Search Results (190)

Research on social media-based health communication has largely focused on non-credentialed influencers or single platforms, leaving limited empirical insight into how credentialed health professionals negotiate professional and personal identity across platform environments. Addressing this research gap, the present exploratory pilot study examines how health opinion leaders (HOLs)—credentialed health professionals active on social media—express professional and personal identities across Instagram and TikTok, and how these expressions vary by gender. Using a quantitative, multiple-case design, the study analyzes 1237 posts and Stories from four Instagram accounts and two TikTok accounts belonging to Norwegian HOLs. Drawing on theories of platform affordances and identity performativity, the analysis traces content-level patterns in how expertise, authenticity, and engagement are staged within specific platform environments. Rather than offering generalizable platform effects, this study identifies contrasting tendencies within a small set of cases: Instagram content more frequently blends professional and personal narratives—especially among female HOLs—while TikTok content is oriented toward more streamlined, expert-focused presentation. Engagement dynamics further differ across platforms, suggesting that visibility and interaction are shaped by distinct platform logics. This study contributes theoretically by demonstrating that professional identity expression in health communication is platform-conditioned and gendered, extending dramaturgical perspectives to contemporary platform infrastructures. More broadly, this study demonstrates how data-based analysis of digital trace content can illuminate shifting boundaries of expertise and identity within digital societies. Finally, given the emergence of HOLs as a socio-professional phenomenon, these findings serve as a stepping stone for larger-scale research and raise practical concerns about trust, professional boundaries, and the adequacy of existing guidelines in increasingly hybrid professional–personal online practices. Full article

This article examines gender inequalities in scientific research in the Mediterranean, with a particular focus on STEM disciplines. It draws on qualitative data from the STEP (STEM and Equality, Diversity and Inclusion for Research Enhancement in Portugal) project, a European Commission-funded initiative aimed at embedding equality, diversity, and inclusion (EDI) principles across partner institutions in Portugal, Italy, France, and Spain. Using semi-structured interviews with five scientific leaders and an inductive thematic analysis, the study explores early-stage mechanisms in the institutionalisation of EDI policies and women’s empowerment trajectories from an intersectional perspective. The analysis identifies emergent patterns suggesting: (i) a gradual strengthening of EDI mainstreaming in contexts with initially limited awareness; (ii) the role of transnational collaboration in enhancing visibility, mentoring, and peer learning; and (iii) the potential of time-bounded initiatives to catalyse participant-observed shifts and institutional routines in formation. Rather than measuring longitudinal impact, the article traces how legitimation, routinisation, and network diffusion may enable EDI principles to extend beyond project lifespans and become embedded in governance structures. These mechanism-focused insights offer a transferable framework for future European cooperation initiatives and contribute to ongoing debates on sustainable gender and EDI policy implementation in Mediterranean research contexts. Full article

To address the challenges of dynamic obstacles, limited perception, and multi-UAV coordination constraints in complex urban environments, a hierarchical control framework based on a virtual leader-follower architecture is proposed, covering global planning, local obstacle avoidance, and formation coordination. In the global planning layer, a dynamic adaptive strategy rapidly exploring random tree star (DASRRT*) algorithm is proposed. To address the low sampling efficiency and limited path extension in dense environments that affect traditional RRT*, a hybrid guided sampling strategy, inefficient node optimization strategy, and perception-based adaptive step size strategy are designed. Additionally, a multi-objective cost function is introduced to provide smoother trajectories that better comply with dynamic constraints for trajectory tracking. In the local obstacle-avoidance layer, a distributed controller is constructed based on an improved artificial potential field method, integrating collision avoidance control laws derived from a spring-damper model, dynamic obstacle-avoidance laws that account for obstacle velocities, and formation coordination control laws grounded in consensus theory. In the coordination control layer, a real-time local target selection strategy is established to guide the virtual leader to precisely track the global path, and a dual-mode switching mechanism based on environmental complexity is constructed to dynamically adjust the priority between formation maintenance and autonomous obstacle-avoidance tasks. Comparative experimental results show that the proposed DASRRT* algorithm reduces path planning time by an average of 34.78% and shortens path length by 1.15%. Simulation results for formation flight demonstrate that the proposed hierarchical control framework can adaptively adjust control modes in response to changes in environmental complexity, exhibiting strong adaptability to complex environments and a good ability to generalize to various scenes. Full article

UAV path planning in complex three-dimensional obstacle environments requires a balance between search efficiency and flight feasibility. However, existing RRT*-based methods often fail to satisfy this requirement, as their random sampling lacks directional guidance and makes limited use of environmental information. To this end, this paper proposes an environment-aware cooperative bidirectional RRT* algorithm (EAC-Bi-RRT*). In the sampling stage, the sampling probability of each direction is adaptively adjusted according to the obstacle distribution across 26 directional sectors and the relative goal orientation, so that the search receives stronger directional guidance. During bidirectional expansion, the two trees are assigned leader and follower roles according to the local expandability on the start and goal sides, and their cooperative search is combined with an environment-adaptive step size and a climbing-angle constraint to balance search efficiency and flight reachability. When an expanding node approaches an obstacle, a repulsive-only local directional correction suppresses oscillation, and the initial path is then smoothed by a curvature-constrained B-spline to form a continuous flight trajectory. Across all test scenarios, EAC-Bi-RRT* achieves a 100% planning success rate. Compared with the baseline algorithms, it reduces planning time by approximately 54–90% and path length by approximately 5–18% while maintaining low average turning angles, which demonstrates competitive overall performance. Full article

Bond ETF rebalancing is difficult to describe with return and risk objectives alone, because a portfolio that looks attractive on paper may still be impractical if it requires large and unstable trades. This paper proposes a topology-aware multi-objective particle swarm optimization framework for bond ETF allocation under credit-risk-related constraints. The method jointly considers annualized return, CVaR, and diversification, while enforcing long-only, exposure, and hard maximum-step turnover constraints. The central idea is to treat the swarm as a communication graph: particles exchange information through an explicit topology, and this topology affects how feasible regions are explored and how leaders are selected. When a candidate portfolio update violates the turnover budget, it is repaired toward the feasible set before evaluation, so that the search remains tied to tradable rebalancing decisions. We test the framework in a walk-forward out-of-sample backtest on U.S. bond ETFs from 2008 to 2024. The empirical analysis compares stronger classical and evolutionary baselines, four communication topologies, hard-versus-soft turnover control, stress-period behavior, and a synthetic scalability proxy. The results suggest that hard turnover repair is effective in truncating extreme rebalancing events, while communication topology changes the return–risk–turnover profile. In our experiments, the ring topology gives the most stable default behavior. Overall, the evidence suggests that topology is not just an implementation detail in swarm-based portfolio search, but a design choice that affects constrained multi-objective allocation. Full article

Cultured meat holds the potential to reduce environmental impacts and offer ethical advantages while replicating the nutritional, taste, and texture attributes of conventional meat. To date, most research on consumer acceptance of meat has focused on European and North American markets. In contrast, Muslim-majority countries remain underexplored, particularly regarding the compatibility of cultured meat with Islamic dietary laws. These societies are experiencing rising meat consumption, and countries such as Saudi Arabia and Malaysia rely heavily on meat imports. This narrative perspective article aims to systematically examine how specific stages of cultured meat production align with, or challenge, Islamic dietary (halal) principles. To this end, we adopt a stage-based analytical approach, mapping key technological steps in cultured meat production onto core requirements of Islamic jurisprudence. To this end, we critically and comprehensively examine the intersection between cultured meat production methods and the Islamic concept of halal, which extends beyond ingredient permissibility to encompass ethical, spiritual, and hygienic dimensions of food production. Key challenges to halal certification include the origin and status of starter cells, whether donor animals were slaughtered according to Islamic law, the permissibility of biopsied tissue, and the use of fetal bovine serum in growth media. The analysis indicates that while halal-compliant cultured meat is scientifically feasible, its adoption remains constrained by unresolved religious interpretations, regulatory fragmentation, and limited availability of halal-certified inputs. We emphasize the need for interdisciplinary collaboration among Islamic scholars, food scientists, certification bodies, and policymakers. From a policy perspective, harmonized halal standards, targeted investment in serum-free and animal-free culture media, and early regulatory engagement with Islamic authorities are essential to facilitate responsible market entry. Therefore, we suggest a multi-level governance and stage-gated halal decision framework for cultured meat. Proactive regulation and open dialogue with religious leaders are vital to ethically introduce cultured meat into Muslim markets, aligning innovation with Islamic values while supporting national sustainability and food security goals. Full article

Background: Engaging diverse populations, including Muslims, in research activities is important to support patient-centered research and improve health equity. Objectives: The research aimed to describe the community engagement steps that informed conducting research with five distinctively diverse U.S. Muslim communities. Methods: This work provides methodological reflections on engaging diverse Muslim communities in the U.S. Researchers built trust-based partnerships with community healthcare organizations and engaged with administrative leaders, advisory members, and people from five diverse communities. Strategies to support sampling, recruitment, multi-language interpretation methods, and how to engage communities and address their concerns are discussed. Results: A total of 22 participants were included in the original study. The research team successfully engaged five of the six planned communities, utilizing multiple interpretation methods and participating in community events to support recruitment and relationship-building. Direct-to-participant recruitment efforts were strengthened by personal connections with trusted community members. Conclusions: Flexibility and adaptability are integral in recruitment and data collection, as diverse communities may respond differently to methods successfully used elsewhere. Attention to gender-related cultural norms, the inclusion of language-concordant researchers, and respect for communities’ autonomy in deciding whether and how to participate collectively contributed to more effective and culturally grounded engagement with Muslim communities. Full article

Rigid-body chain following on piecewise analytic paths is a fundamental subroutine in motion planning and multibody simulation. The problem is nontrivial when only the leader trajectory of the first node is available: enforcing fixed inter-node distances reduces to circle–curve intersection, which is generally multi-valued and becomes particularly challenging near non-smooth junctions. We present a Dichotomy Geometric Path Search (DGPS) framework that converts each constraint into a one-dimensional root-finding task and resolves the branch selection through no-backtracking ordering: at every time step, the admissible solution for the current node is the nearest feasible root in the past relative to its immediately preceding node. DGPS combines backward bracketing with bisection, achieving robust convergence. Compared with the inverse Jacobian method, which maps end-effector velocities to joint velocities via explicit inversion, the proposed approach avoids Jacobian inversion and globally coupled nonlinear solves. We further characterize the local structure of the zero set and establish monotonicity/uniqueness conditions that justify stable root selection across piecewise junctions. Extensive tests on representative piecewise trajectories (line–arc–line, polylines with corners, piecewise sinusoids, and time reparameterization) show that DGPS enforces distance constraints to near machine precision, produces interpretable speed/acceleration transients around non-smooth events, and exhibits computational costs consistent with iteration difficulty. The results support DGPS as a general, efficient solver requiring only the prescribed leader trajectory. Full article

This article advances a constructive theological account of Human-Centered Artificial Intelligence (HCAI) for Faith-Based Organizations (FBOs) engaged in interreligious dialogue (IRD). Drawing on a practical–theological methodology, the study follows four interrelated steps—descriptive–empirical, interpretive, normative, and pragmatic—to examine how AI-enabled practices such as translation, textual analysis, and cross-scriptural synthesis are reshaping contemporary forms of dialogue among religious and non-religious communities. Through the empirical mapping of current AI applications, interdisciplinary interpretation informed by social and ethical analysis, and normative theological evaluation, the study identifies both the opportunities and risks of AI-mediated IRD. On this basis, it synthesizes three interdependent dimensions that structure the proposed framework: (1) Ethics, which clarifies the moral purpose and values guiding AI use; (2) Technology, which addresses mediation, governance, and power in AI systems; and (3) Humans, which centers institutional responsibility, agency, and sustainability within FBOs. From this synthesis, the article introduces an AI–IRD Integration Framework that translates theological and ethical reflection into practical guidance for responsible AI adoption. The study contributes an original interdisciplinary perspective that equips religious leaders, theologians, policymakers, and faith communities to engage AI not merely as a tool, but as a human-centered partner in fostering inclusive, sustainable, and ethically grounded dialogue in an era of AI–human coexistence. Full article

Background/Objective: The quality of care afforded to rural, remote, and First Nations Peoples is dependent on access to a health workforce with the capacity to contextualize healthcare and practice to the needs and expectations of these populations. In Australia, the lack of representation of rural health in undergraduate and post graduate health professional education undermines this preparedness and consideration of rural practice uptake and longevity, compounding the inequities confronted by 7 million Australians residing in these locations. Urgent educational reforms are required to address this omission, the deficit discourses used to characterize rural healthcare, and the persistent health workforce shortages experienced. This paper presents the Anabranch Framework for the Ruralization of Health Professional Education, a high-level strategy to transform rural healthcare provision, professional practice, and health workforce outcomes. Methods: The framework was developed through an iterative process involving a series of systematic steps. The process included the following: individual and group critical dialogues with internal academic educators, external health service leaders, metropolitan academic allies, and leaders of other rural health academic departments; an internal review of empirical studies of relevance to the ruralization of health professional education and practice; the visualization of a place-based framework; the academic conceptualization of the framework; and further critical dialogues to test the framework’s face validity. Results: The Anabranch Framework comprises four inter-related rural domains: theories, pedagogies, practices, and connectivity; four constructs: knowledge acquisition and generation, immersion in rural curriculum, knowledge translation and sharing, and relational practice; and two structural elements: spiraled and scaffolded curriculum and duration and the quality of rural placement and practice. Conclusions: The Anabranch Framework is a high-level strategy to ruralize health professional worldviews, advance rural person-centered practice, enable a deeper understanding of rural places and the development of an equity-orientated, sustainable and rural-literate health workforce. Full article

Mountaintop towers are highly exposed to self-initiated upward lightning flashes. Accurate estimation of their effective height—the equivalent flat-ground height yielding the same lightning exposure—is essential for reliable exposure assessment, for interpreting and calibrating measurement data at instrumented mountaintop towers, and for comparison with established protection guidelines. This study applies a two-step numerical framework that couples finite-element electrostatic simulations with a leader-inception and propagation model for representative tower–terrain configurations reflecting reference instrumented mountaintop sites in lightning research. For each configuration, the stabilization field, the minimum background electric field enabling continuous upward leader propagation to the cloud base, is determined, from which effective heights are obtained. The simulated results agree with the analytical formulation of Zhou et al. (within ~10%), while simplified or empirical approaches by Shindo, Eriksson, and Pierce exhibit larger deviations, especially for broader mountains. A normalized analysis demonstrates that the tower-to-mountain slenderness ratio (h/a) governs the scaling of effective height, following a power-law dependence with exponent −0.17 (R² = 0.94). This compact relation enables direct estimation of effective height from geometric parameters alone, complementing detailed leader-inception modeling. The findings validate the proposed physics-based framework, quantify the geometric dependence of effective height for mountaintop towers, and provide a foundation for improving lightning-exposure assessments, measurement calibration and design standards for elevated structures. Full article

This work proposes an Enhanced Chimp Optimization Algorithm (EChOA) for solving continuous and constrained data science and engineering optimization problems. The EChOA integrates a self-adaptive DE/current-to-pbest/1 (with jDE-style parameter control) variation stage with the canonical four-leader ChOA guidance and augments the search with three lightweight modules: (i) L’evy flight refinement around the incumbent best, (ii) periodic elite opposition-based learning, and (iii) stagnation-aware partial restarts. The EChOA is compared with more than 35 optimizers on the CEC2022 single-objective suite (12 functions). The results shows that the EChOA attains state-of-the-art results at both $D=10$ and $D=20$. At $D=10$, it ranks first on all functions (average rank $1.00$; $12/12$ wins) with the lowest mean objective and the smallest dispersion relative to the strongest competitor (OMA). At $D=20$, the EChOA retains the best overall rank and achieves top scores on most functions, indicating stable scalability with problem dimension. Pairwise Wilcoxon signed-rank tests ($\alpha =0.05$) against the full competitor set corroborate statistical superiority on the majority of functions at both dimensions, aligning with the aggregate rank outcomes. Population size studies indicate that larger populations primarily enhance reliability and time to improvement while yielding similar terminal accuracy under a fixed iteration budget. Four constrained engineering case studies (including welded beam, helical spring, pressure vessel, and cantilever stepped beam) further confirm practical effectiveness, with consistently low cost/weight/volume and tight dispersion. Full article

This paper examines the intersection between Christian theological principles and contemporary cybersecurity challenges, with a focus on the specific vulnerabilities and responsibilities of faith-based organizations. Recognizing that digital threats emerge not only from technological weaknesses but also from human motives and ethical failings, this study introduces a Biblically Framed Cybersecurity (BFCy) Model that integrates scriptural ethics with established security practices. Through a narrative literature review and comparative analysis, the research synthesizes Christian concepts, such as stewardship, vigilance, and integrity, with technical standards (including the CIS Controls v8, NIST CSF 2.0, and ISO 27001:2022), mapping biblical narratives to contemporary risks like social engineering, insider threats, and identity theft. The findings underscore that robust cybersecurity requires more than technical solutions; it also demands a culture of moral accountability and spiritual awareness. Practical recommendations, including tables linking biblical values to operational controls, highlight actionable steps for church leaders and faith-based organizations. This study concludes that effective cybersecurity in these contexts is best achieved by aligning technical measures with enduring ethical and spiritual commitments, offering a model that may inform religious and broader organizational approaches to digital risk and resilience. Full article

Apple is an important fruit worldwide, but it is quite susceptible to various diseases. In particular, apple scab disease (Venturia Inaequalis) is a common fungal infection that causes serious yield losses in apple production. This disease causes spots on both leaves and fruits, negatively affecting product quality and marketability. Early diagnosis and management of apple diseases are critical to increase productivity in apple production. Traditional methods are usually time-consuming and costly; therefore, image processing and artificial intelligence technologies have become important tools in disease detection. In this study, a new approach is developed for the classification of healthy and scab apples by combining image processing, deep learning and optimization methods. First, the dataset is enriched using data augmentation techniques such as rotation, mirroring, zooming, shifting, brightness adjustment, and noise addition. Then, the images are analyzed with Shearlet Transform (ST), and frequency and spatial features are extracted in detail. The features obtained from the ST are reconstructed with the inverse transformation, and the original images are given as inputs to deep learning architectures, specifically AlexNet, VGG-16 and ResNet-18. In each model, deep features are extracted to classify healthy and scab apple images, and a feature pool is created by combining these features. The selection process of features that will increase performance in the classification process is carried out with the Red Deer Optimization (RDO) algorithm. This algorithm, inspired by the natural life cycle of male deer, includes the steps of determining the leader deer, creating a harem, mating and selecting the next generations. By selecting the best male leaders and optimizing the mating process, the algorithm ensures that the most effective feature combinations are chosen to enhance classification performance. As a result, this hybrid method presents an innovative approach to accurately classifying healthy and scab apple images, contributing to more efficient and reliable disease detection in apple production. Full article

Although our world produces more than enough food, hunger and malnutrition remain widespread. This is not simply a problem of production—it is about how we manage and organize our food systems. Many researchers and practitioners see food security either as a business challenge to be solved with technical and managerial tools, or as a social movement rooted in justice, sovereignty, and sustainability. These two schools of thought rarely meet, and their disconnect holds back real progress. This paper brings these perspectives together, through the introduction of the “Managerial Architecture of Food Justice”—a new way to think about food management that does not pick sides but aims to transform how food systems are governed. By blending the best of strategic management with values of equity, agency, and ecological stewardship, this framework shows how management practices can help build food systems that are not only resilient and efficient, but also fair and truly sustainable. Grounded in public and collaborative governance theory, this approach provides practical steps for policymakers and food system leaders who want to break the cycle of hunger and injustice. The proposed model supports the Sustainable Development Goals by making sustainability defining features of food security efforts. Full article