Search Results (802)

Over-the-Air updates have emerged as a critical competitive frontier in the Software-Defined Vehicle era. While offering value creation opportunities, automakers face strategic uncertainty regarding pricing models (e.g., subscription vs. one-time purchase). To clarify these dynamics, this study develops an evolutionary game model of duopolistic pricing competition. Unlike traditional studies with exogenous payoff assumptions, we innovatively employ the Hotelling model to endogenously derive firm profit functions based on consumer utility maximization. The highlights of this study include: (1) We establish an integrated “static–dynamic” framework connecting micro-level consumer choice with macro-level strategy evolution; (2) We identify that product differentiation is the decisive variable governing market stability; (3) We demonstrate that under moderate differentiation, the market exhibits a robust self-correcting tendency towards “Tacit Collusion” (mutual high pricing). However, simulation results also warn that an asymmetric disruptive strategy by a market leader can override this robustness, forcing the market into a low-profit equilibrium. These findings provide theoretical guidance for automakers to optimize pricing strategies and avoid value-destroying price wars. Full article

The growth of generative artificial intelligence (GAI), remarkably, Large Language Models (LLMs) such as ChatGPT, converts the educational environment by empowering intelligent, data-driven education and curriculum design innovation. This study aimed to assess the integration of LLMs into higher education to foster curriculum design, learning outcomes, and innovative work behaviour (IWB). Specifically, this study investigated how LLMs’ perceived usefulness (PU) and perceived ease of use (PEOU) can support educators to be engaged in IWB—idea generation (IG), idea promotion (IP), opportunity exploration (OE), and reflection (Relf)—employing a web-based survey and targeting faculty members. A total of 493 replies were obtained and found to be valid to be analysed with partial least squares structural equation modelling (PLS-SEM). The results indicated that PU and PEOU have a significant positive impact on the four dimensions of IWB in the context of LLMs for curriculum development. The evaluated model can assist in bridging the gap between AI technology acceptance and educational strategy by offering some practical evidence and implications for university leaders and policymakers. Additionally, this study offered a data-driven pathway to advance higher education IWB through the adoption of LLMs. Full article

This study explores the empowerment of local communities through homestay management as an innovative strategy for sustainable rural tourism in Yogyakarta. Using a qualitative research design, data were collected through in-depth interviews, focus group discussions, and participant observation with homestay owners, community leaders, and local tourism stakeholders. The findings reveal that homestay management not only enhances economic opportunities for rural households but also strengthens cultural preservation and community participation in tourism governance. Moreover, the integration of traditional hospitality practices with innovative management approaches fosters visitor satisfaction while ensuring sustainability. The study contributes to the literature by highlighting how homestay management can serve as a model of community-based tourism development, offering practical implications for policymakers, local governments, and tourism practitioners in promoting inclusive and resilient rural tourism. Full article

Introduction: Nursing educators and clinical leaders face persistent challenges in engaging the next generation of nurses, often characterized by short attention spans, frequent phone use, and underdeveloped communication skills. This article describes the design and delivery of a full-day interactive teaching workshop for nursing faculty, senior clinical nurses, and nurse leaders, developed using a design-thinking approach supported by generative AI. Methods: The workshop comprised four thematic sessions: (1) Learning styles across generations, (2) Interactive teaching methods, (3) Application of interactive teaching strategies, and (4) Lesson planning and transfer. Generative AI was used during planning to create icebreakers, discussion prompts, clinical teaching scenarios, and application templates. Design decisions emphasized low-tech, low-prep strategies suitable for spontaneous clinical teaching, thereby reducing barriers to adoption. Activities included emoji-card introductions, quick generational polls, colored-paper reflections, portable whiteboard brainstorming, role plays, fishbowl discussions, gallery walks, and movement-based group exercises. Participants (N = 37) were predominantly female (95%) and represented multiple generations of X, Y, and Z. Mid- and end-of-workshop reflection prompts were embedded within Sessions 2 and 4, with participants recording their responses on colored papers, which were then compiled into a single Word document for thematic analysis. Results: Thematic analysis of 59 mid- and end-workshop reflections revealed six interconnected themes, grouped into three categories: (1) engagement and experiential learning, (2) practical applicability and generational awareness, and (3) facilitation, environment, and motivation. Participants emphasized the workshop’s lively pace and hands-on design. Experiencing strategies firsthand built confidence for application, while generational awareness encouraged reflection on adapting methods for younger learners. The facilitator’s passion, personable approach, and structured use of peer learning created a psychologically safe and motivating climate, leaving participants recharged and inspired to integrate interactive methods. Discussion: The workshop illustrates how AI-assisted, design-thinking-driven professional development can model effective strategies for next-generation learners. When paired with skilled facilitation, AI-supported planning enhances engagement, fosters reflective practice, and promotes immediate transfer of interactive strategies into diverse teaching settings. Full article

In the context of the rapid growth of the Internet of Things and mobile health services, sensors and smart wearable devices are continuously collecting and uploading dynamic health data. Together with the long-term accumulated electronic medical records and multi-source heterogeneous clinical data from healthcare institutions, these data form the cornerstone of intelligent healthcare. In the context of medical data sharing, previous studies have mainly focused on privacy protection and secure data transmission, while relatively few have addressed the issue of incentive mechanisms. However, relying solely on technical means is insufficient to solve the problem of individuals’ willingness to share their data. To address this challenge, this paper proposes a three-party Stackelberg-game-based incentive mechanism for medical data sharing. The mechanism captures the hierarchical interactions among the intermediator, electronic device users, and data consumers. In this framework, the intermediator acts as the leader, setting the transaction fee; electronic device users serve as the first-level followers, determining the data price; and data consumers function as the second-level followers, deciding on the purchase volume. A social network externality is incorporated into the model to reflect the diffusion effect of data demand, and the optimal strategies and system equilibrium are derived through backward induction. Theoretical analysis and numerical experiments demonstrate that the proposed mechanism effectively enhances users’ willingness to share data and improves the overall system utility, achieving a balanced benefit among the cloud platform, electronic device users, and data consumers. This study not only enriches the game-theoretic modeling approaches to medical data sharing but also provides practical insights for designing incentive mechanisms in IoT-based healthcare systems. Full article

Amid global sustainability transitions, campus energy systems confront growing pressure to balance operational efficiency, resilience to extreme weather events, and sustainable development education. This study proposes an artificial intelligence-driven framework for smart campus microgrids that synergistically advances environmental sustainability and disaster resilience, while deepening students’ understanding of sustainable development. The framework integrates an enhanced multi-scale gated temporal attention network (MS-GTAN+) to realize end-to-end meteorological hazard-state recognition for adaptive dispatch mode selection. Compared with Transformer and Informer baselines, MS-GTAN+ reduces prediction RMSE by approximately 48.5% for wind speed and 46.0% for precipitation while maintaining a single-sample inference time of only 1.82 ms. For daily operations, a multi-intelligence co-optimization algorithm dynamically balances economic efficiency with carbon reduction objectives. During disaster scenarios, an improved PageRank algorithm incorporating functional necessity and temporal sensitivity enables precise identification of critical loads and adaptive power redistribution, achieving an average critical-load assurance rate of approximately 75%, nearly doubling the performance of the traditional topology-based method. Furthermore, the framework bridges the divide between theoretical knowledge and educational practice via an educational digital twin platform. Simulation results demonstrate that the framework substantially improves carbon footprint reduction, resilience to power disruptions, and student sustainability competency development. By unifying technical innovation with pedagogical advancement, this study offers a holistic model for educational institutions seeking to advance sustainability transitions while preparing the next generation of sustainability leaders. Full article

Mobile Ad Hoc Networks (MANETs) exhibit diverse deployment forms, such as unmanned swarms, mobile wireless sensor networks (MWSNs), and Vehicular Ad Hoc Networks (VANETs). While providing significant social application value, MANETs also face the challenge of accurately and efficiently evaluating connectivity reliability. Building on existing studies—which mostly rely on the assumptions of imperfect nodes and perfect links—this paper comprehensively considers link communication quality and channel capacity, and extends the imperfect link assumption to analyze and evaluate the connectivity reliability of MANETs. The Couzin-leader model is used to characterize the ordered swarm movement of MANETs, while various probability models are employed to depict the multiple actual failure modes of network nodes. Additionally, the Free-Space-Two-Ray Ground (FS-TRG) model is introduced to quantify link quality and reliability, and the probability of successful routing path information transmission is derived under the condition that channel capacity follows a truncated normal distribution. Finally, a simulation-based algorithm for solving the connectivity reliability of MANETs is proposed, which comprehensively considers node characteristics and link states. Simulation experiments are conducted using MATLAB R2023b to verify the effectiveness and validity of the proposed algorithm. Furthermore, the distinct impacts of link communication quality and channel capacity on the connectivity reliability of MANETs are identified, particularly in terms of transmission quality and network lifetime. Full article

In GNSS-denied environments, achieving accurate and reliable positioning for unmanned aerial vehicle (UAV) formations remains a major challenge. This paper presents a cooperative navigation framework for UAV formations based on LSTM and dynamic model information fusion to enhance formation navigation performance under GNSS-denial. The framework employs a dual-driven hierarchical architecture that integrates an LSTM-based dynamic state predictor with historical motion features, including velocity, acceleration, airflow angle, or thrust, thereby enhancing the robustness and positioning accuracy of the leader UAV layer. Furthermore, a multi-source optimal selection strategy based on consistency evaluation is developed to dynamically fuse pseudo-GNSS (P-GNSS), barometric altitude (BA), and wind-speed consistency information, optimizing node allocation between the leader and follower layers. In addition, an IMM-based resilient fusion filtering algorithm is introduced for the follower UAV layer, incorporating UWB, wind-speed, and external-force estimations to maintain reliable navigation under UWB outages and leader-node degradation. Experimental results demonstrate that the proposed framework significantly improves positioning accuracy and formation stability, exhibiting strong adaptability in complex GNSS-denied environments. Full article

The study assesses the level and concentration of revitalisation measures in Poland’s county-level cities across two dimensions: spatial–cultural and social. We compiled comparable indicators from the Local Data Bank (2020–2023) and municipal revitalisation programmes for 63 cities, constructing ten stimulus variables (five spatial–cultural; five social). Indicators were normalised to (0–1) and aggregated into two synthetic indices—IRSC (spatial–cultural) and IRS (social)—followed by a standard-deviation-based classification into four types/groups. Results show pronounced inter-city variation with no clear voivodeship pattern. Several cities emerge as consistent leaders across dimensions, while others perform unevenly—e.g., cases with high IRSC but moderate IRS, and vice versa—highlighting different strategic emphases of programmes. We also note large disparities in financial effort (per area and per resident) and low counts of actions per unit in many cities, contrasted with a few high-activity cases. The findings indicate that roughly one-third of cities leverage revitalisation effectively in both dimensions. The study advocates complementing synthetic, comparative assessment with practice-informed models that adapt solutions proven in top-performing cities, rather than relying solely on unified, centrally framed approaches. Full article

Greenhouse gas emissions from the energy sector are the main driver of global warming, which has led to an increase in the average surface temperature of the Earth by more than 1 °C above pre-industrial levels. Responding to the urgent need for energy transition, the countries of the European Union have set themselves the goal of achieving climate neutrality by 2050. The main objective of this article is to comprehensively assess the progress of decarbonization in the 27 European Union countries between 2004 and 2024, using an advanced multi-criteria model. The study used the quantitative Analytical Hierarchy Process (AHP) method to construct a multidimensional decision-making model. Eight energy technologies were evaluated through the prism of 13 criteria grouped into three pillars of sustainable development: economic (including technical), environmental, and social. Based on the weights of each criterion, estimated by a group of experts, a synthetic decarbonization index (DI) was calculated for each technology. In the next stage, a cumulative decarbonization index (CDI) was formulated for each country, reflecting the structure of its energy mix. The analysis revealed a fundamental divergence between conventional and zero-emission technologies. Renewable sources and nuclear energy have the highest positive impact on decarbonization (highest DI): hydropower (27.5), nuclear (20.7), wind (20.3). The lowest, unfavorable values of the index are characteristic of fossil fuels: oil (3.6), coal (3.9), and gas (4.8). The average cumulative decarbonization index (CDI) for the EU-27 rose from 14.0 in 2004 to 26.4 in 2024, demonstrating the effectiveness of the EU’s common policy. The leaders of the transition are countries with diversified, green mixes, such as Luxembourg (CDI = 40.4), Lithuania (CDI = 39.6), Portugal (38.5), Austria (36.9), and Spain (33.6). Despite starting from the lowest level in 2004 (CDI = 5.2), Poland recorded one of the most dynamic increases in 2024 (CDI = 17.7), mainly due to a reduction in the share of coal from 93% to 53.5%. The analysis confirms the effectiveness of the EU’s common climate and energy policy and demonstrates the usefulness of the methodology presented for a comprehensive assessment of the decarbonization process. The results indicate the need to further increase the share of zero-emission energy sources in the energy mix in order to achieve the objectives of the European Green Deal. The varying pace of transformation among Member States requires an individualized approach and support for countries with a historical dependence on fossil fuels. Full article

This paper investigates the consensus tracking problem for a class of high-order nonlinear multi-agent systems subject to actuator faults, sensor faults, unknown disturbances, and model uncertainties. To effectively address this problem, a hierarchical fault-tolerant control framework with fuzzy adaptive mechanisms is proposed. First, a distributed output predictor based on a finite-time differentiator is constructed for each follower to estimate the leader’s output trajectory and to prevent fault propagation across the network. Second, a novel state and actuator-fault observer is designed to reconstruct unmeasured states and detect actuator faults in real time. Third, a sensor-fault compensation strategy is integrated into a backstepping procedure, resulting in a fuzzy adaptive consensus-tracking controller. This controller guarantees the uniform boundedness of all closed-loop signals and ensures that the tracking error converges to a small neighborhood of the origin. Finally, numerical simulations validate the effectiveness and robustness of the proposed method in the presence of multiple simultaneous faults and disturbances. Full article

The research elaborates on and empirically verifies an integrative model that describes how the combination of various workplace resources results in the improvement of employee and organizational outcomes. It is based on the Job Demands–Resources model and the Resource-Based View to conceptualize Employee Experience Capital (EEC) as a higher-order construct, consisting of seven interrelation drivers, including digital autonomy, inclusive cognition, sustainability alignment, AI synergy, mindful design, learning agility, and wellness technology. This study examines the effect of these resources in developing two psychological processes, work resonance and employee vitality, which subsequently improves organizational performance. It also examines how the well-being of employees can be a contextual moderator that determines such relationships. The study, based on a cross-sectional design and the diversified sample of the employees who work in various digitally transformed industries, proves that EEC is a great way to improve resonance and vitality, which are mutually complementary mediators between resource bundles and performance outcomes. Employee well-being turns out to be a factor of performance, as opposed to a circumscribed condition. The results put EEC as one of the strategic types of human capital that values digital, sustainable, and wellness-oriented practices to employee well-being and sustainable organizational performance and provides new theoretical contributions and practical guidance to leaders striving to create resource-rich, high-performing workplaces. Full article

Unmanned aerial vehicles (UAVs) are widely utilized for environmental monitoring, precision agriculture, infrastructure inspection, and various defense missions, including reconnaissance and surveillance. Their cybersecurity is essential because any compromise of communication, navigation, or control systems can cause mission failure and introduce significant safety and security risks. Therefore, this paper examines the existing literature on UAV cybersecurity and highlights that most previous surveys focus on listing different types of attacks or communication weaknesses, rather than evaluating the problem from a control systems perspective. Considering control systems is important because the safety and performance of a UAV depend on how cyberattacks affect its sensing, decision-making, and actuation loops; modeling these attacks and their impact on system behavior provides a clearer foundation for designing secure, resilient, and stable control strategies. Based on a comprehensive review of the literature, it presents a mathematical framework for characterizing common cyberattacks on UAV communication and sensing layers, including time-delay switch, false data injection, denial of service, and replay attacks. To demonstrate the impacts of these attacks on UAV control systems, a simulation of a two-UAV leader-follower multi-agent system is conducted in MATLAB. Defense algorithms from the existing literature are then organized into a hierarchical framework of prevention, detection, and mitigation, with detection and mitigation further categorized into model-based, learning-based, and hybrid approaches that combine both. The paper concludes by summarizing key findings and highlighting challenges with current defense strategies, including those insufficiently addressed in existing research. Full article

This paper addresses the issues of target unreachability and local optima in traditional artificial potential field (APF) methods for UAV swarm path planning by proposing an improved collaborative obstacle avoidance algorithm. By introducing a virtual target position function to reconstruct the repulsive field model, the repulsive force exponentially decays as the UAV approaches the target, effectively resolving the problem where excessive obstacle repulsion prevents UAVs from reaching the goal. Additionally, we design a dynamic virtual target point generation mechanism based on mechanical state detection to automatically create temporary target points when UAVs are trapped in local optima, thereby breaking force equilibrium. For multi-UAV collaboration, intra-formation UAVs are treated as dynamic obstacles, and a 3D repulsive field model is established to avoid local optima in planar scenarios. Combined with a leader–follower control strategy, a hybrid potential field position controller is designed to enable rapid formation reconfiguration post-obstacle avoidance. Simulation results demonstrate that the proposed improved APF method ensures safe obstacle avoidance and formation maintenance for UAV swarms in complex environments, significantly enhancing path planning reliability and effectiveness. Full article

As the English language spreads worldwide, debate has intensified over introducing it early in multilingual school systems. In the Arab world, this question is especially sensitive because Arabic is closely linked to cultural and religious identity, and early English policies may shift the language balance in primary education. This review synthesizes 31 peer-reviewed studies on childhood English learning and early English teaching practices, addressing key aspects of age of acquisition, bilingual outcomes, and language maintenance or identity. Using transparent search and selection reporting, we examined studies published between 2000 and 2025. Findings cluster around four themes: age of acquisition, mother-tongue maintenance and identity, teacher preparation and pedagogy, and social outcomes. The evidence from the review shows that earlier exposure can support pronunciation, fluency, and metalinguistic awareness, but the strength and direction of these gains depend primarily on program quality and bilingual model design. Additive approaches that maintain and value Arabic literacy while providing rich, high-quality English input are often associated with better learning outcomes than subtractive arrangements that reduce Arabic use. However, effects vary by context and implementation quality. Where Arabic is reduced without adequate support, learners may face risks such as weaker first-language development and heightened identity-related strain. However, these outcomes are not inevitable and are moderated by factors such as teacher preparation, instructional design, and school–home language support. We propose a balanced early-English design that builds progressive English proficiency while maintaining continuous Arabic-medium literacy, supported by targeted teacher professional development, family and community engagement, and continuous Arabic-medium literacy. The review concludes with policy and practice implications for curriculum designers, school leaders, and decision-makers, and calls for longitudinal, classroom-based research on identity trajectories and English-medium instruction in Arab primary education. Full article