Search Results (1,247)

In the search for alternative protein sources, single cell proteins have gained increasing attention in recent years. Among them, proteins derived from yeast represent a promising but still underexplored option. To enable their application in food product design, their techno-functional properties must be understood. In order to investigate the impact of precipitation pH on their emulsion-stabilizing properties, yeast proteins from Saccharomyces cerevisiae were isolated via precipitation at different pH (pH 3.5 to 5) after cell disruption in the high-pressure homogenizer. Emulsions containing 5 wt% oil and ~1 wt% protein were analyzed for stability based on their droplet size distribution. Proteins precipitated at pH 3.5 stabilized the smallest oil droplets and prevented partitioning of the emulsion, outperforming proteins precipitated at higher pH values. It is hypothesized that precipitation under acidic conditions induces protein denaturation and thereby exposes hydrophobic regions that enhance adsorption at the oil–water interface and the stabilization of the dispersed oil phase. To investigate the stabilization mechanism, the molecular weight of the proteins was determined using SDS-PAGE, their solubility using Bradford assay, and their aggregation behavior using static laser scattering. Proteins precipitated at pH 3.5 possessed larger molecular weights, lower solubility, and a strong tendency to aggregate. Overall, the findings highlight the potential of yeast-derived proteins as bio-surfactants and suggest that pH-controlled precipitation can tailor their functionality in food formulations. Full article

The automotive suspension must perform competently to support comfort and safety when driving. Traditionally, car suspension control tuning is performed through trial and error or with classical techniques that cannot guarantee optimal performance under varying road conditions. The study aims at designing a Linear Quadratic Regulator-based Bacterial Memetic Algorithm (LQR-BMA) for suspension systems of automobiles. BMA combines the bacterial foraging optimization algorithm (BFOA) and the memetic algorithm (MA) to enhance the effectiveness of its search process. An LQR control system adjusts the suspension’s behavior by determining the optimal feedback gains using BMA. The control objective is to significantly reduce the random vibration and oscillation of both the vehicle and the suspension system while driving, thereby making the ride smoother and enhancing road handling. The BMA adopts control parameters that support biological attraction, reproduction, and elimination-dispersal processes to accelerate the search and enhance the program’s stability. By using an algorithm, it explores several parts of space and improves its value to determine the optimal setting for the control gains. MATLAB 2024b software is used to run simulations with a randomly generated road profile that has a power spectral density (PSD) value obtained using the Fast Fourier Transform (FFT) method. The results of the LQR-BMA are compared with those of the optimized LQR based on the genetic algorithm (LQR-GA) and the Virus Evolutionary Genetic Algorithm (LQR-VEGA) to substantiate the potency of the proposed model. The outcomes reveal that the LQR-BMA effectuates efficient and highly stable control system performance compared to the LQR-GA and LQR-VEGA methods. From the results, the BMA-optimized model achieves reductions of 77.78%, 60.96%, 70.37%, and 73.81% in the sprung mass displacement, unsprung mass displacement, sprung mass velocity, and unsprung mass velocity responses, respectively, compared to the GA-optimized model. Moreover, the BMA-optimized model achieved a −59.57%, 38.76%, 94.67%, and 95.49% reduction in the sprung mass displacement, unsprung mass displacement, sprung mass velocity, and unsprung mass velocity responses, respectively, compared to the VEGA-optimized model. Full article

Cobalt is an essential trace element involved in key biological processes. It serves most notably as a component of vitamin B₁₂ (cobalamin) and a regulator of erythropoiesis. While cobalt deficiency can lead to disorders such as megaloblastic anemia, excess cobalt poses toxicological risks to the thyroid, cardiovascular, and hematopoietic systems. In recent years, cobalt ions (Co²⁺) have gained attention for their ability to mimic hypoxia and promote angiogenesis. This represents a crucial mechanism for tissue regeneration. Cobalt mediates this effect mainly by stabilizing hypoxia-inducible factor 1α (HIF-1α) under normoxic conditions, thereby upregulating angiogenic genes, including VEGF, FGF, and EPO. Experimental studies—from cell culture to animal models—have demonstrated cobalt-induced enhancement of endothelial proliferation, migration, and microvascular formation. Emerging evidence also indicates that Co²⁺-stimulated macrophages secrete integrin-β1-rich exosomes. These exosomes enhance endothelial motility and tubulogenesis independently of VEGF. Furthermore, cobalt-modified biomaterials have been developed to deliver cobalt ions in a controlled manner. Examples include cobalt-doped β-tricalcium phosphate or bioactive glasses. These materials support both angiogenesis and osteogenesis.This review summarizes current findings on cobalt’s role in angiogenesis. The emphasis is on its potential in cobalt-based biomaterials for tissue engineering and regenerative medicine. Full article

As lithium-ion batteries (LIBs) gain widespread use, detecting electrolyte–vapor emissions during early thermal runaway (TR) remains critical to ensuring battery safety; yet, it remains understudied. Gas sensors integrating oxide nanostructures offer a promising solution as they possess high sensitivity and fast response, enabling rapid detection of various gas-phase indicators of battery failure. Utilizing this approach, 3D ordered tin oxide (SnO₂) nanostructures were synthesized using polystyrene sphere (PS) templates of varied diameters (200–1500 nm) and precursor concentrations (0.2–0.6 mol/L) to detect key electrolyte–vapors, especially ethyl methyl carbonate (EMC), released in the early stages of TR. The 3D ordered SnO₂ nanostructures with ring- and nanonet-like morphologies, formed after PS template removal, were characterized, and the effects of template size and precursor concentration on their structure and sensing performance were investigated. Among various nanostructures of SnO₂, nanonets achieved by a 1000 nm PS template and 0.4 mol/L precursor showed higher mesoporosity (~28 nm) and optimal EMC detection. At 210 °C, it detected 10 ppm EMC with a response of ~7.95 and response/recovery times of 14/17 s, achieving a 500 ppb detection limit alongside excellent reproducibility/stability. This study demonstrates that precise structural control of SnO₂ nanostructures using templates enables sensitive EMC detection, providing an effective sensor-based strategy to enhance LIB safety. Full article

This study evaluated the effects of early-life creep feeding with a high-protein, high-energy diet on growth performance, ruminal fermentation, and gut microbiota in Hanwoo calves (n = 10). Calves were assigned to control or treatment groups from birth to 6 months of age. No significant differences were observed in body weight, average daily gain (ADG), or feed conversion ratio (FCR), but ADG and dry matter intake (DMI) tended to be higher in the treatment group. Ruminal pH, NH₃-N, and volatile fatty acid (VFA) concentrations showed no significant differences. Fecal VFA profiles exhibited numerical trends suggesting higher propionate at 3 months and lower acetate, butyrate, and total VFA at 6 months in the treatment group, potentially reflecting altered substrate availability or absorption capacity, though these mechanisms were not directly measured. Microbiota analysis indicated stable ruminal alpha diversity, with numerical increases in fecal Bacteroidetes and genera such as Fournierella and Flavonifractor in the treatment group. These results suggest that early creep feeding with high-nutrition diets can support intake and promote potential shifts in hindgut microbiota composition without compromising overall microbial stability. Further research with larger sample sizes is needed to confirm these trends and assess long-term impacts on calf health and productivity. Full article

Given the rising importance of cost-effective solutions in battery research, this study employs an accessible testing approach using low-cost, sensor-equipped platforms that enable broader research and educational applications. It presents a comparative evaluation of lithium-ion battery degradation under two charging strategies: static charging (constant current at 1.2 A) and dynamic charging (stepped current from 400 mA to 800 mA) over 200 charge–discharge cycles. A custom-built, low-cost test platform based on an ESP32 microcontroller was developed to provide real-time monitoring of voltage, current, temperature, and internal resistance, with automated control and cloud-based data logging. The results indicate that static charging provides greater voltage stability and a lower increase in internal resistance (9.3%) compared to dynamic charging (30.17%), suggesting reduced electrochemical stress. Discharge time decreased for both strategies, by 6.25% under static charging and 18.46% under dynamic charging, highlighting capacity fade and aging effects. Internal resistance emerged as a reliable indicator of degradation, closely correlating with reduced runtime. These findings underscore the importance of selecting charging profiles based on specific application needs, as dynamic charging, while offering potential thermal benefits, may accelerate battery aging. Furthermore, the low-cost testing platform proved effective for long-term evaluation and degradation analysis, offering an accessible alternative to commercial battery cyclers. The insights gained contribute to the development of adaptive battery management systems that optimize performance, lifespan, and safety in electric vehicle applications. Full article

To address the challenges of model uncertainty, strong nonlinearities, and controller tuning in high-precision trajectory tracking for hydraulic servo systems, this paper proposes a hierarchical GA-PID-MPC fusion strategy. The architecture integrates three functional layers: a Genetic Algorithm (GA) for online parameter optimization, a Model Predictive Controller (MPC) for future-oriented planning, and a Proportional–Integral–Derivative (PID) controller for fast feedback correction. These modules are dynamically coordinated through an adaptive cost-aware blending mechanism based on real-time performance evaluation. The MPC module operates on a linearized state–space model and performs receding-horizon control with weights and horizon length $\theta =[q,r,T_p]$ tuned by GA. In parallel, the PID controller is enhanced with online gain projection to mitigate nonlinear effects. The blending coefficient $\sigma \left(t\right)$ is adaptively updated to balance predictive accuracy and real-time responsiveness, forming a robust single-loop controller. Rigorous theoretical analysis establishes global input-to-state stability and $H_{\infty }$ performance under average dwell-time constraints. Full article

This study presents a hybrid adaptive control strategy that integrates genetic algorithm (GA) optimization with an adaptive neuro-fuzzy inference system (ANFIS) for precise thermal regulation of single-photon avalanche diodes (SPADs). To address the nonlinear and disturbance-sensitive dynamics of SPAD systems, a performance-oriented dataset is constructed through multi-scenario simulations using settling time, overshoot, and steady-state error as fitness metrics. The genetic algorithm (GA) facilitates broad exploration of the proportional–integral–derivative (PID) controller parameter space while ensuring control stability by discarding low-performing gain combinations. The resulting high-quality dataset is used to train the ANFIS model, enabling real-time, adaptive tuning of PID gains. Simulation results demonstrate that the proposed GA-ANFIS-PID controller significantly enhances dynamic response, robustness, and adaptability over both the conventional Ziegler–Nichols PID and GA-only PID schemes. The controller maintains stability under structural perturbations and abrupt thermal disturbances without the need for offline retuning, owing to the real-time inference capabilities of the ANFIS model. By combining global evolutionary optimization with intelligent online adaptation, this approach improves both accuracy and generalization, offering a practical and scalable solution for SPAD thermal management in demanding environments such as quantum communication, sensing, and single-photon detection platforms. Full article

This paper proposes an anti-disturbance model predictive fault-tolerance control strategy for open-circuit faults of five-phase flux intensifying fault-tolerant interior permanent magnet (FIFT-IPM) motors. This strategy is applicable to electric agricultural equipment that has an open winding failure. Due to the rich third-harmonic back electromotive force (EMF) content of five-phase FIFT-IPM motors, the existing model predictive current fault-tolerant control algorithms fail to effectively track fundamental and third-harmonic currents. This results in high harmonic distortion in the phase current. Hence, this paper innovatively proposes a multi-plane virtual vector model predictive fault-tolerant control strategy that can achieve rapid and effective control of both the fundamental and harmonic planes while ensuring good dynamic stability performance. Additionally, considering that electric agricultural equipment is usually in a multi-disturbance working environment, this paper introduces an adaptive gain sliding-mode disturbance observer. This observer estimates complex disturbances and feeds them back into the control system, which possesses good resistance to complex disturbances. Finally, the feasibility and effectiveness of the proposed control strategy are verified by experimental results. Full article

The rapid growth in large-scale distributed video conferencing, remote education, and real-time broadcasting poses significant challenges to traditional centralized streaming systems, particularly regarding scalability, cost, and reliability under high concurrency. Centralized approaches often encounter bottlenecks, increased bandwidth expenses, and diminished fault tolerance. This paper proposes a novel decentralized real-time broadcasting system employing a peer-to-peer (P2P) chain topology based on IPv6 networking and the Secure Reliable Transport (SRT) protocol. By exploiting the global addressing capability of IPv6, our solution simplifies direct node interconnections, effectively eliminating complexities associated with Network Address Translation (NAT). Furthermore, we introduce an innovative chain-relay transmission method combined with distributed node management strategies, substantially reducing reliance on central servers and minimizing deployment complexity. Leveraging SRT’s low-latency UDP transmission, packet retransmission, congestion control, and AES-128/256 encryption, the proposed system ensures robust security and high video stream quality across wide-area networks. Additionally, a WebSocket-based real-time fault detection algorithm coupled with a rapid fallback self-healing mechanism is developed, enabling millisecond-level fault detection and swift restoration of disrupted links. Extensive performance evaluations using Video Multi-Resolution Fidelity (VMRF) metrics across geographically diverse and heterogeneous environments confirm significant performance gains. Specifically, our approach achieves substantial improvements in latency, video quality stability, and fault tolerance over existing P2P methods, along with over tenfold enhancements in frame rates compared with conventional RTMP-based solutions, thereby demonstrating its efficacy, scalability, and cost-effectiveness for real-time video streaming applications. Full article

In this paper, a robust optimized controller is implemented in the photovoltaic generator system (PVGS). The PVGS is composed of individual photovoltaic (PV) cells, which convert solar energy to electrical energy. To optimize the efficiency of the PVGS under variable solar irradiance and temperatures, a maximum power point tracking (MPPT) controller is necessary. Additionally, the PVGS output voltage is typically low for many applications. To achieve the MPPT and to gain the output voltage, an increasing boost converter (IBC) is employed. Then, two issues should be considered in MPPT. At first, a smooth control signal for adjusting the duty cycle of the IBC is important. Another critical issue is the PVGS and IBC unknown sections, i.e., the total system uncertainty. Therefore, to address the system uncertainties and to regulate the smooth duty cycle of the converter, a robust dynamic sliding mode control (DSMC) is proposed. In DSMC, a low-pass integrator is placed before the system to suppress chattering and to produce a smooth actuator signal. However, this integrator increases the system states, and hence, a sliding mode observer (SMO) is proposed to estimate this additional state. The stability of the proposed control scheme is demonstrated using the Lyapunov theory. Finally, to demonstrate the effectiveness of the proposed method and provide a reliable comparison, conventional sliding mode control (CSMC) with the same proposed SMO is also implemented. Full article

Urinary extracellular vesicles (U-EVs) are gaining increasing interest as non-invasive liquid biopsy tools for clinical use. Prostate cancer (PCa) is amongst the highest cancer-related cause of death in men, and therefore, the identification of non-invasive robust biomarkers is of high importance. This study assessed U-EV profiles from individuals affected by PCa at Gleason scores 6–9, compared with healthy controls. U-EVs were characterised and assessed for proteomic cargo content by LC-MS/MS analysis. The U-EV proteomes were compared for enrichment of gene ontology (GO), KEGG, and Reactome pathways, as well as disease–gene associations. U-EVs ranged in size from 50 to 350 nm, with the majority falling within the 100–200 nm size range for all groups. U-EV protein cargoes from the PCa groups differed significantly from healthy controls, with 16 protein hits unique to the GS 6–7 and 88 hits to the GS 8–9 U-EVs. Pathway analysis showed increased enrichment in the PCa U-EVs of biological process GO (5 and 37 unique to GS 6–7 and GS 8–9, respectively), molecular function GO (3 and 6 unique to GS 6–7 and GS 8–9, respectively), and cellular component GO (10 and 22 unique to GS 6–7 and GS 8–9, respectively) pathways. A similar increase was seen for KEGG pathways (11 unique to GS 8–9) and Reactome pathways (102 unique to GS 8–9). Enrichment of disease–gene associations was also increased in the PCa U-EVs, with highest differences for the GS 8–9 U-EVs (26 unique terms). The pathway enrichment in the PCa U-EVs was related to several key inflammatory, cell differentiation, cell adhesion, oestrogen signalling, and infection pathways. Unique GO and KEGG pathways enriched for the GS 8–9 U-EVs were associated with cell–cell communication, immune and stress responses, apoptosis, peptidase activity, antioxidant activity, platelet aggregation, mitosis, proteasome, mRNA stability oxytocin signalling, cardiomyopathy, and several neurodegenerative diseases. Our findings highlight U-EVs as biomarkers to inform disease pathways in prostate cancer patients and offer a non-invasive biomarker tool for clinical use. Full article

Natural-fiber-reinforced polypropylene (PP) composites are gaining increasing interest as lightweight, sustainable alternatives for various packaging and applications. This study investigates the effect of filler addition sequence on the mechanical, morphological, thermal, and dynamic mechanical properties of PP-based composites reinforced with graphite nanoplatelets (GnP) and kenaf fiber (KF). Two filler incorporation sequences were evaluated: GnP/KF/PP (GnP initially mixed with KF before PP addition) and GnP/PP/KF (KF added after mixing GnP with PP). The GnP/KF/PP composite exhibited superior mechanical properties, with tensile strength and flexural strength increasing by up to 25% compared to the control, while GnP/PP/KF showed a 13% improvement. SEM analyses revealed that initial mixing of GnP with KF significantly improved filler dispersion and interfacial bonding, enhancing stress transfer within the composite. XRD and DSC analyses showed reduced crystallinity and lower crystallization temperatures in the addition of KF due to restricted polymer chain mobility. Thermal stability assessed by TGA indicated minimal differences between the composites regardless of filler sequence. DMA results demonstrated a significantly higher storage modulus and enhanced elastic response in the addition of KF, alongside a slight decrease in glass transition temperature (T_g). The results emphasize the importance of optimizing filler addition sequences to enhance mechanical performance, confirming the potential of these composites in sustainable packaging and structural automotive applications. Full article

Power hardware-in-the-loop (PHIL) creates a safe test environment to connect simulations with real hardware under test (HuT). Therefore, an interface algorithm (IA) must be chosen. The ideal transformer method (ITM) and the partial circuit duplication (PCD) are popular IAs, where a distinction is made between voltage- (V-) and current-type (C-) IAs. Depending on the sample time of the simulator and further delays, simulation accuracy is reduced and instability can occur due to negative feedback in the V-ITM and C-ITM control loops, which makes PHIL operation impossible. In the case of positive feedback, such as with the V-PCD and C-PCD, the delay causes destructive interference, which results in a phase shift and attenuation of the output signal. In this article, a novel damped Smith predictor (SP) for positive feedback PHIL IAs is presented, which significantly reduces destructive interference while allowing stable operation at low linking impedances at V-PCD and high linking impedances at C-PCD, thus reducing losses in the system. Experimental results show a reduction in phase shift by 21.17° and attenuation improvement of 24.3% for V-PCD at a sample time of 100 µs. The SP transfer functions are also derived and integrated into the listed negative feedback IAs, resulting in an increase in the gain margin (GM) from approximately one to three, which significantly enhances system stability. The proposed methods can improve stability and accuracy, which can be further improved by calculating the HuT impedance in real-time and dynamically adapting the SP model. Stable PHIL operation with SP is also possible with SP model errors or sudden HuT impedance changes, as long as deviations stay within the presented limits. Full article

In complex marine environments, ramp-based recovery systems for autonomous underwater vehicles (AUVs) often encounter engineering challenges such as reduced docking accuracy and success rate due to disturbances in the capture window attitude. In this study, a desktop-scale physical experimental platform for recovery compensation was designed and constructed. The system integrates attitude feedback provided by an attitude sensor and dual-motor actuation to achieve active roll and pitch compensation of the capture window. Based on the structural and geometric characteristics of the platform, a dual-channel closed-loop control strategy was proposed utilizing midpoint tracking of the capture window, accompanied by multi-level software limit protection and automatic centering mechanisms. The control algorithm was implemented using a discrete-time PID structure, with gain parameters optimized through experimental tuning under repeatable disturbance conditions. A first-order system approximation was adopted to model the actuator dynamics. Experiments were conducted under various disturbance scenarios and multiple control parameter configurations to evaluate the attitude tracking performance, dynamic response, and repeatability of the system. The results show that, compared to the uncompensated case, the proposed compensation mechanism reduces the $MSE$ by up to 76.4% and the $MaxAE$ by 73.5%, significantly improving the tracking accuracy and dynamic stability of the recovery window. The study also discusses the platform’s limitations and future optimization directions, providing theoretical and engineering references for practical AUV recovery operations. Full article