When patients cannot be reached yet risk exists, GP trainees must make proportionate safety decisions without the stabilising anchor of a live consultation. This 'unreachable patient' problem is common in results handling, radiology follow-up, medication monitoring, and safeguarding, but is rarely taught explicitly as a clinical skill. The Unreachable Patient Algorithm (UPA) is a structured educational intervention that trains trainees to treat reachability as a variable in clinical reasoning. It combines rapid risk stratification, a graded action ladder, and a documentation standard that supports confidentiality, accountability, and loop closure. Supervisors introduce a single trigger (for example, a critical laboratory alert, abnormal imaging report, or safeguarding concern) and ask trainees to state the risk tier, the minimum safe action, the escalation threshold, and the fallback plan if contact remains unsuccessful. Teaching occurs through inbox simulation, case-based tutorials, and supervised 'pause and plan' moments in real clinics. A one-page note template and brief portfolio prompts reinforce consistency across placements. Early reflections suggest trainees escalate more appropriately, write clearer contingency plans, and rely less on vague statements such as 'tried to call'. UPA is low-cost, fits routine GP training, and offers a replicable method to reduce avoidable harm when contact fails in practice.
Brachial plexus birth injury (BPBI) results in individualized impairments in upper extremity (UE) mobility. A patient-specific understanding of these movement limitations is critical for optimizing decision-making and outcomes; however, current assessments may fail to capture the entirety of a patient's UE mobility. Reachable workspace quantifies an individual's global UE mobility by measuring the regions that can be reached by their hand. Despite most UE activities of daily living requiring adequate close-to-body function, current workspace approaches only assess far-from-body mobility. This study assessed the ability of a motion capture-based workspace approach to evaluate inner, close-to-body UE mobility in children with BPBI. It was hypothesized that the BPBI affected limb would have less inner, close-to-body workspace than the unaffected limb, especially in regions requiring UE movements commonly impaired in BPBI. Fifteen children with unilateral BPBI were assessed with motion capture using real-time visual feedback to measure UE workspace in all regions surrounding the body. All inner, close-to-body points reached by the hand were recorded. A two-way repeated measures ANOVA evaluated percentage workspace reached in each region surrounding the head, thorax, and abdomen. The affected limb had significantly less workspace reached than the unaffected limb for 8 of 9 regions (mean interlimb differences by region, 17.0-49.2%). Affected limb workspace deficits corresponded to common movement impairments in BPBI demonstrating the clinical relevance of this tool. Assessment of inner, close-to-body reachable workspace may provide a valuable new perspective on UE mobility to help guide clinical decision-making and outcomes assessment.
This study validates a clinically accessible approach for quantifying the Upper Extremity Reachable Workspace (UERW) using monocular AI-driven Markerless Motion Capture (MMC). Objective validation of such techniques for clinically oriented tasks is essential to support their adoption in clinical motion analysis. Nine adults without impairments performed the standardized UERW task, reaching targets distributed across a virtual sphere centered on the torso and displayed via VR headset. Movements were simultaneously captured with a marker-based system and eight FLIR cameras; monocular analysis was applied to two videos representing frontal and offset camera configurations. Agreement was assessed by comparing the percentage workspacereached across six of eight workspace octants between the systems. The frontal camera demonstrated strong agreement with the marker-based reference (mean bias: 0.61±0.12% reachspace per octant), whereas the offset view underestimated workspace reached -5.66±0.45%. Depth-related errors in the frontal configuration were confined to posterior octants, whereas the offset view introduced inaccuracies in both contralateral and posterior octants. These findings support the feasibility of a frontal monocular camera for UERW assessment, particularly for anterior workspace evaluation. While posterior accuracy remains limited by depth estimation and anatomical occlusion errors, the overall results demonstrate clinical potential for practical, monocular-camera assessments.
This paper formulates the "Minimum Vertex Cut with Reachable Set" (MVCRS) problem as an optimization framework to suppress botnet propagation in networked systems, and clarifies its computational complexity and algorithmic solutions. Building a firewall to minimize damage is essential for addressing botnet propagation in Internet of Things (IoT) networks. We define the basic MVCRS problem as minimizing the sum of the weight of the deployed resources and the resulting propagation scope. While we demonstrate that the constrained version of the problem is NP-complete, we show that the fundamental trade-off optimization model can be solved in polynomial time by reducing it to the maximum flow-minimum cut problem. This provides a theoretical baseline for optimal resource allocation in cybersecurity. Experimental evaluations reveal the limitations of conventional heuristics. In community-structured networks, the degree-based greedy algorithm overlooks critical bridge nodes, yielding an optimality gap of up to 72.6% above the theoretical minimum cost. Conversely, our exact algorithm consistently guarantees the optimal minimum cost (a 0% gap) with high statistical stability across diverse topologies. Furthermore, it scales efficiently to solve 100,000-node IoT networks within practical time limits, proving to be a reliable and efficient foundation for botnet suppression in complex real-world systems.
Community health volunteers (CHVs) are an important resource for supporting health service delivery, surveillance, and social programmes. However, retention and attrition of CHVs remain a big challenge. This study explored factors affecting the retention and attrition of CHVs working as village reporters (VRs) responsible for community-based death notification in the Malaria Vaccine Implementation Program (MVIP) in Malawi. This mixed-methods exploratory study, which intersected with the case studies, was conducted from November 2022 to March 2023 in nine rural districts in southern and central Malawi. Purposive sampling was used to select 64 study participants for qualitative interviews. Using case studies, we conducted six in-depth interviews (IDIs) with CHVs who had dropped out, were reachable, and agreed to be interviewed-many were dispersed, hesitant to attend meetings, or unreachable for focus group discussions (FGDs). We held five FGDs (n = 50) with CHVs who remained in the MVIP for shared norms and experiences and eight key informant interviews (KIIs) with health workers, opinion leaders, and program staff to provide insights into health workers motivation, supervisory and program perspectives. Thematic analysis and the social capital framework (roles, relationships, and empowerment) were used to analyse and interpret the qualitative data. The qualitative study was complemented by a cross-sectional survey involving 696 randomly selected participants from a pool of 2,861 CVHs to demonstrate the trends of retention over time. Descriptive statistics were computed from quantitative survey data, with retention as a primary outcome (defined as whether a CHVs was willing to stay in the program). At the start of the program in 2019, a total of 2,861 CHVs were recruited by March 2023; only 295 (10.3%) had dropped out. Among 696 CHVs surveyed, the most commonly reported factors associated with retention were incentives (643; 92%), participation in exchange visits (377; 54%), and managing a small geographical area (275; 40%). Qualitative data from FGDs and KIIs corroborated these findings and identified compassion for serving others, financial and non‑financial incentives, and flexibility to work across multiple programmes as key motivators for continued participation. Factors associated with attrition included experiences of ridicule or disrespect, lack of opportunities for personal development, and limited career progression. IDIs with CHVs who left the programme provided in‑depth accounts of these individual‑level drivers, which helped explain patterns observed in the survey. Engagement of CHVs in community-based programs can be promoted by offering opportunities to serve others, incentives, and flexibility to work on multiple programmes. However, it is also important to address ridicule-making fun or rude comments and limited personal and career development, which act as barriers to the continued engagement of lay health workers.
Imprecise probability models generated from data represent epistemic uncertainty by replacing the precise empirical distribution with a set of compatible probability distributions. When this set is described by reachable probability intervals, the induced bounds are tight, so the represented imprecision is not inflated by unattainable interval limits. This paper studies the informational effect of this replacement through the epistemic entropy gap, defined as the difference between the maximum entropy over the induced credal set and the Shannon entropy of the empirical distribution. The gap is a differential quantity: it measures the additional uncertainty introduced by the imprecise model beyond the observed frequencies. We analyze it for three reachable interval models generated from multinomial data: the Imprecise Dirichlet Model, the ϵ-contamination model and the approximated Non-Parametric Predictive Inference model. The analysis covers its main properties, its asymptotic behavior and its role in entropy equivalent calibration of model parameters. The results show that the entropy gap offers a common informational scale for comparing how different imprecise models represent the same empirical evidence, and helps interpret the degree of caution associated with limited data reliability and with empirical distributions that may otherwise lead to overconfident uncertainty assessments.
Generative artificial intelligence (GenAI) is rapidly entering consumer health information environments, yet patient readiness for safe adoption in cancer survivorship remains unclear. This study assessed preparedness for GenAI adoption among Chinese cancer survivors and identified correlates relevant to equitable implementation. We conducted a multi-center, cross-sectional online survey among digitally reachable adult cancer survivors recruited via clinical encounters, WeChat patient groups, and peer referral across three oncology centers in Sichuan, China. The questionnaire was administered on Wenjuanxing. The primary outcome was a theory-informed, study-specific 0-100 GenAI adoption preparedness composite derived from five Likert items: perceived usefulness, perceived ease of use, access to guidance/support, privacy concern after reverse coding, and near-term intention. Secondary outcomes included GenAI awareness and prior use, willingness for report explanation and symptom advice scenarios, and health information ability. Multivariable linear regression with robust standard errors estimated adjusted associations with preparedness, with sensitivity analyses addressing data quality flags and recruitment pathway. From 1,062 survey visits, 876 participants comprised the analytic sample. Mean preparedness was 57.8 (SD 24.2) with acceptable internal consistency (Cronbach's alpha 0.75). Awareness of GenAI was 61.6 and 40.6% reported prior use. Near-term intention to try GenAI for survivorship information tasks was endorsed by 51.3%. Willingness was higher for test report explanation (56.1% agree/strongly agree) than for symptom advice with referral prompts (40.3%). Preparedness was lower among participants older than 60 years versus 18-45 years (beta -8.1, 95% CI -12.0 to -4.3) and higher with prior generative AI use (beta 9.3, 95% CI 5.7-12.9), higher self-rated generative AI knowledge (beta 5.4 per 1-point, 95% CI 3.7-7.0), and greater health information ability (beta 2.0 per 10 points, 95% CI 1.2-2.7). The model explained 36% of variance (R2 0.36). Among digitally reachable cancer survivors in Sichuan, preparedness for GenAI adoption was moderate and strongly use-case dependent, with lower readiness among older survivors. The online-only sampling strategy means that the observed preparedness level may overestimate readiness in the broader survivorship population. Implementation should begin with lower-risk applications, such as report explanation and question preparation, paired with guidance on verification, privacy protection, and clear escalation to clinicians.
The epidemic potential of infectious diseases depends on how contacts connect individuals over time-a form of temporal connectivity that has rarely been quantified in resource-poor settings. We used the forward-reachable path (FRP)-the proportion of population reachable from an index person via direct or indirect connections-to quantify temporal connectivity in contact networks relevant for acute respiratory transmission. From empirical social-contact data collected in rural and urban Tamil Nadu, India, we derived contact-location-specific network statistics. These statistics were used to parameterize dynamic network models, simulate daily networks over one year, and compute FRPs. In both rural and urban networks, mean FRPs rose sharply on day 1, then either increased steadily at school and work or plateaued at home and at locations included in the other layer (that is, locations other than home, school, and work). By day 365, mean FRPs followed the order: home (0.06% [rural] and 0.03% [urban]) < school (11.96% [rural] and 9.14% [urban]) < work (12.55% [rural] and 26.72% [urban]) < other (40.54% [rural] and 67.99% [urban]). The mean FRP peaked at home among those aged ≥60 years, at school among those aged 10-19 years, and at work among those aged 40-59 years. Although FRP at home was bounded by household size, reachability expanded substantially through school, work, and other contacts. These findings indicate high temporal connectivity and substantial epidemic potential for acute respiratory transmission in these settings.
In deep scattering tissue, vascular geometry strongly modulates the optical fluence field, thereby complicating quantitative photoacoustic imaging and the assessment of multi-view illumination. This study establishes a geometry-consistent basis for comparing multi-view photoacoustic illumination across complex vascular geometries under a unified fluence-side criterion. Illumination directions are drawn from a fixed candidate view pool and accumulated as a stable prefix, while the unified reachability threshold is calibrated once from a baseline reference view and then applied to all geometries and all view counts. This protocol enables direct comparison of union-reachability coverage Ω reach , fluence nonuniformity V G , low-fluence-tail response, and view-response redundancy. Monte Carlo simulations on five aneurysm-like vascular geometries show that Ω reach increases rapidly at small view numbers and then approaches geometry-dependent saturation levels of approximately 0.514 - 0.553, a behavior preserved under moderate optical-transport perturbations and controlled variations in thresholding, photon number, view ordering, and view selection. Matched-coverage analysis further shows that comparable reachability does not imply comparable homogenization, because V G and the local response coefficient ηG * remain geometry dependent within the same coverage window. Spectral analysis of the view-response matrix reveals geometry-constrained effective low dimensionality, with terminal Neff values of approximately 4.2 - 7.4, far below the nominal number of accumulated views. These results identify reachable-domain expansion, fluence homogenization, and response redundancy as coupled but non-equivalent aspects of multi-view photoacoustic illumination, providing physically interpretable criteria for assessing view accumulation in complex vascular geometries.
Empirical investigation requires dealing with fundamental uncertainty. In experimental psychology, research questions are often addressed using Null Hypothesis Significance Testing (NHST), an approach rooted in the frequentist statistical tradition. In scenarios that do not consent to reject the null hypothesis using the NHST paradigm (i.e., results are non-significant), researchers may be tempted to reframe their analysis in the Bayesian framework, either as a complementary alternative or alongside the original NHST approach. In fact, the Bayesian approach is gaining increasing appeal in the social sciences as an alternative to the frequentist NHST framework, and Bayesian methods for hypothesis testing (i.e., the Bayes Factor) can be used to help determine whether a failure to reject the null hypothesis reflects merely insufficient evidence for the alternative hypothesis or provides affirmative evidence for the (point) null hypothesis. Nevertheless, using the two approaches interchangeably carries the risk of conceptual confusion, as NHST and Bayesian frameworks address different inferential questions. This study provides an empirical, real-world opportunity to examine how NHST and Bayesian methods can be applied to the same hypothesis test when using Generalized Linear Mixed Models with a Binary Outcome. Importantly, this application incorporates common experimental constraints into the design-analysis planning, defining a reachable, realistic albeit underpowered sample size, assuming the classical 0.80 power threshold. This research report provides a valuable opportunity to examine how Bayesian and NHST approaches potentially differ in their workflow, performance, and inferential interpretation under realistic experimental conditions.
This article presents time-dependent outputfeedback and state-feedback sampled-data control strategies for achieving both state and output reachability in permanent magnet synchronous generator-based wind turbine systems using a fuzzy approach. First, a nonlinear wind turbine model is represented as a set of fuzzy linear subsystems subject to bounded disturbances and parametric uncertainty. Unlike conventional sampled-data control schemes, a unified samplingtime- dependent fuzzy control framework is developed for both state-feedback and output-feedback cases. The framework varies across sampling periods and incorporates Bernoulli random packet dropouts, thereby forming a closed-loop system. Next, the fundamental Lyapunov component is modified by incorporating aperiodic sampling with various weighting levels. A samplingvariable- dependent discontinuous Lyapunov-Krasovskii functional, combined with a fuzzy membership function-dependent $\mathcal {H}\_\infty$ technique, is employed to derive sufficient reachability conditions. Finally, the simulation results, including comparative studies with existing approaches, demonstrate the applicability of the proposed control strategies and confirm improvements in terms of allowable maximum sampling period, reduced H$\mathcal {H}\_\infty$ performance bounds, tighter reachable-set ellipsoids, and fewer decision variables.
The design of active pneumatic upper limb exoskeletons is complicated by the challenge of reliably determining a kinematically safe workspace. Existing analytical kinematic methods are not sufficient to predict geometric collisions between elements of closed kinematic chains, which poses risks of mechanical damage and threats to user safety during exoskeleton operation. This paper proposes a hybrid algorithm for verifying the workspace of a pneumatic exoskeleton, combining analytical modelling in MATLAB R2020b based on the Product of Exponentials (PoE) method with high-performance static simulation in the Unity environment. At the initial stage, a discrete set comprising 758 million positions of the upper exoskeleton manipulator was generated. Subsequently, a multithreaded two-stage filtering process was implemented: analytical verification of rod stroke limits and angular constraints, followed by the detection of physical intersections of solid-state meshes using the PhysX engine. The results indicate that while the analytical model filters out 99.6% of invalid configurations. Yet, among the remaining positions-formally correct from a mathematical standpoint-up to 50% lead to critical geometric collisions or breaks in the kinematic chain. The computational efficiency of the proposed architecture enabled full static workspace verification in under 20 min. A reachable zone topology was established, revealing pronounced asymmetry and the presence of a "manoeuvrability core" in the user's anterior hemisphere. The developed algorithm generates a verified set of kinematically safe exoskeleton states, providing a foundation for the kinematic safety layer of a hierarchical control system. These findings demonstrate the necessity of complementing analytical kinematics with physical collision detection when designing hybrid kinematic mechanisms, and the approach can be applied to verify collision-free movement trajectories in various robotic systems. The approach can be applied to verify collision-free movement trajectories in simulation, with physical validation deferred to future work.
Background: Extracorporeal cardiopulmonary resuscitation (ECPR) can improve outcomes following refractory out-of-hospital cardiac arrest (OHCA); however, access is constrained by geography and resources. This study compared two strategies against the current system in Nara Prefecture, Japan: a two-stage hospital model using chest-pain network hospitals as ECPR-initiation sites, and a prehospital ECPR model using physician-staffed ambulances from two extracorporeal membrane oxygenation (ECMO)-ready hospitals. Methods: A geographic information system (GIS)-based simulation was conducted using emergency medical service (EMS) records of witnessed cardiac-origin OHCA cases (2017-2022). Isochrone analyses estimated areas reachable within a 60 min arrest-to-ECMO target. In the two-stage hospital model, patients located within a 15 min transport radius from chest-pain network hospitals were considered geographically covered. In the prehospital ECPR model, a physician-staffed ambulance was assumed to reach arrest sites within a 25 min travel-time radius from ECMO-ready hospitals. The study outcome was geographic coverage, defined as the proportion of cases within each service area; the two strategies were compared using McNemar's test for paired proportions. Results: Among 1476 included cases, the coverage rate was as follows: current system, 28.7%; two-stage hospital model, 65.2%; prehospital model, 70.4% (p < 0.001). Certain eastern and southern mountainous regions remained outside both coverage areas. Conclusions: Using real-world EMS data, a mobility-focused prehospital ECPR strategy provided broader potential geographic access without requiring additional fixed hospital infrastructure than expanding hospital-based initiation sites. Optimization of prehospital deployment may represent a geographically feasible approach to expanding ECPR access in mixed urban-rural regions, though operational feasibility and cost-effectiveness require further evaluation.
Following traumatic finger amputation, successful replantation depends on several preoperative factors, with proximity to a registered hand trauma center being a key determinant. These centers represent the standard for treatment, making timely access within a critical window essential. Research has demonstrated that increased travel distances and longer times to replantation correlate with poorer functional outcomes. Using data from the American Society for Surgery of the Hand, the locations of registered hand trauma centers were mapped with advanced isochrone application programming interface modeling software, which uses complex algorithms to map and analyze areas reachable within specific time or distance thresholds. A shaded region was generated to indicate areas within a 6-hour and 12-hour travel radius of a registered hospital center. The District of Columbia (1.5), Oklahoma (0.98), and Minnesota (0.88) had the highest number of trauma centers per 1 million residents, whereas multiple states such as Nevada, Montana, and Kansas had 0 trauma centers. In terms of geospatial mapping, regions such as Houston, Texas; Indianapolis, Indiana; Columbus and Cincinnati, Ohio; and large cities in the Northeast such as New York, Philadelphia, the District of Columbia, and Boston, all have greater than 6 hand trauma centers. At a 6-hour catchment, 13.3% of the U.S. population lacks access to a registered hand trauma center; even when extended to 12 hours, 7.6% remain without coverage. Limited hospital and trauma center density remains a significant barrier to high-quality surgical care. Reducing travel times, increasing access in rural areas, and improving urban infrastructure could enhance replantation outcomes by ensuring that more patients reach registered hand trauma centers within the critical time window.
M. Wysoczynski, D.-M. Shin, M. Kucia, and M. Z. Ratajczak, "Selective Upregulation of Interleukin-8 by Human Rhabdomyosarcomas in Response to Hypoxia: Therapeutic Implications," International Journal of Cancer 126, no. 2 (2010): 371-381, https://doi.org/10.1002/ijc.24732. This Expression of Concern is for the above article, published online on 8 July 2009 and available in Wiley Online Library (wileyonlinelibrary.com), and has been published by agreement between the journal Editor-in-Chief, Prof. Christoph Plass; the Union for International Cancer Control; and John Wiley & Sons Ltd. The Expression of Concern was agreed due to concerns raised by a third party after publication regarding an overlap between the "RH30" and "RH30 scrambled" panels in Figure 5d. The corresponding author confirmed the overlap in Figure 5 but could not provide the original data given the time that had elapsed. An investigation by the University of Louisville concluded that there was no evidence of misconduct and that the overlap likely resulted from an inadvertent error during manuscript preparation. However, the journal is issuing this Expression of Concern because the concerns regarding the integrity of the data and the results presented cannot be resolved. The authors M. Z. Ratajczak and M. Wysoczynski agree to this Expression of Concern. D.-M. Shin and M. Kucia were not reachable.
In order to fit with National Institute for Health and Care Excellence (NICE) guidelines on arthritis, we established a 'very early arthritis clinic' (v-EAC) to evaluate patients within 48 h of a medical referral for any suspected arthritis of <1-month duration. We describe the characteristics of patients referred to the v-EAC from 2 October 2023 to 1 October 2024. Delay of referral was recorded. A systematic and standardized clinical, biological and ultrasound evaluation of all consecutive patients was conducted. Patients and disease characteristics are described. A total of 900 patients were evaluated within a median delay of 1 day after referral: 51% were women and the median age was 55 years (IQR 42-67). Among these patients, 37% were referred by emergency departments, 28% by rheumatologists, 18% by general practitioners and 17% by other specialists. For 46% of patients, it was the first episode of a swollen joint, with a median symptom duration of 14 days (IQR 5-44) and a mean number of 1.3 (s.d. 1.9) swollen joints. Inflammatory arthritis was confirmed in 63% of cases and diagnoses were flare of immune-mediated inflammatory disease (IMID)-related arthritis in 35% of cases (half of which represented the first manifestation of an IMID-related arthritis), crystal-related arthritis in 27% (mostly gout) and infectious disease in 8.0% (including 34 bursitis and 13 septic arthritis). Joint aspiration was performed in 414 patients (46%). Corticosteroid-derivative injections were administered to 31% of patients and hospitalization was required in 10% of cases [median stay 6 days (IQR 4-8)]. A dedicated care pathway can make the NICE guidelines regarding management of arthritis reachable.
The optimal prehospital transport strategy for patients eligible for mechanical thrombectomy remains debated. We evaluated how direct versus secondary transfer to a comprehensive stroke center (CSC) influences outcomes in a large metropolitan stroke network. This prospective registry-based cohort study included all patients undergoing thrombectomy between 2015 and 2022 in a city of 1.7 million inhabitants. The network comprised 11 primary stroke centers (PSCs) referring to a single CSC, all reachable within 60 minutes. Among 2,017 patients, 242 (12.0%) were transported directly to the CSC, whereas 1,775 (88.0%) were secondarily transferred after initial assessment at a PSC. Functional outcome was assessed using the modified Rankin Scale (mRS) at 90 days. Multivariable logistic and Cox regression models were used to identify independent predictors of good functional outcome and survival. The median onset-to-CSC admission time was shorter after direct versus secondary transfer (101 [7-1,071] vs. 240 [40-1,411] min, P<0.001). A good functional outcome (mRS 0-2) was more frequent after direct transport (50.3% vs. 40.5%, P=0.015). After adjustment for age, National Institutes of Health Stroke Scale score, thrombolysis, posterior circulation stroke, and comorbidities, direct transport independently predicted good functional outcome (odds ratio: 1.74; 95% confidence interval [CI] 1.25-2.42; P=0.001). Among patients receiving combined thrombolysis and thrombectomy (n=1,110), direct transport was associated with improved long-term survival (log-rank P=0.026; adjusted hazard ratio: 0.67; 95% CI 0.47-0.95; P=0.026). Even within a metropolitan network with 1-hour access, direct transport to a CSC shortens treatment delays, improves functional recovery, and enhances survival among patients undergoing combined reperfusion therapy.
Contrast detection autofocus (CDAF) performance in industrial machine vision is shaped by platform capability as well as by the focus measure and search strategy. CDAF is analyzed through a platform capability framework and a unified frame-level transaction chain across three platforms: a capability upper-bound platform (P1), a bridging platform (P2), and an industrial black-box platform (P3). In experiments covering six scene categories, four initial conditions, five fixed-rule strategies, and 30 repetitions per condition, the dominant observable tail on P3 is localized after control submission, in the command-to-actuation segment. On P2, controlled one-factor perturbations using a physically calibrated sample position mismatch intensity (σalign) and an actuation chain variability coordinate (λact) reproduce the main P3 degradation directions, providing a mechanism-level account in terms of sample position mismatch and command-to-actuation variability. Platform capability sets the reachable performance boundary, within which strategies trade speed, final quality, and failure risk. On P3, S1-S4 form the main engineering trade-off band, whereas S5 shows condition-dependent upper-quantile quality gains without a stable frontier advantage. The resulting deployment logic combines capability tiering, segment-wise bottleneck localization, and strategy band selection and treats CDAF as a capability-conditioned speed-quality-risk trade-off rather than a platform-independent strategy ranking.
Safety-critical Industrial Internet of Things (IIoT) sensor networks deployed in disaster scenarios require intelligent routing mechanisms that prioritize mission-critical packets without relying on centralized coordination. Federated learning on resource-constrained edge nodes presents three primary challenges: the absence of an interpretable supervisory signal, the inability to act conservatively based on per-inference confidence, and vulnerability to partial node availability. The proposed FedCARE framework addresses these issues by employing a Mamdani Fuzzy Inference System to generate traceable criticality labels from multi-modal sensor telemetry, a dropout-aware aggregation protocol that normalizes over only reachable nodes, and a confidence-gated resolver that defers to symbolic fuzzy classification when model confidence is insufficient, otherwise applying an auditable maximization rule to prevent under-prioritization of safety-critical data. Evaluation on 50-, 100-, and 200-node Watts-Strogatz topologies under fault rates up to 50%, using the Edge-IIoTset and WUSTL-IIoT-2021 benchmarks, demonstrates 99.00% critical recall and up to 1.8× higher overall-packet delivery compared to RPL-RP under severe fault conditions. Routing improvements are primarily attributed to fuzzy criticality labeling and multi-path replication. These findings indicate that fuzzy-supervised federated inference offers a practical and interpretable solution for safety-critical IIoT routing, with an observed energy overhead of 7.8% per delivered packet.
Hexapod robots are attractive for operation in cluttered and uneven environments, but their walking stability is strongly affected by the coupled effects of leg morphology and foot-end trajectory planning. In many existing designs, leg-segment proportions, reachable workspace, and swing-phase trajectory smoothness are considered separately, which makes it difficult to clarify how structural parameters and motion planning jointly influence locomotion stability. To address this issue, this study presents a spider-leg-inspired hexapod robot with a simplified three-degree-of-freedom leg configuration. Selected functional characteristics of spider legs, including segmented limb structure and compliant distal contact, were abstracted into an engineering-feasible hexapod platform rather than directly reproducing spider anatomy. A parametric workspace analysis was conducted under a fixed total leg length to compare six candidate femur-to-tibia ratios. Based on forward reach, vertical foot-lifting capability, stride potential, and structural compactness, a 4:6 femur-to-tibia ratio was selected. In addition, an eleventh-order Bézier curve was developed for swing-phase foot trajectory planning and compared with a conventional composite cycloid trajectory under identical tripod-gait conditions. Simulation and straight-line walking experiments showed that the Bézier-based trajectory reduced body-attitude fluctuation and produced smoother angular-velocity variation than the composite cycloid trajectory. The results indicate that the proposed structural design and Bézier-based trajectory can improve flat-ground walking stability of the hexapod robot. This work provides a practical reference for biomimetic structural design and gait-trajectory optimization of multi-legged robots, while further validation on more complex terrain remains necessary.