Corrosion Management, ISSN:1355-5243 is an international, interdisciplinary, open-access, peer-reviewed journal that publishes original research and review articles on the interface between Chemistry and Management and Social Sciences. The journal focuses on the exploration of the neural mechanisms underlying consciousness, cognition, perception, and behavior from a quantum perspective. Corrosion Management is published half-yearly. A journal of the Institute of Corrosion Corrosion Management aims to promote scientific dialogue and collaboration among researchers, practitioners, and scholars in the fields of Chemistry and Management and Social Sciences, psychology, philosophy, and related disciplines. The journal welcomes empirical, theoretical, and methodological contributions that enhance our understanding of the mind-brain relationship and the fundamental principles of quantum mechanics that apply to the study of cognition and behavior. Some of the key topics covered in Corrosion Management include quantum information processing in the brain, quantum coherence and entanglement in neural networks, quantum-inspired models of brain function, quantum-inspired models of perception and decision making, quantum consciousness, and the philosophical and ethical implications of quantum approaches . Corrosion Management is committed to providing high-quality, rigorous, and timely peer review and publication services to authors worldwide. The journal operates on a continuous publishing model, with accepted articles Peer Review Corrosion Management has a blind review process. All manuscripts submitted to Corrosion Management are circulated to two field referees after initial screening process. If the referee's report contradict one another, a further expert opinion will be sought. 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Artificial Intelligence and Internal Control Systems: A Theoretical Examination of Risk Detection, Monitoring, Compliance, and Organizational Accountability in Businesses
Internal control systems exist to give organizations reasonable assurance that operations are effective, financial reporting is reliable, and applicable laws and regulations are followed — an assurance that has historically rested on periodic testing performed by people who can be asked, directly, why a particular control judgement was made. Artificial intelligence (AI) is now performing a growing share of the risk-detection and monitoring work internal control systems depend on, and this paper offers a theoretical examination of what that shift means for the control environment as a whole, not merely for the efficiency of individual control activities. Integrating the COSO Internal Control framework, agency theory, the Institute of Internal Auditors' Three Lines Model, and the NIST AI Risk Management Framework with recent empirical evidence on AI-enabled monitoring, automation, and algorithmic decision authority, the paper argues that AI strengthens two of internal control's traditional components — risk assessment and monitoring activities — more reliably than it strengthens a third, less often theorized requirement: organizational accountability for control failures. Because AI systems increasingly perform the detection and monitoring work that control frameworks assume a human occupies, the traditional assumption that a specific, identifiable person can explain why a control operated as it did becomes harder to satisfy exactly as detection and monitoring become more comprehensive and more automated. Drawing on the distinction between post-hoc explanation and interpretability by design, the paper proposes that this accountability gap is not simply a communication problem to be solved with better explanation tools, but a structural design choice organizations make when they select complex, opaque models over simpler, inherently interpretable ones for control-relevant decisions. The paper develops a four-dimensional theoretical model, compares the governance vocabulary of COSO, the Three Lines Model, and the NIST framework directly, and considers what the resulting account implies for how internal control systems, and the professionals who operate them, need to adapt.
Copyright (c) 2025 Corrosion Management ISSN:1355-5243 https://corrosion-management.com/
ARTIFICIAL INTELLIGENCE ADOPTION IN ACCOUNTING: A THEORETICALL EXAMINATION OF OPPORTUNITIES, CHALLENGES AND IMPLICATION FOR THE FUTURE OF PROFESSIONAL ACCOUNTING PRACTICE
Artificial Intelligence (AI) is being adopted across the accounting profession at a pace that has outrun the profession's theoretical understanding of what that adoption actually involves. Much of the existing literature treats AI adoption as a technical event — a system installed, a tool switched on — rather than as the organizational and professional process it actually is. This paper offers a theoretical examination of that process, integrating adoption theory (the Technology–Organization–Environment framework, the Technology Acceptance Model, and the Unified Theory of Acceptance and Use of Technology) with recent empirical evidence from accounting, auditing, and professional services research. It argues that AI adoption in accounting should be understood as a socio-technical process operating simultaneously at three levels — organizational readiness, individual acceptance, and professional identity — each of which can independently stall adoption regardless of a technology's capability. The paper reviews the opportunities AI creates across audit quality, fraud detection, predictive analytics, and advisory work, and sets these against the governance, ethical, and workforce challenges that recent field evidence shows are just as consequential. It closes by considering what sustained AI adoption implies for the accountant's professional role, for accounting education, and for the standard-setting bodies that will eventually have to formalize expectations around AI-assisted professional judgement. The contribution is a theoretical account of adoption that treats capability and acceptance as genuinely separate problems, neither of which rersolves the other.
Copyright (c) 2026 Corrosion Management ISSN:1355-5243 https://corrosion-management.com/
Flame-Retardant Cotton Fabric Prepared by Admicellar Polymerization of a Phosphorus-Containing Monomer Using a Cationic Surfactant
A flame-retardant coating was developed on cotton fabric through admicellar polymerization of 2-methacryloyloxyethyl phosphorylcholine (MPC) using cetylpyridinium chloride (CPC) as a cationic surfactant. The adsorption behavior of CPC on cotton fabric was investigated at 30 and 60 °C to determine suitable conditions for admicellar polymerization. The adsorption of CPC increased with increasing temperature and a critical micelle concentration (CMC) of approximately 0.65 mM was obtained. Cotton fabrics coated at 60 °C exhibited higher phosphorus content than fabrics coated at 30 °C, indicating enhanced polymer deposition. Thermogravimetric analysis revealed increased char formation and modified thermal degradation behavior in treated fabrics. The onset degradation temperature decreased due to phosphorus-catalyzed dehydration, whereas the char yield increased significantly. Auto-flammability tests demonstrated substantial improvement in flame retardancy, and the treated fabric containing 7.8 mg phosphorus g⁻¹ cotton exhibited self-extinguishing behavior. The flame-retardant effect was retained after home laundering. The results demonstrate that admicellar polymerization is an effective technique for producing durable phosphorus-containing flame-retardant coatings on cotton fabric.
Determinants of Sustainable Biking as an Alternative Mode of Transport in Ibadan, Nigerian
Rapid urbanization in Nigeria has intensified traffic congestion, environmental degradation, and public health concerns, and economic loss, highlighting the need for sustainable mobility alternatives. Existing research focuses on psychological determinants separately from the environmental, and on the physical and infrastructural determinant within which travel behaviour occurs. Consequently, there is a need for empirical study that integrates all variables for better understanding of the impact on adoption of cycling. This study investigates the determinants of cycling behaviour in Ibadan using an integrated framework based on the Theory of Planned Behavior (TPB) and the Ecological Model of Active Transportation. A convergent mixed-methods design was employed, involving questionnaire surveys administered to 150 urban commuters and semi-structured interviews conducted with 7 commuters, transport planners, and cycling advocates. Quantitative data were analysed using descriptive statistics, multiple linear regression, and binary logistic regression, while qualitative data were examined through thematic analysis. The results indicate that TPB constructs significantly predict cycling intention (R² = 0.41), with perceived behavioural control emerging as the strongest predictor (β = 0.48, p < 0.001). However, actual cycling behaviour was influenced primarily by contextual factors. Perceived infrastructure safety significantly increased the likelihood of regular cycling (OR = 4.20, p < 0.001), whereas private car ownership substantially reduced participation (OR = 0.20, p = 0.001). Qualitative findings further revealed concerns regarding inadequate cycling infrastructure, destination security, and the potential of electric bicycles to overcome physical and environmental constraints.. The study concludes that cycling adoption in Ibadan is shaped more by infrastructural, institutional, and policy conditions than by individual intentions, stressing the need for integrated transport interventions.
Adewumi, Adedotun Joshua1; Adeyemi, Ademola1
Copyright (c) 2026 MSW Management Journal ISSN: 1053-7899 https://mswmanagementj.com/
Thermodynamic Framework for the Formation and Stability of Self-Assembled Nanostructures
Self-assembled nanostructures represent one of the most significant paradigms in modern nanotechnology, offering bottom-up fabrication strategies that exploit thermodynamic driving forces to achieve precise structural organization at the nanoscale. This paper provides a comprehensive examination of the thermodynamic stability mechanisms governing self-assembled nanostructures, including micelles, vesicles, DNA origami complexes, peptide nanofibers, and metal-organic frameworks (MOFs). The interplay between enthalpy and entropy in the free energy landscape is explored in depth, with attention to how temperature, solvent polarity, ionic strength, pH, and molecular concentration modulate structural integrity. we critically evaluate thermodynamic stability parameters such as Gibbs free energy, critical aggregation concentration, and phase transition temperatures. Methodology encompasses a suite of analytical techniques including differential scanning calorimetry (DSC), isothermal titration calorimetry (ITC), dynamic light scattering (DLS), small-angle X-ray scattering (SAXS), and molecular dynamics (MD) simulations. Key results are summarized across four data tables that capture thermodynamic profiles of diverse nanostructure classes. The findings underscore the delicate balance of non-covalent interactions and demonstrate that rational engineering of these forces yields nanostructures with tunable, programmable stabilities applicable to drug delivery, catalysis, sensing, and responsive materials. This review advances understanding of fundamental stability principles and outlines future directions for designing robust, stimuli-responsive nanoarchitectures.
In particular, the lack of immediate human assistance is a major reason why women's safety continues to be a problem in society at large. Traditional safety apps mostly require the user to manually interact with them, so they are not helpful at all in the case of a sudden panic or physical distress. This article presents an AI-powered smart wristband system that can detect unsafe situations in real time and respond to emergencies automatically. The system under discussion uses wearable sensors such as an accelerometer-gyroscope module, a heart rate sensor, a microcontroller, a base embedded platform, and a smartphone interface. Wearable device motion data are used in this research work to locate abnormal or panic-like movement patterns by means of a Temporal Convolutional Network (TCN), whereas physiological stress can be detected on the basis of personalized heart rate analysis committed to individual baseline variations. Furthermore, through GPS-based risk zone analysis and time-dependent rules, the system's sensitivity could be dynamically changed, and hence achievement of contextual awareness. A multi-modal decision framework, which is based on user confirmation, is employed in order to minimize false alarm generation from bio signal interpretation. This mechanism is followed by the incorporation of physical features and personal health features coupled with motion that are then subjected to an analysis through a multi-modal decision framework, which results in the detection of the event and alarm generation. Once the system has figured out that the threat is real, it will not hesitate to send out an SOS alert as well as allow someone to track the live location of the person continuously through a cloud platform. This proposed model is a personalized, multi-sensor fusion and low-latency decision, approach that has been compared to a reliable and scalable solution for women.
Prof. Poonam Kapse, Prof. Vishwayogita A. Savalkar, Sneha Mestri, Sahil Kanse, Shivam Mohite, Saurav Rai, Rishi Chilveri
INTEGRATING EMOTIONAL INTELLIGENCE WITH THE THEORY OF PLANNED BEHAVIOUR TO EXPLAIN EMPLOYEE TURNOVER INTENTION: A PLS-SEM APPROACH
Purpose: The primary aim of the study was to examine the impact of Emotional Intelligence on Turnover Intention based on the Theory of Planned Behaviour. With the aim to obtain a holistic understanding of intention to behave from the point of view of the employees, it was planned to incorporate the concept of emotional intelligence in the three major components of TPB, which are attitudes, subjective norms, and perceived behavioural control. Methodology: In addition to primary research using a questionnaire style of scales, a quantitative research methodology was used. Purposive and convenience sample techniques were used to choose 486 respondents from various industry backgrounds. The measurement and structural models of the collected data were analyzed by using PLS-SEM. Findings: The study confirmed the significance of social pressures in the workplace by showing that subjective norm was the largest predictor of turnover intention, whereas emotional intelligence had a little impact. Turnover intention was found to be influenced by attitude and perceived behavioural control, however this influence was minimal when compared to the subjective norm. Originality: By combining the Theory of Planned Behaviour with Emotional Intelligence, the study added to the corpus of knowledge by developing a comprehensive framework of social cognitive and emotional aspects. It provided organisations with more theoretical and practical guidance on how to create an efficient staff retention plan.
Copyright (c) 2026 Corrosion Management ISSN:1355-5243 Scopus https://corrosion-management.com/
SPATIAL BLUEPRINTING OF PLAY INFRASTRUCTURE: OPTIMIZING RESOURCES AND SUSTAINABLE CONSTRUCTION METHODOLOGIES
Modern urban development increasingly seeks outdoor spaces that harmonize aesthetic appeal with ecological sustainability. This paper presents a multi-disciplinary approach to the design and construction of a sustainable amusement park. The research focuses on minimizing natural resource depletion through the integration of Green Building principles and cost-effective engineering strategies. The technical workflow includes site-condition analysis, soil strength assessment, and high-precision surveying via Total Station. The proposed design was developed using AutoCAD, Revit, and Lumion to ensure structural efficiency and environmental compatibility. The findings demonstrate that sustainable design can mitigate the negative environmental footprints of large-scale recreational projects.
Copyright (c) 2024 Corrosion Management ISSN:1355-5243 Scopus https://corrosion-management.com/
ENHANCING CLOUD RESOURCE ALLOCATION WITH VISION TRANSFORMER, DEEP REINFORCEMENT LEARNING, AND IMPROVED SHRIKE OPTIMIZATION ALGORITHM
Cloud computing relies on efficient resource management to deliver scalable, on-demand services while minimizing costs, energy consumption, and response delays in Infrastructure as a Service (IaaS) environments. Traditional approaches often struggle with dynamic workloads, leading to resource underutilization, high migration overheads, and suboptimal Quality of Service (QoS). This paper introduces a novel hybrid framework that synergistically combines Vision Transformer (ViT) for adaptive load pattern recognition and balancing, Deep Reinforcement Learning (DRL) integrated with an Enhanced Shrike Optimization Algorithm (ESOA) for intelligent resource allocation, and Twin Fold Moth Flame Optimization (TFMFO) for cost-effective virtual machine (VM) migration. The ESOA improves upon the standard Shrike Optimization Algorithm by incorporating adaptive pheromone evaporation, Lévy flights for better global exploration, and stagnation detection to escape local optima, enabling faster convergence and superior handling of multimodal optimization problems typical in cloud scenarios. Extensive simulations using CloudSim with heterogeneous workloads (100–500 tasks) demonstrate significant performance gains: makespan reduced to 770 ms (26–36% improvement), average response time to 280 ms, energy consumption to 6160 J (30–35% savings), operational cost to $9.1, and throughput increased to 119 tasks/sec (20–25% higher) compared to state-of-the-art methods such as DRL-based placement, dual-threshold MBFD, multi-objective RL for edge, and VMP-ER. The proposed framework offers a robust, sustainable solution for modern cloud data centers, advancing efficient load balancing, resource utilization, and energy-aware operations.
Assessment of Livelihood Vulnerability and Adoption of Climate Smart Agriculture among the Farmers in Ghaziabad, Uttar Pradesh
Climate change is posing serious risks for human beings and the environment, and it is affecting agriculture and food security around the world. Food security will be impacted by changes in the climate and agricultural production systems, but farmers will bear the burden of these changes first. For most of the years, Uttar Pradesh’s agricultural growth has continuously lagged behind the national average. From 2005–06 to 2018–19, the agricultural growth rate increased by 3.0% annually (at constant prices of 2011-12). The paper examined the livelihood vulnerability among the farmers and adoption of climate-smart agriculture. The study is based on a primary survey conducted in Ghaziabad district’s four blocks, i.e., Bhojpur, Rajapur, Loni and Murad Nagar. There were 100 responses collected from each block, and a total of 400 responses were collected from the study area. The data has been analyzed using techniques such as the Livelihood Vulnerability Index (LVI), which is a balanced weighted average approach, indexing based on UNDP, and climate-wise agriculture practices based on composite score. The study also used multiple linear regression and the ordered logistic regression model. The areas of Loni and Muradnagar are the most vulnerable, have the least potential for adaptation, and are most exposed to socio-environmental stresses. More people are using cost-effective techniques like mulching and composting. The results imply that adoption of CSA practices is significantly influenced by economic viability.
SYNTHESIS AND UTILIZING NANOPOROUS MATERIAL FROM PARTIALLY LATERISED KHONDALITE ROCK FOR CORROSION STUDIES
Partially Laterised Khondalite (PLK) rocks are a bauxite mining waste generated during the mining of the bauxite for the production of alumina. This waste can be transformed into a nanoporous material after a suitable physical beneficiation followed by a chemical activation process. In this paper, the nanoporous material was synthesized from the partially laterised khondalite (PLK) rocks, which was applied to a mild steel in the medium of hydrochloric acid of one molar (1 M) concentration. The characterisation study has been carried out on the raw material and the nanoporous material. The characterisation study included XRD, FTIR, SEM, TEM, BET. It was observed that the structural transformation occurs with an increase in surface area from 8.4 m²/g to 162.7 m²/g. The different experiments were carried out to reveal that the material acts as an efficient corrosion inhibitor by using electrochemical impedance spectroscopy (EIS), potentiodynamic polarization, and weight-loss experiments. It is found that the maximum inhibition efficiency of 92.5% achieved after dipping in the sea water for 72 hours. The resulting material improves performance and environmental impact while providing a more economical and sustainable option for creating anticorrosive coatings.
The Role of Forensic Accounting in Fraud Detection and Prevention
This paper examines the role of forensic accounting in the detection and prevention of fraud within contemporary organisational environments. Drawing on secondary data from peer-reviewed studies published since 2015, the study analyses how forensic accounting extends beyond traditional auditing by integrating investigative techniques, legal awareness and advanced data analytics. The review highlights that forensic accounting significantly enhances the identification of complex and concealed fraud schemes, particularly those involving management override, collusion and digital transactions. The findings further indicate that forensic accounting contributes to fraud prevention by supporting stronger internal controls, improving governance practices and creating deterrence effects through increased detection likelihood. The study also discusses the influence of technological innovation, professional competence and institutional support on the effectiveness of forensic accounting practices. While acknowledging challenges such as cost constraints and skill gaps, the paper underscores the growing strategic importance of forensic accounting as an integral component of modern fraud risk management frameworks.
ASSESSMENT OF PROTECTIVE COATINGS FOR CORROSION PREVENTION OF CARBON STEEL IN HIGH-CONCENTRATION SULFURIC ACID ENVIRONMENTS
This study evaluates the performance of three advanced protective coatings—Belzona 4341, Carboline Plasite 4550, and DuraPol UHT—on carbon steel subjected to the aggressive conditions of 98% sulfuric acid. The primary objective is to determine the effectiveness of these coatings in preventing corrosion and extending the lifespan of carbon steel in highly corrosive environments.To achieve this, a series of tests were conducted to assess key performance indicators, including adhesion strength, chemical resistance, hardness retention, and weight loss over time. Adhesion strength was measured to evaluate how well each coating bonded to the carbon steel substrate, which is critical for maintaining protective properties under corrosive conditions. Chemical resistance tests were performed to determine the coatings' ability to withstand the harsh effects of concentrated sulfuric acid, while hardness retention assessments provided insights into the coatings' durability and resistance to mechanical wear. Weight loss measurements were taken to quantify the extent of corrosion that occurred over the testing period.The results of the study revealed that Belzona 4341 outperformed the other coatings in all evaluated parameters, demonstrating superior adhesion strength and exceptional chemical resistance. Additionally, it exhibited minimal weight loss, indicating its effectiveness in preventing corrosion. Carboline Plasite 4550 and DuraPol UHT showed varying degrees of performance, but neither matched the overall effectiveness of Belzona 4341.These findings underscore the importance of selecting appropriate protective coatings for carbon steel in industrial applications, particularly in environments characterized by high acidity. By identifying Belzona 4341 as the most effective solution, this research provides valuable insights for industries seeking to enhance the longevity and reliability of their materials in corrosive settings. The study aims to inform future strategies for corrosion prevention and material selection, ultimately contributing to improved operational efficiency and reduced maintenance costs in industrial applications.
OPTIMIZATION OF PROCESS PARAMETERS TO ENHANCE INHIBITION EFFICIENCY IN CORROSION PREVENTION THROUGH SIO2 COATING
This study thoroughly investigates the inhibition of corrosion efficiency using silica sand (SiO2), specifically tailored for coating applications in aerospace, marine, and industrial sectors. The primary objective was to enhance corrosion efficiency beyond 90%, making it suitable for various industrial applications. To further ensure the reliability of this result, the Box-Behnken statistical design, implemented in Minitab, was used to optimize and refine the process parameters to improve corrosion efficiency. Various influencing factors, such as particle size, concentration, and pH, were systematically analyzed to determine their impact on corrosion resistance. Experimental validation confirmed that silica sand (SiO2), coatings significantly enhance durability and performance under harsh environmental conditions. This research provides a foundation for developing advanced protective coatings with enhanced longevity, contributing to cost-effective and sustainable corrosion prevention solutions across multiple industries.
NANO-ZIRCONIA COMPOSITE COATINGS AS EFFECTIVE BARRIERS FOR CORROSION MITIGATION
This work examines the corrosion resistance of nano zircon or zirconia-silica (ZrO₂-SiO₂) composite coatings on metal surfaces opened to corrosive ambient, comprised of acids, salts, and moisture. Looking at the traditional coatings, nano-scale composites upgrade reliability via augmenting hardness, wear resistance, and adhesion aspects. The synthesized zircon, provide greater cleanliness and steadiness over natural zircon, bring forth remarkable benefits toward industrial usages like aerospace and automotive sectors, diminishing maintenance costs and expanding service span. The ZrO₂-SiO₂ composite was artificially produced by extracting nano ZrO₂ out of zircon concentrate and mixing it with fumed SiO₂ in a 1:1 molar ratio, subsequently calcination at temperatures ranging 1000°C to 1400°C. X-ray diffraction (XRD) study established the configuration of monoclinic ZrO₂ and tetragonal ZrSiO₄, with finest crystallinity at 1400°C. Scanning Electron Microscopy (SEM) exposed a dense, regular, and crack-free coating with tough adhesion. The corrosion rate of mild steel coated with ZrO₂-SiO₂ composite is 0.001mm/year in sea water and .004mm/yr in 0.1M H2SO4solution. The results emphasize the prospective of ZrO₂-SiO₂ coatings as a useful resolution for corrosion defence in considerate manufacturing atmospheres.
LIGHTWEIGHT MACHINE LEARNING FOR RAPID AND ACCURATE CORROSION DETECTION
The deterioration of metallic structures due to corrosion presents substantial economic and safety concerns, contributing to nearly 4% of the global GDP. Despite the promise of machine learning in corrosion detection, progress is hindered by the scarcity of standardized datasets. In this research, the ARL-WPI corrosion dataset has been utilized to refine multi-label classification models through computationally efficient feature extraction and optimized machine learning techniques. However, Colour descriptors such as mean and standard deviation of Red, Green, and Blue (RGB) and Hue, Saturation, and Value (HSV) channels have been extracted for enhanced illumination invariance, along with texture-based attributes Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP), and Gray Level Co-occurrence Matrix (GLCM) to encapsulate structural patterns and wavelet-derived features (DWT) for multiresolution frequency analysis. Additionally, Principal Component Analysis (PCA) has been applied to preserve 99% of the variance while significantly reducing computational overhead. The optimization of machine learning models, particularly K-Nearest Neighbour(KNN) with 98.6% accuracy and 28.27s execution time and ensemble methods with 97.2% accuracy and 67.24s execution time has demonstrated superior performance over deep learning frameworks, which exhibit higher computational demands. The findings underscore the efficacy of lightweight classifiers with domain-specific feature representations in expediting corrosion assessment. This study fosters a robust synergy between AI-driven material diagnostics and industrial applications, offering a scalable, high-precision, and computationally viable approach for real-world corrosion monitoring.
Copyright (c) 2025 Corrosion Management ISSN:1355-5243 Scopus https://corrosion-management.com/
NEXT-GEN CORROSION ANALYTICS: INTELLIGENT FORECASTING FOR OIL PIPELINE INTEGRITY
Effective monitoring and precise forecasting of internal CO₂ corrosion are paramount for safeguarding the structural integrity of oil pipelines. This research employs an array of advanced machine learning and deep learning methodologies to predict corrosion progression, leveraging a comprehensive dataset encapsulating critical environmental and operational factors. Various regression techniques, including Support Vector Regression (SVR), Decision Trees, Random Forest, K-Nearest Neighbours (KNN), and Polynomial Regression, were meticulously evaluated. Among linear models, the Random Forest Regressor exhibited superior predictive accuracy with an R² of 0.9936 and an MSE of 0.0080. Notably, third-degree polynomial regression emerged as the most effective nonlinear approach (R² = 0.9988). Regularized regression models underscored Ridge Regression as the optimal choice, achieving an R² of 0.9989. Within deep learning paradigms, the Feed-Forward Neural Network (FNN) and ResNet models demonstrated remarkable efficacy, attaining a R² score of 0.9928. Furthermore, ensemble learning, particularly a hybridized CNN-CNN Forecaster model, exhibited commendable predictive precision. The study emphasizes the necessity of model selection for accurate corrosion forecasting and facilitating proactive maintenance.
CORROSION DETECTION IN CONCRETE USING PHASE-BASED SPEECH ENHANCEMENT AND RECOGNITION TECHNIQUES WITH AI FRAMEWORK
Automatic Speech Recognition (ASR) is one of the oldest signal processing techniques which deals with detecting different types of speech including spoken words, emotions, disease, speaker identity, languages, music, and noise levels. Recently, these standard ASR methods have been adopted into the fields of acoustics, bioinformatics, wildlife, and environmental audio detection. One of the upcoming applications of ASR is in civil engineering which deals with the hammer sound test. This test is one of the non-destructive tests which is performed to detect the concrete quality and surface defects. This research problem has been taken in this paper and investigated with various phase-based audio and statistical features along with speech enhancement techniques to reduce unwanted noises. Phase of the audio signal plays a crucial role in extracting relevant audio information which is less explored in the audio signal processing. The proposed phase-based method has been tested with standard hammer sound datasets and other environmental sound datasets. It has been observed from the analysis of the simulated results that the proposed method has been demonstrating superior performance.
Fuzzy Logic-Based Robustness Assessment of Complex Engineering Systems: Methodologies, Case Studies, and Comparative Analysis
Robustness assessment of engineering systems under uncertainty is a critical factor for ensuring operational reliability and minimizing costly downtime. Traditional probabilistic methods often fall short in addressing the vagueness and ambiguity inherent in real-world data and expert judgments. This study presents an in-depth application of fuzzy logic approaches—including Fuzzy Fault Tree Analysis (FFTA), Fuzzy Reliability Block Diagrams (FRBD), and Fuzzy Bayesian Networks (FBN)—to model and evaluate the robustness of complex engineering systems. Detailed case studies on an automated manufacturing plant and a power distribution system demonstrate how fuzzy models effectively capture uncertainty through membership functions and fuzzy arithmetic, providing a richer reliability assessment than classical methods. Sensitivity analyses identify the most influential parameters affecting system robustness, guiding targeted maintenance and risk mitigation. A comparative evaluation of the fuzzy methods highlights their respective strengths and limitations, informing model selection based on application requirements. The results underscore fuzzy logic’s potential to enhance fault diagnosis, maintenance prioritization, and decision-making under uncertainty. Finally, future research directions emphasize real-time fuzzy monitoring and hybrid AI integration to meet the demands of increasingly complex and interconnected systems.
Legal Responses to Pipeline Bombings and Sabotage as Tools of Domestic Terrorism in Nigeria
Pipeline bombings and sabotage have become prominent forms of domestic terrorism in Nigeria, threatening national security, economic stability, and public safety. This paper examines Nigeria's legal frameworks and judicial responses to pipeline attacks, especially in the Niger Delta region, where insurgent groups and criminal gangs have systematically targeted oil and gas infrastructure. The analysis reveals that while Nigeria has enacted comprehensive counterterrorism laws, significant challenges remain in effectively prosecuting offenders and preventing future attacks. The legal framework includes various statutes, such as the Terrorism Prevention Act, which designates infrastructure sabotage as terrorism, yet enforcement is hampered by weak mechanisms, corruption, and socioeconomic factors underlying the violence. The paper evaluates how courts have interpreted and applied terrorism laws in pipeline sabotage cases and explores the intersection of counterterrorism measures with human rights protections, noting concerns about potential abuses during security operations. The study concludes that Nigeria's legal response, although theoretically robust, requires improvements through enhanced investigative capacity, witness protection, specialized terrorism courts, and addressing root causes like resource conflicts, poverty, and governance issues. Effective strategies should balance punitive legal actions with conflict resolution, community involvement, and equitable resource sharing. This research contributes to understanding how developing nations handle the complex legal challenges of combating domestic terrorism while safeguarding the rule of law and fundamental rights in resource-rich, conflict-affected areas.
Tobiloba AWOTOYE(PhD) Femi Adeoye Alabi (PhD) Dolapo Olutayo(PhD)
An Application of Erikson’s Psychosocial Theory to Anita Desai’s Voices in the City
This paper employs the psychologist Erik Erikson’s psychosocial theory of development, the theoretical framework that represents the psychosocial dimensions of alienation in Anita Desai’s novel Voices in the City are examined. The study analyses resolved undevelopmental problems, related to identity and intimacy, resulting in existential disconnection, emotional isolation, and a fractured sense of self while focusing on the psychological paths of the main characters-Nirode, Monisha, and Amla. Desai’s portrayal of contemporary urban life in post-independence India serves as a crucial background for these psychological conflicts. This paper demonstrates how Desai’s characters represent the psychosocial consequences of alienation by applying Erikson’s stages of Identity vs. Role Confusion and Intimacy vs. Isolation. In addition to providing a deeper understanding of Desai's narrative technique, this interdisciplinary approach reveals the psychological theories enduring significance in literary analysis.
Pardeep kaur and Asst. Prof. Dr. Kanchan Sharma
MAPPING THE LANDSCAPE OF INDIVIDUAL-LEVEL POVERTY ANALYSIS (2012–2025): A SCIENTOMETRIC STUDY
The individual-level poverty analysis is a significant step forward in poverty assessment, highlighting intra-household differences that are often overlooked by traditional household-based measurements. This scientometric study examines the intellectual and collaborative landscape of individual poverty research. This scientometric study uses data from the Scopus database to examine the organization and development of research on individual-level poverty analysis from 2012 to 2025. The study analyzed 73 peer-reviewed papers to map publication trends, identify key authors, and track theme developments. The results show a growing interest in gender-sensitive and multifaceted responses to poverty, with notable international cooperation and thematic shifts toward the capabilities approach, time poverty, and empowerment. In addition to outlining potential research avenues, this work advances our knowledge of the development of individual-level poverty analysis.
"AN ANALYTICAL STUDY OF PATRIOTIC THEMES IN ADVERTISING ART"
This research investigates how patriotic elements are integrated into advertising art and how these elements—such as national symbols, historical imagery, and culturally rooted narratives—influence both consumer behaviour and brand interpretation. Drawing on a mixed-method research framework that blends content analysis, interviews, and survey findings, the study identifies the visual and emotional devices most commonly used to evoke national pride. The results reveal clear generational differences: older audiences tend to interpret patriotic advertisements with warmth and trust, whereas younger individuals are more cautious and often question the sincerity behind such messages. Although patriotic themes can reinforce loyalty and enhance brand credibility—particularly around national celebrations—the study also highlights concerns about the ethical implications of commercialising patriotic sentiment. Overall, the research guides advertisers seeking to employ patriotic themes with authenticity and cultural sensitivity to ensure positive and lasting consumer relationships.
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Assessment of Drinking Water Quality level in Narnaul, City of Haryana
Human existence depends on water accessibility, as well as quantity and quality. Over 50% of water is wasted in the home, agricultural, and industrial sectors as a result of extensive water extraction for these uses. A large portion of the accessible water is becoming unfit to drink due to water contamination. Surface water is the primary source of drinking water for most of Indians. The WQI, or Water Quality Index, calculates an individual value that measures the overall state of the water at a given location and time. The Indian Council of Medical Research (ICMR) and the Bureau of Indian Standards (BIS) provide a standardized method for computing the Water Quality Index, which is used in this work. The aim of this work, is to assess the Water Quality Index (WQI) of drinking water in the Narnaul city, which is based upon both primary and secondary data, The analyzed data is contrasted with the standard values that the ICMR and BIS recommend. Number of water quality parameters, including pH, total alkalinity, calcium, magnesium, chloride, sulfate, and nitrate, are taken into account in this work in order to compute the WQI. Additionally, the periodical, fluctuations of the Physicochemical characteristics of the quality of water Narnaul city are calculated. The results shows According to the WQI values, the east of the city had a higher percentage of drinking water in the excellent water quality category, while the south had neutral water quality.The water quality index (WQI) is different everywhere in the city . The lowest variation was in Sample4 (Rewari Rode bore-well) 15.144339, and the highest variation was in Sample10 (Nalapur bore-well) 222.7897. A more methodical approach, the WQI method makes it easier to compare the drinking water quality of multiple sampling locations.It is clearly visible from the view that only about 50% samples in the city have suitable quality for drinking water. It is very important to treat the remaining 50% sampling stations before use. The quality of water is poor in most parts of the study area.
NAVIGATING LIFE’S CHALLENGES: A JOURNEY TO INNER PEACE AND STRESS MANAGEMENT
Stress triggers a series of physiological and psychological responses that impact both the body and mind. When faced with a stressor, the body activates the fight-or-flight response, a primal survival mechanism designed to prepare for danger. During this response, the body releases stress hormones such as cortsisol and adrenaline, which increase heart rate, elevate blood pressure, and boost energy levels to facilitate quick action. While this response can be lifesaving in emergencies, chronic exposure to stress can have detrimental effects on physical and mental health. Prolonged activation of the stress response can weaken the immune system, disrupt sleep patterns, contribute to digestive issues, and increase the risk of developing anxiety, depression, and other mental health disorders.
Dr P Rajeswari, Dr. P. Sankara Rao, P. Saritha, And Dr S. Naresh kumar Goud
MARGARET ATWOOD’S SURFACING: FEMINISM AND SEXUAL POLITICS
Feminism and Gender inequality are not natural but embedded. They primarily explained Women who were confined to the patriarchal culture-defined roles. Girl-child social conditioning, gender discrimination, and self abnegation facilitate stereotyped roles. Feminism and Sexual Politics in general employed to describe political, social and economic movements. It seeks to give more rights and adequate protection to women. It does not have a single key definition in the context of the American radical feminist author Gloria Steinem , intellectual background is stressed. "The goal of feminism is to liberate the individuality of the person and understand that in every one of us there is a unique human being who is a mixture of heredity and environment."
Historical to Contemporary Urban Services in Narnaul City (1991–2022): A Spatial and Statistical Analysis Priyanka Yadav Dr. Salahuddin Mohd Urban services play a crucial role in determining the quality of life and sustainability of cities. Narnaul, a h
Urban services play a crucial role in determining the quality of life and sustainability of cities. Narnaul, a historic city in Haryana, has witnessed significant demographic and spatial changes since 1991. This paper analyzes the evolution of urban services in Narnaul from 1991 to 2022, combining secondary data with field-based household surveys. The study highlights disparities between the old core city, intermediate zones, and newly developed extensions. Services such as water supply, sanitation, drainage, solid waste management, health, housing, and transportation are examined through statistical comparisons and ward-level observations. Findings reveal that while new colonies enjoy better-planned infrastructure, service delivery remains irregular. Conversely, old wards face infrastructural stress and overcrowding, reflecting inadequate modernization. The analysis emphasizes the need for integrated and participatory urban planning to address spatial inequalities in service delivery.
Effects of Size, Shape, and Polarity of Components in the Binary Mixture of Ionic Liquid ([C₄mim][Cl]) and Cyclic Ether ((1,4-dioxane)) on Speed of Sound at Different Temperatures
The molecular size, shape, and polarity of ionic liquids (ILs) and cyclic ethers significantly influence the acoustic and thermodynamic properties of their binary mixtures. This study investigates the variation in the speed of sound in IL–cyclic ether binary mixtures as a function of temperature (298–328 K) and composition. The ionic liquid 1-butyl-3-methylimidazolium chloride ([C₄mim][Cl]) was mixed with the cyclic ether (1,4-dioxane). Speed of sound measurements were performed using an ultrasonic interferometer. Results demonstrate that structural parameters and dipole interactions affect compressibility and, consequently, the speed of sound. Stronger hydrogen bonding and denser packing led to higher values at lower temperatures, while temperature rise generally decreased sound velocity due to reduced interaction strength. The findings provide insights into molecular-scale interactions and their impact on bulk acoustic behavior.
Kuldeep Kumar Solanki, Dr Arpna, Dr Sunil Kumar Jangra, Sanju
MANAGEMENT STRATEGIES FOR DEALING WITH OCCUPATIONAL STRESS OF WORKING WOMEN OF DELHI-NCR
In this study, researchers attempted to determine which stress-reduction mechanisms were being used by the 300 respondents who said that they were employing various coping techniques provided by their separate businesses. In addition to organizational coping techniques, respondents were asked whether individual or personal coping strategies they utilized to handle stress. In the study, the researcher identified the stress management techniques provided by the firms chosen for the study. This information was obtained through casual interactions with management and certain organizational personnel, as well as from the literature given by the aforementioned organizations.
Multi-Material Shield Design for Nuclear Reactors: A Study on Chemical Composition and Functionality
The safe and efficient operation of nuclear reactors heavily relies on robust radiation shielding systems. This research investigates the formation and performance of nuclear reactor shields with varying chemical compositions to understand their role in attenuating different types of ionizing radiation. By analysing the interaction mechanisms between radiation particles (particularly neutrons and gamma rays) and shielding materials, this study highlights the importance of material selection based on atomic number, density, neutron cross-section, and thermal stability. Through comparative evaluation of traditional materials like lead, concrete, and borated polyethylene, alongside advanced composites and nanomaterials, the research demonstrates how tailored chemical formulations can significantly enhance shielding efficiency in both conventional and next-generation reactors. The findings offer insight into optimizing reactor design, enhancing safety standards, and guiding material innovation in nuclear shielding applications.