Search Results (30)

Electrocardiography (ECG) is a key biosensing technique for assessing cardiac function and autonomic activity. Accurate detection of R-peaks and precise calculation of R-R intervals (RRIs) are essential for heart rate variability (HRV) analysis; however, automated detection algorithms remain vulnerable to local misdetections, such as false positives or missed beats (false negatives), caused by noise, baseline fluctuations, or waveform variability. Conventional correction approaches based on filter or threshold adjustment may introduce new errors outside the target region, highlighting the need for an intuitive and localized manual correction capability. To address this issue, we developed a prototype graphical user interface (GUI)-based ECG viewer implemented in Fortran for high computational efficiency. The system enables interactive insertion and deletion of detected R-peaks, with recalculation of the RRI time series and automatic updating of related analyses, including power spectral density, histograms, Lorenz plots, and polar plots. Validation using synthetic ECG signals at four sampling frequencies (125–1000 Hz) and three display time scales (2, 5, and 10 s) demonstrated correction errors below 0.7% and stable update times within 20–30 ms. When applied to real ECG recordings from the MIT-BIH Arrhythmia Database (records 115, 122, and 209; MLII lead), the GUI-derived RRIs achieved accuracies exceeding 0.985 at a strict ±10 ms tolerance and reached 1.000 at ±20 ms or higher, including recordings with frequent atrial premature contractions. These results indicate that the proposed system provides reliable feedback for localized correction of R-peak misdetections without altering the underlying ECG signal. The proposed algorithm may support future research and experimental applications in biosignal processing. Full article

Cardiac contractility modulation (CCM) is a bioelectronic therapy that delivers precisely timed electrical signals during ventricular refractoriness to modulate myocardial contractility without triggering depolarization. Unlike pacing-based therapies, CCM does not initiate a new depolarization but instead modulates intracellular signaling pathways to enhance myocardial contractility without increasing myocardial oxygen consumption. CCM therefore represents a myocardial conditioning strategy distinct from cardiac resynchronization therapy, conduction system pacing, or neuromodulation. Experimental and translational studies demonstrate that repeated CCM delivery induces sustained myocardial adaptations, including improvements in excitation–contraction coupling, molecular signaling pathways, and structural remodeling that extend beyond transient hemodynamic effects. Across clinical investigations, CCM has been associated with meaningful improvements in exercise tolerance, health-related quality of life, and functional status in carefully selected populations. Observational data further suggest a potential reduction in heart failure-related hospitalizations when therapy is applied within evidence-aligned indications. Recent technological developments—including simplified ventricular lead configurations, rechargeable compact generators, and integrated CCM–defibrillator platforms—have reduced procedural complexity and may broaden clinical applicability, particularly in patients with concomitant implantable cardioverter–defibrillator indications. This review synthesizes mechanistic insights, clinical evidence, patient selection principles, and practical considerations to define the evolving role of CCM within contemporary heart failure care pathways. Full article

Background/Objectives: Magnetic resonance imaging (MRI) can be used to assess muscle function while performing a motion task within the scanner. Quantitative measures such as contraction velocity and strain can be derived from the images. Cine phase contrast (PC) MRI for time-resolved imaging of muscle function relies on the consistently repeated execution of the motion task for several minutes until data acquisition is complete. This may be difficult for patients with neuromuscular dysfunctions. To date, this approach has been applied only in adults, but not pediatric populations. The aim of this pilot study was to investigate the feasibility of PC MRI for assessing calf muscle function during electrically evoked and voluntary motion in children with cerebral palsy (CP) using open-source hardware and software. Methods: Cine PC MRI was performed at 3T in ambulatory pediatric patients with CP and typically developing children under electrical muscle stimulation (EMS) (n = 14/13) and during voluntary plantarflexion (n = 4/4) using a home-built pedal with a force sensor. A visual feedback software was developed to enable synchronized imaging of voluntary muscle contractions. Muscle contraction velocity and strain were calculated from the MRI data. Data quality was rated by two readers. Results: During EMS, the velocity data quality was rated as sufficient in 21% of scans in patients compared with 82% of scans in controls. During the voluntary task, all patients demonstrated increased compliance and greater generated force output than during EMS. Voluntary motion imaging was successful in all controls but none of the patients, as motion periodicity in patients was worse during voluntary than during stimulated contraction. Conclusions: Cine phase-contrast MRI combined with EMS or voluntary motion proved challenging in pediatric patients with CP, particularly in those with more severe baseline muscle dysfunction or reduced tolerance to stimulation. In contrast, the approach was successfully implemented in typically developing children. Although the scope of the patient-based findings is limited by data heterogeneity, the method demonstrates considerable potential as a tool for monitoring treatment-related changes in muscle function, particularly in less severely affected patients. Further refinement of the EMS and voluntary motion protocols, together with a reduction in MRI acquisition time, is required to improve motion periodicity, tolerability, and consequently the overall success rate in the intended pediatric patient cohort. Full article

Integrating emerging Industry 4.0 technologies into smart factories has been widely discussed, particularly challenges regarding the practical use of a blockchain; one remaining challenge is the role of a blockchain beyond logistics and traceability, as well as its ability to support explicit trust measurement in real industrial environments. Existing studies often treat trust as a conceptual or cloud-oriented construction, without linking it to measurable production events. This study proposes a blockchain service-level agreement (SLA) to measure trust at an open-source frugal smart factory (SF). Trust is defined as a dynamic quantitative score derived from measurable process events, including estimated and response times, assembly correctness, and transaction outcomes; all of this is calculated through a smart contract implemented on a blockchain network. The approach is implemented in a tangram puzzle assembly process that integrates cyber-physical systems, edge computing, artificial intelligence, cloud computing, data analytics, cybersecurity, and the blockchain within a unified SF architecture. The framework was experimentally validated across four representative assembly scenarios: (i) the SF delivered the puzzle in time and was correctly assembled (${\lambda }_s$ = 0.1734), (ii) the puzzle was completed within tolerance time (${\lambda }_s$ = 0.0649), (iii) the puzzle was delivered on time and was incorrectly assembled (${\lambda }_s$ = 0.0005), and (iv) the puzzle was completed outside the tolerance time and was correctly assembled (${\lambda }_s$ = 4.91 × ${10}^{-5}$); demonstrating that the model accurately estimates expected assembly times and updates trust without manual intervention during a physical manufacturing task, addressing the limitations of prior conceptual and cloud-based approaches. The main research contributions include an operational SLA-based trust model, the demonstration of the feasibility of applying blockchain-based SLAs in a physical SF environment, and evidence that a blockchain can be justified as a mechanism for managing and measuring trust in SF, rather than solely for traceability or logistics. Full article

Multi-robot systems are increasingly deployed in critical applications such as search and rescue, precision agriculture, and autonomous transportation. However, the presence of Byzantine robots—agents that intentionally transmit false or misleading information—can severely compromise mission success and system safety, highlighting the urgent need for robust fault-tolerant coordination mechanisms. To address the challenge of Byzantine faults in multi-robot systems, we propose a novel approach utilizing a blockchain-based framework, termed RobotOBchain (Robot Observation Blockchain). RobotOBchain permanently records each robot’s own state information and its observed neighboring robots’ states at every time step. By leveraging smart contracts encoded within the blockchain, our method automatically detects state inconsistencies or conflicts among recorded observations, enabling early identification of intentionally deceptive Byzantine robots. Experimental validation demonstrates that RobotOBchain achieves 100% consistent Byzantine identification across all robots, maintains estimation errors within 3% of ground-truth, and exhibits robust tolerance to up to 50% malicious agents. These results significantly surpass the performance of classical W-MSR algorithms, while eliminating the dependency on predefined fault bounds. The framework’s demonstrated capabilities indicate strong potential for practical deployment in dynamic and safety-critical multi-robot applications. Full article

Background: Exercise capacity is markedly impaired in patients with end-stage renal disease (ESRD) due to neuromuscular dysfunction and reduced oxygen delivery. This study aimed to investigate the effects of NMES during HD on exercise capacity in patients with ESRD. It specifically examined neuromuscular and hemodynamic adaptations. Methods: Twenty-two patients with ESRD were randomized to a neuromuscular electrical stimulation training group (NSTG, n = 11) or a control group (CG, n = 11). The NSTG underwent intradialytic quadriceps NMES three times per week for 12 weeks (40 min/session). Exercise capacity was evaluated via sustained isometric contraction at 50% of maximal voluntary contraction (MVC) until exhaustion. Neuromuscular function was assessed through voluntary activation (ΔVA) and potentiated twitch force (ΔQ_tw,pot), while muscle oxygenation (ΔO₂Hb, ΔHHb, ΔTHb) of the vastus lateralis was continuously monitored using near-infrared spectroscopy. Results: After the intervention, the NSTG showed a significant +20% increase in Tlim (103.9 ± 14.4 s to 123.3 ± 16.6 s; p = 0.01) and +30% improvement in MVC (421.3 ± 24.9 N to 550.4 ± 20.3 N; p < 0.01), while no improvements were observed in CG. Resting VA increased by ≈7% in NSTG (90.2 ± 3.7% to 96.8 ± 2.5%; p = 0.012). Improved muscle oxygenation and reduced twitch force suggest enhanced oxidative capacity and greater peripheral fatigue tolerance. Conclusions: Intradialytic NMES elicited robust improvements in exercise capacity, muscle strength, and oxygen utilization in ESRD patients by enhancing both central activation and peripheral oxidative adaptations. These findings support NMES as a feasible and effective rehabilitative strategy to counteract fatigue and functional decline in the ESRD population. Full article

Predefined-time control has been extensively implemented in marine control systems due to its capability to enhance transient performance and achieve superior control specifications. However, inaccurate control execution resulting from faulty actuators can compromise this control strategy and critically undermine system performance. To address this challenge, this paper propose a predefined-time model predictive fault-tolerant control strategy for unmanned surface vessels (USVs) while considering actuator failures and ocean disturbances. Firstly, a novel predefined-time model predictive control (PTMPC) strategy is designed by incorporating contraction constraints derived from an auxiliary predefined-time control system into the proposed optimization framework. This ensures that the resulting control variables guarantee predefined-time convergence of tracking errors when applied to the USV system. Furthermore, a long short-term memory-based neural network for disturbance prediction is integrated into the control strategy, leveraging its exceptional capability in modeling temporal sequences to achieve accurate forecasting of ocean disturbances. Thirdly, the proposed control scheme utilizes its integrated fault observation mechanism to actively compensate for actuator failures through real-time fault estimation, ensuring predefined-time convergence performance while providing rigorous guarantees of closed-loop stability and feasibility. Finally, simulation results demonstrate the efficacy and superiority of the proposed algorithm. Full article

Background/Objectives: Studies of the effects of anchor schemes (perceived intensity vs. relative intensity) on muscular performance have reported mixed findings. Therefore, the present study examined the effects of different anchor schemes on time-to-task failure (TTF), muscular performance, neuromuscular responses, and potentiated twitch torque (PTT). Methods: On separate days, 15 men (age = 21.5 ± 2.3 yrs) performed forearm flexion maximal voluntary isometric contractions (MVICs) before and after sustained tasks anchored to a rating of perceived exertion of 6 (RPEFT) and with the torque at RPE = 6 (TRQFT). Electromyographic amplitude (EMG AMP) and mean power frequency (EMG MPF) were recorded from the biceps brachii (BB). Supramaximal stimuli were delivered to the motor nerve of the BB following the MVICs to quantify the PTT. Repeated measures ANOVAs assessed the mean differences between anchor schemes for MVIC, neuromuscular, and PTT responses. Paired t-tests compared the magnitude of percent changes for the dependent variables. Results: The TTF for the RPEFT was longer (p < 0.001) than the TRQFT, but the MVIC decreased similarly (12.7 ± 9.5% vs. 20.3 ± 7.9%, p = 0.054). Electromyographic AMP did not change (p = 0.288), while EMG MPF decreased (15.7 ± 10.2%, p < 0.011) for the TRQFT only. Mean decreases in PTT were comparable for both tasks (p < 0.003), although the percent change was greater for the TRQFT (49.6 ± 16.1%, p < 0.001). Conclusions: The differences in TTF, but similar decreases in MVIC suggested that participants reached a sensory tolerance limit. Based on EMG MPF and PTT, the TRQFT caused greater peripheral perturbations to contractile function than the RPEFT. Full article

With the extensive application of the Global Navigation Satellite System (GNSS), the intelligent upgrading of the GNSS monitoring system is of particular significance. Traditional GNSS monitoring systems typically rely on a centralized architecture, which possesses certain drawbacks when it comes to data tampering, fault tolerance, and data sharing. This paper presents an intelligently upgraded localized GNSS monitoring system that integrates blockchain and artificial intelligence (AI) technology to achieve the deep integration of security, transparency, and intelligent processing of monitoring data. Firstly, this paper employs blockchain technology to guarantee the integrity and tamper-resistance of GNSS monitoring data and utilizes a distributed ledger structure to realize the decentralization of data storage and transmission, thereby enhancing the anti-attack capability and reliability of the system. Secondly, the LSTM model is utilized to analyze and predict the vast amount of monitoring data in real-time, enabling the intelligent detection of GNSS signal anomalies and deviations and providing real-time early warnings to optimize the monitoring effect. Based on this architecture, we also combine the trained model with smart contracts to realize real-time monitoring and early warnings of GNSS satellites. By integrating the security guarantee of blockchain and the intelligent analysis ability of AI, the localized GNSS monitoring system can offer more efficient and accurate data monitoring and management services. In the study, we constructed a prototype system and tested it in both simulated and real environments. The results indicate that the system can effectively identify and respond to GNSS signal anomalies, and enhance the monitoring accuracy and response speed. Additionally, the application of blockchain enhances the immutability and traceability of data, providing a solid foundation for the long-term storage and auditing of GNSS data. The introduction of AI algorithms, especially the application of the Long Short-Term Memory (LSTM) network in anomaly detection, has significantly enhanced the system’s ability to recognize complex patterns. Full article

In modern electrical grids, the numbers of customer-owned distributed energy resources (DERs) have increased, and consequently, so have the numbers of points of common coupling (PCC) between the electrical grid and customer-owned DERs. The disruptive operation of and out-of-tolerance outputs from DERs, especially owned DERs, present a risk to power system operations. A common protective measure is to use relays located at the PCC to isolate poorly behaving or out-of-tolerance DERs from the grid. Ensuring the integrity of the data from these relays at the PCC is vital, and blockchain technology could enhance the security of modern electrical grids by providing an accurate means to translate operational constraints into actions/commands for relays. This study demonstrates an advanced power system application solution using distributed ledger technology (DLT) with smart contracts to manage the relay operation at the PCC. The smart contract defines the allowable total power factor (TPF) of the DER output, and the terms of the smart contract are implemented using DLT with a Cyber Grid Guard (CGG) system for a customer-owned DER (wind farm). This article presents flowcharts for the TPF smart contract implemented by the CGG using DLT. The test scenarios were implemented using a real-time simulator containing a CGG system and relay in-the-loop. The data collected from the CGG system were used to execute the TPF smart contract. The desired TPF limits on the grid-side were between +0.9 and +1.0, and the operation of the breakers in the electrical grid and DER sides was controlled by the relay consistent with the provisions of the smart contract. The events from the real-time simulator, CGG, and relay showed a successful implementation of the TPF smart contract with CGG using DLT, proving the efficacy of this approach in general for implementing electrical grid applications for utilities with connections to customer-owned DERs. Full article

This study developed a risk assessment tool for contract capacity optimization problems using the ant colony optimization and auto-regression model. Based on the historical data of demand consumption, the Least Square algorithm, the Recursive Levinson–Durbin algorithm, and the Burg algorithm were used to derive the auto-regression model. Then, ant colony optimization was used to search for the auto-regression model’s best p-order parameters. To avoid the risk of setting the contract capacity, this paper designed the risk tolerance parameter β to correct the predicted value of the auto-regression model. Ant colony optimization was also used to search for the optimal contract capacity with risk assessment under the two-stage time-of-use and three-stage time-of-use. This study employed an industrial consumer with high voltage power in Taiwan as the research object, used the AR model to estimate the contract capacity under the risk assessment, and cut back electricity usage to reduce the operation cost. The results can be used as a basis to develop an efficient tool for industrial customers to select contract capacities with risks to obtain the best economic benefits. Full article

With the rapid development of the Internet of Vehicles (IoV), the demand for wireless spectrum resources has significantly increased. Dynamic spectrum sharing technology is regarded as a key solution to alleviate the shortage of spectrum resources. However, during the spectrum sharing process, security issues and a low utilization of the shared spectrum may arise. This study designs a consortium blockchain for trustworthy dynamic spectrum sharing in IoV environments. An improved asynchronous byzantine fault-tolerant algorithm is also designed to address the instability of signals in this scenario, and the allocation and management of spectrum resources between vehicles and base stations are further optimized using the Stackelberg game, ultimately deployed automatically through smart contracts. Simulation results show that our method not only significantly improves the system’s response time but also ensures communication quality and can maintain efficient operation under high network delay and complex scenarios. Full article

Cross-chain transaction technologies have greatly promoted the scalability of cryptocurrencies, which then facilitates the development of Metaverse applications. However, existing solutions rely heavily on centralized middleware (notary) or smart contracts. These schemes lack privacy considerations, and users’ cross-chain transactions are easy to master by other parties. Some signature-based payment schemes have good privacy but do not support multi-party cross-chain protocols or rely heavily on some time assumptions. The uncertainty of user behavior makes it difficult to design a secure multi-party cross-chain protocol. To solve these problems, we investigate how to design a secure multi-party cross-chain transaction protocol with offline tolerance. We propose a new signature algorithm called the pre-adaptor signature scheme, an extension of the adaptor signature scheme. The pre-adaptor signature scheme combines the multi-signature and adaptor signature schemes, which can realize the secret transmission channel between multiple parties. To provide offline tolerance, we encode our protocol into the P2SH script. Our protocol provides better privacy due to no dependence on smart contracts. The performance evaluation was conducted with ten participants. For each participant of our cross-chain protocol, the initialization and execution process can be performed in 3 milliseconds and with 6 k bytes of communication overhead at most. The cost increases linearly with the increase in the number of participants. Full article

Species within the genus Chenopodium hold significant research interest due to their nutritional richness and salt tolerance. However, the morphological similarities among closely related species and a dearth of genomic resources have impeded their comprehensive study and utilization. In the present research, we conduct the sequencing and assembly of chloroplast (cp) genomes from six Chenopodium and related species, five of which were sequenced for the first time. These genomes ranged in length from 151,850 to 152,215 base pairs, showcased typical quadripartite structures, and encoded 85 protein-coding genes (PCGs), 1 pseudogene, 37 tRNA genes, and 8 rRNA genes. Compared with the previously published sequences of related species, these cp genomes are relatively conservative, but there are also some interspecific differences, such as inversion and IR region contraction. We discerned 929 simple sequence repeats (SSRs) and a series of highly variable regions across 16 related species, predominantly situated in the intergenic spacer (IGS) region and introns. The phylogenetic evaluations revealed that Chenopodium is more closely related to genera such as Atriplex, Beta, Dysphania, and Oxybase than to other members of the Amaranthaceae family. These lineages shared a common ancestor approximately 60.80 million years ago, after which they diverged into distinct genera. Based on InDels and SNPs between species, we designed 12 pairs of primers for species identification, and experiments confirmed that they could completely distinguish 10 related species. Full article

Previous studies have demonstrated that the intestinal abundance of Bacteroides uniformis is significantly higher in healthy controls than that in patients with ulcerative colitis (UC). However, what effect B. uniformis has on the development of UC has not been characterized. Here, we show for the first time that B. uniformis F18-22, an alginate-fermenting bacterium isolated from the healthy human colon, protects against dextran-sulfate-sodium (DSS)-induced UC in mice. Specifically, oral intake of B. uniformis F18-22 alleviated colon contraction, improved intestinal bleeding and attenuated mucosal damage in diseased mice. Additionally, B. uniformis F18-22 improved gut dysbiosis in UC mice by increasing the abundance of anti-inflammatory acetate-producing bacterium Eubacterium siraeum and decreasing the amount of pro-inflammatory pathogenetic bacteria Escherichia-Shigella spp. Moreover, B. uniformis F18-22 was well-tolerated in mice and showed no oral toxicity after repeated daily administration for 28 consecutive days. Taken together, our study illustrates that B. uniformis F18-22 is a safe and novel probiotic bacterium for the treatment of UC from the healthy human colon. Full article