Search Results (6,054)

This paper studies the issues of human balancing and stability in the sagittal plane using fractional and integer order time delay feedback control. The neural-mechanical model of human balance is represented as an inverted pendulum controlled by torque. We present a finite-time stability (FTS) analysis for closed-loop neutral time delay systems (NFOTDSs) with fractional order $1<\beta <\alpha \le 2$. By employing a generalized Gronwall inequality, we derive new FTS criteria for these systems in terms of the Mittag-Leffler function. Finally, a suitable numerical example is presented to show the effectiveness of the proposed method. Full article

High-fidelity 3D reconstruction of human-sized objects typically requires multi-sensor scanning systems that are expensive, complex, and rely on proprietary hardware configurations. Existing low-cost approaches often rely on handheld scanning, which is inherently unstructured and operator-dependent, leading to inconsistent coverage and variable reconstruction quality. This limitation necessitates the need for a controlled, repeatable, and affordable scanning method that can generate high-quality data without requiring multi-sensor hardware or external tracking markers. This study presents a marker-less scanning platform designed for human-scale reconstruction. The system consists of a single structured-light sensor mounted on a vertical linear actuator, synchronised with a motorised turntable that rotates the subject. This constrained kinematic setup ensures a repeatable cylindrical acquisition trajectory. To address the geometric ambiguity often found in vertical translational symmetry (i.e., where distinct elevation steps appear identical), the system employs a sensor-assisted initialisation strategy, where feedback from the rotary encoder and linear drive serves as constraints for the registration pipeline. The captured frames are reconstructed into a complete model through a two-step Iterative Closest Point (ICP) procedure that eliminates the vertical drift and model collapse (often referred to as “telescoping”) common in unconstrained scanning. To evaluate system performance, a modular anthropometric benchmark object representing a human-sized target (1.6 m) was scanned. The reconstructed model was assessed in terms of surface coverage and volumetric fidelity relative to a CAD reference. The results demonstrate high sampling stability, achieving a mean surface density of $0.760{\mathrm{points}/\mathrm{mm}}^2$ on front-facing surfaces. Geometric deviation analysis revealed a mean signed error of −1.54 mm ($\sigma =$ 2.27 mm), corresponding to a relative volumetric error of approximately 0.096% over the full vertical span. These findings confirm that a single-sensor system, when guided by precise kinematics, can mitigate the non-linear bending and drift artefacts of handheld acquisition, providing an accessible yet rigorously accurate alternative to industrial multi-sensor systems. Full article

Pancreatic exocrine secretion is regulated by the physicochemical properties and nutrient composition of gastric and intestinal chyme. The present study examined integrative feedback mechanisms involved in the physiological control of pancreatic secretion, with particular emphasis on interactions between pancreatic juice, bile, and gut-derived regulatory and metabolic signals. A chronic porcine model enabling selective withdrawal and controlled reintroduction of pancreatic juice and bile into defined intestinal segments was employed. Duodenal and ileal exposure to pancreatic juice suppressed pancreatic enzyme secretion, while intraduodenal administration of pancreatin elicited a biphasic inhibitory response. Interruption of bile flow to the duodenum resulted in increased pancreatic protein output and was associated with reduced circulating cholecystokinin concentrations. In contrast, intraduodenal infusion of bile acids attenuated pancreatic exocrine secretion. Prolonged bile deprivation led to sustained pancreatic hypersecretion accompanied by a marked reduction in biliary leptin output. Collectively, these findings indicate that pancreatic exocrine secretion in pigs is regulated by multiple interacting feedback pathways operating along the gastrointestinal tract. The observed responses support functional contributions of protease-dependent luminal feedback, distal intestinal sensing, hormone-dependent regulation, and bile-associated metabolic modulation. Full article

Management Information Systems (MIS) are increasingly expected to support real-time, evidence-based decision-making and to automate routine workflows. Nevertheless, many organizations still struggle to transform heterogeneous, high-velocity data into trustworthy decision support and process execution at scale. Adopting a socio-technical systems perspective, this study explores the interplay between data infrastructure, analytics capabilities, and decision-making processes. We adopted a mixed-methods design, which incorporated (i) a cross-sectional survey of MIS professionals (n = 150) from organizations across three industries (retail, healthcare, and financial services) and (ii) 12 semi-structured stakeholder interviews. The survey data show that the performance outcomes of the organizations reporting a higher level of BDA and maturity in real-time processing are stronger, characterized by self-reported average revenue growth of 12% among retailers, a material decrease in operational costs, and improvements in overall system efficiency. These figures reflect respondents’ estimates rather than audited financial statements. BDA, real-time processing, and data infrastructure readiness were statistically significant predictors in an OLS regression model of perceived organizational performance, explaining a substantial percentage of variance (R² = 0.72). The insights provided by the interviews explain how these effects were achieved: performance improvements materialized through real-time feedback loops where streaming and batch pipelines were integrated, data-quality controls were embedded in ingestion, and decision outputs were linked to workflow automation. The research contributes a holistic view to the MIS capability framework, linking data infrastructure decisions to the timeliness of decisions and automation preparedness, while contributing to the theoretical refinement of MIS capability frameworks and offering practical guidance for governance and technology selection. Full article

N-Acetyl-L-glutamate kinase (NAGK) catalyzes the first committed step in arginine biosynthesis in organisms that perform the cyclic pathway of ornithine synthesis. In cyanobacteria and most Archaeplastida, the activity of NAGK is controlled by the PII signal transduction protein. During evolution, representatives of the class Mamiellophyceae, Ostreococcus and Bathycoccus lost the gene encoding PII, while Micromonas retained this gene. Here, we perform coupled enzyme and pull-down assays and show that M. commoda NAGK is activated by N-acetyl-L-glutamate and inhibited by arginine but is not controlled by PII proteins. This loss may have been compensated for by the enzyme’s low sensitivity to arginine. In contrast, M. commoda PII relieved Chlamydomonas reinhardtii NAGK from feedback inhibition by arginine. These observations suggest that M. commoda NAGK possesses a unique feature: it has lost the ability to interact with PII protein. The findings are discussed in the context of the relationship between NAGK control and the PII role in Mamiellophyceae. Full article

AI-driven assistance can help the user perform complex teleoperated tasks, introduce autonomous patterns, or adapt the workbench to objects of interest. On the other hand, the level of assistance should be responsive to the user’s response and adapt accordingly to promote a positive and effective experience. Envisaging this final goal, this article investigates whether physiological signals can be used to estimate the user’s performance and response in a teleoperation setup, with and without AI-driven assistance. In more detail, a teleoperated pick-and-place task was performed with or without AI-driven assistance during the grasping phase. A deep-learning algorithm for affordance detection provided assistance, helping participants align the robotic hand with the target object. Physiological and kinematic data were measured and processed by machine learning models to predict the effects of AI assistance on task performance during teleoperation. Results showed that AI-driven assistance, as expected, affected pick-and-place performance. Beyond this, the assistance affected the participant’s fatigue level, which the machine learning models could predict with an average accuracy of 84% based on the physiological response. In addition, the success or failure of the pick-and-place task could be predicted with an average accuracy of 88%. These findings highlight the potential of integrating deep learning with biometric feedback and gesture-based control to create more intuitive and adaptive HRI systems. Full article

This work presents a novel soft gripper concept featuring integrated force feedback and a compact, resource-efficient geometry. The gripper is designed to provide a low-cost, adaptable, and precise solution for manipulating delicate and irregularly shaped objects. By embedding force feedback directly into the structure, the system reliably detects contact and enables controlled, gentle gripping of fragile items. The design was developed for collaborative and assistive robotic applications, where safety and human–robot interaction are prioritized. The prototype is fabricated using consumer-grade 3D-printed components and employs a simple cable-driven actuation system. The hybrid soft–rigid architecture combines compliant fingers with a rigid, sensorized thumb, preserving the adaptive grasping characteristics of soft robotics while simplifying sensing integration and construction. A motor-based control mechanism synchronizes finger motion through cable traction, ensuring reliable and repeatable performance. Experimental evaluations demonstrate secure, damage-free handling across diverse object types, highlighting the gripper’s potential in assistive robotics, cobot environments, biomedical contexts, and other domains requiring safe and delicate manipulation. Full article

Background: Rare cancers combine low incidence with high biological heterogeneity and multi-institutional care trajectories. These features make single-center learning structurally incomplete and render pathway fragmentation a dominant driver of preventable harm, variability, and waste. In this context, care quality is best understood as a property of pathway integrity across routing, diagnostics (imaging/biopsy planning), multidisciplinary intent-setting, definitive treatment, and surveillance—rather than as a department-level attribute. Objective: To define a pragmatic, transferable operating blueprint for a rare-cancer Learning Health System (LHS) that turns routine care into continuous, auditable learning under explicit governance, while maintaining claims discipline and protecting measurement validity. Approach: We synthesize an implementation-oriented operating model using the Swiss Sarcoma Network (SSN) as an exemplar. The blueprint couples clinical governance (Integrated Practice Unit logic, hub-and-spoke routing, auditable multidisciplinary team decision systems) with an interoperable real-world-time data backbone designed for benchmarking, pathway mapping, and feedback. The operating logic is expressed as a closed-loop control cycle: capture → harmonize → benchmark → learn → implement → re-measure, with explicit owners, minimum requirements, and failure modes. Results/Blueprint: (i) The model specifies a minimal set of data primitives—time-stamped and traceable decision points covering baseline and tumor characteristics, pathway timing, treatment exposure, outcomes and complications, and feasible longitudinal PROMs and PREMs; (ii) a VBHC-ready, multi-domain measurement backbone spanning outcomes, harms, timeliness, function, process fidelity, and resource stewardship; and (iii) two non-negotiable validity guardrails: explicit applicability (“N/A”) rules and mandatory case-mix/complexity stratification. Implementation is treated as a governed step with defined workflow levers, fidelity criteria, balancing measures, and escalation thresholds to prevent “dashboard medicine” and surrogate-driven optimization. Conclusions: This perspective contributes an operating model—not a platform or single intervention—that enables credible improvement science and establishes prerequisites for downstream causal learning and minimum viable digital twins. By distinguishing enabling infrastructure from the governed clinical system as the primary intervention, the blueprint supports scalable, learnable excellence in rare-cancer care while protecting against gaming, inequity, and inference drift. Distinct from generic LHS or VBHC frameworks, this blueprint specifies validity gates required for rare-cancer benchmarking—explicit applicability (“N/A”) rules, denominator integrity/capture completeness disclosure, anti-gaming safeguards, and escalation governance. These elements are critical in rare cancers because small denominators, high heterogeneity, and multi-institutional pathways otherwise make benchmarking prone to artifacts and unsafe inferences. Full article

This paper conceptualises burnout in Project-Based Organisations (PBOs) as a systemic emergent property arising from the non-linear interaction between structural demands and human capital. Utilising a System Dynamics (SD) methodology, the study constructs a Causal Loop Diagram (CLD) to visualise the feedback architecture governing the burnout cycle. The analysis identifies the dynamic tension between the Reinforcing Loop of exhaustion (R1) and the Balancing Loop of adaptation (B1). A key theoretical contribution is the positioning of the Project Manager’s Emotional Intelligence (EI) not merely as a soft skill but as a systemic control lever (B2) capable of reducing information delays and shifting the system from reactive to proactive homeostasis. Crucially, the study operationalises these conceptual findings into a Burnout Risk Management Framework (BRMF), accompanied by a practical diagnostic dashboard. This tool offers managers a set of leading and lagging indicators for early detection, bridging the gap between theoretical plausibility and applied risk management in high-entropy project environments. Full article

The shoulder may be an effective central site for continuous oxygen saturation (SpO2) monitoring but studies of shoulder-mounted pulse oximetry technology are limited. We hypothesized that an alternative location would be similar in function and user acceptance to a standard FDA-cleared finger-based pulse oximeter. We conducted a quantitative and descriptive pilot study of two prototype biosensor designs in patients with clinical suspicion of hypoxic episodes at an outpatient sleep center. Participants wore two prototype biosensors—the primary a shoulder-mounted adhesive and the secondary a combination ring–bracelet—in addition to a control FDA-approved finger-based pulse oximeter. We assessed the comfort of the devices based on a survey. We monitored 27 patients during an overnight polysomnography study. Participants rated the shoulder-mounted device more highly than the control device on a Likert scale survey of comfort (4.6 out of 5 versus 3.1 out of 5). Open-ended questionnaires showed that the two major criticisms of the control and ring devices were devices falling off and disruption to sleep, while only one participant commented on the shoulder device specifically. We also investigated SpO2 agreement between the primary shoulder-mounted prototype and the control finger-based pulse oximeter. This study confirms that alternative configurations for SpO2 monitoring offer potential as well-tolerated devices with preliminary findings of acceptable agreement. Problems with traditional pulse oximetry, such as false readings of hypoxia due to device removal or noisy data, were encountered less frequently in shoulder-mounted pulse oximetry than in the commercial finger-based device. Future directions include studies of additional populations that are at risk of respiratory collapse and surveys to elicit specific feedback on the configurations, whether positive or negative. Full article

Parallel inverter systems constitute the fundamental units of AC microgrids and distributed renewable energy generation systems, wherein accurate power sharing among units represents a critical challenge for stable operation. Conventional droop control fails to share the harmonic power in proportionality to the capacity of inverters due to disparities on line impedance, leading to circulating currents, degraded power quality, and reduced system load capability. To address these issues, this paper proposes a harmonic power-sharing control strategy based on perturbative virtual impedance injection. Under the premise that fundamental power sharing according to capacity ratios has been ensured, the strategy first converts the harmonic power information of each inverter into a small-signal perturbation, which is injected into the virtual impedance of its fundamental control loop. Subsequently, by detecting the resulting variations in fundamental power coefficients induced by this perturbation, a closed-loop feedback is constructed to adaptively adjust the virtual impedance value of each inverter at harmonic frequencies. This adjustment enables the automatic matching of the harmonic power distribution ratio to the inverter capacity ratio, ultimately achieving precise harmonic power sharing. The proposed strategy operates without requiring inter-unit communication links or sampling the voltage at the common coupling point, relying solely on local information, thereby enhancing system reliability. Finally, the effectiveness of the proposed control strategy in achieving harmonic power sharing under conditions of line impedance mismatch is validated through an RT-LAB hardware-in-the-loop platform. Full article

To address the low application accuracy and poor spreading uniformity of conventional lime spreaders, an electromagnetic vibration-assisted variable-rate lime spreader integrating a shaftless screw metering mechanism was developed. The overall configuration and operating principle are presented. Considering the physicochemical characteristics of lime powder, including fine particle size, strong drift tendency, and poor flowability, a shaftless screw metering unit was designed to improve discharge stability and metering accuracy. To enhance dispersion uniformity, a vertical electromagnetic vibration device was developed, and its key parameters were determined through a theoretical analysis of vibration frequency and amplitude. In addition, the structure and kinematic parameters of the spreading disc were optimized by analyzing particle trajectories and outlet distribution patterns. A closed-loop feedback control strategy was implemented to enable precise variable-rate application. Static bench tests demonstrated a metering accuracy of 96.42%, and the dispersion uniformity was at least 84.14% at an electromagnetic vibration frequency of 10 to 18 Hz. Field evaluations further showed that the coefficient of variation for transverse uniformity was no more than 17.88%, while the maximum coefficient of variation for longitudinal stability was 18.09%. These results indicate that the proposed spreader satisfies the operational requirements for accurate and uniform variable-rate application of lime powder. Full article

Aiming at the problem that the position control accuracy of the traditional semi-closed-loop control and the vibration caused by the nonlinear characteristics of the system are easily affected by the full closed-loop control, a double-loop position feedback control based on the state information feedback of the motor and the load is proposed. Based on the double-loop position feedback control framework, a vibration suppression method combining the linear extended state observer, torque feedback compensation and speed feedforward is introduced. The simulation results show that the proposed control method effectively suppresses load vibration, improves the system’s servo control performance, and maintains position control accuracy. Full article

Peptide-based vaccines offer a modular and readily manufacturable platform for both prophylactic and therapeutic immunization. However, their broader translation has been constrained by the limited capacity to predict protective immunity directly from sequence-level features. Recent advances in systems vaccinology and high-throughput immune profiling have substantially expanded the experimental evidence, while generative artificial intelligence now enables de novo design of peptide immunogens and multi-epitope antigens under precisely controlled constraints. This review approaches how these complementary developments are transforming peptide vaccine research, moving beyond classical reverse vaccinology and conventional epitope prediction toward integrated, data-driven design frameworks. We discuss key generative model architectures and conditioning strategies aligned with vaccine objectives, including approaches that account for structural presentation, antigen processing and population-level human leukocyte antigen (HLA) diversity. Central to this perspective is the requirement for rigorous experimental validation and for strengthening the computational–experimental feedback loop through iterative in vitro and in vivo testing informed by systems-level immune readouts. We highlight representative applications spanning infectious diseases, cancer immunotherapy and vector-borne vaccinology, and we outline major technical and translational challenges that must be addressed to enable robust real-world deployment. Finally, we propose future directions for precision peptide vaccinology, emphasizing standardized functional benchmarks, the development of richer curated datasets linking sequence space to immune outcomes, and the early incorporation of formulation and delivery constraints into generative design pipelines. Full article

Despite the widespread adoption of digital technologies in modern implant dentistry, a comprehensive synthesis of error propagation across the entire workflow of full-arch implant rehabilitation remains absent. This narrative review aimed to synthesize current evidence on cumulative error propagation throughout the digital workflow of full-arch implant rehabilitation. Rather than focusing on isolated accuracy metrics, this article proposes a conceptual “Error Control Framework” to elucidate how minor deviations introduced at different workflow stages interact and amplify. A comprehensive literature search (2015–2025) was conducted to analyze error generation across five interrelated phases: Planning, Acquisition, Processing, Output, and Feedback. The evidence indicates that inaccuracies in full-arch implant rehabilitation behave as a cascading system (snowball effect) rather than isolated events. Errors introduced during early stages establish an irreversible baseline that is magnified during digital processing and manufacturing. Consequently, reactive verification at delivery alone is insufficient. To address this, this article proposes a proactive Error Control Framework that integrates a “Front-End Loading” strategy (necessitating strict upstream standardization of scanning strategies and scan-body geometry), alongside “Critical Control Points” (enforcing mandatory physical verification prior to final manufacturing). Viewing digital full-arch rehabilitation as a cumulative error system allows clinicians to implement preventive strategies and verification checkpoints, improving passive fit and long-term mechanical and biological outcomes. Full article