A hybrid evolution strategies algorithm for non-permutation flow shop scheduling problems.

Flow shop scheduling has garnered significant attention from researchers over the past ten years, establishing itself as a prominent area of study within the field of scheduling. Nevertheless, there exists a paucity of research dedicated to addressing Non-Permutation Flow Shop Scheduling Problems. In this study, a Hybrid Evolution Strategies (HES) is suggested by combining the exploitation ability of Nawaz, Enscore, and Ham (NEH) Heuristic, the exploration ability of Improved Evolution Strategies (IES), and a Local Search Technique to minimize the makespan of NPFSSP.

pNPs-CapsNet: Predicting Neuropeptides Using Protein Language Models and FastText Encoding-Based Weighted Multi-View Feature Integration with Deep Capsule Neural Network.

Neuropeptides (NPs) are critical signaling molecules that are essential in numerous physiological processes and possess significant therapeutic potential. Computational prediction of NPs has emerged as a promising alternative to traditional experimental methods, often labor-intensive, time-consuming, and expensive. Recent advancements in computational peptide models provide a cost-effective approach to identifying NPs, characterized by high selectivity toward target cells and minimal side effects.

TransBind allows precise detection of DNA-binding proteins and residues using language models and deep learning.

Identifying DNA-binding proteins and their binding residues is critical for understanding diverse biological processes, but conventional experimental approaches are slow and costly. Existing machine learning methods, while faster, often lack accuracy and struggle with data imbalance, relying heavily on evolutionary profiles like PSSMs and HMMs derived from multiple sequence alignments (MSAs). These dependencies make them unsuitable for orphan proteins or those that evolve rapidly.

A fine-tuned convolutional neural network model for accurate Alzheimer's disease classification.

Alzheimer's disease (AD) is one of the primary causes of dementia in the older population, affecting memories, cognitive levels, and the ability to accomplish simple activities gradually. Timely intervention and efficient control of the disease prove to be possible through early diagnosis. The conventional machine learning models designed for AD detection work well only up to a certain point.

A mathematical framework of HIV and TB co-infection dynamics.

The biological processes involved in diseases like human immunodeficiency virus (HIV) and tuberculosis (TB) require extensive research, particularly when both diseases occur together. This piece of research delves to explore a new fractional-order mathematical model that examines the co-dynamics of HIV and TB, taking into account the treatment effects. Although no definitive vaccine or cure for HIV exists, antiretroviral therapy (ART) can slow disease spread and prevent subsequent complications.

A simplified framework for assessing waste prevention and minimisation in developing countries within the context of CE, SDGs and ESG principles.

Waste minimisation and prevention are crucial for the circular economy (CE), sustainable development goals (SDGs) and environmental, social and governance (ESG) principles, focusing on waste elimination and resource efficiency. However, there are significant gaps in implementing effective waste minimisation strategies, mainly due to the lack of standardised waste prevention terminologies and indicators. This article introduces a novel simplified and comprehensive framework for assessing waste prevention and minimisation measures tailored to developing countries.

Comparison of CMIP6 GCMs historical precipitation with measured precipitation over Pakistan.

Comparison of Coupled Model Intercomparison Project Phase 6 (CMIP6) General Circulation Models (GCMs) with observations under different climatic conditions is necessary to determine their respective strengths and differences. In the current study, ten CMIP6 GCMs are compared with measured gauge precipitation data of 51 stations across Pakistan. Results show reasonable agreement between the CMIP6-GCMs with measured data in capturing precipitation days of ≤10 mm/day.

Mechanically robust and thermally insulating natural cotton fiber-reinforced biocomposite panels for structural applications.

Natural cotton fiber-reinforced heat-insulating biocomposites with high mechanical strength were designed and developed in the present research for various structural applications. Novel cotton-reinforced polypropylene (PP) composites with improved interfacial adhesion were fabricated through uniform blending of cotton and PP fibers of different volume fractions for high mechanical strength. The developed cotton-PP specimens were characterized by examining their tensile, flexural and impact strength, heat barrier properties, and thermal stability at high temperatures.

Effect of Eu ion concentration on optical and magnetic properties of oriented GdO/CTAB nanoparticles as multifunctional optical-magnetic probes in biomedicine.

The GdO:Eu nanoparticles were synthesized using a multi-step chemical method with urea as a reactant to control the ratio of different Eu activation centers: 2, 4, 6, 8, 10, and 12 mol% combined with CTAB surfactant to improve surface quality. The study aimed to determine the optimal concentration of Eu in the presence of CTAB to increase biocompatibility and achieve the best fluorescence. The structure, surface morphology, optical properties, and magnetic properties of the materials were analyzed through FSEM, XRD, HRTEM, XPS, UV-vis, fluorescence, fluorescence excitation, time-resolved fluorescence, vibrating sample magnetometry (VSM), and magnetic heating measurements.

Thermally induced phase separation (TIPS) to fabricate chitosan/pectin based absorbent macroporous sponge with tranexamic acid for stable hemostasis.

Uncontrolled bleeding is one of the major causes of death in military and civilian trauma. Hence, designing a hemostatic agent that ensures safety, quality, and efficiency to stop bleeding is a challenging research area. Although many commercial agents focus on instant blood clotting, the issue of intrinsic coagulation followed by blood clot stabilization is ignored.

Tailoring adversarial attacks on deep neural networks for targeted class manipulation using DeepFool algorithm.

The susceptibility of deep neural networks (DNNs) to adversarial attacks undermines their reliability across numerous applications, underscoring the necessity for an in-depth exploration of these vulnerabilities and the formulation of robust defense strategies. The DeepFool algorithm by Moosavi-Dezfooli et al. (2016) represents a pivotal step in identifying minimal perturbations required to induce misclassification of input images.

Hollow-core PCF for terahertz sensing: A new approach for ethanol and benzene detection.

Terahertz (THz) spectroscopy is becoming a powerful technique for non-destructive, label-free chemical sensing with applications ranging from medicinal research to security screening. Enhancing THz spectroscopy's sensitivity and selectivity is crucial to maximizing its potential. In this work, we offer a novel optical fiber design, square shape core PCF that is tailored to exploit improved optical features at exterior in the THz region.

Optimizing power generation in a hybrid solar wind energy system using a DFIG-based control approach.

The rising demand for renewable energy has recently spurred notable advancements in hybrid energy systems that utilize solar and wind power. The Hybrid Solar Wind Energy System (HSWES) integrates wind turbines with solar energy systems. This research project aims to develop effective modeling and control techniques for a grid-connected HSWES.

Progress in heterostructures for photoelectrocatalytic reduction of carbon dioxide into fuels and value-added products.

Carbon capture and utilization (CCU) technology offers a sustainable option to simultaneously address both energy crisis and environmental pollution such as catalytic reduction of carbon dioxide (CO) into value-added fuel products (e.g., C-C).

Global advancement of solar photovoltaic thermal technologies integrated with membrane distillation systems: a comprehensive review.

Energy is crucial to progress toward development, modernization, and economic prosperity. Energy and water are both crucial to human survival and play significant roles in the growth and development of society. The need for desalination has risen significantly as freshwater environments have declined.

wQFM-TREE: highly accurate and scalable quartet-based species tree inference from gene trees.

Motivation: methods are becoming increasingly popular for species tree estimation from multi-locus data in the presence of gene tree discordance. Accurate Species TRee Algorithm (ASTRAL), a leading method in this class, solves the Maximum Quartet Support Species Tree problem within a constrained solution space, while heuristics like Weighted Quartet Fiduccia-Mattheyses (wQFM) and Weighted Quartet MaxCut (wQMC) use weighted quartets and a divide-and-conquer strategy. Recent studies showed wQFM to be more accurate than ASTRAL and wQMC, though its scalability is hindered by the computational demands of explicitly generating and weighting quartets.

Machine learning predictions of high-strength RCA concrete utilizing chemically activated fly ash and nano-silica.

This study explores the potential of RCA combined with nano silica and chemically activated fly ash to produce sustainable and high strength concrete. The research addresses the challenges posed by RCA's inferior mechanical and durability properties by incorporating SCM. A comprehensive experimental program includes 420 and 240 samples for compressive strength and acid resistance.

Stabilization of expansive clay soil using shells based agricultural waste ash.

This study investigates the effectiveness of using various agricultural waste ashes-namely eggshell ash (ESA), clamshell ash (CSA), cockle shell ash (CKSA), and oyster shell ash (OSA)-for the stabilization of expansive clay soils (ECS). Laboratory experiments assessed the impacts of these ashes on the soil's consistency limits, shrink-swell behavior, compaction properties, unconfined compressive strength (UCS), shear strength, and mineralogical and microstructural characteristics. The results demonstrated that the inclusion of agricultural waste ashes significantly improved the ECS properties by reducing plasticity from 29.

Pattern reconfigurable quasi Yagi antenna with Origami inspired magic spiral cubes for dynamic indoor IoT applications.

This paper presents a novel deployable antenna design with reconfigurable radiation patterns suitable for various indoor Internet of Things (IoT) applications. Inspired by origami, the antenna comprises a central monopole patch housed on a magic cube (MC-2) and two other modular units comprising stacks of magic cubes (MCs). In compact form, i.

A general simulation-based study on printability of inks in direct ink writing.

Direct ink writing (DIW)-based 3D printing process has gained significant attention due to its ability to accommodate versatile ink materials. However, the most challenging aspect of DIW is the development of inks with suitable printability. To address this, we demonstrate a numerical approach using the finite element method (FEM) to model the printability of inks.

Extreme heat prediction through deep learning and explainable AI.

Extreme heat waves are causing widespread concern for comprehensive studies on their ecological and societal implications. With the ongoing rise in global temperatures, precise forecasting of heatwaves becomes increasingly crucial for proactive planning and ensuring safety. This study investigates the efficacy of deep learning (DL) models, including Artificial Neural Network (ANN), Conolutional Neural Network (CNN) and Long-Short Term Memory (LSTM), using five years of meteorological data from Pakistan Meteorological Department (PMD), by integrating Explainable AI (XAI) techniques to enhance the interpretability of models.

Recent advances of silver nanoparticle-based polymer nanocomposites for biomedical applications.

Silver nanoparticle-polymer nanocomposites (AgNP-PNCs) represent a transformative advancement in biomedical material science, integrating the potent antimicrobial properties of AgNPs with the structural versatility of polymer matrices. This synergy enables enhanced infection control, mechanical stability, and controlled drug delivery, making these nanocomposites highly suitable for applications such as wound healing, medical coatings, tissue engineering, and biosensors. Recent progress in synthesis and functionalization has led to greater control over particle morphology, dispersion, and stability, optimizing AgNP-PNCs for clinical and translational applications.

Textile wastewater treatment by heterogeneous catalytic ozonation using microcellulose loaded ZIF 67 catalyst.

The textile industry, being a significant consumer of fresh water, generates large amounts of wastewater, leading to serious environmental problems. The high levels of pollutants present in textile wastewater pose a significant risk to the environment and disrupt the ecosystem. As a result, it is essential to implement effective treatment steps to mitigate these harmful effects.

Enhancing output efficiency in self-powered hybrid nanogenerators with micro-pyramid surface design using ceramic/polymer film for flexible wearable electronic devices.

Self-powered sensors are increasingly valued for their eco-friendly and energy-efficient design, making them ideal for sustainable applications. As global energy demand rises and carbon emissions increase, there is a shift toward renewable energy sources like solar and wind. Advanced sustainable energy devices, such as piezoelectric and triboelectric nanogenerators, show promises for capturing untapped energy, supporting the development of portable, green devices.