Search Results (886)

This paper presents a two-layer consistency control framework for the collaborative assembly of multiple aircraft in complex environments, comprising a low-level control layer and a high-level guidance layer. The control layer develops a robust anti-interference law by integrating an extended state observer (ESO) with Backstepping for attitude control and employing constrained Backstepping for velocity regulation. The guidance layer ensures safe and coordinated assembly. A time-varying communication topology is adopted to guarantee collision-free maneuvers. An assembly trajectory is generated for each aircraft based on a position allocation strategy and the Dubins path planning method. To achieve time-coordinated arrival, a speed consensus protocol is designed, guiding the aircraft into a sparse formation. Subsequently, consensus-based control laws for both attitude and velocity are implemented to transition into a tight formation. The effectiveness of the proposed framework is validated through aircraft six-degree-of-freedom (6-DoF) simulations, which confirm that it significantly improves the safety and robustness of the multi-aircraft assembly process. Full article

In fish gut microbiome studies, there are no standardized protocols regarding sampling region or post-feeding time, nor clear consensus on whether analyses should target resident (autochthonous) or transient (allochthonous) bacteria. This study examined the dynamics and interactions of both microbial communities in the anterior and posterior intestine of farmed gilthead sea bream and evaluated the resident microbiome at 24 and 48 h post-feeding. Microbial DNA was sequenced using the Oxford Nanopore Technology platform. Data were analyzed through statistical and discriminant approaches, as well as a Bayesian network framework to assess bacterial interactions. Transient communities showed higher richness and diversity, regardless of intestinal section, suggesting a more specialized and stable microbial environment in the mucus layer. The two communities differed markedly in structure and composition. Variations associated with intestinal region were less pronounced, particularly for autochthonous bacteria, and post-feeding fluctuations in the resident microbiome were minimal. Functionally, results indicated relevant synergies between communities. Protein metabolism pathways were enriched in autochthonous bacteria, whereas allochthonous microorganisms contributed mainly to bile acid and carbohydrate metabolism. Overall, resident and transient bacteria constitute distinct communities in the gut of gilthead sea bream, with numerous genera present in both but most being differentially represented and interconnected. Full article

The integration of Software-Defined Networking (SDN), blockchain (BC), and machine learning (ML) has emerged as a promising approach to securing Internet of Things (IoT) and Industrial IoT (IIoT) networks. This paper conducted a comprehensive review of recent studies focusing on multi-layered security across device, control, network, and application layers. The analysis reveals that BC technology ensures decentralised trust, immutability, and secure access validation, while SDN enables programmability, load balancing, and real-time monitoring. In addition, ML/deep learning (DL) techniques, including federated and hybrid learning, strengthen anomaly detection, predictive security, and adaptive mitigation. Reported evaluations show similar gains in detection accuracy, latency, throughput, and energy efficiency, with effective defence against threats, though differing experimental contexts limit direct comparison. It also shows that the solutions’ effectiveness depends on ecosystem factors such as SDN controllers, BC platforms, cryptographic protocols, and ML frameworks. However, most studies rely on simulations or small-scale testbeds, leaving large-scale and heterogeneous deployments unverified. Significant challenges include scalability, computational and energy overhead, dataset dependency, limited adversarial resilience, and the explainability of ML-driven decisions. Based on the findings, future research should focus on lightweight consensus mechanisms for constrained devices, privacy-preserving ML/DL, and cross-layer adversarial-resilient frameworks. Advancing these directions will be important in achieving scalable, interoperable, and trustworthy SDN-IoT/IIoT security solutions. Full article

Members of the microsporidial genus Encephalitozoon have the capacity to infect both mammals and birds, and E. cuniculi is most commonly found in rabbits. With a seroprevalence ranging up to 85%, E. cuniculi can be a problem in pet rabbits as well as in food production and laboratory animal science. While most infections are likely subclinical, there are three main clinical presentations: neurological, renal, and ocular. Typical clinical signs including vestibular disease and phacoclastic uveitis may develop with initial or relapsing infection, while renal infection is usually progressive and associated with non-specific clinical signs. High-sensitivity/specificity ante mortem diagnostic options are lacking, and serological testing most often provides adjunct rather than definitive information such that physical examination and other diagnostics are used more so for ruling out other differentials and comorbidities, rather than confirming infection. In the veterinary community, treatment regimens are variable given the lack of thorough studies and a consensus. The aim of this document is to present the available literature to give a concise review of this organism and its infection of rabbits as well as to propose guidelines and protocols for diagnostics and treatment regimens. In addition, the current challenges and recommendations for further studies are discussed. Full article

This paper investigates the bipartite consensus problem for multi-agent systems subject to both switching dynamics and external disturbances within an event-triggered control (ETC) framework. The investigation commences with an analysis of time-invariant systems to establish bipartite consensus, and subsequently expands the framework to accommodate the complexities of switched systems. In time-invariant systems, agents update their states only when the event-triggering threshold is exceeded; the convergence of this mechanism can be rigorously established via an error dynamics mode. For switched systems, the system state is also updated solely when the event-triggering condition is met. Once all subsystems are stabilized, we design an appropriate mean sojourn time to mitigate state jumps caused by switching, thus ensuring bipartite consensus. Finally, four case studies based on numerical simulations to verify the theoretical results. Full article

The entire system collapses due to the issues of inadequate centralized storage capacity, poor scalability, low storage efficiency, and susceptibility to single point of failure brought on by huge power consumption data in the smart grid; thus, an alliance chain-driven multi-cloud storage and verifiable deletion method for smart grid data is proposed. By leveraging the synergy between alliance blockchain and multi-cloud architecture, the encrypted power data originating from edge nodes is dispersed across a decentralized multi-cloud infrastructure, which effectively mitigates the danger of data loss resulting from single-point failures or malicious intrusions. The removal of expired and user-defined data is guaranteed through a transaction deletion algorithm integrated into the indexed storage deletion chain and strengthens the flexibility and security of the storage architecture. Based on the Practical Byzantine Fault-Tolerant Consensus Protocol with Ultra-Low Storage Overhead (ULS-PBFT), by the hierarchical grouping of nodes, the system communication overhead and storage overhead are reduced. Security analysis proves that the scheme can resist tampering attacks, impersonation attacks, collusion attacks, double spend attacks, and replay attacks. Performance evaluation shows that the scheme improves compared to similar methods. Full article

This paper investigates the problem of fixed-time event-triggered consensus control for distributed unmanned underwater vehicle systems subject to communication and energy constraints. The systematic integration control framework is developed, where each unmanned underwater vehicles updates their control inputs only at event-triggered instants instead of continuously, thereby reducing unnecessary communication and actuation efforts. By designing a fixed-time consensus protocol, it is guaranteed that the group of unmanned underwater vehicles achieves time-synchronized consensus within the convergence time, independent of the initial conditions. The stability and convergence of the proposed scheme are rigorously proved using Lyapunov theory and fixed-time stability analysis. Furthermore, a zeno-free triggering condition is established to ensure the feasibility of practical implementation. Numerical simulations are carried out on a team of unmanned underwater vehicles to demonstrate the effectiveness of the proposed method in achieving precise coordination, reducing communication burden, and enhancing energy efficiency in distributed marine operations. Full article

Background: Rate-dependent depression of the Hoffmann reflex (RDD-HR) is a neurophysiological marker of spinal inhibition altered in several neurological conditions, yet no consensus exists on optimal stimulation frequency, number of stimuli, or the feasibility of upper limb recordings. This study aimed to define practical, standardized parameters for reliable RDD-HR assessment in upper and lower limbs of healthy adults, as a first step toward clinical application. Methods: In this observational study, bilateral Hoffmann reflexes were recorded from the flexor carpi radialis and soleus muscles in 21 healthy adults. Stimulation was delivered using three 10-pulse trains at seven frequencies (0.1–5 Hz). RDD-HR was quantified as the median H-reflex area, expressed as a percentage of the first response (lower values indicate greater depression). Optimal frequencies and minimal stimuli were identified by sigmoid fitting and confidence analyses, with train and stimulus effects tested by two-way ANOVA. Results: RDD-HR displayed a sigmoidal frequency–response across all limbs. Maximal depression occurred at 1–5 Hz, with no significant differences between these frequencies, supporting 1 Hz as optimal. Depression was greater in lower limbs (~30%) than upper limbs (~47%). Reliable estimates were obtained using a single train of seven stimuli, with no benefit from averaging across trains. Upper limb recordings required lower stimulation intensities. Conclusions: RDD-HR can be reliably assessed using a simplified protocol based on a single seven-pulse train at two key frequencies. This standardized approach provides a methodological foundation for future clinical validation of RDD-HR as a biomarker of spinal inhibitory dysfunction. Full article

Background: Early surgical intervention is associated with improved outcomes in hip fracture care, yet in patients using Direct Oral Anticoagulants (DOACs), surgery is frequently delayed due to concerns about increased intraoperative bleeding. Despite the increasing prevalence of hip fracture patients on DOACs, no consensus exists on optimal surgical timing. This has led to substantial practice variation between hospitals, with some operating within 24 h of last DOAC intake and others delaying surgery beyond 24 h. This study hypothesizes that early surgery within 24 h results in a non-inferior blood transfusion risk compared to delayed surgery 24 h or more after last DOAC intake in hip fracture patients on DOACs. This protocol describes the design and methodological rationale of a natural experiment. Methods and analysis: A multicenter cohort study designed as a natural experiment will be conducted across seven Dutch level 2 trauma centers, using predefined and standardized prospectively collected variables from electronic health records. Centers will adhere to distinct local surgical timing protocols, forming two cohorts: early surgery within 24 h and delayed surgery 24 h or more after last DOAC intake. Patients presenting with an isolated hip fracture who are using a DOAC and have taken their last dose within 24 h before admission will be included. The primary endpoint is postoperative blood transfusion. Secondary endpoints include additional bleeding-related outcomes, thrombotic and postoperative complications, and hospital length of stay. The primary analysis will be conducted on a per-protocol basis, with an intention-to-treat analysis performed as a supplementary assessment. Non-inferiority will be established if the upper bound of the one-sided 95% confidence interval for the risk difference does not exceed the predefined margin of 5%. Ethics and dissemination: Ethical approval was obtained from the Medical Ethics Committee United, Utrecht, The Netherlands. As this is a cohort study without altering clinical care, individual informed consent is not required. All data will be pseudonymized, and findings will be disseminated through peer-reviewed journals and scientific conferences. Registration details: Medical Ethics Committee United, Utrecht, The Netherlands, registration number W25.034. Full article

Improved survival rates for paediatric cancer patients represent a major medical achievement, but they have simultaneously brought the long-term sequelae of oncological treatments into sharp focus. Cardiotoxicity stands out as one of the most serious complications, being the leading cause of non-relapse-related morbidity and mortality among childhood cancer survivors. This comprehensive review analyses the current landscape, highlighting the significant gap in evidence that hinders optimal care. This paper constitutes a comprehensive narrative and scoping review based on a critical analysis of current clinical guidelines, landmark studies, and consensus papers in paediatric cardio-oncology. Crucially, it assesses the heterogeneity and limitations of existing evidence regarding standardized surveillance protocols, primary prevention strategies, and acute/late-onset cardiovascular complication management. The review then identifies and critically discusses key areas for future research and clinical development. A critical gap in evidence persists in paediatric cardio-oncology, leading to significant variability in clinical practice and the underdiagnosis/undertreatment of cardiovascular risk factors in this vulnerable population. To bridge this gap, there is an urgent need for international collaborative research. The overarching goal is to transform paediatric cardio-oncology into a predictive and preventive speciality, ensuring that all childhood cancer survivors achieve not only extended life expectancy but also improved cardiovascular quality of life. Full article

Background: Dysglycemia remains a persistent challenge in hospital care. Despite advances in outpatient diabetes technology, inpatient insulin management largely depends on intermittent point-of-care glucose testing, static insulin dosing protocols and rule-based decision support systems. Artificial intelligence (AI) offers potential to transform this care through predictive modeling and adaptive insulin control. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines, a scoping review was conducted to characterize AI algorithms for insulin dosing and glycemic management in hospitalized patients. An interdisciplinary team of clinicians and engineers reached consensus on AI definitions to ensure inclusion of machine learning, deep learning, and reinforcement learning approaches. A librarian-assisted search of five databases identified 13,768 citations. After screening and consensus review, 26 studies (2006–2025) met the inclusion criteria. Data were extracted on study design, population, AI methods, data inputs, outcomes, and implementation findings. Results: Studies included ICU (N = 13) and general ward (N = 9) patients, including patients with diabetes and stress hyperglycemia. Early randomized trials of model predictive control demonstrated improved mean glucose (5.7–6.2 mmol/L) and time in target range compared with standard care. Later machine learning models achieved strong predictive accuracy (AUROC 0.80–0.96) for glucose forecasting or hypoglycemia risk. Most algorithms used data from Medical Information Mart for Intensive Care (MIMIC) databases; few incorporated continuous glucose monitoring (CGM). Implementation and usability outcomes were seldom reported. Conclusions: Hospital AI-driven models showed strong algorithmic performance but limited clinical validation. Future co-designed, interpretable systems integrating CGM and real-time workflow testing are essential to advance safe, adaptive insulin management in hospital settings. Full article

Background: Irisin, an exercise-induced myokine, has emerged as a potent neuroprotective factor, though a systematic synthesis of its role across neurological disorders is lacking. This review systematically evaluates clinical and preclinical evidence on irisin’s association with neurological diseases and its underlying mechanisms. Methods: Following PRISMA 2020 guidelines, a systematic search of PubMed/MEDLINE, Scopus, Web of Science, Embase, and Cochrane Library was conducted. The review protocol was prospectively registered in PROSPERO. Twenty-one studies were included, comprising predominantly preclinical evidence (n = 14), alongside clinical observational studies (n = 6), and a single randomized controlled trial (RCT) investigating irisin in cerebrovascular diseases, Parkinson’s disease (PD), Alzheimer’s disease (AD), and other neurological conditions. Eligible studies were original English-language research on irisin or FNDC5 and their neuroprotective effects, excluding reviews and studies without direct neuronal outcomes. Risk of bias was independently assessed using SYRCLE, the Newcastle–Ottawa Scale, and RoB 2, where disagreements between reviewers were resolved through discussion and consensus. Results were synthesized narratively, integrating mechanistic, pre-clinical, and clinical evidence to highlight consistent neuroprotective patterns of irisin across disease categories. Results: Clinical studies consistently demonstrated that reduced circulating irisin levels predict poorer outcomes. Lower serum irisin was associated with worse functional recovery and post-stroke depression after ischemic stroke, while decreased plasma irisin in PD correlated with greater motor severity, higher α-synuclein, and reduced dopamine uptake. In AD, cerebrospinal fluid irisin levels were significantly correlated with global cognitive efficiency and specific domain performance, and correlation analyses within studies suggested a closer association with amyloid-β pathology than with markers of general neurodegeneration. However, diagnostic accuracy metrics (e.g., AUC, sensitivity, specificity) for irisin as a standalone biomarker are not yet established. Preclinical findings revealed that irisin exerts neuroprotection through multiple mechanisms: modulating microglial polarization from pro-inflammatory M1 to anti-inflammatory M2 phenotype, suppressing NLRP3 inflammasome activation, enhancing autophagy, activating integrin αVβ5/AMPK/SIRT1 signaling, improving mitochondrial function, and reducing neuronal apoptosis. Irisin administration improved outcomes across models of stroke, PD, AD, postoperative cognitive dysfunction, and epilepsy. Conclusions: Irisin represents a critical mediator linking exercise to brain health, with consistent neuroprotective effects across diverse neurological conditions. Its dual ability to combat neuroinflammation and directly protect neurons, demonstrated in preclinical models, positions it as a promising therapeutic candidate for future investigation. Future research must prioritize the resolution of fundamental methodological challenges in irisin measurement, alongside investigating pharmacokinetics and sex-specific effects, to advance irisin toward rigorous clinical evaluation. Full article

Traditional Human Resource Management (HRM) systems are criticized for lacking transparency, being inefficient, and offering ample opportunities for fraud because of their centralized design and reliance on manual processes. This work proposes a blockchain-enabled framework for HRM that enhances the transparency, trust, and global mobility of talents by integrating distributed ledgers, consensus protocols, and smart contract networks into Human Resources (HR) functions. A four-layer theoretical model—data, consensus, smart contract, and application layers—is developed and comparatively examined against traditional HR systems to show how blockchain principles can be systematically mapped into HR processes. This study shows how blockchain-driven HRM can ensure tamper-evident employee records, automate contractual and payroll operations, and enhance auditability and compliance. By informing the framework with established technology adoption perspectives, this paper extends both the theoretical and managerial understanding of blockchain in HR. In comparison with previous studies that were limited to either recruitment or credential verification, this article presents an overarching, cross-layer synthesis that connects blockchain architectures with end-to-end HR functions, thus providing a clear conceptual foundation for its future enterprise adoption in the digital economy. Full article

Background/Objectives: Caffeine citrate represents the standard pharmacological intervention for apnea of prematurity (AOP) and episodes of intermittent hypoxia (IH). Despite its widespread use, consensus regarding the necessity of routine serum monitoring, optimal dosing protocols, and precise clinical indications remains elusive. The primary objective of this investigation was to evaluate the longitudinal trajectory of serum caffeine concentrations in preterm infants and to analyze their correlation with the incidence of AOP and IH episodes. Furthermore, we sought to determine whether blood caffeine concentrations varied significantly across gestational ages throughout the postnatal period. Methods: This multicenter, prospective observational study enrolled preterm infants with a gestational age of ≤30 weeks. Participants were administered a standard loading dose of caffeine citrate within the first 24 h of life, followed by a standardized maintenance regimen. Serum caffeine levels were quantified on a weekly basis. The cohort was stratified into two distinct groups based on gestational age: Group 1 (23–27 weeks) and Group 2 (28–30 weeks). Results: The study yielded 588 serum caffeine measurements from a cohort of 104 preterm infants, characterized by a median gestational age of 28 weeks (range: 23–30 weeks) and a mean birth weight of 1034 ± 296 g. Statistical analysis revealed no significant disparities in serum caffeine concentrations across gestational age groups (p > 0.05). Notably, during the third week of life, infants with apneic episodes demonstrated significantly lower caffeine levels than those without apnea (p = 0.016). Furthermore, a significant negative correlation was identified between serum caffeine concentrations and the frequency of IH episodes during the third, fourth, and fifth weeks of life across multiple oxygen saturation thresholds. Conclusions: While serum caffeine concentrations in preterm infants did not vary significantly with gestational age, lower levels were associated with a higher incidence of AOP and IH episodes. These results suggest that while routine monitoring or dose adjustment based solely on gestational age may not be warranted, maintaining adequate serum levels is critical for symptom management. Future research should prioritize randomized controlled trials with expanded sample sizes, extended follow-up periods, and a rigorous analysis of adverse effects. Full article

Background/Objectives: Cytoreduction status is a critical prognostic factor in ovarian cancer, yet preoperative selection of patients suitable for primary debulking surgery and accurate prediction of surgical outcome remain challenging. This study aimed to evaluate the prognostic ability of MRI-based Fagotti score and Peritoneal Cancer Index (PCI) for predicting resectability of peritoneal disease in ovarian cancer patients. Methods: This was a prospective single-center observational study. Patients with suspected primary ovarian cancer who underwent preoperative MRI of the abdomen and pelvis with a dedicated protocol were considered. MRI-based Fagotti score and PCI were determined by two readers independently, using a combination of T2W, Diffusion-Weighted Imaging (DWI), and contrast-enhanced T1W sequences. In cases of discordance, a third radiologist reviewed the scans and consensus was reached. ROC analysis and logistic regression were used to evaluate prognostic performance. The reference standard to predict resectability was optimal cytoreduction defined as residual disease ≤1 cm. Results: Forty-six women with epithelial ovarian cancer (mean age 56.3 ± 2.6 years) who underwent preoperative MRI, followed by laparoscopy and/or laparotomy, were included in the study. Both MRI-based Fagotti score and PCI showed high predictive value for predicting resectability (AUC 0.92 and 0.94, respectively). Optimal cut-offs were ≤6 for Fagotti score and ≤20 for PCI. Patients with scores below these thresholds had >60-fold (Fagotti) and >100-fold (PCI) increased odds for successful primary cytoreduction (p < 0.001). Conclusions: MRI-based Fagotti score and PCI may serve as powerful noninvasive predictors of surgical outcome in ovarian cancer. MRI may reliably guide treatment decisions, reducing unnecessary laparotomies and optimizing patient selection. Full article