling nonlinear differential equations and extracting exact solutions systematically.

Dynamical behavior of analytical solutions and bifurcation analysis for a novel structured (2+1)-dimensional Kadomtsev-Petviashvili equation via analytic approach.

This work represents a novel integration of both frameworks in a single analytical context by using the modified extended (ME) mapping approach to get new, exact solutions of the (2+1)-dimensional Kadomtsev-Petviashvili and Shallow Water Wave equations. This model is essential for simulating multidimensional wave propagation processes, and it has several applications in shallow water hydrodynamics, plasma physics, nonlinear optics, and the investigation of long internal waves in stratified fluids. Bright soliton, dark soliton, singular soliton, and singular periodic, periodic, hyperbolic, exponential, Jacobi elliptic (JE), and Weierstrass elliptic doubly periodic solutions are among the many unique structures that are constructed in this study.

Synthesis and characterization of hematite nanomaterials imprinted with acetone, ethanol and methanol for AI-Based IoT gas sensor arrays.

Indoor air pollution, primarily caused by volatile organic compounds (VOCs) such as acetone, ethanol, and methanol, poses significant health risks and necessitates the development of efficient, real-time monitoring solutions. Conventional gas sensors often operate at high temperatures and are limited to detecting a single gas type, which restricts their applicability for comprehensive air quality management. To address these challenges, this study presents the development and evaluation of an advanced electronic nose (E-nose) system capable of detecting acetone, ethanol, and methanol at low operating temperatures.

Modeling and simulation of optical wireless communication channels in IoUT considering water types turbulence and transmitter selection.

The Internet of Underwater Things (IoUT) is revolutionizing underwater communication by enabling real-time data exchange, environmental monitoring, and exploration in aquatic environments. Among emerging technologies, optical wireless communication (OWC) has gained prominence due to its high-speed data rates and superior efficiency compared to traditional acoustic and radio frequency (RF) methods. This paper presents a comprehensive study of OWC channel modeling and simulation tailored for IoUT applications.

Exploring highly dispersive optical solitons and modulation instability in nonlinear Schrödinger equations with nonlocal self phase modulation and polarization dispersion.

This study explores the dynamics of highly dispersive optical solitons in nonlinear Schrödinger equations (NLSE) with non-local self-phase modulation (SPM) and polarization-mode dispersion (PMD). These nonlinear effects significantly influence soliton propagation and stability in advanced optical communication systems. Employing the Improved Modified Extended Tanh-Function Method (IMETFM), we derive exact soliton solutions, including bright, dark, singular, and combo solitons, under specific parametric conditions.

Generic optimal power flow solution associated with technical improvements and emission reduction by multi-objective ARO algorithm.

In modern power engineering, the optimal operation aims to achieve the basic requirements of the electrical power grid, meet various technical and economic aspects, and preserve the environmental limits within their accepted bounds. In this line, the current paper finds the optimal operational scheduling of the power generation units that cover the load requirements, considering different frameworks of the optimal power flow (OPF) problem involving single- and multi-objective functions. Technical, economic, and emissions objective functions are considered.

Hybrid deep learning optimization for smart agriculture: Dipper throated optimization and polar rose search applied to water quality prediction.

Modern sustainable farming demands precise water management techniques, particularly for crops like potatoes that require high-quality irrigation to ensure optimal growth. This study presents a novel hybrid metaheuristic framework that combines Dipper Throated Optimization (DTO), a bio-inspired algorithm modeled on bird foraging behavior, with Polar Rose Search (PRS) to enhance deep learning models in predictive water quality assessment. The proposed approach integrates binary feature selection and metaheuristic optimization into a unified optimization process, effectively balancing exploration and exploitation to handle complex, high-dimensional datasets.

Application of andesite and hydrolyzed poly acrylonitrile andesite composite for adsorption of Al(III), Fe(III), CHSH, and HS form aqueous solutions.

The hydrolyzed poly acrylonitrile andesite composite (HPAA) was prepared and characterized using BET analysis, zeta potential measurements, XRD and XPS before and after the adsorption process. Both the HPAA composite and andesite were analyzed using FTIR spectroscopy. The effect of adsorption on the surface morphology and crystallinity of andesite was evaluated using SEM imaging.

High efficiency adsorption of hexavalent chromium using bioderived activated carbon kinetics, isotherms, and thermodynamics.

Hexavalent chromium (Cr), a toxic pollutant in industrial wastewater, poses serious environmental and health risks. This study investigates H₃PO₄-treated palm frond-derived activated carbon (PFTACs) as a low-cost, sustainable adsorbent for Cr removal. PFTACs achieved 99.

Machine learning approaches for forecasting compressive strength of high-strength concrete.

Identifying the mechanical properties of High Strength Concrete (HSC), particularly compressive strength, is critical for safety purposes. Concrete compressive strength is determined by using laboratory experiments, which are costly and time-consuming. Artificial intelligence (AI) methods reduce time and money.

Sustainable cobalt (II) removal from wastewater using an electrospun Ag-MOF/polycaprolactone-chitosan nanofiber membrane: Optimization and regeneration performance.

A novel nanofiber membrane integrating a silver metal-organic framework (Ag-MOF), polycaprolactone (PCL), and chitosan (CS) was developed through electrospinning techniques to create the Ag-MOF/PCL-CS nanofiber membrane aimed at the real removal of cobalt (II) ions from aqueous solutions. The successful synthesis of this hybrid membrane was rigorously characterized using FT-IR, XRD, XPS, SEM-EDX, and nitrogen adsorption/desorption isotherms. Additionally, batch adsorption studies were conducted to assess various parameters, counting pH, initial Co(II) concentration, contact duration, temperature, and the dosage of the adsorbent.

Sustainable removal of Cd(II) using β-Cyclodextrin/Polyethylenimine hydrogel beads embedded with silver-MOFs: Synthesis, characterization, mechanism, and process optimization.

This research focuses on the development of a biopolymeric hydrogel adsorbent that is both environmentally friendly and highly effective. It is specifically designed to remove cadmium (II) ions from various water sources and industrial wastewater. The innovative adsorbent, designated as Ag-MOF/CD-PEI hydrogel beads, was formulated by embedding a silver based metal-organic framework (Ag-MOF) within a β-cyclodextrin (CD) and polyethylenimine (PEI) structure, which was chemically crosslinked with epichlorohydrin.

Compact Frequency-Agile and Mode-Reconfigurable Antenna for C-Band, Sub-6-GHz-5G, and ISM Applications.

This article presents the design and evaluation of a compact-sized antenna targeting heterogenous applications working in the C-band, 5G-sub-6GHz, and the ISM band. The antenna offers frequency reconfigurability along with multi-operational modes ranging from wideband to dual-band and tri-band. A compact-sized antenna is designed initially to cover a broad bandwidth that ranges from 4 GHz to 7 GHz.

Enhanced phenol removal from wastewater via sulfuric acid activated eggshell derived carbon.

This research explores the development of an innovative activated carbon adsorbent (ACES) derived from waste eggshells through sulfuric acid activation to effectively remove phenol from simulated wastewater. Optimization of adsorption parameters was conducted using Design-Expert 13 software and response surface methodology (RSM). Under optimal conditions (initial phenol concentration of 25.

Effect of column and pile configuration and dimension on shear performance of reinforced concrete pile caps.

Despite extensive research on reinforced concrete (RC) pile caps, the influence of column and pile configuration and dimensions on their shear performance remains unexplored. This study investigates the structural behavior of RC pile caps through experimental and numerical analyses, focusing on how variations in column and pile geometry affect shear capacity. Two pile cap specimens (700 mm long × 300 mm wide) with heights of 250 mm (SB1) and 350 mm (SB2) were tested under shear-dominated conditions.

Characterization of lightning-induced overvoltages in wind farms.

Wind farms are exposed to various weather hazards, including lightning strikes, which can pose significant risks. However, the impact of different wind farm topologies on the magnitude of lightning-induced overvoltages has not been extensively studied, creating a gap in existing literature. This paper addresses this gap by analyzing the characteristics of lightning-induced overvoltages injected into the grid for various wind farm topologies.

Diverse solitons wave structures for coupled NLSEs in birefringent fibers with higher nonlinearities using the modified extended mapping algorithm.

This work is a thorough investigation of mathematical modeling with an emphasis on efficiency and performance optimization. Our research is centered on the cubic-quartic nonlinear Schrödinger equation, specifically concerning birefringent fibers exhibiting nonlinearity in the cubic-quintic-septic-nonic continuum. This work makes a unique and significant addition to the field of science.

A metaheuristic optimization-based approach for accurate prediction and classification of knee osteoarthritis.

Knee osteoarthritis (KOA) is a severe arthrodial joint condition with significant global socioeconomic consequences. Early recognition and treatment of KOA is critical for avoiding disease progression and developing effective treatment programs. The prevailing method for knee joint analysis involves manual diagnosis, segmentation, and annotation to diagnose osteoarthritis (OA) in clinical practice while being highly laborious and a susceptible variable among users.

Enhancing CO emissions prediction for electric vehicles using Greylag Goose Optimization and machine learning.

Electric vehicle (EV) [Formula: see text] emissions should be predicted and mitigated, which requires lowering EV emissions in line with global sustainability goals. Such accurate forecasting supports policymakers and other industry stakeholders make marketing decisions to reduce environmental impacts and optimize resource utilization. In this research, a novel Greylag Goose Optimization (GGO) algorithm is integrated with a Multi-Layer Perceptron (MLP) model to improve [Formula: see text] emissions prediction.

Unveiling diverse solitons in the quintic perturbed Gerdjikov-Ivanov model via a modified extended mapping method.

The quintic perturbed Gerdjikov-Ivanov equation, a non-linear model in optics and quantum field theory, describes the propagation of optical pulses in nonlinear media with quintic nonlinearity and perturbation effects. This study aims to derive exact traveling wave solutions for the quintic perturbed Gerdjikov-Ivanov equation using the modified extended mapping method. The method efficiently generates a broad spectrum of solutions, including bright, dark, periodic, singular periodic, hyperbolic, plane, Weierstrass, and Jacobi elliptic forms, extending the known solution space.