Review Paper on Stakeholder Identification and Analysis in Construction Industry: A Strategic Approach
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.
Talent Development and Supply Chain Leadership: Building the Next Generation of Supply Chain Leaders
The evolution of supply chain management in the digital age has created unprecedented challenges for talent development and leadership cultivation. This study examines the critical intersection of talent development and supply chain leadership within the United States, analyzing how technological disruption, sustainability imperatives, and globalization are reshaping the competencies required for next-generation supply chain leaders. Through comprehensive analysis of industry trends, educational frameworks, and organizational strategies, this research identifies key gaps in current talent development approaches and proposes innovative solutions for building robust supply chain leadership pipelines. The findings reveal that traditional supply chain education and development programs are insufficient to meet the demands of Industry 4.0 and autonomous supply chains, necessitating a fundamental reimagining of how organizations cultivate supply chain talent.
Human-AI Collaborative Security Operations: Optimizing SOC Analyst Cognitive Load Through Augmented Intelligence Frameworks
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.
Human-Powered Water Filtration Mechanism for Rural and Remote Applications
Pure and clean drinking water is essential for every household, as human life cannot be sustained without it. In rural and remote regions, electricity supply is often unreliable or completely unavailable, making conventional water purifiers ineffective for everyday use. To address this challenge, a pedal-operated water filtration system is proposed, which utilizes human muscle power through a pedal-driven mechanism to filter water without the need for electricity. This project is specifically aimed at communities where access to safe drinking water is limited due to erratic water supply and power shortages, and where sources of potable water are often located far from residential areas. The system is designed to be lightweight and detachable, allowing it to be easily transported or relocated with minimal effort and modification.
Studies On Development of Model for Sentiment Analysis with ML: A Review
Sentiment analysis refers to the automated process of identifying the underlying emotional tone conveyed in a text. This task has become increasingly crucial today due to the exponential growth of opinion-oriented text generation on various online platforms including social media and e-commerce websites. The process of sentiment analysis has evolved through different phases like the use of handcrafted rules, sentiment-oriented dictionaries, machine learning, deep learning, transformer models, and large language models. These different phases have enriched the field of sentiment analysis and enabled it to get better insights into text for sentiment analysis. One of the most impactful developments in sentiment analysis was the creation of embedding’s for words or sentences, which are useful for representing unstructured data as structured data. These embedding are representing words or sentences as numerical vectors in a high-dimensional space and have proven effective in capturing the semantic relationships between words. By leveraging the power of embedding’s, sentiment analysis models can understand the complexities of human language and provide more precise insights into people's emotions and opinions. However, word embedding’s not inherently generated for sentiment analysis, which means that they are not inherently sentiment-oriented. Therefore, many researchers have focused on developing sentiment-oriented word embedding’s for sentiment words, but they have overlooked the importance of intensity words such as “little”, “high”, and “extremely”. These intensity words determine the level of sentiment expressed in text, which is particularly useful for fine-level sentiment analysis. To address this issue, a review proposes an intensity-aware word embedding development model and sentiment analysis with ML.
The Impact of AI-Driven Algorithmic Trading on Market Efficiency and Volatility: Evidence from Global Financial Markets
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.
Educational Legislation's Impact on Child Psychology: A Review in the USA and Africa
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.
The Role of Artificial Intelligence in Predictive Maintenance for Industrial Engineering Systems
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.
Indian Sensibility and Indigenization: The Poetry of Jayanta Mahapatra
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.