Search Results (290)

Background/Objectives: This study aimed to compare the effects of low- and medium-frequency NMES, combined with a standard physical therapy (SPT) program, on functional capacity in critically ill patients. Methods: Fifty-four critically ill patients admitted into Intensive Care Unit (ICU) and on mechanical ventilation participated in this randomized, single-blinded, experimental study. Participants were randomly assigned to a Control group, who received a lower limb SPT program; the Low-frequency NMES group received lower limb SPT + NMES at 100 Hz; and the Medium-frequency NMES group received lower limb SPT + NMES at 100 Hz with a carrier frequency of 2500 Hz. The outcomes, encompassing functional capacity in the hospital, included muscle strength, handgrip strength, functional status, degree of independence for activities of daily living, functional and dynamic mobility, quality of life, and total days hospitalized. Results: Both NMES protocols combined with SPT improved functional capacity compared to the control group. Medium-frequency NMES provided additional benefits on dynamic balance, in the degree of independence to perform activities of daily living and quality of life (all p < 0.001) prior to hospital discharge. It also promoted larger gains on functional status prior to ICU discharge and on knee extension strength (both p < 0.05) prior to intermediate care unit discharge. Medium-frequency NMES also enhanced handgrip strength earlier than low-frequency NMES when compared to the control group. Notably, medium-frequency NMES was the only intervention associated with a significant reduction in total hospital stay duration (p < 0.05). Conclusions: Medium-frequency NMES, along with an SPT program in critically ill patients, showed greater benefits on functional capacity during recovery than low-frequency NMES. (Trial registration: This trial is registered on ClinicalTrials.gov: NCT05287919). Implications for rehabilitation: 1. Medium-frequency NMES may enhance physical functionality and quality of life in critically ill patients with ICU-acquired weakness. 2. Medium-frequency NMES can reduce the number of hospitalization days. 3. NMES combined with SPT represents a feasible and effective option for patients unable to engage in active rehabilitation during critical illness. Full article

Machine learning techniques have revolutionized physical layer security (PLS) and provided opportunities for optimizing the performance and security of modern communication systems. In this study, we propose the first machine learning-assisted random communication system (ML-RCS). It comprises a pretrained decision tree (DT)-based receiver that extracts binary information from the transmitted random noise carrier signals. The ML-RCS employs skewed alpha-stable ($\alpha$-stable) noise as a random carrier to encode the incoming binary bits securely. The DT model is pretrained on an extensively developed dataset encompassing all the selected parameter combinations to generate and detect the $\alpha$-stable noise signals. The legitimate receiver leverages the pretrained DT and a predetermined key, specifically the pulse length of a single binary information bit, to securely decode the hidden binary bits. The performance evaluations included the single-bit transmission, confusion matrices, and a bit error rate (BER) analysis via Monte Carlo simulations. The fact that the BER reached 10⁻³ confirms the ability of the proposed system to establish successful secure communication between a transmitter and legitimate receiver. Additionally, the ML-RCS provides an increased data rate compared to previous random communication systems. From the perspective of security, the confusion matrices and computed false negative rate of 50.2% demonstrate the failure of an eavesdropper to decode the binary bits without access to the predetermined key and the private dataset. These findings highlight the potential ability of unconventional ML-RCSs to promote the development of secure next-generation communication devices with built-in PLSs. Full article

The Internet of Vehicles (IoVs) uses vehicles as the main carrier to communicate with other entities, promoting efficient transmission and sharing of traffic data. Using real identities for communication may leak private data, so pseudonyms are commonly used as identity credentials. However, existing anonymous authentication schemes have limitations, including large vehicle storage demands, information redundancy, time-dependent pseudonym updates, and public–private key updates coupled with pseudonym changes. To address these issues, we propose a certificateless strong anonymous privacy protection authentication scheme that allows vehicles to autonomously generate and dynamically update pseudonyms. Additionally, the trusted authority transmits each entity’s partial private key via a session key, eliminating reliance on secure channels during transmission. Based on the elliptic curve discrete logarithm problem, the scheme’s existential unforgeability is proven in the random oracle model. Performance analysis shows that it outperforms existing schemes in computational cost and communication overhead, with the total computational cost reduced by 70.29–91.18% and communication overhead reduced by 27.75–82.55%, making it more suitable for privacy-sensitive and delay-critical IoV environments. Full article

Power converters in the smart grid systems are essential to link renewable energy sources with all grid appliances and equipment. However, this raises the possibility of electromagnetic interference (EMI) between the smart grid elements. Hence, spread spectrum (SS) modulation techniques have been used to mitigate the EMI peaks generated from the power converters. Consequently, the performance of the nearby communication systems is affected under the presence of EMI, which is not covered in many situations. In this paper, the behavior of the G3 Power Line Communication (PLC) channel is evaluated in terms of the Shannon–Hartley equation in the presence of SS-modulated EMI from a buck converter. The SS-modulation technique used is the Random Carrier Frequency Modulation with Constant Duty cycle (RCFMFD). Moreover, The analysis is validated by experimental results obtained with a test setup reproducing the parasitic coupling between the PLC system and the power converter. Full article

With the growing deployment of latency-sensitive applications, the Age of Information (AoI) has emerged as a key performance metric for the evaluation of data freshness in networked systems. While prior studies have extensively explored the AoI under centralized scheduling or random-access protocols such as carrier sense multiple access (CSMA) and ALOHA, most assume that sources generate independent information. However, in practical scenarios such as environmental monitoring and visual sensing, information correlation frequently exists among correlated sources, providing new opportunities to enhance network timeliness. In this paper, we propose a novel analytical framework that captures the interplay between CSMA channel contention and spatial information correlation among sources. By leveraging the stochastic hybrid systems (SHS) methodology, we jointly model random backoff behavior, medium access collisions, and correlated updates in a scalable and mathematically tractable manner. We derive closed-form expressions for the average AoI under general correlation structures and further propose a lightweight estimation approach for scenarios where the correlation matrix is partially known or unknown. To our knowledge, this is the first work that integrates correlation-aware modeling into AoI analysis under distributed CSMA protocols. Extensive simulations confirm the accuracy of the theoretical results and demonstrate that exploiting information redundancy can significantly reduce the AoI, particularly under high node densities and constrained sampling budgets. These findings offer practical guidance for the design of efficient and timely data acquisition strategies in dense or energy-constrained Internet of Things (IoT) networks. Full article

Objectives: A randomized, double-blind, placebo-controlled intervention study aimed to evaluate whether hawthorn fruit (HF) supplementation can influence facial skin phenotypes and leukocyte telomere length (TL) and whether these effects differ by genetic polymorphisms related to TL. Participants/Methods: Among 41 male and female adults aged 25–75 years who participated in the study, 36 completed initial and follow-up examinations over 6 months. The HF supplementation group (n = 17) was instructed to take a powdered HF supplement (900 mg/day), while controls (n = 19) were to take a cornstarch placebo (900 mg/day). Facial skin phenotypes, including pigmentation, pores, hydration, wrinkles, and elasticity, were measured before and after the intervention, and changes in these phenotype scores were calculated. Sequencing of telomerase reverse transcriptase (TERT) polymorphisms, such as rs7705526 (C>A) and rs2853669 (A>G), was conducted. Results: The HF supplementation group exhibited significantly improved hydration scores compared to the control group; the mean changes (follow-up measure—baseline measure) [standard deviation] in hydration scores over 6 months were 1.71 [8.18] and −3.00 [8.42] for the supplementation group and control group, respectively (p < 0.05) (Cohen’s d = 0.57). However, changes in other phenotypes and leukocyte TL were similar between groups. The genotype-specific analysis revealed that the improvement of hydration state was most noticeable among carriers with the CC genotype of rs7705526 (p < 0.05) (Cohen’s d = 1.50) and that the HF supplementation group exhibited reduced wrinkle scores while the control group showed increased scores among carriers of the AA genotype of rs2853669 (p < 0.05) (Cohen’s d = 1.40). In correlation analysis for all participants, hydration scores were positively correlated with leukocyte TL (Spearman correlation coefficient: 0.36; p < 0.05). Conclusions: These findings suggest that HF consumption may have potential anti-skin-aging effects. Future studies may need to elucidate the biological mechanisms underlying these effects. Full article

Background/Objectives: Checkpoint inhibitors (ICIs) are key therapies for NSCLC, but current selection criteria, such as excluding mutation carriers and assessing PD-L1, lack sensitivity. As a result, many patients receive costly treatments with limited benefit. Therefore, this study aimed to predict which NSCLC patients would achieve durable survival (≥24 months) with immunotherapy. Methods: A comprehensive ensemble radiomics approach was applied to pretreatment CT scans to prognosticate overall survival (OS) and predict progression-free survival (PFS) in a cohort of 220 consecutive patients with inoperable NSCLC treated with first-line ICIs (pembrolizumab or atezolizumab, nivolumab or prolgolimab) as monotherapy or in combination. The radiomics pipeline evaluated four normalization methods (none, min-max, Z-score, mean), four feature selection techniques (ANOVA, RFE, Kruskal–Wallis, Relief), and ten classifiers (e.g., SVM, random forest). Using two to eight radiomics features, 1680 models were built in the Feature Explorer (FAE) Python package. Results: Three feature sets were evaluated: clinicopathological (CP) only, radiomics only, and a combined set, using 6- and 12-month PFS and 24-month OS endpoints. The top 15 models were ensembled by averaging their probability scores. The best performance was achieved at 24-month OS with the combined CP and radiomics ensemble (AUC = 0.863, accuracy = 85%), followed by radiomics-only (AUC = 0.796, accuracy = 82%) and CP-only (AUC = 0.671, accuracy = 76%). Predictive performance was lower for 6-month (AUC = 0.719) and 12-month PFS (AUC = 0.739) endpoints. Conclusions: Our radiomics pipeline improved selection of NSCLC patients for immunotherapy and could spare non-responders unnecessary toxicity while enhancing cost-effectiveness. Full article

This study presents a novel approach to predicting ship speed based on real-time voyage observation data, aiming to enhance maritime safety and operational efficiency. Observational data from a 20,000-ton bulk carrier, including variables such as latitude, longitude, GPS orientation, wind direction, wind speed, and main engine parameters, were collected and preprocessed to mitigate noise and handle missing values. Six machine learning models—the Backpropagation (BP) Neural Network, Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), XGBoost, and LightGBM—were employed to develop predictive models. Among these, the LightGBM model demonstrated the highest prediction accuracy, achieving a Root Mean Squared Error (RMSE) of 0.188, Mean Absolute Error (MAE) of 0.149, and a coefficient of determination (R²) of 0.978. The results highlight the potential of the LightGBM model in optimizing ship navigation and improving maritime operational efficiency. These findings offer a reliable foundation for further advancements in predictive maritime technologies and route optimization. Full article

Background and Objectives: The development of non-dairy probiotic products is a challenge for the food industry, while cereals, as probiotic carriers, provide the means to incorporate probiotics, prebiotics, and fiber into the human diet. The present study investigated the effects of Lactococcus cremoris spp. immobilized on oat flakes on blood and urine biomarkers in a randomized placebo-controlled single-blind clinical trial. Materials and Methods: Fifty-four eligible participants were randomized into a placebo or probiotic group that consumed 5 g of oat flakes daily for 12 weeks. Blood and urine samples were collected at the baseline, 6 weeks, and 12 weeks to assess the glycemic, lipemic, inflammatory, immunological, and antioxidant biomarkers, as well as the vitamin levels. Results: The intervention group exhibited a significant reduction in their hs-CRP levels (p = 0.002) and a trend toward decreased IL-6 levels (p = 0.035) at week 12 compared to the control group, suggesting a potential anti-inflammatory effect. Additionally, a significant reduction in insulin levels was observed in the probiotic group at week 6, with a clinical trend toward differentiation despite the absence of statistically significant differences between the groups. Furthermore, there were promising results regarding certain biomarkers, such as vitamin B12 and cortisol levels, in the probiotic group. Conclusions: The twelve-week consumption of Lactococcus cremoris spp. immobilized on oat flakes resulted in improvements in inflammatory, metabolic, and stress-related biomarkers. These results support the examined concept of non-dairy probiotic products, though further research is needed to confirm their efficacy and clarify their underlying mechanisms. Full article

Frequency agile radar (FAR) exhibits robust anti-jamming capabilities and a superior low probability of intercept performance due to its randomized carrier frequency (CF) and pulse repetition frequency (PRF) hopping sequences. The advent of compressed sensing (CS) theory has effectively addressed the coherent processing challenges of frequency agile signals. Nonetheless, the reconstructed results often suffer from elevated sidelobe levels, which lead to significant sparse recovery errors. The performance of sparse reconstruction is greatly influenced by the correlation between the dictionary matrix columns. Specifically, weaker correlation usually means better target detection performance and lower false alarm probability. Consequently, this paper adopts the maximum coherence coefficient (MCC) between the dictionary matrix columns as the cost function. In addition, in order to reduce the correlation of the dictionary matrix and improve the target detection performance, a genetic algorithm (GA) is employed to jointly optimize the CF hopping coefficients and PRFs of the FAR. The echo of optimized signals is subsequently reconstructed using the alternating direction method of multipliers (ADMM) algorithm. Simulation results demonstrate the effectiveness of the proposal. Full article

Covert wireless communication is designed to securely transmit hidden information between two devices. Its primary objective is to conceal the existence of transmitted data, rendering communication signals difficult for unauthorized parties to detect, intercept, or decipher during transmission. In this paper, we propose a Noise-like Multi-Carrier Random Phase Communication System (NRPCS) to enhance covert wireless communication by significantly complicating the detection and interception of transmitted signals. The proposed system utilizes bipolar modulation and Cyclic Code Shift Keying (CCSK) modulation, complemented by a random sequence generation mechanism, to increase the randomness and complexity of the transmitted signals. A mathematical model of the NRPCS waveform is formulated, and detailed analyses of the system’s time-domain basis functions, correlation properties, and power spectral characteristics are conducted to substantiate its noise-like behavior. Simulation results indicate that, compared to traditional fixed-frequency transmission methods, NRPCS substantially improves both the Low Probability of Detection (LPD) and the Low Probability of Interception (LPI). Further research results demonstrate that unauthorized eavesdroppers are unable to effectively demodulate signals without knowledge of the employed modulation scheme, thus significantly enhancing the overall security of communication. Full article

A road network is an important spatial carrier for the efficient and reliable operation of urban services and material flows. In recent years, the “high road density, small block size” trend has become a major focus in urban planning practices. However, whether high-density road networks are highly resilient lacks quantitative evidence. This study presents a multi-scale analytical framework for measuring road network resilience from a topological perspective. We abstract 186 ideal orthogonal grid density models from an actual urban road network, quantifying resilience under two disturbance scenarios: random failures and intentional attacks. The results indicate that road network density indeed has a significant impact on resilience, with both scenarios showing a trend where higher densities correlate with greater resilience. However, the increase in resilience value under the intentional attack scenario is significantly higher than that under the random failure scenario. The findings indicate that network density plays a decisive role in determining resilience levels when critical edges fail. This is attributed to the greater presence of loops in denser networks, which helps maintain connectivity even under intentional disruption. In the random failure scenario, network resilience depends on the combined effects of the node degree and density. This study offers quantitative insights into the design of resilient urban forms in the face of disruptive events, establishing reference benchmarks for road network spacing at both meso- and micro-scales. The results provide practical guidance for resilient city planning in both newly developed and existing urban areas, supporting informed decision-making in urban morphology and disaster risk management. Full article

On the basis of the space voltage vector pulse width modulation (SVPWM) technique, the random pulse width modulation (RPWM) technique, which can reduce harmonics, is investigated based on the vector control system of permanent magnet synchronous motor (PMSM) to address the problem of generating a large number of high-amplitude harmonics at the carrier frequency and its multiplier frequency. Firstly, the root causes of the large number of high-amplitude harmonics at the carrier frequency and its multiplier frequency are analyzed in depth, and the RPWM technique is explained in detail on how to reduce the amplitude of these harmonics effectively. Secondly, to address the problem of insufficient random performance in the traditional RPWM technique, an innovative optimization scheme is proposed, i.e., the introduction of a two-state Markov chain and, based on the immune algorithm for transition probability and random gain, the optimization of two key parameters. Ultimately, through experimental verification, the proposed method significantly improves the spectral distribution of the current waveform compared with the traditional RPWM, which makes the distribution more uniform and effectively reduces the high-amplitude harmonics concentrated near the carrier frequency and its octave frequency, thus enhancing the overall performance of the system. Full article

The development of drug carriers with efficient absorption and controlled delivery properties is crucial for advancing medical treatments. Metal–organic frameworks (MOFs) with tunable porosity and a large surface area represent a promising class of materials for this application. Among them, NUIG4 stands out as a biocompatible MOF that exhibits exceptionally high doxorubicin (Dox) absorption (1995 mg dox/g NUIG4) and pH-controlled release properties. In this study, we report the synthesis and characterisation of multivariate MOFs (MV-NUIG4), which are analogues of NUIG4 that maintain the same topology while incorporating different functional groups within their framework. Eight new MV-NUIG4 MOFs have been synthesised through in situ reactions of the corresponding 4-aminobenzoic acid derivative with 4-formylbenzoic acid. The compounds were thoroughly characterised using a range of techniques, including powder X-ray diffraction, infrared spectroscopy, ¹H-NMR, and single-crystal X-ray crystallography. The experimental ratio of the reagents and ligand precursors for the synthesis of MV-NUIG4 MOFs matched the ratio of the linkers in the final products. These structures incorporate additional functional groups, such as methyl and hydroxyl, in varying ratios. Computational modelling was used to provide further insight into the crystal structure of the MOFs, revealing a random distribution of the functional groups in the framework. The Dox absorption and release capacity of all analogues were studied, and the results revealed that all analogues displayed high drug absorption in the range of 1234–1995 mg Dox/g MOF. Furthermore, the absorption and release rates of the drug are modulated by the ratio of functional groups, providing a promising approach for controlling drug delivery properties in MOFs. Full article

Picking fruits from tall fruit trees manually is laborious and inefficient. Rotary-wing drones, a low-altitude carrier platform, can enhance the picking efficiency for tall fruit trees when combined with picking robotic arms. However, during the operation of rotary-wing drones, the wind field changes dramatically, and the center of gravity of the drone shifts at the moment of picking, leading to poor aerodynamic stability and making it difficult to achieve optimized attitude control. To address the aforementioned issues, this paper constructs a drone and wind field testing platform and employs the Lattice Boltzmann Method and Large Eddy Simulation (LBM-LES) algorithm to solve the high-dynamic, rapidly changing airflow field during the transient picking process of the drone. The aerodynamic structure of the drone is optimized by altering the rotor spacing and duct intake ratio of the harvesting drone. The simulation results indicate that the interaction of airflow between the drone’s rotors significantly affects the stability of the aerodynamic structure. When the rotor spacing is 2.8R and the duct ratio is 1.20, the lift coefficient is increased by 11% compared to the original structure. The test results from the drone and wind field experimental platform show that the rise time ($t_r$) of the drone is shortened by 0.3 s, the maximum peak time ($t_p$) is reduced by 0.35 s, and the adjustment time ($t_s$) is accelerated by 0.4 s. This paper, by studying the transient wind field of the harvesting drone, clarifies the randomness of the transient wind field and its complex vortex structures, optimizes the aerodynamic structure of the harvesting drone, and enhances its aerodynamic stability. The research findings can provide a reference for the aerodynamic optimization of other types of drones. Full article