Strategic Calibration AI Framework: Adaptive Imbalance in Dynamic Environments

This paper focuses on D2D communication for 5G/6G in dynamic environments where the D2D network topology changes through time due to D2D Devices’ mobility. Specifically, we consider a scenario within the coverage area of a Base Station (BS) serving a number of User Devices (UEs), with variations in speed and direction causing changes in the D2D network topology, either via direct connections or via D2D single-hop or D2D Multi-hop paths. Based on this scenario, we formulate a problem aiming to maximise the total Spectral Efficiency (SE) whilst minimising the total Power Consumption (PC), by selecting the best transmission mode that the D2D devices will operate. In order to address the aforementioned problem, the Distributed Artificial Intelligence Solution (DAIS) plan proposed in our earlier work and designed for static environments is extended to consider the mobility (i.e., speed, direction and indirectly link distance change) of the D2D Device, targeting the dynamic creation of stable and efficient clusters and good backhauling links towards the gateway. The enhanced DAIS performance is comparatively evaluated in terms of SE and PC against selected variations in UE speed and direction, changes in the link Transmission Power (TP), and an increase in the number of Devices in the D2D network. Overall, the results obtained demonstrated superior performance of enhanced DAIS over all the other related investigated approaches (i.e., Distributed Sum Rate (DSR), Single Hop Relay Approach (SHRA), Distributed Random (DR)), in terms of Spectral Efficiency (SE) and Power Consumption (PC). Also, the ability of enhanced DAIS to react and adapt quickly and efficiently to D2D network topology changes, with reduced signalling overhead and control delay in responding to changes, shows that it is a well-poised approach to be used in a Dynamic D2D Environment.

In today's rapidly evolving business environment, organizations must continuously adapt to remain competitive. However, traditional strategic planning often results in complex documents that are rarely implemented effectively, limiting organizations' ability to achieve their goals. This paper explores the role of strategic planning and adaptation in fostering organizational success by reviewing key concepts, the importance of strategic planning, its components, and the necessity of strategic adaptation. The study highlights that strategic planning is a structured approach to defining organizational goals, allocating resources, and guiding long-term decision-making. However, rigid planning structures often fail in dynamic business environments, necessitating an integrated approach combining strategic planning and adaptability. Strategic adaptation enables organizations to respond effectively to external uncertainties, market shifts, and emerging opportunities. Organizations can enhance their agility and resilience by leveraging environmental scanning, strategic flexibility, and participatory planning. Findings suggest that while strategic planning provides a clear roadmap for achieving objectives, its effectiveness is significantly improved when paired with adaptation strategies. Organizations that integrate flexibility into their planning processes are better positioned to navigate uncertainties and drive sustainable growth. Moreover, fostering a culture of strategic employee participation enhances engagement, innovation, and alignment with organizational goals. This paper concludes that achieving organizational success requires a dynamic, integrated approach that balances strategic planning and adaptability. By embedding flexibility into their strategic frameworks, organizations can remain resilient, responsive, and competitive in an ever-changing business landscape, ultimately driving long-term growth and success.

Control Systems and Incentives as Moderators of the Positive Association between Perceived Environmental Uncertainty and Willingness to Recommend Strategic Change

Autonomous Unmanned Surface Vehicle (USV) operations in complex ocean engineering scenarios necessitate robust navigation, guidance, and control technologies. These systems require reliable sensor-based object detection and efficient, safe, and energy-aware path planning. To address these multifaceted challenges, this paper proposes a novel synergistic AI framework. The framework integrates (1) a novel adaptation of the Swin-Transformer to generate a dense, semantic risk map from raw visual data, enabling the system to interpret ambiguous marine conditions like sun glare and choppy water, enabling real-time environmental understanding crucial for guidance; (2) a Transformer-enhanced A-star (T-ASTAR) algorithm with spatio-temporal attentional guidance to generate globally near-optimal and energy-aware static paths; (3) a domain-adapted TD3 agent featuring a novel energy-aware reward function that optimizes for USV hydrodynamic constraints, making it suitable for long-endurance missions tailored for USVs to perform dynamic local path optimization and real-time obstacle avoidance, forming a key control element; and (4) CUDA acceleration to meet the computational demands of real-time ocean engineering applications. Simulations and real-world data verify the framework's superiority over benchmarks like A* and RRT, achieving 30% shorter routes, 70% fewer turns, 64.7% fewer dynamic collisions, and a 215-fold speed improvement in map generation via CUDA acceleration. This research underscores the importance of integrating powerful AI components within a hierarchical synergy, encompassing AI-based perception, hierarchical decision planning for guidance, and multi-stage optimal search algorithms for control. The proposed solution significantly advances USV autonomy, addressing critical ocean engineering challenges such as navigation in dynamic environments, object avoidance, and energy-constrained operations for unmanned maritime systems.

Job analysis for a changing workplace

The maritime sector, particularly in offshore tugboat and support vessel operations, faces multifaceted strategic management challenges due to rapid technological advancements, environmental regulations, and market conditions. This paper introduces an innovative strategic decision-making framework for this industry, integrating Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis with Fuzzy Decision Making Trial and Evaluation Laboratory-DEMATEL and Order of Preference by Similarity to Ideal Solution-TOPSIS methodologies. A thorough extensive literature review and expert contributions identified 20 key factors impacting the sector across 4 SWOT categories, with balanced weights of 25.14% (strengths), 25.32% (weaknesses), 24.68% (opportunities), and 24.86% (threats). The hybrid approach uniquely highlights the interdependencies among these factors, offering a prioritized list of 8 strategic options. The analysis revealed that the WO1 strategy (focusing on cost reduction through technological innovation) emerged as the most effective strategy with a closeness coefficient of 0.798, followed by SO2 (operational efficiency enhancement) at 0.642, and ST1 (leveraging experienced crew and advanced technology) at 0.550. These results show that operational cost reduction through technological innovation and emerging market entry is a strategic priority. This study contributes significantly to the existing literature by providing a robust strategic management model tailored to address the unique challenges of offshore maritime operations. These insights are useful for improving operational efficiency for industrialists with sustainability-related concerns and for furthering their competitive advantage in an increasingly dynamic global environment. Moreover, the study also provides a replicable model that can be applied to other industries where similarly complex decisions can be made.

In the dynamic business environment of Indonesia and Southeast Asia (SEA), organizations must navigate rapid technological advancements, shifting consumer expectations, and intensifying global competition. The convergence of Artificial Intelligence (AI), Big Data analytics, and sustainability orientation offers unprecedented opportunities for strategic marketing transformation, yet empirical research integrating these dimensions remains limited in emerging market contexts. Drawing on Dynamic Capabilities Theory (Teece, 2016), the Technology Acceptance Model (Davis, 1989), and the Sustainable Marketing Framework (Belz & Peattie, 2012), this study develops and empirically tests a comprehensive model linking AI adoption, Big Data analytics capability, and sustainability orientation to marketing agility, innovation performance, and competitive advantage. Data were collected from 412 senior marketing executives in Indonesia, Malaysia, Singapore, Thailand, and Vietnam, representing the FMCG, retail, technology, hospitality, and manufacturing sectors. Using Structural Equation Modeling–Partial Least Squares (SEM-PLS), results indicate that AI adoption, Big Data analytics capability, and sustainability orientation each significantly enhance marketing agility and innovation performance, which in turn strongly predict competitive advantage (R² = 0.68). All seven hypotheses were supported, confirming the robustness of the model. The findings contribute theoretically by integrating technological and sustainability perspectives within a strategic marketing framework for emerging markets, and offer actionable insights for managers in SEA seeking to leverage digital transformation for sustained market leadership.

The explosion of Internet of things (IoT) devices has led to pretty much saturated network infrastructures, thus congestion often becomes severe, especially in applications where users require low latency, high throughput and real-time responses. Hence, traditional congestion control mechanisms like static routing protocols, Active Queue Management (AQM) and TCP variants are not suitable for this dynamic and heterogeneous IoT environment as they are reactive, rigid and cannot adapt to dynamic changes. The contribution is to investigate the potential of AI paradigms—such as ML, DL, RL and hybrid models- to provide Intelligent and proactive congestion Control in IoT systems. The paper compares the strengths and weaknesses of each method, such as RL’s generalizability and DL’s ability to capture patterns, as well as limitations in terms of scalability, interpretability, and computational resource requirements. Moreover, it emphasizes important research gaps in model generalization process, evaluation criteria and cross-platform fusion. Next steps in research discussions on the future of research take into account lightweight AI architectures, Explainable AI (XAI) frameworks, scalability of FL, and standard benchmarking data sets. The hybrid-AI congestion prediction model built in this work is validated across simulation tools and real-data sets and is observed to result in a significant decrease in latency, packet loss and energy consumption. This paper paves the way for scalable, intelligent and secure AI-based congestion management solutions for various IoT networks.

In the highly dynamic environment of civil aviation, inflight teams serve as the frontline in shaping passengers' perceptions of airline service quality. However, discrepancies often arise between passenger expectations and the actual experiences delivered, primarily due to misaligned or inconsistent communication practices. This study explores the critical role of strategic communication in bridging expectation gaps between cabin crew and passengers, thereby enhancing the overall inflight experience. Through a mixed-methods approach combining passenger feedback analysis, crew interviews, and service quality audits across multiple airlines, the research identifies key communication breakdowns and their impact on passenger satisfaction. Findings reveal that unclear service announcements, inconsistent interpersonal communication, and cultural misinterpretations are major contributors to expectation gaps. Moreover, reactive rather than proactive communication strategies by inflight teams tend to escalate service failures and passenger dissatisfaction. The study proposes a strategic communication framework tailored for inflight operations that emphasizes proactive engagement, emotional intelligence, situational awareness, and real-time information flow. The framework advocates for structured pre-flight briefings, personalized passenger interactions, and adaptive communication protocols to manage service variability and individual passenger needs. Implementation of this model was piloted with select crew cohorts, resulting in measurable improvements in Net Promoter Scores (NPS), complaint reduction, and reported crew confidence. By addressing communication not merely as an operational tool but as a strategic asset, airlines can enhance service delivery consistency and foster trust among diverse passenger demographics. This research underscores the importance of training inflight personnel in communication strategy, emphasizing clarity, empathy, and cross-cultural competence. It concludes that closing expectation gaps through strategic communication is not only vital to improving passenger experience but also essential to strengthening brand loyalty in an increasingly competitive global aviation market.

The main purpose of this paper was to propose a strategic conceptual framework using the Theory of Planned Behavior in the context of entrepreneurship intention among young creative entrepreneurs. The dynamic environment of the COVID-19 pandemic has significantly affected the cultural resilience of art and design. The present study identified eight hypotheses for pathways of strategic direction for young creative entrepreneurs within and after the pandemic. The conceptual framework developed examines the influence of entrepreneurship education on attitudes, subjective norms, and perceived behavioral control. Furthermore, this study also outlines guidance for young creative entrepreneurs based on attitudes toward entrepreneurship intention, entrepreneurship self-identity, subjective norms surrounding entrepreneurship intention, perceived behavioral control, and entrepreneurship commitment and enactment. Keywords: entrepreneurship education, Theory of Planned Behavior, entrepreneurship self-identity, entrepreneurship plan enactment, entrepreneurship commitment

The study explores adaptive enterprise management as a strategic framework for maintaining optimal efficiency in dynamic and uncertain environments. It was stated that modern technological platforms play a dual role, either accelerating or constraining adaptive management processes. On one hand, traditional data distribution across departments can limit real-time visibility required for timely and accurate economic judgments. On the other hand, cloud-based infrastructures with integrated data repositories empower managers to analyze aggregated indicators swiftly and adjust strategies based on updated patterns. As was declared, adaptive management in contemporary organizations faces challenges stemming from the complexity of data sources, such as transactional databases, consumer feedback, operational logs, and financial reports. Subsequently, these diverse and increasingly sophisticated data sources necessitate advanced analytical methodologies capable of extracting meaningful insights from unstructured text, high-dimensional numerical data, and time-series signals reflecting system performance. Relational databases, widely used in transactional processing and resource management, often rely on numerical and categorical metrics like query execution times and error counts. However, these metrics fail to capture the broader semantic context of issues documented in textual logs and feedback. BERT embeddings transform raw textual inputs, such as error messages or policy documentation, into high-dimensional vectors retaining semantic detail. Simultaneously, LSTM networks manage temporal dependencies in performance trends, selectively retaining long-term patterns while discarding irrelevant fluctuations. The combined feature vectors—comprising BERT embeddings and numerical metrics—are fed into LSTM layers to model fluctuations in database performance dynamically. The research emphasizes the critical role of real-time analytics and adaptive decision-making facilitated by advanced information architectures. It highlights the economic value of integrating data-driven insights into adaptive enterprise management, enabling rapid optimization of processes and resource allocation. However, technological advancements alone are insufficient without organizational readiness to act on insights, balanced incentives, and a culture of adaptability. The study underscores the balance between centralized oversight for compliance and decentralized decision-making for local adaptability. It was concluded, that while centralization supports risk management standardization, excessive control can stifle frontline innovation. Conversely, excessive decentralization risks incoherence and inefficiency. This work proposes an economic model that integrates organizational structures and technological infrastructures to improve decision-making processes in real-time.

This paper offers an overview of mixed-income housing in African cities, emphasising its importance, challenges, and potential solutions. As urbanisation increases, mixed-income housing becomes crucial for addressing housing inequality, fostering social inclusion, and promoting sustainable urban development. However, its application in African contexts is under-researched. Through bibliometric analysis and systematic review, the study utilised VOSviewer to examine existing research, identifying publication trends, dominant themes, emerging trends, and mapping out research gaps of mixed-income housing in the African context. The study highlights geographical biases and calls for diverse research settings to understand mixed-income housing complexities across different African cities. The emerging trends further underscore the need for tailored policies, community integration, sustainable financing, and long-term impact assessments. However, gaps remain in understanding environmental sustainability, economic impacts, and community dynamics in mixed-income housing. The paper concludes with recommendations for future research, advocating interdisciplinary collaboration and mixed-method approaches to formulate a comprehensive strategic framework. Addressing these gaps can guide evidence-based policies to promote inclusive, sustainable mixed-income housing in African cities, aligning with Sustainable Development Goals (SDGs) and fostering resilient urban futures.

The inherent variability in human performance introduces stochastic perturbations into manufacturing environments, undermining the seamless coordination required for effective human-robot collaboration (HRC) systems. While human cognitive flexibility enhances adaptability, it simultaneously acts as a source of operational uncertainty, complicating the modeling and optimization of integrated robotic systems. Given these challenges, there is an urgent need to substantially expand the adaptability of robotic systems through real-time detection, algorithmic analysis and dynamic behavioral adjustments in response to human performance fluctuations. The systematic development of such systems capable of precisely detecting task-specific variations, analyzing them via advanced AI algorithms and adapting their behavior accordingly remains a critical focus of contemporary research. To evaluate progress in this domain, this study conducts a systematic literature review, synthesizing advancements across 124 publications and identifying underexplored research frontiers. The findings reveal a persistent misalignment between current technical capabilities and the requirements of adaptive collaboration in dynamic industrial environments. Key gaps include the absence of explainable AI frameworks for transparent decision-making, limited generalizability of adaptive control architectures and a lack of proactive strategies that anticipate rather than merely react to performance deviations.

The rapid proliferation of Internet of Things devices has led to a surge in real-time data generation, requiring efficient and accurate anomaly detection mechanisms, especially in resource-constrained edge environments. Traditional centralized methods often suffer from high latency, excessive bandwidth usage, and privacy concerns, making them less suitable for modern IoT deployments. This paper proposes a novel bio-inspired Edge AI framework designed for real-time anomaly detection in distributed IoT sensor networks. Drawing inspiration from biological systems such as the human immune response, the framework integrates adaptive temporal feature extraction with an Artificial Immune System based anomaly detection module. It is optimized for lightweight, decentralized processing to support low-power edge devices. The system architecture includes preprocessing, bio-inspired feature computation, self-organizing anomaly classification, and efficient communication modules. Comparative evaluations against baseline algorithms Support Vector Machine and Isolation Forest demonstrate the superiority of the proposed model in terms of detection accuracy (94.6%), reduced latency (28 ms), and lower energy consumption (15 mJ). These results confirm the framework’s effectiveness for real-time deployment in dynamic IoT environments. The proposed approach enables scalable, energy-efficient, and privacy preserving anomaly detection, paving the way for next-generation edge intelligence in mission-critical applications such as smart grids, healthcare, and industrial monitoring

Academic literature is currently lacking on the topic of how to acquire, deploy, integrate, and reconfigure AI technologies to achieve HRM goals. This void is filled largely from the viewpoint of technology-driven human resource management. This research fills that knowledge vacuum by proposing a new standard for the architectural framework of human resource management strategies that make use of artificial intelligence. This paper presents an all-encompassing operational architectural framework for AI-powered HRM, drawing on theory of management and AI integration literature. It addresses the following areas: corporate setting, HRM(AI) architecture's inner workings, its connection to HRM domain-specific outcomes, organizational level conclusions, and subsequent performance. Additional exploration into the suggested AI framework for linked strategic architecture might be facilitated by a plethora of viable research ideas.