University of Wasit

The smart application of nanopesticides marks a significant shift toward environmentally responsible and sustainable pest management. By utilising nanotechnology to encapsulate and deliver active ingredients with high precision, nanopesticides offer targeted action against pests while minimising collateral damage to non-target organisms and ecosystems. Unlike conventional pesticides, these nanoscale systems reduce the overall chemical load required for effective pest control, thereby mitigating environmental pollution and human health risks. Advanced delivery strategies such as stimuli-responsive release, ligand-receptor targeting and controlled degradation enhance the specificity, stability and efficiency of these formulations. Innovative nanocarriers, including polymeric nanoparticles, liposomes and mesoporous silica nanoparticles, enable the controlled release of active compounds in response to environmental triggers such as pH fluctuations, temperature changes, or enzymatic activity. These technological breakthroughs align with the principles of sustainable agriculture by reducing chemical runoff, groundwater contamination and pesticide residues in the food chain. Additionally, the decreased frequency and quantity of applications contribute to lower carbon emissions and resource consumption. To ensure the safe integration of these technologies, robust regulatory frameworks and comprehensive risk assessment models are essential.

General Background: Polymer materials have become essential components in modern technology due to their lightweight structure, chemical stability, and adaptable physical characteristics. Specific Background: Advances in polymer science have enabled the modification of molecular structures and the integration of nanoparticles, allowing polymers to perform functions beyond traditional insulating and structural roles. Knowledge Gap: Despite extensive use in industrial and technological fields, a comprehensive conceptual synthesis linking polymer structure, classification, properties, and emerging nanotechnology applications remains necessary to clarify their functional relationships. Aims: This study analyzes the fundamental principles of polymer science, including structural composition, polymerization mechanisms, classifications, and key physical and chemical properties, while highlighting emerging nanopolymer applications. Results: The analysis demonstrates that polymer performance is strongly related to molecular structure, degree of polymerization, and intermolecular interactions. The integration of metal and oxide nanoparticles within polymer matrices introduces additional optical, electrical, and catalytic functionalities. Novelty: The work provides an integrated conceptual overview connecting classical polymer theory with modern nanocomposite developments in materials science. Implications: Understanding the structure–property relationship of polymers supports the development of advanced materials for applications in electronics, renewable energy systems, biosensors, and biomedical technologies, while also emphasizing the importance of sustainable polymer research. Keywords: Polymer Science, Nanopolymer Materials, Polymer Structure, Polymer Nanocomposites, Materials Engineering Key Findings Highlights Polymer molecular structure determines mechanical, thermal, and electrical material behavior. Nanoparticle integration introduces new optical and electronic functionalities in polymer systems. Polymer nanocomposites support emerging technologies in energy systems, sensing devices, and biomedical materials.

In this study, a novel anomaly detection-based hybrid model is realized for the prediction of ransomware attacks by monitoring network traffic. The signature or rule-based traditional approach to combating polymorphic ransomware and zero-day attack is under severe strain, especially in the presence of encrypted network traffic, that requires using more adaptive and flexible approaches that analyse behaviours. The proposed framework exploits temporal patterns of traffic by LSTM autoencoder and identifies statistically anomalous values with Isolation Forest. The outputs from the two models are then combined by a weighted merge mechanism to a final anomaly score to determine if traffic is benign or malicious. The proposed model was tested on an enhanced version of UGRansome_Dataset_2024 dataset having 111,904 records in unbalanced form, and balanced by SMOTE-ENN technique up to 149,197 records containing benign and malicious traffic. The simulation results demonstrate the superiority of the proposed model with 99.66% accuracy, 99.78% precision, 99.85% recall, 99.81% F1-score. These results demonstrate that the combined approach can effectively reduce false alarms, while the ability to detect attacks is significantly improved, and therefore can be used in practice networked environments.

Artificial Intelligence (AI) has become a key driver of transformation across industrial sectors, significantly enhancing efficiency, production quality, cost reduction, and natural resource conservation. This study examines AI’s influence on sustainable industrial development by analyzing its role in improving environmental and operational performance while balancing economic, social, and ecological dimensions. A descriptive analytical approach was adopted, using both quantitative and qualitative data from multiple sources. A purposive sample of 50 specialists in AI and industrial development contributed insights. Findings indicate that AI plays a decisive role in operational optimization, waste reduction, and improved resource management. However, technical and administrative barriers, including infrastructure and skilled human resource gaps, hinder wider AI adoption. The study recommends national strategies supporting digital industrial transformation and human capital development to ensure sustainable and resilient industrial growth.

General Background: Hydrogen generated through electrochemical water splitting is a promising clean energy carrier due to its high energy density and carbon-free combustion. Specific Background: The hydrogen evolution reaction (HER) requires efficient cathode catalysts, but platinum-group metals are limited by scarcity and cost, encouraging the exploration of nickel-based electrocatalysts. Knowledge Gap: The relationship between synthesis strategy, nanostructure formation, and HER electrocatalytic behavior of advanced nickel heterostructures and phosphosulfide systems remains insufficiently clarified. Aims: This study evaluates Ni/Ni(OH)₂ heterostructures and nickel phosphosulfide (Ni-P-S) nanocatalysts by examining their preparation, structural characteristics, and HER performance in alkaline media. Results: Structural analysis using XRD, XPS, and TEM confirmed successful nanostructure formation, including core–shell Ni/Ni(OH)₂ structures and porous nanosheet Ni-P-S networks. Electrochemical measurements showed that Ni-P-S required an overpotential of 78 mV at 10 mA cm⁻² and maintained stability for more than 50 hours in 1 M KOH, while Ni/Ni(OH)₂ exhibited higher overpotential values. Novelty: The study provides a comparative evaluation linking synthesis routes, nanostructure morphology, and HER electrocatalytic behavior of two nickel-based catalysts. Implications: These findings support the development of scalable non-precious metal catalysts for alkaline water electrolysis and sustainable hydrogen production technologies. Highlights:• Porous Nanosheet Architecture Provides High Electrochemically Active Surface Area.• Charge Transfer Resistance Decreases at the Catalyst and Electrolyte Interface.• Long-Term Operation Exceeding Fifty Hours Demonstrates Durability in Alkaline Medium. Keywords: Nickel Based Nanocatalysts, Hydrogen Evolution Reaction, Alkaline Water Splitting, Electrocatalysis, Nanostructured Catalysts

A substantial percentage of the world’s energy consumption (almost 40%) and carbon dioxide (CO2) emissions (around 37%) come from the construction industry, especially schools. This work presents a new hybrid artificial intelligence (AI) engineering model that aims to maximize energy performance on campuses in a holistic way. Modules for data-driven forecasting, metaheuristic optimization, and real-time adaptive control are all part of the concept. A thorough energy simulation of a university campus building is used in conjunction with the AI model to assess its performance through a co-simulation framework. Findings show that yearly peak electricity demand may be reduced by 18.7% and total site energy consumption by 22.4% when compared to a baseline building management system, all while keeping indoor thermal comfort levels high. According to the study, one effective way to make school buildings smart, eco-friendly, and energy efficient is to use a hybrid AI-driven method.

Emotions directly affect essential functions in human cognition, decision-making, and interactions. Any change in a person’s emotions can lead to different patterns in brain wave signals. Electroencephalography (EEG) is one of the key methods used to measure the brain's electrical activity. Emotion identification through EEG data has become a crucial element of human-computer interaction. Recently, the increasing need for monitoring brain activity has been measured by EEG devices, and the high price of these devices, which are not accessible outside of the laboratory for personal use, has prompted the development of low-cost wearable EEG devices. This paper introduces the design and implementation of an EEG system using available and low-cost components. This EEG system applies to emotion identification using the STM32F103C8T6 microcontroller, two-stage amplifiers, and filtering by bandwidth of (0.5-48) Hz. These data are logged and processed using MATLAB 2024, where Power Spectral Density (PSD) is applied for feature extraction and then the Support Vector Machine (SVM) for emotion detection, such as boredom, calm, fear, and happiness. Results show that this system can achieve an accuracy of a range between (79-83) % for the four emotion classifications. This study concludes by summarizing the practical importance of Electroencephalography (EEG) signals in emotion recognition, focusing on its potential for future applications.

Organic semiconductors have revolutionized electronics, but their amorphous nature hinders performance and stability. Crystallinity overcomes these limitations; however, the design of materials that combine high crystallinity, optimal thermal properties, and ease of synthesis remains a significant challenge. A machine learning (ML)‐assisted approach combined with the density functional theory (DFT) study was employed to generate a vast chemical space of crystallizable organic semiconductors. By breaking retrosynthetically interesting chemical species (BRICS), over 1700 new organic semiconductors with promising synthetic accessibility (SA) were designed. ML algorithms, specifically extra trees (ET) and random forest, were used to predict the melting temperatures ( T m ) of these semiconductors, yielding good R ‐squared ( R 2 ) values of 0.94–0.96. Dimensionality reduction analysis reveals that the t‐distributed stochastic neighbor embedding (t‐SNE) components 1 and 2 of these semiconductors ranged within the same value (−5 to 5), indicating a high degree of similarity. Furthermore, analysis of SA showed that new organic semiconductors with SMILES lengths between 15 and 40 are more likely to be easily synthesized. Based on these findings, 20 new candidate semiconductors were identified for practical synthesis and analysis. DFT calculations were employed to study the optoelectronic properties of chromophores. This study demonstrates the power of ML‐assisted design in generating crystallizable organic semiconductors with enhanced SA. The findings are expected to contribute significantly to the development of high‐performance organic electronics.

Objective: Estrogen is a fundamental sex steroid hormone that not only governs female reproductive physiology but also exerts wide-ranging effects on multiple organ systems, including the cardiovascular, nervous, immune, and musculoskeletal systems. Methods: The fall of estrogen, characteristic of postmenopausal women, has been believed to be one of the strongest players for developing all these problems associated with aging, including cardiovascular disease, neurodegenerative disorder, metabolic syndrome, osteoporosis, sarcopenia, and frailty [4, 5]. Results: Estrogen exerts its biological actions through its receptors, called estrogen receptors (ERs). Estrogen-related receptors (ERRs: α, β, and γ), orphan nuclear receptors with high structural homology and overlapping transcriptional targets to ERs, have also been implicated in estrogen action [64], [65]. New emerging evidence emphasizes the pivotal involvement of both ERs and ERRs in skeletal muscle biology, such as the regulation of muscle mass, adaptation to exercise, and regeneration. Novelty: This review is highlighted on the the putative regulatory roles of ERs and ERRs exert on skeletal muscle physiology, with special attention in mediating mitochondrial function and metabolic homeostasis.