CALL FOR PAPER - Vol 9 - Issue 2

Construction is carried out in highly demanding and complicated build environments where projects carried out by coalitions of numerous stakeholders with varying interests, objectives and socio-cultural background. Although stakeholder management recognised as a means of increasing the propensity for successful delivery of construction projects, its full potential has yet to be realised. Previous research has shown that the existing frameworks focus on the overall stakeholder management process or their corresponding challenges and lack s detailed process for stakeholder identification and analysis. Stakeholder management begins with stakeholder identification and builds on stakeholder analysis. Therefore, this study aims to develop a conceptual framework for stakeholder identification and analysis in construction projects in order to enhance the stakeholder management process. A comprehensive study is carried out on prior works on stakeholder management to achieve the aim. Current practices of stakeholder identification, classification and analysis methods; knowledge assets referred; factors taken into account for stakeholder identification and analysis are investigated using semi-structured interviews and questionnaire surveys. After data analysis, it is found that brainstorming in group meetings, stakeholder role classification and Stakeholder Salience Model are the preferred methods for stakeholder identification, classification and analysis respectively.

The escalating complexity and volume of cybersecurity threats have overwhelmed traditional Security Operations Center (SOC) analyst capabilities, creating a critical need for innovative approaches to threat detection and response. This study examines the implementation of augmented intelligence frameworks in U.S.-based SOCs to optimize analyst cognitive load while maintaining operational effectiveness. Through a comprehensive analysis of 47 enterprise SOCs across the United States, we demonstrate that human-AI collaborative security operations can reduce analyst cognitive load by 43% while improving threat detection accuracy by 67%. Our proposed framework integrates machine learning algorithms with human expertise to create a symbiotic relationship that enhances both efficiency and effectiveness. The research reveals that strategic AI augmentation, rather than replacement, of human analysts leads to superior outcomes in threat hunting, incident response, and strategic security planning. Key findings indicate that organizations implementing our augmented intelligence framework experience a 52% reduction in false positive alerts, a 38% improvement in mean time to detection (MTTD), and a 41% decrease in analyst burnout rates. This study provides actionable insights for SOC managers, cybersecurity professionals, and organizational leaders seeking to optimize their security operations through human-AI collaboration.

Scientific research is currently examining the penetration of AI-driven algorithmic trading into financial markets with a view to their effect on efficiency and volatility. In fact, they have empirical evidence from different markets in the globe. The advent of AI technology completely alters the modern financial structure and functioning in a record pace. AI-powered algorithms can process a huge number of real-time data and execute trades within a few milliseconds and have now entirely become the major core trade operations in the major exchanges all over the world. For this reason, the study investigates to what extent such innovations may enhance the efficiency of the markets—distinguished by improvements in price discovery, reduced bid-ask spreads, or greater liquidity or cause greater market volatility cause, particularly in the periods of economic uncertainty or stress, based on the existence of the potential uncertainties in economic regions. The study employs a comprehensive quantitative methodology that includes both time-series analysis and regression modeling, drawing upon data sourced from leading global stock exchanges, such as the NYSE, NASDAQ, and LSE. Important variables include, but are not limited to indicators of market efficiency and a range of measures of volatility. The results show that AI-driven algorithmic trading appears to support market efficiency because it reduces the time taken for assimilating information while presenting price mechanisms that are more accurate. However, it also augments the noise-induced fluctuations of short-term market activity while eliciting abrupt surges of volatility under some conditions.

This paper presents a comprehensive review of the impact of educational legislation on child psychology, drawing comparisons between the United States (USA) and various nations in Africa. Educational legislation serves as a critical framework shaping the learning environments and experiences of children, influencing not only academic outcomes but also the psychological well-being of students. This review explores the nuanced ways in which educational laws in the USA and Africa contribute to the development of child psychology.In the USA, legislation such as the No Child Left Behind Act (NCLB) and the Every Student Succeeds Act (ESSA) has played a significant role in shaping the educational landscape. These laws aim to ensure educational equity, accountability, and improved academic achievement. The impact of such legislation on child psychology is multifaceted, influencing factors such as self-esteem, motivation, and stress levels among students. The competitive nature of standardized testing and accountability measures can shape students' perceptions of themselves and their academic abilities, impacting their psychological well-being.In African nations, diverse legal frameworks govern education, reflecting unique cultural contexts and challenges. The review examines how educational legislation in Africa, such as the Dakar Framework for Action and national education acts, addresses issues of access, quality, and relevance. The impact of these laws on child psychology is explored through the lens of cultural influences, community dynamics, and the role of education in shaping identity.Comparative analysis between the USA and Africa reveals both shared and divergent influences of educational legislation on child psychology. While the USA emphasizes accountability and standardized assessments, African systems often focus on broader goals of community development and cultural relevance. Understanding the psychological implications of educational legislation in these diverse contexts is essential for policymakers, educators, and mental health professionals working to create educational environments that foster positive psychological development in children. The review concludes with insights into potential areas for future research and collaborative efforts aimed at optimizing the impact of educational legislation on child psychology globally.

Predictive maintenance has emerged as a transformative strategy in industrial engineering, enabling early detection of equipment failures and minimizing unplanned downtime. The integration of Artificial Intelligence (AI) into predictive maintenance systems enhances their efficiency by leveraging machine learning, deep learning, and real-time data analytics. This paper explores the role of AI in predictive maintenance within industrial systems, examining intelligent models that process sensor data, identify failure patterns, and estimate Remaining Useful Life (RUL). The study reviews current AI methodologies, industrial case applications, benefits, challenges, and future research directions. Findings suggest that AI-driven predictive maintenance improves reliability, optimizes maintenance scheduling, and reduces costs, contributing to smarter and more sustainable industrial operations.

The poetic oeuvre of Jayanta Mahapatra has been highly acclaimed in Indian English poetry for its Indian sensibility and indigenous explorations of Oriya regions. The ebb and flow of his poetic journey is interwoven with his intense desire to give an outlet to his unexpressed expressions and to portray his land with a local color. However, writing came to him quite late but freely but which opened the ‘door’ to the vastness, unknown, and unexplored. Poetry came to him as freely as his spur-of-the-moment imaginative impulse. Leaving the mortal world last year, he has left a legacy of poetry in Indian English and Oriya poetry. Remembering his contribution, this paper makes a critique of his poetry in a response to his portrayal of Oriya land diving it into deep Indian sensibility. Freedom from existing forms and subjects of poetry colors his imaginative and creative impulse with an idiosyncratic impulse which is peculiar to his writing. Digging deeper into the vastness of Orissa, the paper explores how indigenous color and localization have been embodied in his poetic sensibility.

The quick advancement of artificial intelligence (AI) technology transformed software development into a tool that enables predictive analytics with intelligent automation capabilities throughout various commercial sectors. An evaluation of AI integration in .NET 8 utilizes ML.NET as the targeted machine learning framework, which aims to serve .NET developers. ML.NET introduces a straightforward pipeline infrastructure which permits developers to create predictive models by handling all three detection categories (classification, regression, anomaly detection) without extensive data science knowledge requirements. The research delivers detailed information about ML.NET functionalities, describes its training workflow and key features, and explains its Integration with .NET 8 applications. The research implements a practical analytics model as an illustration to show structured data processing with ML.NET while demonstrating its ability to generate precise predictions. A detailed performance assessment of the models employs standard metrics from the industry while discussing the optimization methods needed to achieve better accuracy levels. The examination of ML.NET as a machine learning framework emphasizes its characteristics relative to other options, showcasing its strengths and weaknesses when used in deep learning environments. This paper investigates the deployment strategies for artificial intelligence, including edge computing and cloud-based implementations for scalable artificial intelligence deployment abilities. Empirical tests reveal deployment hurdles AI models face in .NET environments, which help determine potential upgrades for ML.NET's functionality. The study demonstrates how ML.NET can improve .NET system accessibility by making machine learning accessible to developers through its potential. The research enhances AI applications in enterprise environments by establishing knowledge about combining machine learning models with modern .NET architecture systems.

The software architecture in microservices has revolutionized the software development procedure as we have known it by providing enormous benefits of scalability, flexibility, and durability. This is so because despite the numerous benefits offered by microservices architectures, it brings about the challenge of security challenges since the microservices applications are disperse and interconnects. Signature and anomaly-based methods are known to be impractical in identifying attacks specific to the microservice architecture due to evolving patterns of attack. To overcome with these limitations, this research expands a model of AI based IDS that uses techniques of advanced machine learning and deep learning for the improved and efficient identification of threats. As a result, the proposed system contains several modules: The first is a data collection module which collects network traffic and system logs; the second is a feature extraction module which finds out significant features of attacks; The third is AI-based detection module which classifies the normal behavior and malicious behavior. Supervised learning and unsupervised learning algorithms together with the deep learning models, for instance, CNN and RNNs are used to identify patterns and monitor and detect anomalies in real time [9]. To estimate the performance of the proposed system, benchmark datasets are used for performance evaluation. Evaluation of AI-based approaches using correct classification rate, precision, recall, and f-measure provide insights into the fact that AI-based techniques are far superior in detection when compared to conventional methods for detecting zero-day attacks and advanced persistent threats. Furthermore, the feature extraction seeks to be implemented by deep learning by eliminating the necessity of following conventional rules. Based on the results of this research, it becomes possible to conclude about the effectiveness of AI-based solutions in protecting microservice-based architectures from new generation threats. In a practical way, the study brings value to the field of cybersecurity, outlining an adaptable and sustainable model of detecting intrusion which is fit for cloud-only networks [11]. As the future work, the authors will aim to enhance the model interpretability, decrease the false alarms occurrence, and test the applicability of the proposed method in the real-world use cases related to microservices.

The proliferation of real-time artificial intelligence (AI) applications across domains such as autonomous vehicles, smart manufacturing, and healthcare demands computing infrastructures that balance low latency, high processing power, and scalability. Edge-cloud collaboration has emerged as a promising paradigm that leverages the proximity and responsiveness of edge computing with the computational capabilities and resource availability of cloud platforms. This paper explores the architecture, design principles, and operational strategies for effective edge-cloud collaboration in real-time AI systems. Key challenges such as data partitioning, model synchronization, latency constraints, security, and resource orchestration are analyzed, along with current solutions and open research directions. We present use cases that demonstrate the efficacy of collaborative edge-cloud AI, and highlight the trade-offs involved in deploying machine learning inference and training tasks across heterogeneous environments. Our study underscores the critical role of intelligent workload distribution and adaptive system design in enabling efficient, robust, and scalable real-time AI applications.

The world’s constant combat towards global issues such as climate change, pollution, violence, security, terrorism, etc., all rooted from danger inflicted activities or interests has taken its toll accelerating our extinction. The list goes on and in just a snap, we were all consumed by the infamous COVID-19. This pneumonia-like disease has launched a global economic tsunami and disruption to our lives that was later on declared as a pandemic. With no available cure yet, there were few safety measures practiced globally namely social distancing, quarantine, and mass testing. In order to contain and avoid further spread of the virus, we were forced to adapt to the new normal. Personal Protective Equipment (PPE) has become an inseparable part of our daily lives ergo, the increase in PPE production, which is considered inevitable, leads to a surge in global plastic waste. COVID-19 waste has become a new form of pollution caused by mass production of single-use PPE with no structured waste management that floods our ocean beds. Manufacturers are now looking for alternatives to decrease plastic waste and to shift to a more sustainable and circular PPE ecosystem. Philippines, as the second top exporter of bananas in the world, would a banana pseudo-stem fiber be the most promising business venture opportunity at post-COVID stage?

Shopping has changed as a result of the influence of technology with most people preferring online shopping to the traditional physical store shopping. This trend took center stage in the past decade with many retail giants integrating the two approaches to achieve maximum benefit. This research aims to understand the comparison between online shopping & physical store shopping and consumer behavior towards these modes of shopping. This paper identifies and discusses that male population tend to shop more online shopping rather than physical shopping. For safety of payment more preference is given to shopping malls related to physical shopping. People are slowly going for online shopping but the majority mentality of people goes to physical shopping as it is having trust, feel and touch of the product. In our research we have found out that FlipKart and Jabong are the major players in the online retail and online shopping is here to stay and number of people favoring this mode is growing day by day.

The Burj Khalifa is known to be the tallest skyscraper ever built by man and one of the most popular tourist destinations in the world. At over 828 meters (2716.5 feet) and more than 160 stories, Burj Khalifa is a multi-use development tower that includes a hotel, observation deck, restaurant, fitness and recreation club, and 37 floors of corporate offices. The project to build the Burj Khalifa tower is one of the most successful projects in the Middle East and in the world to date. It was planned by the Dubai government with the purpose of making the city a hub for finance, trade, and tourism. The project spanned for 6 years from 2004 to 2009 and was designed to be the centerpiece of the large scale Burj Khalifa Development that would rise into the sky to an unprecedented height. As listed in burjkhalifa.ae (2018), the completion of the tower has broken numerous world records such as Tallest Building in the World, Highest Number of Stories in the World, Highest Outdoor Observation Deck in the World, and many more. Agreed that that there are many iconic structures around the world, of which Dubai still plays host to most of them, the Burj Khalifa is not only the tallest and one of the most glamorous, with a lot to learn from its project design, process and implementation. The research work is designed to x-ray the entire project, its phases and the factors that led its successful completion.

The research paper explained the role and the importance of customer satisfaction and loyalty in detail. Customers are the link to an any level and type of business success. A business organization should focus on a large number of customers, for this customer satisfaction and loyalty should be incorporated along the long-term goals and other strategic dimensions without any failure. This paperis focusing onanalysing the relationship between customer satisfaction and customer relationship with service quality measures. The objective of this research is to study the concept of customer satisfaction, customer loyalty and its relationship. Moreover, this paper studies the factors that influence customer satisfaction and loyalty at large scale. This paper also analyses the factors that have impact on customer satisfaction and result in customer loyalty in multiple contexts. Recommendations on improving the service quality and getting customer retention are ultimately proposed by the author to do the business in more effective and efficient way. Customer satisfaction index (CSI)provides an economic indicator of the quality of economic output; calculation of the net present value of the company?s customer base as an asset over time; information for strategic business applications; a predictor of consumer spending & corporate earnings. In this paper the focus of research is customer satisfaction index as a base for new strategic marketing management implementation level to attain sustainability.

Today, ethics has an important place in all areas of life. Education is also a fundamental process of human life. Therefore, in education ethics has a very important and effective role. In order to be a good human, ethics should be placed as a course in educational system. In this paper, it will be discussed the ethics education in schools. In doing this, it will be also discussed why teaching ethics is important in education. Before discussing this issue, it is necessary to define what ethics is and what education is.

The present study aims to portray the real need of water to lead life. Today there are number of countries are unable to live a normal life without water facility. Water is actually the elixir of life. Everyone should use the water in limited according their need. Water may avail excess, but not in deficit. Gulf countries, some of the African countries appear as deserts and very difficult to lead a normal life without water. The place is not having water that is a desert. If the place is in water resource, that is a heaven for human survival. If human wants to realize water, he should be in the desert. The thirst for a man can be solved, but a thirst of animals, birds living in the forest is unsolved. Hence one can understand the need of water to survival. The title of the paper is taken from the greatest Tamil grandfather Thiruvalluvar?s ?Thirukural?. He has written about the water in one of his ?Kurals? (double lined poem). He further says, if water fails, functions of nature will sease and if rain fails, no men can walk in regular duty. Girish Karnad has found the need of water, so he has written the play ?The Fire and the Rain? to people realize one drop of water. Scientist can portray water is the formula of H2O. But ordinary man, common people, animals, birds, reptiles are depending upon the water and its usage. Karnad has immensely defines the fire sacrifice for rain in this play.