Search Results (267)

Despite its significance, ulwaluko has continuously faced challenges, including, inter alia, fatalities, botched circumcisions, and psychosocial distress, which have often been linked to inadequate support. The enactment of the Customary Initiation Act No. 2 of 2021 represented a legislative effort to regulate, enhance safety, and create space for professional interventions, while explicitly excluding social workers. This qualitative study used focus group discussions with traditional leaders, medical practitioners, parents, recent initiates, and COGTA representatives in OR Tambo, Amathole, and Alfred Nzo districts of the Eastern Cape, South Africa. Six themes emerged: stakeholder roles and responsibilities; preparation and permission processes; psychological and mental health assessment; gender considerations in social work involvement; challenges, fatalities, and government interventions; and cultural competence and collaboration. The findings revealed support for the Act but identified limitations in its emphasis on medical assessments without corresponding psychosocial assessment. The study demonstrates the importance of integrating social workers to provide holistic, culturally sensitive psychosocial support within ulwaluko. Full article

Multi-objective optimization plays an important role in modern design and complex engineering applications. However, achieving an effective balance between the convergence and diversity of Pareto-optimal solutions remains challenging. This paper proposes a Sobol-driven Multi-objective Whale Migration Algorithm (SMOWMA), which extends the Whale Migration Algorithm within a non-dominated sorting and elite-selection framework. A maximin scrambled Sobol initialization scheme is first employed to improve the distribution of the initial population. An archive-guided adaptive Student-t flight mechanism is then incorporated into the leader-whale position update to dynamically balance global exploration and local exploitation. In addition, archive crowding information and archive-entry success feedback are jointly used to adjust the search behavior according to both environmental diversity and recent search performance. SMOWMA is evaluated on five widely used multi-objective benchmark suites, namely ZDT, DTLZ, WFG, UF, and CF, using four performance indicators: generational distance (GD), inverted generational distance (IGD), spacing (SP), and hypervolume (HV). The results, together with Friedman tests and Holm-adjusted Wilcoxon tests, demonstrate that SMOWMA achieves competitive overall performance in terms of convergence, diversity, and objective-space coverage, although its relative advantage remains problem-dependent. The practical applicability of SMOWMA is further examined using multi-objective welded-beam design formulations, a bi-objective four-bar truss design problem, and a five-objective car side-impact design problem. The engineering results show that SMOWMA can obtain competitive and stable approximation sets for constrained design problems with different numbers of objectives, supporting its effectiveness and applicability in multi-objective engineering optimization. Full article

In long-range sequential orbital pursuit-evasion game scenarios, traditional Nash equilibrium algorithms rely on symmetric simultaneous decision-making assumptions. Such algorithms fail to fully exploit the sequential advantages of the leader-follower paradigm in practical space missions, and often yield conservative strategies with excessive fuel consumption. This paper proposes a Stackelberg equilibrium-based control algorithm for sequential pursuit-evasion games involving satellites with impulsive orbital maneuvers. A multi-stage impulsive maneuver game model incorporating orbit determination delays is established, and a bi-level nested optimization architecture is designed. The outer layer employs the Pattern Search algorithm to derive the optimal maneuver strategy of the pursuer, while the inner layer uses the Sequential Quadratic Programming (SQP) algorithm to obtain the optimal response strategy of the evader. Simulation results demonstrate that, compared with the traditional Nash-equilibrium Action-Reaction Search (ARS) algorithm and the greedy algorithm neglecting evader maneuvers, the proposed algorithm achieves a shorter terminal relative distance with lower fuel consumption. Full article

Cooperative adaptive cruise control (CACC) platoons rely on Vehicle-to-Vehicle communication and GPS to maintain sub-second headways, creating cyberattack surfaces underrepresented in standard surrogate-safety metrics. We built a fully equation-based, Routh–Hurwitz- and Lp-string-stability-verified simulation of a ten-follower (eleven-vehicle, including the leader) CACC platoon (point-mass dynamics, actuator lag, PD spacing control) and subjected it to a two-channel GPS-spoofing attack corrupting both the attacked vehicle’s control loop and its broadcast position; velocity and acceleration broadcasts, and the CACC feed-forward term they drive, are left uncorrupted, so the reported boundaries are conditional on this restricted, single-channel threat model and should be read as a lower bound on attack severity rather than a worst case. Across a 64-cell severity–duration grid (2–20 m, 1–10 s; h = 0.6 s), minimum time-to-collision fell from 31.7 s to a simulated collision in 6/64 cells (9.4%), driven more by magnitude than duration; the disturbance decays sharply after the first hop rather than cascading down the platoon, so the resulting risk is local, not cascading. A 48-cell headway grid showed h ≥ 0.7 s eliminated all collisions at the originally tested attack duration (3/8 → 0/8 at fixed severity), a result that held under two alternative controller-gain sets tested for sensitivity and was largely, though not universally, robust to a substantially stiffer third set. A position sweep found risk invariant across nine of ten platoon positions. Batch-computed first-hop propagation and tail-to-origin amplification ratios showed the disturbance transiently amplifies (ratio > 1) at its first hop in a third of tested attacks despite decaying three orders of magnitude by the platoon’s tail, a behavior distinct from the front-injected Lp string stability verified separately. Peak root-mean-squared jerk stayed within the comfortable range (≤1 m/s³) in every tested cell, including collisions, showing collision and comfort risk are governed by different parameters. Embedding a representative detection and elastic-control layer alongside headway optimization eliminated collisions within the tested range and remained robust at three times that severity, where headway alone failed; because the detector’s residual is computed directly from the true offset magnitude and detector failure is not modeled, this joint-defense result is illustrative rather than a validated-detector-calibrated estimate. These results give a reproducible, quantified basis for headway- and detection-based mitigation policy in connected-vehicle platoons. Full article

This study investigates the fixed-time quasi-consensus and quasi-containment control for multi-agent systems (MASs) under non-periodic unknown denial-of-service (DoS) attacks. Most available strategies fail to construct fixed-time observers and feasible corresponding parameter tuning rules to guarantee the precise fixed-time convergence of observer states to the convex hull trajectory spanned with multiple leaders under arbitrary non-periodic DoS interference. Moreover, most existing relevant fixed-time cooperative control methods for MASs commonly impose restrictive assumptions on system input matrices, requiring the matrix to be square and invertible, which severely limits their practical applicability. To overcome these limitations, the observers and corresponding parameter selection conditions are designed in this study, which can ensure that the observer states converge to the target trajectory formed by the leaders within a fixed time under non-periodic unknown DoS attacks. Then, based on the linear transformation of the state space and the theory of sliding mode control, a novel observer-based controller is proposed to solve the fixed-time quasi-consensus and quasi-containment control problems. The proposed approach remains effective even when, under mild conditions, the input matrix is non-square or non-invertible—a challenge that many existing methods cannot address. Finally, numerical simulations demonstrate that the proposed control strategy enables MASs with non-square input matrices suffering from unknown non-periodic DoS attacks to achieve fixed-time quasi-consensus and quasi-containment under mild conditions. Full article

Objective: Women in recovery from opioid use disorder (OUD) experience sexual and reproductive health disparities which worsen health outcomes and may negatively impact recovery. While in recovery programs, women engage with different healthcare professionals, but how these professionals provide sexual and reproductive healthcare (SRH) is not well known. The purpose of this study is to identify perceived barriers and strategies to overcome barriers to providing SRH to women in recovery from OUD from the perspective of OUD recovery healthcare professionals. Methods: Guided by the social ecological framework, we used a qualitative descriptive study design to conduct individual semi-structured interviews with healthcare professionals (nurses, doctors, leaders/managers, peer support specialists, therapists, and case managers). Data was analyzed using thematic analysis. Results: Among individual interviews with N = 34 healthcare professionals, we identified two overarching themes: Barriers and Facilitators. Barriers included unmet provider training needs, navigating patient stigma and shame surrounding SRH, and fragmented healthcare systems. Facilitators included leveraging communities of shared experiences and developing ideal models of SRH programming. This included co-location of SRH and recovery spaces and the importance of peer support specialists in women’s recovery journeys. Conclusions: Multi-level challenges to address SRH in recovery require discipline specific training of healthcare professionals, empowering peer professionals, and policy and advocacy to address systemic issues. Full article

To address the challenges faced by UAV swarms in local narrow-space penetration missions, including constrained passages, dense obstacles, and the difficulty of balancing formation stability and traversability, this paper proposes a local penetration method that integrates virtual–center consensus-based formation control with a V-shaped formation self-reconfiguration strategy. First, a virtual geometric center is introduced as the consensus reference to replace the traditional physical leader node, thereby reducing the risk of single-point failure and improving swarm coordination consistency. Second, geometric constraints, including the effective channel width, lateral formation width, and safety margin, are incorporated to construct a channel-constraint-driven formation self-reconfiguration mechanism, enabling the swarm to contract its formation, avoid obstacles during traversal, and recover the formation after passing through the constrained region. Finally, a digital twin-based virtual–real interactive validation platform is constructed to verify the formation maintenance, formation reconfiguration, and virtual–real trajectory consistency of the proposed method. Experimental results show that the proposed method can maintain favorable formation consistency and safe inter-UAV distances in constrained channel environments while demonstrating good stability and scenario adaptability during formation adjustment and recovery. Full article

The optimal design of a three-element dynamic vibration absorber (TEDVA) involves a fundamental trade-off between minimizing the peak amplitude magnification ($H_{\infty }$ norm) and the broadband energy absorption ($H_2$ proxy), a conflict that is further complicated by the lack of closed-form solutions, even for undamped primary systems. In this paper, we present an algorithm that extends the golden jackal optimizer with dynamic multi-map chaotic initialization, a Pareto-guided two-leader search structure driven by crowding distance, and a Pareto-gated self-adaptive differential evolution mutation to jointly ensure convergence and diversity. The algorithm is validated on the benchmark TEDVA case with mass ratio $\mu = 0.1$ and primary damping ${\zeta }_1 = 0.3$, and benchmarked against standard multi-objective algorithms (NSGA-II and MOPSO) as well as the single-objective AM-PSO baseline. Simulation results indicate that MODCGJO achieves a 7.3% reduction in peak amplitude compared to the state-of-the-art single-objective adaptive multi-swarm particle swarm optimization (AM-PSO), while maintaining a competitive $H_2$ performance and converging to the same Pareto-optimal region as NSGA-II and MOPSO. Comprehensive Pareto metrics—hypervolume, generational distance, spread, and spacing—are adopted, validating the front’s superior quality and uniform distribution. Sensitivity analyses on both physical design parameters (spring and damping ratios) and algorithmic control parameters (population size, iteration count, and archive size) confirm the robustness of the obtained solution and the stability of MODCGJO’s performance across varying configurations. The results show that MODCGJO is an effective and reliable tool for the multi-objective design of vibration absorbers, providing a superior trade-off between conflicting performance criteria, with the Pareto front offering engineers flexible design choices for different application requirements. Full article

Background: Artificial intelligence is increasingly influencing health care and policy, yet the global research landscape linking artificial intelligence and health care policy remains underexplored. This study aimed to map publication trends, major contributors, collaboration networks, citation structures, and emerging themes in this field. Methods: A bibliometric analysis was conducted using the Web of Science Core Collection. The search was performed on 3 May 2026, and covered publications from 2000 to 3 May 2026. The final dataset included 347 peer-reviewed English-language original research and review articles. Biblioshiny, VOSviewer, and CiteSpace were used to analyze publication trends, country and institutional contributions, author and journal productivity, collaboration networks, citation and co-citation structures, keyword patterns, and thematic evolution. Results: Publications increased markedly after 2020 and reached their highest annual output in 2025. The 2026 publication count was lower because data for that year were partial at the time of database retrieval. Researchers from 82 countries and 900 institutions contributed to the field, with the United States leading in output, followed by China, England, Canada, and India. Harvard Medical School was the most productive institution, whereas Harvard University had the highest institutional centrality. Frontiers in Public Health published the most articles, and PLOS ONE was the most frequently co-cited journal. The most cited article was “Artificial intelligence and the future of global health.” Key research themes included machine learning, COVID-19, health policy, risk, large language models, interpretable machine learning, neural network-assisted screening, socioeconomic perspectives, and public health applications. Conclusions: Research on artificial intelligence and health care policy has expanded rapidly, particularly in recent years, and is increasingly centered on predictive modeling, public health decision-making, and emerging artificial intelligence technologies. These findings highlight influential contributors, evolving themes, and future directions for researchers, policymakers, and health care leaders. Full article

Battery management systems (BMSs) in electric vehicles (EVs) are instrumented with an increasing number of heterogeneous sensors, many of which contribute redundant or noisy measurements that increase computational cost without improving diagnostic accuracy. This paper proposes a Binary Hybrid Particle Whale Optimization Algorithm (BHPWOA) for multi-objective feature selection targeting three-class BMS fault diagnosis: OK, Warning, and Critical. The method is evaluated using an 18-feature EV charging dataset with $n=500$ samples. BHPWOA encodes candidate feature subsets as binary masks in a continuous $\left[\mathrm{0,1}\right]$ position space. It executes a Binary Particle Swarm Optimization (BPSO) phase during the first 50 iterations to rapidly identify a promising subset region, then transfers the global-best mask as the Whale Optimization Algorithm (WOA) leader for the remaining 50 iterations of bubble-net exploitation. A multi-objective fitness function simultaneously penalises classifier error and subset size, directly optimising the accuracy–cost trade-off. BHPWOA selects four features out of 18, corresponding to a $77.8\mathrm{\%}$ reduction, and achieves accuracy $=0.710$ and macro-F1 $=0.4455$ on the held-out test set. It outperforms all-feature KNN $\left(F_1=0.2997\right)$, standalone BPSO with six selected features $\left(F_1=0.4603\right)$, BWOA with two selected features $\left(F_1=0.4026\right)$, and BSFSA with five selected features $\left(F_1=0.4216\right)$ on the Pareto-dominant combined fitness objective. The selected subset $\left\{{\mathrm{C}\mathrm{e}\mathrm{l}\mathrm{l}\mathrm{V}\mathrm{o}\mathrm{l}\mathrm{t}\mathrm{a}\mathrm{g}\mathrm{e}}_V,{\mathrm{C}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{C}\mathrm{u}\mathrm{r}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{t}}_A,{\mathrm{S}\mathrm{O}\mathrm{C}}_{\mathrm{\%}},{\mathrm{C}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e}\mathrm{P}\mathrm{o}\mathrm{w}\mathrm{e}\mathrm{r}}_{kW}\right\}$ achieves the best fitness score of $-0.5555$, enabling a $77.8\mathrm{\%}$ sensor-cost reduction while improving fault detection. Stability analysis across five independent random seeds confirms a mean feature count of $4.0\pm 0.7$ and a mean macro-F1 of $0.441\pm 0.021$, demonstrating algorithmic robustness. Full article

This article situates school leadership in the Canadian Prairies within a socio-spatial context and ongoing impact of coloniality. It draws on Black geographies to position Black school leaders as occupying diasporic spaces in white settler contexts of the Prairies, exploring how spatial relations are constituted in contemporary social arrangements characterized by longstanding institutional oppression. This spatial gaze exposed four themes for deeper analysis, namely Black school leadership as spatially situated power; the production of “visible difference” in leadership space; navigating whiteness as spatial norm; and loneliness, spatial isolation, and the cost of being “the only one”. The article demonstrates how Black school leadership in the Canadian Prairies cannot be relegated to simply an individual experience nor made reducible to interpersonal discrimination but rather is constituted through historically produced spatial arrangements that regulate belonging, authority, and legitimacy. Full article

High-altitude, low-pressure environments significantly reduce the insulation strength of air gaps, posing severe risks to live-line working on Ultra High Voltage and Extra High Voltage (UHV/EHV) transmission lines. To address this challenge and ensure operational safety, this paper proposes a predictive gap discharge voltage calculation model based on the dynamic coupling of time-varying electric fields and space charge. Unlike existing approaches that rely on static, geometry-dependent empirical corrections, the proposed model achieves high predictive capability by intrinsically mapping air relative density and absolute humidity to dynamically modify key microscopic discharge parameters, including the effective ionization coefficient, attachment coefficient, and streamer internal electric field strength. This physical framework enables the successful simulation of the complete progression from streamer inception to leader development and final breakdown, thereby calculating the 50% breakdown voltage under varying altitudes and gap distances. To rigorously validate the proposed model, breakdown tests were conducted using simplified sphere–plane gaps and full-scale simulated gaps between a human worker and a tower window at altitudes of 23 m and 2100 m. Additionally, third-party experimental datasets were utilized for comprehensive comparative analysis. The results demonstrate that the model’s predictive values align excellently with multi-source experimental data, establishing its high accuracy and practical engineering value for complex electrode configurations under diverse high-altitude conditions. Full article

This paper proposes an integrated formation control framework with a finite-time nonlinear disturbance observer (FT-NDO) for automated guided vehicles (AGVs) operating in port environments, where constrained workspace, narrow formation spacing, and complex external disturbances pose significant challenges. An adaptive leader–follower formation strategy with dynamic inter-vehicle spacing is developed to enhance maneuverability during turning. Within a hierarchical control structure that decouples lateral and longitudinal dynamics, two sliding mode controllers (SMCs) are designed: (a) a lateral SMC that prioritizes heading accuracy, limiting yaw angle error to within ±2°; and (b) a nonsingular terminal SMC (NTSMC) for longitudinal control, improving error convergence speed compared to conventional SMC. An FT-NDO is further incorporated into both control loops to estimate and compensate for external disturbances in real time, achieving a disturbance estimation accuracy of over 95% and significantly attenuating the impact of environmental disturbances. Validation through simulation and physical experiment of a dual-AGV formation in a realistic port scenario demonstrates that the proposed approach restricts formation deviation to 0.015 m and maintains stable operation under various disturbance conditions. This study provides a practical solution for dual-AGV collaborative transportation in spatially constrained and dynamically disturbed environments, with direct implications for improving operational efficiency and safety in port logistics. Full article

Information disorders condition electoral processes, becoming a major institutional concern. In response, the European Union and various fact-checking organisations co-organised the Elections24Check project to curb disinformation in the 2024 European elections. This research analyses the activities, strategies, and editorial behaviour of the five Spanish fact-checking agencies that are integrated into the initiative. Through a content analysis applied to 3256 publications, the findings demonstrate the maturity of the Spanish ecosystem, which led the project by contributing 32.8% of the total content. Strategically, reactive action predominated, except for Newtral, which prioritised prebunking (62.6%). Political scrutiny was minor (6.6%), focusing on major coalitions and far-right leaders. Thematically, highlights included war conflicts, migration, and national/regional frameworks utilised for emotional polarisation, displacing the focus from the strictly European debate. In conclusion, Spain consolidates itself as a cornerstone of European fact-checking. However, the results reveal inefficiencies in the project’s extended timeframe, suggesting more constrained and effective frameworks for election campaigns. Furthermore, the persistence of narratives anchored to local agendas evidences a strategic fragmentation that hinders the construction of a fully pan-European public space. Full article

Ninhydrins represent a promising chemical space for the search for new biologically active molecules with antimicrobial, antiprotease, and antitumor properties. In the present work, new ninhydrin derivatives were synthesized, and for the first time, a systematic in silico study of ninhydrins as multitarget ligands for five pharmacologically significant targets (HER1/HER4, HER2/HER3, Trk-B, PPAR-α, and LTβR) was conducted, whose amplification or overexpression plays a key role in the pathogenesis and progression of certain aggressive cancer types. Among the studied ninhydrin derivatives, compound 1 (2,2-dihydroxy-5,6-dimethoxy-1H-indene-1,3(2H)-dione) stands out as the most potentially active molecule. It exhibits high affinity for HER1/HER4, Trk-B, and PPAR-α, opening up potential applications in oncology (HER family and Trk-B inhibition during BDNF overexpression), neurodegenerative diseases (Trk-B modulation), and metabolic disorders (PPAR-α activation). Compound 4 (2,2-Dihydroxy-5-trifluoromethylindane-1,3-dione) is a leader in LTβR binding and also holds promise for immuno-oncology and anti-inflammatory strategies. Full article