Speculative Execution Attacks and Cloud Security

Operational Technology (OT) systems are facing increasing cybersecurity risks due to IT/OT convergence, legacy systems, and evolving regulatory demands. Security Operations Centers (SOCs) play a central role in monitoring and responding to these threats. However, existing literature predominantly focuses on IT-centric SOCs, leaving OT SOC models underexplored. This study examines the structure and implementation of SOC models designed for OT systems, based on 14 qualitative interviews with security professionals and industry stakeholders. The findings reveal a spectrum of SOC models, including integrated, dedicated, in-house, outsourced, and vendor-operated. Each possesses distinct trade-offs in visibility, contextual awareness, cost, and operational resilience. The study identifies key factors influencing the selection of the SOC model for industrial clients, including organizational size, OT complexity, and regulatory pressures. It also outlines future directions for integrated SOCs, process-aware monitoring, and federated models. By bridging empirical insights with existing literature, this work contributes a comparative framework for evaluating OT SOC models and informs both academic research and industry practices in securing critical OT infrastructures.Operational Technology (OT) systems are facing increasing cybersecurity risks due to IT/OT convergence, legacy systems, and evolving regulatory demands. Security Operations Centers (SOCs) play a central role in monitoring and responding to these threats. However, existing literature predominantly focuses on IT-centric SOCs, leaving OT SOC models underexplored. This study examines the structure and implementation of SOC models designed for OT systems, based on 14 qualitative interviews with security professionals and industry stakeholders. The findings reveal a spectrum of SOC models, including integrated, dedicated, in-house, outsourced, and vendor-operated. Each possesses distinct trade-offs in visibility, contextual awareness, cost, and operational resilience. The study identifies key factors influencing the selection of the SOC model for industrial clients, including organizational size, OT complexity, and regulatory pressures. It also outlines future directions for integrated SOCs, process-aware monitoring, and federated models. By bridging empirical insights with existing literature, this work contributes a comparative framework for evaluating OT SOC models and informs both academic research and industry practices in securing critical OT infrastructures.

The implementation of artificial intelligence (AI) and machine learning (ML) in the architecture, engineering, and construction (AEC) domain has gained significant attention within academic research and construction technology companies. The scientific field often relies on information collected from the industry for theoretical development and sometimes test cases. However, to the best of the authors’ knowledge, there has been no systematic effort to compare academic and industry trends and developments. In this work, we examine the development and application of ML in the AEC domain from both academic and industry perspectives. To investigate both perspectives, we implement a mixed-methods approach including an academic literature review, a web-based mapping of construction technology companies worldwide, and an online questionnaire targeting practitioners and technology companies. Overall, our mixed methods analysis reveals a strong alignment between academic research and industry practice in targeting early design and post construction phases, with both communities prioritizing the application of ML for energy efficiency, facility management, and site safety, particularly within the context of Building Information Modeling (BIM). However, industry devotes more effort to sustainability, cost and scheduling solutions, whereas academia lags in these areas and often relies on limited or synthetic datasets. Finally, both sectors identify data availability and quality, particularly the scarcity of large, labeled, domain-specific repositories, as the primary barrier to wider ML adoption in the AEC industry.

This study investigates the role of influencer marketing in the fashion retail sector of Delhi NCR, focusing on qualitative insights from PR agencies that actively manage influencer campaigns. With the growing dominance of digital marketing, influencer collaborations have become a key strategy for brands seeking to enhance consumer engagement and brand positioning. Using QDA Lite for data analysis, the research identifies critical themes shaping influencer marketing strategies, including authenticity, appeal, trustworthiness, competence, and inventiveness.Through in-depth interviews with PR professionals, this study examines how these attributes influence consumer perception and purchasing behavior. The research adopts a systematic coding approach to extract meaningful insights from interview data, allowing for a comprehensive understanding of the factors that drive successful influencer-brand partnerships. Findings suggest that authenticity and trustworthiness serve as fundamental pillars of effective influencer collaborations, ensuring credibility and long-term consumer loyalty. Additionally, appeal and inventiveness play a crucial role in capturing audience attention and fostering deeper engagement, while competence enhances an influencer's ability to communicate brand messages effectively.The study also highlights the challenges faced by PR agencies, including measuring campaign effectiveness, selecting the right influencers, and managing audience skepticism. It offers practical recommendations for brands and PR agencies to refine their influencer marketing strategies by leveraging industry expertise and addressing real-world obstacles. By bridging the gap between academic research and industry practice, this study provides valuable insights for marketing professionals looking to optimize influencer collaborations in the dynamic and competitive landscape of Delhi NCR's fashion retail sector.

Artificial intelligence (AI) has profoundly altered the financial services industry by offering new paradigms for data analysis, decision-making, and operational efficiency. This chapter provides an extensive review of the core concepts, recent advancements, and emerging applications of AI in the financial sector. Based on a critical review of 162 high-impact scientific articles, the chapter identifies and evaluates key theme areas, including financial forecasting, fraud detection, explainable AI (XAI), and the nexus between AI and FinTech. The findings demonstrate the importance of state-of-the-art AI techniques such as deep learning, reinforcement learning, and hybrid models for enhancing risk assessment, raising forecast precision, and ensuring regulatory compliance. The chapter also discusses how AI could transform several financial processes, including portfolio management, credit assessment, and anti-money laundering efforts. The creation of interpretable AI frameworks, the incorporation of multimodal data sources, and the examination of AI's societal ramifications regarding sustainability and financial inclusion are just a few of the new opportunities and research gaps that are highlighted in this chapter. By bridging the gap between academic research and industry practice, this chapter provides useful insights for both researchers and practitioners. To harness AI's transformative potential while upholding moral standards and public trust, it encourages ethical, interdisciplinary collaboration. The chapter concludes with a strategic roadmap for the innovative and long-term use of AI technology in contemporary financial ecosystems.

Purpose – The purpose of this paper is to document the development path of a specific concept during its first 20 years. Design/methodology/approach – Evidence was extracted of the citation-counts of relevant articles, uses of the term in other articles that do not cite the original articles, and uses of terms with similar meanings. Examination of the data took into account insights from epidemiology, memetics and diffusion of innovations theory. Findings – The concept has had insufficient impact to overcome the weaknesses in theory and practice that it was intended to address. It has lacked champions. It has proven to be sufficiently fit to survive, but not to flourish. Research limitations/implications – Google Scholar has a wide catchment area, and hence provides a basis for tracking the path of development of new ideas. However, the tools remain fairly blunt, and do not, for example, enable efficient extraction of patterns of citation over time, or the nature of the uses made of terms by the citing articles. Practical implications – Neologisms take on a life of their own, losing the associations that they were intended to have with other ideas, and shedding their embedment in a body of theory. For a new term to successfully project a meme, its proponent must enthuse a critical mass of early adopters to apply it, and to generate a further round of adopters. Originality/value – Concepts are seldom tracked over time. This paper shows that a new term and its associated body of theory require more than publications in top-level journals if they are to have significant impacts on academic research and industry practice.

Integrating Artificial Intelligence (AI) in supply chain management (SCM) signifies a significant advancement with profound implications for modern businesses, including those in Uganda. This research paper critically examines the challenges and opportunities associated with this integration, using Uganda as a case study. A comprehensive analysis of existing literature and specific insights from the Ugandan context identifies critical challenges such as data integration, technology adoption, and organizational readiness within the country. Additionally, it explores AI's diverse opportunities in optimizing supply chain processes for Ugandan businesses, including demand forecasting, inventory management, and logistics optimization within Uganda's unique operational landscape. Furthermore, the paper discusses the potential impact of AI integration on various stakeholders within Uganda's supply chain ecosystem, including suppliers, manufacturers, distributors, and customers. By synthesizing insights from academic research and industry practices in Uganda, this paper provides valuable insights for Ugandan businesses aiming to leverage AI technologies in their SCM strategies. Ultimately, this research contributes to a deeper understanding of the complexities of integrating AI in SCM within the Ugandan context and offers recommendations for addressing challenges while maximizing the opportunities presented by this transformative technology.

As the deployment of 5G technology accelerates globally, understanding the foundational rollout of 4G remains critically relevant. This article presents a comparative analysis of 4G deployment strategies across key regions including the United States, China, South Korea, and European countries. It delves into the various approaches adopted by different nations and operators, examining the impacts of liberalization and privatization within the telecom sector, spectrum allocation, and regulatory frameworks. By analyzing data from both academic research and industry practices, this study explores how geographical, economic, and socio-political factors influence the development of 4G network infrastructures. Furthermore, it assesses government initiatives regarding productive capacities, market competition, and technological advancements. This comprehensive review not only highlights the challenges and success factors encountered during 4G rollouts but also provides valuable insights for stakeholders navigating the complexities of telecommunications deployment in an era transitioning to 5G.

Subtitling has increasingly become a central mode of audiovisual translation, particularly with the rise of streaming platforms as the predominant medium for global entertainment consumption. This rapid expansion has intensified the demand for high-quality subtitling to ensure both audience satisfaction and broader market accessibility. However, despite the noticeable disjunction between established academic conventions and the realities of professional subtitling practice, researchers have not sufficiently explored this in academia. The present study seeks to address this gap by conducting a systematic analysis of the contradictions and inconsistencies in professional subtitling and captioning practices across multiple domains within six major streaming platforms operating in the Middle East and North Africa region. Employing a comparative content analysis informed by established theoretical frameworks in audiovisual translation studies, the research adopts a mixed-methods design that integrates qualitative textual analysis with quantitative measures. Through this methodological synthesis, the study not only seeks to map the extent of divergence between academic research and industry practices but also aims to enable academics to develop theoretical frameworks to more accurately reflect contemporary subtitling and captioning realities, while simultaneously offering practitioners research-driven insights into misalignments and strategies for reconciling them through enhanced approaches to readability and accessibility.

Artificial Intelligence (AI) agents are revolutionizing industries by enabling autonomous decision-making, task execution, and multi-agent collaboration. This paper provides a comprehensive review of AI agent frameworks, focusing on their architectures, applications, and challenges in financial services. We conduct a comparative analysis of leading frameworks, including LangGraph, CrewAI, and AutoGen, evaluating their strengths, limitations, and suitability for complex financial tasks such as trading, risk assessment, and investment analysis. The integration of AI agents in financial markets presents both opportunities and challenges, particularly in terms of regulatory compliance, ethical considerations, and model robustness. We examine agentic AI design patterns, multi-agent systems, and the deployment of AI agents advancing the proposal to use them for fraud detection and risk management. By synthesizing insights from academic research and industry practices, this review identifies key trends and future directions in AI agent development. This work contributes to the growing discourse on AI-driven automation by outlining technical considerations and open challenges in deploying AI agents at scale. We highlight the need for enhanced transparency, interpretability, and security in AI-driven Agentic systems. Our findings provide valuable insights for researchers and practitioners seeking to harness AI agents for more efficient and intelligent decision-making.

Runtime and memory requirements for typical formulations of energy system models increase non-linearly with resolution, computationally constraining large-scale models despite state-of-the-art solvers and hardware. This scaling paradigm requires omission of detail which can affect key outputs to an unknown degree. Recent algorithmic innovations employing decomposition have enabled linear increases in runtime and memory use as temporal resolution increases. Newly tractable, higher resolution systems can be compared with lower resolution configurations commonly employed today in academic research and industry practice, providing a better understanding of the potential biases or inaccuracies introduced by these abstractions. We employ a state-of-the art electricity system planning model and new high-resolution systems to quantify the impact of varying degrees of spatial, temporal, and operational resolution on results salient to policymakers and planners. We find models with high spatial and temporal resolution result in more realistic siting decisions and improved emissions, reliability, and price outcomes. Errors are generally larger in systems with low spatial resolution, which omit key transmission constraints. We demonstrate that high temporal resolution cannot overcome biases introduced by low spatial resolution, and vice versa. While we see asymptotic improvements to total system cost and reliability with increased resolution, other salient outcomes such as siting accuracy and emissions exhibit continued improvement across the range of model resolutions considered. We conclude that modelers should carefully balance resolution on spatial, temporal, and operational dimensions and that novel computational methods enabling higher resolution modeling are valuable and can further improve the decision support provided by this class of models.

Information Systems (IS) as a discipline draws on diverse areas including, technology, organisational theory, management and social science. The field is recognized as very broad and encompassing many themes and areas. However, the development of artefacts, or information systems development (ISD), in the broadest sense, is a central concern of the discipline. Significantly, ISD impacts on the organisational and societal contexts through the use of the artefacts constructed by the development. Today, that impact also needs to be evaluated in terms of its effects on the environment. Sustainable, or green, IT is a catch-all term used to describe the development, manufacture, management, use and disposal of ICT in a way that minimizes damage to the environment. As a result, the term has many different meanings, depending on the role assumed in the life span of the ICT artefact. The theme of the proposed work is to critically examine the whole range of issues around ISD from the perspective of sustainability. Sustainable IT is an emerging theme in academic research and industry practice in response to an individual concern for the environment and the embryonic regulatory environments being enacted globally to address the environmental impact of ICT. In this work we intend to bring together in one volume the diverse research around the development of sustainable IS.

This study investigates the current state of industry-academia research collaboration within the Philippine civil engineering management sector, emphasizing its significance to engineering management practices. Recognizing the essential role such partnerships play in fostering innovation, addressing industry challenges, and improving operational efficiencies, the research explores the key barriers and opportunities for strengthening these collaborations. An online survey was administered to civil engineers from both academia and industry to gather insights into their experiences, perspectives, and perceived challenges regarding joint research activities. The findings reveal that limited time, resources, and a lack of clear research incentives within the industry are substantial impediments to active participation. Institutional misalignment, administrative bottlenecks, and funding constraints further hinder sustained collaboration. Despite these challenges, the study identifies a shared interest among professionals in fostering strategic partnerships, particularly in areas critical to engineering management, such as construction project management, infrastructure sustainability, and technological innovation. The research highlights the need for proactive strategies to bridge the gap between academic research and industry practice. Recommendations include establishing structured engagement platforms, incentivizing collaborative initiatives, and aligning academic outputs with industry needs. This study contributes valuable insights into the field of engineering management by exploring how industry-academia collaboration can improve decision-making processes, resource allocation, and project management within the civil engineering sector. It provides practical recommendations for policymakers, academic leaders, and industry stakeholders, aiming to enhance professional development and the impact of civil engineering practices in the Philippine context.

Social Life Cycle Assessment (SLCA) has emerged as a crucial element in sustainability discussions, gaining traction in academic research and industry practices. In response to increasing consumer awareness, governmental regulations, and corporate responsibility, sustainable production models have been widely adopted, influencing engineering projects to address not only environmental but also social impacts. This systematic review aims to explore the application of SLCA within the engineering field, focusing on key trends, methodologies, and challenges engineers face when implementing SLCA. To achieve this purpose, 196 peer-reviewed studies published between 2010 and 2024 were analysed through bibliometric and content analysis. The results reveal an increasing trend in SLCA research, with key contributions from energy, mechanical engineering, and chemical engineering fields. Despite its growing relevance, several methodological challenges persist, including a lack of standardisation, data availability issues, and difficulties in integrating SLCA with environmental and economic assessments. The findings of this research can facilitate academic researchers, industrial managers, and policymakers in implementing SLCA practices in engineering applications for a comprehensive sustainability assessment.

Reputation is a critical factor in the health insurance industry, impacting business performance and public health outcomes. However, despite its acknowledged importance, the reputation of health insurers has received limited attention in both academic research and industry practice. Gaining a comprehensive understanding of health insurers' reputation could help insurers strategically manage their public image while providing policymakers with valuable insights to develop policies that improve health outcomes. This study aims to identify the factors shaping the reputation of health insurers and to assess the broader impacts of reputation within the industry and the health care system. A scoping review was conducted to map the existing literature on the reputation of health insurers. Using the PRISMA-ScR framework, it identified key determinants and outcomes of health insurer reputation, clarified concepts of reputation and trust, and highlighted gaps in the current body of literature. A total of 3385 records were identified, of which 397 were fully screened for eligibility, resulting in the inclusion of 45 studies. The majority of the included studies conducted surveys (n = 40), while the minority employed qualitative interviews (n = 6) and focus group discussions (n = 7). The total number of participants involved in the included studies is 31'344. The analysis identified 23 descriptives themes, which were organised into two main analytical categories: determinants and outcomes. The determinants included 17 descriptive themes which have been further classified into three groups based on whether they pertain to policyholders, insurers or providers. The outcomes consist of six key descriptive themes. This review provides a thorough analysis of research that have investigated different aspects of health insurance reputation and identify significant gaps in the literature. It maps key determinants of reputation - such as corporate culture, product structure, and service quality - that insurers can directly influence. Additionally, factors like the quality of healthcare providers and targeted communication efforts may also be linked to reputation. The analysis underscores the importance of reputation for core business activities (e.g., client acquisition and retention) and its role in preventive healthcare. Notably, there is a lack of research on the regulatory implications of insurers' reputation, suggesting a need for further studies to enhance preventive strategies and integrate insurers into a broader public health framework.

Nowadays Convolutional Neural Networks (CNNs) are being employed in a wide range of industrial technologies for a variety of sectors, such as medical, automotive, aviation, agriculture, space, etc. This paper reviews the state-of-the-art in both the field of CNNs for image classification and object detection and Autonomous Driving Systems (ADSs) in a synergetic way. Layer-based details of CNNs along with parameter and floating-point operation number calculations are outlined. Using an evolutionary approach, the majority of the outstanding image classification CNNs, published in the open literature, is introduced with a focus on their accuracy performance, parameter number, model size, and inference speed, highlighting the progressive developments in convolutional operations. Results of a novel investigation of the convolution types and operations commonly used in CNNs are presented, including a timing analysis aimed at assessing their impact on CNN performance. This extensive experimental study provides new insight into the behaviour of each convolution type in terms of training time, inference time, and layer level decomposition. Building blocks for CNN-based object detection are also discussed, such as backbone networks and baseline types, and then representative state-of-the-art designs are outlined. Experimental results from the literature are summarised for each of the reviewed models. This is followed by an overview of recent ADSs related works and current industry activities, aiming to bridge academic research and industry practice on CNNs and ADSs. Design approaches targeted at solving problems of automakers in achieving real-time implementations are also proposed based on a discussion of design constraints, human vs. machine evaluations and trade-off analysis of accuracy vs. size. Current technologies, promising directions, and expectations from the literature on ADSs are introduced including a comprehensive trade-off analysis from a human-machine perspective.