Search Results (692)

Background/Objectives: Previous studies show that allocating carbohydrates earlier and vegetables/protein later in late-evening meals improves glycemic control in both healthy individuals and those with type 2 diabetes. However, evidence remains insufficient regarding the effects of distributing carbohydrate intake across the day by dividing three regular meals into five smaller meals. Methods: We conducted a randomized, controlled, crossover trial to compare the effects of two dietary patterns: (1) a conventional three-meal pattern with simultaneous intake of all food components, and (2) a five-meal pattern incorporating divided carbohydrate portions and a fixed food order—vegetables first, followed by protein, and then carbohydrates. Eighteen healthy young women consumed the same test meals under both patterns. Glucose fluctuations were monitored using an intermittently continuous glucose monitoring system. Results: The five-meal pattern with food sequencing significantly improved the mean amplitude of glycemic excursions (MAGE; 2.56 ± 0.13 vs. 3.49 ± 0.32 mmol/L, p < 0.01), glucose peak, and incremental area under the glucose curve for breakfast, lunch, and dinner, and the time above the target glucose range [>7.8 mmol/L; 1.4 ± 0.6 vs. 4.2 ± 1.0%, p < 0.01] compared to the three-meal pattern. Conclusions: These findings suggest that divided carbohydrate intake and food order ameliorates the MAGE in healthy young women. Full article

Ectropis grisescens (Lepidoptera: Geometridae) is a destructive pest in tea plantations, leading to significant economic losses through defoliation. Existing control strategies, including chemical insecticides and biological agents, are often limited by environmental concerns, resistance, and variable efficacy. Recent evidence suggests that bacteria influence insect physiology and could be leveraged for pest management, but the postmortem microbial ecology of E. grisescens remains uncharacterized. In this study, we employed 16S rRNA sequencing to investigate temporal changes in the bacterial communities of E. grisescens cadavers at 0, 7, and 21 days following cryogenic mortality. Our results indicate a time-dependent decline in microbial diversity, while species richness initially increased before subsequent reduction. The dominant endosymbiont Wolbachia gradually diminished after host death, whereas Enterobacter remained abundant. Notably, non-dominant genera including Lysinibacillus and Sporosarcina exhibited a transient increase in abundance at day 7 before reverting to control levels by day 21. This study presents the first comprehensive analysis of postmortem microbial succession in a lepidopteran system, highlighting dynamic shifts in bacterial composition and offering potential avenues for microbiome-based pest management strategies. 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

This study presents the design and evaluation of AquaSignalNet, a deep learning-based system for recognizing underwater visual commands to enable the autonomous navigation of a Remotely Operated Vehicle (ROV). The system is built on a ResNet50 V2 architecture and trained with a custom dataset, UVSRD, comprising 33,800 labeled images across 12 gesture classes, including directional commands, speed values, and vertical motion instructions. The model was deployed on a Raspberry Pi 4 integrated with a TIVA C microcontroller for real-time motor control, a PID-based depth control loop, and an MPU9250 sensor for orientation tracking. Experiments were conducted in a controlled pool environment using printed signal cards to define two autonomous trajectories. In the first trajectory, the system achieved 90% success, correctly interpreting a mixed sequence of turns, ascents, and speed changes. In the second, more complex trajectory, involving a rectangular inspection loop and multi-layer navigation, the system achieved 85% success, with failures mainly due to misclassification resulting from lighting variability near the water surface. Unlike conventional approaches that rely on QR codes or artificial markers, AquaSignalNet employs markerless visual cues, offering a flexible alternative for underwater inspection, exploration, and logistical operations. The results demonstrate the system’s viability for real-time gesture-based control. Full article

In this study, the RUSITEC system was used to study the regulation of rumen-derived Pseudomonas sp. PSC001 (PSC001) on the rumen environment contaminated by Zearalenone (ZEN). The rumen fluid of dairy cows was selected as the fermentation broth, and four experimental groups were set up: control group (CON), Pseudomonas group (PS), ZEN pollution group (ZEN), and PS and ZEN co-treatment group (PS + ZEN). The NH₃-N, microbial protein (MCP), and volatile fatty acid (VFA) in the rumen fermentation broth were measured after culturing, and the changes in microbial community structure in rumen fluid were analyzed by 16S rRNA gene sequencing. After adding PSC001, the concentration of propionic acid, valeric acid, and butyric acid increased, and the acetate to propionate ratio and concentration of isovaleric acid decreased. ZEN exposure can lead to an abnormal increase in NH₃-N, valeric acid, and isovaleric acid content and a decrease in MCP content. The content of NH₃-N, valeric acid, and isovaleric acid decreased and the content of MCP increased in the PS + ZEN combined treatment group. The addition of PSC001 and ZEN significantly or extremely significantly increased the abundance of 18 genera and significantly or extremely significantly decreased the relative abundance of 5 genera in rumen fluid, respectively. It is worth noting that with the addition of both at the same time, the abundance of four genera in the PS + ZEN group was significantly or extremely significantly increased among the five genera with decreased abundance in the ZEN group. Among the 18 genera with increased abundance in the ZEN group, 10 genera in the PS + ZEN group decreased significantly or extremely significantly. In summary, the addition of PSC001 alleviated the negative impact of ZEN on the internal environment of rumen fermentation, and it also had a positive regulatory effect on rumen fermentation. Full article

With the increasing penetration of renewable energy generation, analysis of the transient characteristics of doubly fed induction generators, as the mainstream wind turbine configuration, is made highly significant both theoretically and practically. However, manufacturers treat the control parameters as confidential commercial secrets, rendering them a “black box”. Parameter identification is fundamental for studying transient characteristics and system stability. Existing identification methods achieve accurate results only under moderate or severe voltage dip faults. To address this limitation, this paper proposes a control parameter identification method based on the adaptive differential evolution algorithm, suitable for DFIG time-domain simulation models. This method enables accurate parameter identification even during mild voltage dips. Firstly, a trajectory sensitivity analysis is employed to evaluate the difficulty of identifying each parameter, establishing the identification sequence accordingly. Secondly, based on the control loop where each parameter resides, the time-domain expressions are discretized to formulate the fitness function. Finally, the identified control parameters are compared against their true values. The results demonstrate that the proposed identification method achieves high accuracy and robustness while maintaining a rapid identification rate. Full article

Modeling mobile robots is crucial to odometry estimation, control design, and navigation. Classical state-space models (SSMs) have traditionally been used for system identification, while recent advances in deep learning, such as Long Short-Term Memory (LSTM) networks, capture complex nonlinear dependencies. However, few direct comparisons exist between these paradigms. This paper compares two multivariate modeling approaches for a differential drive robot: a classical SSM and an LSTM-based recurrent neural network. Both models predict the robot’s linear (v) and angular ($\omega$) velocities using experimental data from a five-minute navigation sequence. Performance is evaluated in terms of prediction accuracy, odometry estimation, and computational efficiency, with ground-truth odometry obtained via a SLAM-based method in ROS2. Each model was tuned for fair comparison: order selection for the SSM and hyperparameter search for the LSTM. Results show that the best SSM is a second-order model, while the LSTM used seven layers, 30 neurons, and 20-sample sliding windows. The LSTM achieved a FIT of 93.10% for v and 90.95% for $\omega$, with an odometry RMSE of 1.09 m and 0.23 rad, whereas the SSM outperformed it with FIT values of 94.70% and 91.71% and lower RMSE (0.85 m, 0.17 rad). The SSM was also more resource-efficient (0.00257 ms and 1.03 bytes per step) compared to the LSTM (0.0342 ms and 20.49 bytes). The results suggest that SSMs remain a strong option for accurate odometry with low computational demand while encouraging the exploration of hybrid models to improve robustness in complex environments. At the same time, LSTM models demonstrated flexibility through hyperparameter tuning, highlighting their potential for further accuracy improvements with refined configurations. Full article

Genetic fusion of a tag sequence to a target protein, or protein of interest (POI), is one of the most widely used technologies for recombinant expression. Tag-fusion proteins can enhance soluble expression, prolong half-life, increase binding avidity, and facilitate protein purification or refolding. In addition, tag-fusion proteins can be used to identify POI-binding partners through pull-down or immunoprecipitation assays. Beyond these classical applications, tags have evolved to serve as multifunctional tools, enabling real-time imaging, spatial localization, targeted delivery, and regulation of protein activity in living systems. Some engineered tags also allow conditional control, such as pH or ligand-dependent stabilization, thus expanding their utility in synthetic biology and therapeutic design. Here, we summarize protein-based and peptide-based tags, as well as methods for tag removal. While not fully comprehensive, this review aims to help researchers design suitable tag formats for specific goals. Full article

To address the challenges of high fuel consumption and emissions in traditional diesel-powered inland law enforcement vessels, this study proposes a methanol–electric hybrid propulsion system retrofitted with a novel energy management strategy (EMS) based on the integration of Dynamic Programming (DP) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The DP-ANFIS algorithm combines the global optimization capability of DP with the real-time adaptability of ANFIS to achieve efficient power distribution. A high-fidelity simulation model of the hybrid system was developed using methanol engine bench test data and integrated with models of other powertrain components. The DP algorithm was used offline to generate an optimal control sequence, which was then learned online by ANFIS to enable real-time energy allocation. Simulation results demonstrate that the DP-ANFIS strategy reduces total energy consumption by 78.53%, increases battery state of charge (SOC) by 3.24%, decreases methanol consumption by 64.95%, and significantly reduces emissions of CO, HC, NOx, and CO₂ compared to a rule-based strategy. Hardware-in-the-loop tests confirm the practical feasibility of the proposed approach, offering a promising solution for intelligent energy management in marine hybrid propulsion systems. Full article

Studies of early syn-rift successions in the Duwi Basin have revealed repetitive lacustrine carbonate deposits exhibiting regressive sequences and early diagenetic processes. Two main informal stratigraphic units (Units 1 and 2), spanning the Late Oligocene to Early Miocene, have been identified in the area. Unit 1 primarily consists of lacustrine limestone and calcrete deposits that formed in a palustrine environment, whereas Unit 2 is composed of dolomites and cherts, which developed during times of lake evaporation and desiccation under arid climatic conditions. A wide variety of pedogenic features, including brecciation, nodulization, rhizocretions, fissuring, microkarsts, and circumgranular cracks, dominate the carbonate sequence, indicating deposition in a marginal lacustrine setting. Integrated petrographic, mineralogical, geochemical, and isotopic studies of carbonate facies reveal two distinct evolutionary stages in the Duwi Basin, with dolomitization and silicification characterizing the late stage. Their isotopic compositions show a wide range of δ¹³C and δ¹⁸O values, ranging from −9.00‰ to −7.98‰ and from −10.03‰ to −0.68‰, respectively. Dolomite beds exhibit more negative δ¹³C and δ¹⁸O values, whereas palustrine limestones display higher (less negative) values. The upward trend of δ¹⁸O enrichment in carbonates suggests that the lake became hydrologically closed. Trace element concentrations serve as potential markers for distinguishing carbonate facies, aiding with paleoenvironmental and diagenetic interpretations. Our findings indicate that the studied dolomites and cherts formed under both biogenic and abiogenic conditions in an evaporative, alkaline-saline lake system. Biogenic dolomite and silica likely resulted from microbial activity, whereas abiogenic formation was driven by physicochemical conditions, including decreasing pH values and the presence of smectite clays. Tectonics, local climate, and provenance played crucial roles in controlling the overall diagenetic patterns and evolutionary history of the lake basin system during the Late Oligocene to Early Miocene. Full article

Access to high-performance sports is crucial for the holistic development and well-being of athletes with intellectual impairment (II). However, ensuring fair and equitable participation requires effective eligibility systems. This study investigates how basketball-specific inductive reasoning impacts athletes with II. A total of 92 elite male players with II (average age 26.3 ± 7 years) and 128 control players without II participated. This study used a tailored test to assess the ability to quickly and accurately arrange 12 photo series depicting basketball sequences. Athletes with II were hypothesized to require more time and arrange the image sequences less accurately compared to their peers. The results indicated that athletes with II took significantly more time (41.2 s ± 20.2 s) and arranged the image sequences less accurately than senior players without II (19.2 s ± 5.9 s). A discriminant function analysis classified 84.1% of players accurately, confirming that athletes with II performed at a lower level in basketball-specific activities that require inductive reasoning. These findings contribute to the development of Phase 3 of the classification model for athletes with II, which consists of 4 phases. This helps establish the eligibility system boundaries in basketball for individuals with II, promoting equitable access for athletes to high-performance sports. Full article

The increasing frequency of climate- and cyber-induced blackouts in modern distribution networks calls for restoration strategies that are both resilient and control-aware. Traditional black-start schemes, based on predefined energization sequences from synchronous machines, are inadequate for inverter-dominated grids characterized by high penetration of distributed energy resources and limited system inertia. This paper proposes a novel multi-layered black-start planning framework that explicitly incorporates the dynamic capabilities and operational constraints of grid-forming energy storage systems (GFESs). The approach formulates a multi-objective optimization problem solved via the Non-Dominated Sorting Genetic Algorithm III (NSGA-III), jointly minimizing total restoration time, voltage–frequency deviations, and maximizing early-stage load recovery. A graph-theoretic partitioning module identifies restoration subgrids based on topological cohesion, critical load density, and GFES proximity, enabling localized energization and autonomous island formation. Restoration path planning is embedded as a mixed-integer constraint layer, enforcing synchronization stability, surge current thresholds, voltage drop limits, and dispatch-dependent GFES constraints such as SoC evolution and droop-based frequency support. The model is evaluated on a modified IEEE 123-bus system with five distributed GFES units under multiple blackout scenarios. Simulation results show that the proposed method achieves up to 31% faster restoration and 46% higher voltage compliance compared to MILP and heuristic baselines, while maintaining strict adherence to dynamic safety constraints. The framework yields a diverse Pareto frontier of feasible restoration strategies and provides actionable insights into the coordination of distributed grid-forming resources for decentralized black-start planning. These results demonstrate that control-aware, partition-driven optimization is essential for scalable, safe, and fast restoration in the next generation of resilient power systems. Full article

Several types of self-balancing vehicles have been successfully developed and commercialized in the past two decades, both as manned vehicles and as autonomous mobile robots. At the same time, due to their characteristic instability and underactuation, a large body of research has been devoted to their control. However, despite this practical and theoretical interest, the current publicly available literature does not cover their systematic design and development. In particular, overall processes that lead to a finished vehicle starting from a set of requirements and specifications have not been examined in the literature. Within this context, this paper contributes a comprehensive mechatronic, dynamics-based procedure for the design of this class of vehicles; to promote clarity of exposition, the procedure is systematically presented using Model-Based Systems Engineering tools and principles. In particular, the proposed design method is developed and formalized starting from an original description of the vehicle, which is treated as a complex system composed of several interconnected multi-domain components that exchange power and logical flows through suitable interfaces. A key focus of this work is the analysis of these exchanges, with the goal of defining a minimal set of quantities that should be necessarily considered to properly design the vehicle. As a salient result, the design process is organized in a logical sequence of steps, each having well-defined inputs and outputs. The procedure is also graphically outlined using standardized formalisms. The design method is shown to cover all the mechanical, electrical, actuation, measurement and control components of the system, and to allow the unified treatment of a large variety of different vehicle variants. The procedure is then applied to a specific case study, with the goal of developing the detailed design of a full-scale vehicle. The main strengths of the proposed approach are then widely highlighted and discussed. Full article

Identifying the botanical origin of honey is essential for ensuring its quality, preventing adulteration, and protecting consumers. Traditional techniques, such as melissopalynology, physicochemical analysis, and PCR, are often labor-intensive, time-consuming, or limited to the detection of only known species, while advanced DNA sequencing remains prohibitively costly. In this study, we aim to develop a deep learning-based approach for identifying pollen grains extracted from honey and captured through microscopic imaging. To achieve this, we first constructed a dataset named VNUA-Pollen52, which consists of microscopic images of pollen grains collected from flowers of plant species cultivated in the surveyed area in Hanoi, Vietnam. Second, we evaluated the classification performance of advanced deep learning models, including MobileNet, YOLOv11, and Vision Transformer, on pollen grain images. To improve performances of these model, we proposed data augmentation and hybrid fusion strategies to improve the identification accuracy of pollen grains extracted from honey. Third, we developed an online platform to support experts in identifying these pollen grains and to gather expert consensus, ensuring accurate determination of the plant species and providing a basis for evaluating the proposed identification strategy. Experimental results on 93 images of pollen grains extracted from honey samples demonstrated the effectiveness of the proposed hybrid fusion strategy, achieving 70.21% accuracy at rank 1 and 92.47% at rank 5. This study demonstrates the capability of recent advances in computer vision to identify pollen grains using their microscopic images, thereby opening up opportunities for the development of automated systems that support plant traceability and quality control of honey. Full article

Monkeypox virus (MPXV) experienced an unprecedented global outbreak in 2022, characterized by a significant departure from historical patterns: a rapid spread of the epidemic to more than 110 non-traditional endemic countries, with more than 90,000 confirmed cases; a fundamental shift in the mode of transmission, with human-to-human transmission (especially among men who have sex with men (MSM)) becoming the dominant route (95.2%); and genetic sequencing revealing a key adaptive mutation in a novel evolutionary branch (Clade IIb) that triggered the outbreak. These features highlight the significant evolution of MPXV in terms of host adaptation, transmission efficiency, and immune escape ability. The aim of this paper is to provide insights into the viral adaptive evolutionary mechanisms driving this global outbreak, with a particular focus on the role of immune escape (e.g., novel mechanisms of M2 proteins targeting the T cell co-stimulatory pathway) in enhancing viral transmission and pathogenicity. At the same time, we systematically evaluate the cross-protective efficacy and limitations of existing vaccines (ACAM2000, JYNNEOS, and LC16), as well as recent advances in novel vaccine platforms, especially mRNA vaccines, in inducing superior immune responses. The study further reveals the constraints to outbreak control posed by grossly unequal global vaccine distribution (e.g., less than 10% coverage in high-burden regions such as Africa) and explores the urgency of optimizing stratified vaccination strategies and facilitating technology transfer to promote equitable access. The core of this paper is to elucidate the dynamic game between viral evolution and prevention and control strategies (especially vaccines). The key to addressing the long-term epidemiological challenges of MPXV in the future lies in continuously strengthening global surveillance of viral evolution (early warning of highly transmissible/pathogenic variants), accelerating the development of next-generation vaccines based on new mechanisms and platforms (e.g., multivalent mRNAs), and resolving the vaccine accessibility gap through global collaboration to build an integrated defense system of “Surveillance, Research and Development, and Equitable Vaccination,” through global collaboration to address the vaccine accessibility gap. Full article