Search Results (829)

Burial and mourning practices in Africa have long been embedded in deeply rooted cultural, spiritual and communal value systems. However, these traditions are undergoing significant transformation through modernisation, urbanisation, migration, religious change and economic pressure. This study examines the changing dynamics of burial and mourning practices in Southern Africa, with particular attention to Zimbabwe and Lesotho. Drawing on qualitative insights from existing scholarship, cultural analysis and the researchers’ contextual observations, the paper explores how mourning periods, communal participation, ritual ceremonies, ancestral relations and attachment to burial land are being reconfigured rather than simply abandoned. In both countries, prominent shifts include the professionalisation and commercialisation of funerals, the influence of Pentecostal Christianity, the growing role of funeral assurance schemes and the disruption of expected rites by COVID-19. Using functionalist, symbolic interactionist and African Indigenous Knowledge Systems (AIKS) perspectives, the paper identifies a continuing tension between cultural identity and contemporary practical realities. It argues that some indigenous practices have weakened, while others have been adapted or hybridised into new cultural forms. The comparison contributes to debates on cultural continuity, genealogy, identity and social change by showing how communities preserve the social and spiritual functions of mourning even when its outward forms change. The paper is positioned primarily within cultural studies, specifically the interdisciplinary study of religion, ritual and social change. Among the factors considered, Pentecostal reinterpretation and ritual substitution, the professionalisation and commodification of funerals, and migration, together with pressure on burial land, appear most influential. COVID-19 is retained as a historically bounded accelerator rather than the dominant continuing force. Full article

Purpose: This study examines how large multinational corporations translate sustained macroenvironmental volatility into deliberate strategic reconfiguration processes, and how adaptive mechanisms differ across business models and sectors under shared environmental pressures. Design/Methodology: The study employs a qualitative comparative multiple-case design with abductive logic and a longitudinal perspective covering 2019–2024. Apple, Amazon, and McDonald’s were selected through theoretical sampling for their sector heterogeneity, their shared global regulatory and operational exposure, and their contrasting adaptive architectures—vertical integration, platform diversification, and franchising, respectively—rather than a uniform majority-international-revenue criterion. A corpus of 78 primary and secondary documents was analysed through open coding, axial coding, thematic aggregation, cross-case comparison, and pattern matching. Findings: All three firms converge around digitalisation, regulatory compliance, and sustainability as environmental legitimacy requirements rather than differentiating strategic choices. Divergence emerges in execution mechanisms: Apple deploys anticipatory vertical integration; Amazon converts operational complexity into structural barriers; McDonald’s exploits franchise flexibility for local adaptation while preserving brand coherence. The analysis yields an original Adaptive Reconfiguration Cycle (ARC Framework) comprising five iterative stages, evidenced for each firm across the full cycle rather than only its dominant stage. Theoretical Contribution: The study develops an integrated macroenvironment–capability reconfiguration framework that bridges PESTEL analysis, dynamic capabilities theory, and contingency theory, addressing an under-explored integration gap in each tradition. Three theory-building propositions, generated inductively from the three comparative cases and not presented as empirically established relationships, are advanced together with their moderating conditions. Practical Implications: Firms must institutionalise environmental sensing as a permanent strategic function, treat compliance capabilities as competitive assets, and build adaptive capacity as a standing organisational competency. Originality/Value: This study is among the few comparative analyses to integrate PESTEL trigger structures with dynamic capabilities reconfiguration logic across heterogeneous sectors using longitudinal evidence. The ARC Framework is offered as an analytically transferable, theory-generating model with explicit boundary conditions and testable propositions, rather than as a broadly generalisable one. Full article

Reliable coordination of unmanned aerial vehicle (UAV) swarms is significantly challenged by random denial-of-service (R-DoS) attacks, which introduce stochastic packet loss, time-varying communication interruptions, and strong concealment. Existing fault-tolerant consensus approaches typically assume known attack information or treat attack detection, topology recovery, and control design as separate processes, resulting in limited resilience under dynamically evolving attack conditions. To address this issue, this paper proposes a detection-guided resilient consensus control framework for UAV swarms under R-DoS attacks. A dual-dimensional statistical detection method is developed by jointly modeling packet reception rate (PRR) and inter-arrival time (IAT), enabling real-time identification of attack-induced anomalies through spatio-temporal feature fusion. Based on the detection results, a distributed topology reconstruction strategy is designed, incorporating redundant node identification and cluster-based dynamic communication reconfiguration. The communication graph is adaptively updated via online adjustment of adjacency and Laplacian matrices, and robustness guarantees for the resulting consensus process are analytically established. Hardware-in-the-loop simulation experiments under both single-leader and multi-leader architectures demonstrate that the proposed method can accurately detect attacked nodes, effectively reconstruct the communication topology, and maintain stable formation coordination under severe R-DoS attacks. The position tracking error is constrained within 0.4 m, validating the effectiveness and robustness of the proposed framework. This study is limited to defensive cyber-resilience in a closed hardware-in-the-loop simulation environment and does not address reconnaissance payloads, weaponization, target selection, or operational attack execution. Unlike methods that assume known attack schedules or treat detection, topology recovery, and control separately, this study focuses on their online coupling under unknown random packet loss; its validation is limited to the stated closed HIL impairment model. Full article

This study examines employees’ readiness for AI-related organizational change by integrating AI perception, work reconfiguration, organizational conditions, and trust within a socio-technical perspective. A cross-sectional survey of 276 employees from six sectors in Croatia combined theory-guided operational composites, reliability assessment, exploratory principal component analysis, nonparametric comparisons, and multiple linear regression. AI perception (β = 0.195, p = 0.003) and AI-related work reconfiguration (β = 0.350, p < 0.001) were positively associated with adaptive capacity and readiness for change. Organizational change culture and trust and organizational relations showed substantial overlap, limiting interpretation of their separate adjusted coefficients. Significant sectoral differences were observed across all five measured domains. Open-ended responses highlighted perceived benefits associated with automation and work processes alongside concerns about accuracy, privacy, ethics, employment, and human oversight. The findings suggest that organizational AI implementation should consider changes in work practices, transparent communication, employee involvement, competence development, and trust. Interpretation is limited by the cross-sectional design, purposive nonprobability sampling, self-reported measures, and the absence of independent confirmatory measurement validation. Full article

Owing to their small size, micron-scale accuracy, rapid response, and biocompatibility, microrobots are emerging as a promising tool for biomedical applications, especially in targeted drug delivery and minimally invasive microsurgery. The architectural configuration of microrobots governs their locomotion performance, environmental adaptability, and functional integration capacity. Although existing reviews have systematically organized this field by actuation strategies, material categories, or application scenarios, a comprehensive summary centered on the structural evolution paradigm remains conspicuously absent. This review aims to fill this gap by systematically tracing the evolutionary trajectory of microrobot structures from rigid architectures through soft configurations to rigid–soft integrated systems. First, the foundational principles underlying structural evolution were introduced, encompassing the connotation of structure, fluid dynamics constraints, and the drivers of structural innovation. Then, rigid microrobot architectures were systematically elucidated, including geometric asymmetric and surface asymmetric designs. Subsequently, soft microrobot structures, covering both predefined deformation structures and dynamically reconfigurable architectures, were discussed. Finally, the rigid–soft integrated systems reconciling compliance and performance through spatial heterogeneity or temporal stiffness modulation were systematically surveyed. This evolutionary trend reflects a transition from optimizing individual performance parameters toward achieving balanced functional synergy across distinct task phases. Based on the current research progress, this review also presents future research directions in data-driven structural optimization, reconfigurable architectures, and autonomous structural intelligence, offering strategic guidance for next-generation microrobot design. Full article

Addressing the opportunistic behavior of retailers operating within the unique spaces of transportation hub ecosystems, which exploit strong temporal and spatial constraints and geographic monopolies, this paper constructs a three-party evolutionary game model involving transportation hub authorities, micro-merchant enterprises, and transient consumers. Using China’s high-speed rail hubs as the research context, this study develops a theoretical evolutionary game model with numerical simulations and endogenously embeds the backlash from informal digital public opinion into the regulatory decision-making framework. The study thoroughly deconstructs the mechanisms of behavioral evolution and control pathways within highly constrained micro-spaces. The study reveals that: (1) Static, constant regulation, lacking negative feedback linked to the system’s state, inevitably causes the system to fall into a non-convergent “regulation-speculation” cyclical oscillation; (2) A state-dependent dynamic regulatory mechanism can effectively serve as an adaptive damper to quell strategic oscillations; however, because it fails to eliminate speculative premiums, the system becomes locked in a mediocrity trap characterized by low compliance rates; (3) Endogenous digital public opinion leverage can reconfigure the phase space structure and trigger topological bifurcations in the system, breaking the attractor region of the mediocrity equilibrium and driving the game system to undergo a structural transition, with asymptotic convergence to the pure-strategy ideal point. Furthermore, digital empowerment drives consumers to exhibit an inverted U-shaped transient behavioral trajectory of “forced silence—awakening to rights advocacy—rational self-governance,” thereby scientifically deconstructing the public’s dual silence paradox in the digital age. This study not only provides a mathematical basis for the public sector to achieve a paradigm shift from rigid regulation to flexible adaptive governance and structural safe retreat at the micro level, but also offers strategic insights for intertemporal compliance management by micro-merchants. Full article

Criminal ecosystems are durable social systems in which differentiated criminal functions become recurrently interdependent across organizations, networks, illicit markets, institutional actors, financial facilitators, territorial brokers, and social environments. The concept applies when coercion, illicit accumulation, political protection, territorial regulation, and other specialized capacities depend on one another over time and become structurally coupled with institutional, sociocultural, spatial, or technological conditions. A criminal ecosystem is therefore not defined by the presence of a particular organization, market, or territory, nor by resilience itself. It is identified by functional differentiation, recurrent interdependence, structural coupling, and temporal reproduction. Complex adaptive systems theory explains how decentralized interactions can generate adaptation, substitution, and emergent order, while autopoietic social systems theory explains how recurring communications stabilize expectations and reproduce boundaries. Digital infrastructure can mediate financial and transnational functions without constituting a separate operational dimension in the present framework. This distinction allows resilience, reconfiguration, displacement, and decline to be examined as empirical outcomes rather than assumed properties of the ecosystem. Full article

Aiming at the vulnerability of nodes within collaborative task execution links and the slow response of link reconfiguration under limited communication conditions and sudden equipment failure scenarios at sea, and noting that existing approaches mainly rely on static optimization or offline planning without an integrated online reconfiguration framework, this paper proposes an A-star enhanced Multi-Objective Collaborative Reconfiguration (A-MOCR). By constructing a node state probability model and a multi-layer complex network framework, the proposed method sets maximization of task completion probability and network resilience as dual optimization objectives, and leverages the NSGA-III algorithm to generate primary links and corresponding backup link pools. Link reconfiguration is activated through real-time monitoring of node anomaly indicators; available surviving routes from the backup pool are prioritized for matching. If matching cannot be achieved, local A-star search or global path replanning will be initiated. Comparative simulation experiments against two baseline approaches demonstrate that the proposed A-MOCR achieves comparable mission success rates and link survival duration while significantly reducing the average reconfiguration time by over 50%. With equivalent task success probability, A-MOCR completes link reconfiguration within shorter time and maintains steady performance advantages under varying communication coverage parameters. Unlike conventional methods that require global re-planning after each failure, A-MOCR decouples offline optimization from online matching, enabling millisecond-level switching. The method realizes millisecond-level link switching and provides effective technical support for the adaptive reconfiguration of collaborative task execution links for marine multi-platform systems. Full article

With the increasing penetration of renewable energy sources, active distribution networks are facing severe operational challenges caused by stochastic power fluctuations, which may lead to voltage stability degradation and frequent topology adjustment requirements. Existing dynamic reconfiguration methods mainly rely on repeated power flow calculations or predefined stability evaluations, resulting in high computational burdens and limited adaptability under uncertain renewable scenarios. To address these issues, this paper proposes a stability perception-driven dynamic reconfiguration framework for active distribution networks with high renewable penetration. First, a data-driven voltage stability margin perception model is developed to rapidly characterize the security state under diverse operating conditions. By constructing the nonlinear mapping between network topology, renewable generation, load variations, and stability indicators, repetitive stability calculations during online optimization are effectively reduced. Subsequently, a multi-objective rolling reconfiguration strategy is established to coordinate voltage security, active power loss, and load distribution under stochastic renewable fluctuations. An adaptive evolutionary optimization method is employed to search feasible topology schemes, and the TOPSIS approach is adopted to determine the final compromise solution. Case studies conducted on the IEEE 33-bus distribution system demonstrate that the proposed framework improves voltage stability performance, reduces active power losses, and decreases computational costs compared with conventional approaches. The results verify the effectiveness of the proposed method in enhancing the adaptability and operational resilience of active distribution networks under high renewable penetration conditions. Full article

As artificial intelligence transforms the way educational systems interpret students, personalise teaching and automate pedagogical decisions, the persistent use of learning styles as a basis for personalisation demands renewed critical review. Although learning styles remain attractive to teachers because they offer an accessible language for recognising learner diversity, their empirical fragility becomes more problematic when transferred to adaptive platforms, recommender systems and data-based learning environments. This scoping review examines the evidence supporting learning-style models as factors for personalisation and analyses how recent literature reconfigures this debate in the context of AI-mediated education. The findings suggest that the most defensible contribution of learning styles does not lie in their classificatory capacity, but in the pedagogical concern they express, namely avoiding uniform teaching. However, AI does not solve the problem by classifying students better. Its educational value lies in supporting dynamic, revisable, and teacher-mediated interpretations based on observable, contextual, and ethically used evidence about learning. Full article

Commercial laser-processing systems are typically designed for a specific manufacturing process, limiting their adaptability in research and prototyping environments. This study presents the staged evolution of a laboratory-scale prototype originally conceived for metal powder bed fusion into a reconfigurable laser-processing platform. Successive mechanical, optical, electrical, and control developments are consolidated, including the integration and calibration of a 200 W fibre laser, a galvanometric scanning system, motorised powder feed and build platforms, a recoater mechanism, and a combined LabVIEW and weldMARK control architecture. The capabilities of the resulting platform were assessed through optical commissioning by laser marking and two experimental case studies involving single-layer fusion of AISI 316L powder and laser transmission welding of dissimilar thermoplastics. The marking trials provided qualitative confirmation of beam delivery, focal adjustment, and programmed path reproduction. In contrast, the powder experiments produced continuous fused regions, demonstrating controlled laser–powder interaction without constituting full multilayer powder-bed-fusion validation. Thermoplastic welding generated mechanically resistant joints, with failure occurring cohesively within the foam substrate rather than at the welded interface. These results demonstrate the potential of the developed system as a reconfigurable research platform for different laser-processing operations. Nevertheless, fully automated multilayer powder bed fusion still requires improvements in platform levelling, machine-zero integration, process synchronisation, and atmosphere monitoring. Full article

The transition toward unmanned and hybrid vessels increases the requirements for diagnosability and continuity of power supply to critical consumers under physical and information-layer faults. The aim of this study is to develop a probabilistic digital twin of a shipboard microgrid that combines nonlinear state estimation, Bayesian mode inference, adaptive measurement-quality accounting, and risk-aware power reconfiguration. Field validation was performed using a long-term operational measurement program. The database comprises 12 classes of operating and fault events, with 30 field records per class, yielding 360 thirty-minute records, 180 h of observations, and 162,360 time samples at a 4 s sampling interval. Three diagnostic and control methods were applied to the same field records, producing 1080 paired algorithmic evaluations. Relative to the deterministic digital twin, the proposed method reduced voltage RMSE by 37.3%, SOC RMSE by 32.7%, and power RMSE by 30.1%, while reducing ENS by 13.3%. Mean fault-detection delay did not differ significantly from that of the deterministic approach; however, the missed-detection rate decreased from 12.7% to 7.3%. Compared with threshold-based protection, detection latency and recovery time were reduced by 25.7% and 30.8%, respectively. The field evidence therefore supports the proposed architecture as a supervisory state-estimation, diagnosis, and energy-reconfiguration layer. Because the field records originate from one operator and predominantly conventional tanker vessels, transfer to hybrid and unmanned platforms requires prospective onboard validation on the target vessel classes. Full article

Digital technologies, particularly artificial intelligence (AI) and big data analytics (BDA), are increasingly recognized as drivers of competitive advantage, yet the mechanisms through which they enhance organizational performance remain insufficiently understood. Grounded in Teece’s dynamic capabilities framework, this scoping review examines how digital technologies enable sensing, seizing, and reconfiguring capabilities and their impact on organizational performance. Following PRISMA-ScR guidelines, 1349 records from Scopus and Web of Science were screened, resulting in 21 eligible studies (2017–2025). A thematic synthesis and a complementary random-effects meta-analysis of 15 quantitative studies (N = 6924) were conducted, representing an integrated scoping review with embedded quantitative synthesis. The findings show that AI and BDA strengthen sensing through advanced data analytics, AI and digital platforms enhance seizing via real-time decision support, and IoT and blockchain facilitate reconfiguring by improving process optimization and organizational flexibility. Dynamic capabilities consistently mediate the relationship between digital technologies and performance. The meta-analysis confirms a significant positive overall effect (β = 0.356, 95% CI: 0.258–0.454), remaining robust after publication bias correction (β = 0.338). This review further identifies three generative tensions, breadth–depth, speed–deliberation, and flexibility–rigidity, that underpin capability development. An integrative framework and future research agenda are proposed, extending dynamic capability theory by conceptualizing digital technologies as active enablers of organizational adaptation and sustained performance. Full article

Agrivoltaic systems configure photovoltaic power generation and agricultural production within the same land space, providing a new pathway for alleviating the conflict between energy development and farmland conservation; however, array shading and structural constraints also alter crop growth and agricultural equipment operating environments. Following the PRISMA process, this review searched studies published from 2010 to 2026 in the Web of Science Core Collection, Scopus, and China National Knowledge Infrastructure, and ultimately included 206 publications. The review focuses on array-induced environmental reconfiguration, equipment adaptation, light–thermal sensing and prediction, and coordinated agrivoltaic operation. Existing evidence indicates that the environmental effects of agrivoltaic systems are clearly influenced by climate and array configuration; in representative vertical or tracking systems, annual-scale photosynthetically active radiation decreased by approximately 11% to 34%. Mismatch among module height, row spacing, and implement width reduces field-operation efficiency, which fell to approximately 45% under severe mismatch in some experiments; elevated arrays also cause GNSS signal attenuation and increase the difficulty of continuous positioning beneath the panels. Because existing studies differ considerably in site conditions, evaluation indicators, and validation periods, the above results mainly reflect representative performance ranges. Matching criteria between photovoltaic arrays and agricultural machinery have not yet been established, long-term field-measured data in complex field environments are insufficient, model adaptability across regions is limited, and coordinated scheduling of agricultural production and photovoltaic operation and maintenance remains inadequate. Overall, coordinated design of arrays and agricultural machinery, multi-sensor fusion navigation, environmental prediction corrected for array structure, and hierarchical scheduling under safety constraints are the main development directions for intelligent coordinated operation of agrivoltaic systems. Full article

Software-defined radio (SDR) has become an attractive platform for radar research due to its flexibility, compact size, and ability to reconfigure radar methods for different situations. However, achieving high-resolution time-domain radar with SDR remains challenging due to demanding requirements for data throughput, sampling rate, and hardware configuration. Frequency-modulated radar implementations face similar limitations, often suffering from slow data acquisition and increased system complexity including exact waveform linearity, phase coherence, frequency-stepping control, and computationally intensive signal processing. In this work, a high-resolution impulse radar is implemented on a USRP X310 equipped with a UBX-160 daughterboard. The system fully exploits the hardware’s wide instantaneous bandwidth, enabling high range resolution in real time. Detailed hardware requirements and software configurations are presented step-by-step to ensure reliable operation at high data rates. To maximize performance and flexibility, the radar system is implemented using the low-level USRP Hardware Driver C++ API rather than graphical programming frameworks. This enables improved memory handling, reduced processing overhead, and precise timing control. The resulting modular design permits adaptability for different radar modalities with minimal code modification. The proposed platform provides a practical and reproducible framework for developing wideband, real-time SDR-based radar systems. Full article