Search Results (101,142)

Whey protein concentrate (WPC-80) was reconstituted to 10% (m/v) and pumped through a pulsed electric field (PEF) system using three treatment conditions. The PEF-treated whey solution was assessed for viscosity, whereas dried whey was resolubilized and tested for protein structure integrity by circular dichroism (CD), fluorescence, and differential scanning calorimetry (DSC), and functionality was assessed by measuring solubility, foamability, emulsification, and particle size. PEF treatment resulted in a reduction in apparent viscosity (from 2.74 cP down to 2.57 cP) and particle size (from 325.9 nm down to 297.6 nm), and increased solubility (from 90.41% up to 92.34%) and emulsification stability (from 1727 min up to 4821 min), while emulsification stability decreased initially (from 1.645 m²/g to 1.283 m²/g) then increased at the high treatment level (1.915 m²/g). The foamability and molecular weight profile did not change with PEF treatment. Exposure to PEF resulted in no statistically significant changes to protein structure based on data obtained from CD, fluorescence, or DSC. This study represents the first instance of a WPC-80 being treated with a commercially available, scalable, continuous flow PEF system at a higher concentration (10% m/v), resulting in favorable changes to the physical and functional properties of the whey solution and dried powder. Full article

A global interest in the cultivation of Kalibaus (Labeo calbasu) has emerged due to decreasing natural stocks and a consistent rise in market value and demand. Given these concerns, understanding the species’ physiological responses to environmental changes is crucial. The present research aimed to assess the effect of varying environmental temperatures on metabolism, haemato-biochemical indices, hormonal concentrations and immune responses in L. calbasu. This study was conducted in triplicate using 100 L glass aquariums at four different temperatures: 22, 26, 30, and 34 °C. The highest weight and length gain were observed at 30 °C, while the lowest occurred at 22 °C. Notably, the best feed conversion ratio (FCR) of 1.51 ± 0.03 was also recorded at 30 °C. Although haematological and biochemical parameters remained within normal ranges, they varied with temperature changes. Indicators of cold and heat stress were evident through lower hematocrit levels and higher white blood cell (WBC) counts. Biochemical indicators such as serum albumin (1.84 ± 0.05 g dL⁻¹), serum globulin (1.64 ± 0.06 gdL⁻¹), HCO₃ (30.93 ± 0.62), Na⁺ (115.60 ± 3.72 mmolL⁻¹), alkaline phosphatase (93.33 ± 9.39 AP, IUL⁻¹), and AST/SGOT (21.00 ± 4.55 UL⁻¹) were significantly higher at 30 °C. Regarding hormonal responses, peak levels of growth hormone (GH), triiodothyronine (T3) (1.44 ± 0.07 ngmL⁻¹), and thyroxine (T4) were recorded at 30 °C. Meanwhile, serum cortisol (1.62 ± 0.06 µgdL⁻¹) and adrenocorticotropic hormone (ACTH) (18.01 ± 3.26 pgmL⁻¹) were highest at 34 °C. Immune responses were strongest between 26 and 30 °C. In conclusion, the results suggest that L. calbasu should ideally be cultured between 26 and 30 °C for optimum growth and health, making it ideal for commercial farming. Full article

This paper presents an architecture and strategy for on-orbit software updating of satellite payload control systems, based on a tightly coupled DSP and FPGA design. The architecture achieves tight coupling between the DSP and FPGA via the XINTF interface, integrating the DSP program and data into the FPGA bitstream. This enables synchronous updating of both chips with a single software package, significantly reducing both uplink data volume and update time. The system features a dual-flash redundant boot design and a mutual supervision mechanism between the DSP and FPGA, enabling cross-monitoring and autonomous reset, thereby significantly enhancing the system’s fault tolerance and reliability in orbit. Experimental results demonstrate a substantial improvement in fault recovery, with the weighted mean recovery time reduced from 27.09 s to 1.56 s, a relative improvement of 94.25% compared to conventional methods. Ground-based environmental tests confirm the system’s stability and engineering viability under extreme space conditions. Full article

Background/Objectives: This study examined the clinical utility of diffusion-weighted whole-body magnetic resonance imaging with background body signal suppression (DWIBS) for differentiating infectious from non-infectious aortitis. Methods: The study included 32 patients with suspected inflammatory aortitis who underwent non-contrast computed tomography (NCCT) and magnetic resonance imaging. We evaluated the diagnostic performance of DWIBS using the spinal cord as a reference, NCCT, and their combination. The diagnosis of infectious aortitis was adjudicated based on imaging, clinical, and laboratory findings. We conducted a sensitivity analysis using a stricter definition of infectious aortitis that required both surgical and microbiological confirmation. Results: Fifteen patients were diagnosed with infectious aortitis. The sensitivity, specificity, and areas under the receiver operating characteristic curves were 93.3%, 70.6%, and 0.82, respectively, for NCCT; 93.3%, 76.5%, and 0.85, respectively, for DWIBS; and 86.7%, 94.1%, and 0.90, respectively, for the combination of both modalities. In the sensitivity analysis, the combined DWIBS and NCCT approach demonstrated a specificity of 87.5% and a sensitivity of 70.8%. Conclusions: DWIBS using the spinal cord as a reference appears to be a promising diagnostic tool for differentiating infectious from non-infectious aortitis, especially when combined with NCCT. Full article

Engineering programmes have been giving more weight to experiential learning, largely because many students still find it difficult to see how classroom theory connects to the work that engineers handle on the ground. With this in mind, a robotics-centred Project-based Learning (PBL) module was introduced to first-year general engineering students as part of the faculty’s engineering spine. The module asks students to design, build, and program small autonomous robots capable of navigating and competing in a set arena. Even a simple task of this kind draws together multiple strands of engineering. Students shift between sketching mechanical layouts, wiring basic circuits, writing code, testing prototypes, and negotiating the usual challenges that arise when several people share responsibility for the same piece of hardware. To explore how students learned through the module, a mixed-methods evaluation was carried out using survey responses alongside reflective pieces written by the students themselves. Certain patterns appeared repeatedly. Many students felt that their technical skills had grown, particularly in breaking down a messy problem into smaller, more workable components. Teamwork also surfaced as a prominent theme. Groups often had to sort out issues such as a robot veering off course due to a misaligned sensor or a block of code producing unpredictable behaviour. These issues were undoubtedly challenging for the students, but they also had a certain pedagogical flavour, with many students describing them as a source of frustration as well as a learning opportunity. Later iterations of the module may benefit from more targeted support at key stages. Despite the many challenges, robotics has been shown to be an attractive way for students to step into engineering practice. The project helped them build technical capability, but it also encouraged habits that matter just as much in real work, such as planning, communicating clearly, and returning to a problem until it behaves as expected. Taken together, the experience offers useful guidance for curriculum designers seeking to create early learning environments that feel authentic and manageable and for motivating students who are just beginning their engineering journey. Full article

A 74-year-old man with type 2 diabetes presented with fever, urinary retention, and urinary difficulties. Initial abdominal Computed Tomography (CT) suggested acute pyelonephritis, but a low-density area in the prostate was overlooked. Following the confirmation of methicillin-resistant Staphylococcus aureus (MRSA) in blood and urine cultures, comprehensive screening for metastatic abscesses was necessitated. Diffusion-weighted whole-body imaging with background suppression (DWIBS) was utilized and clearly identified a prostatic abscess (PA), nephritis, urethritis, and subcutaneous cysts. These findings also raised suspicion of pyogenic vertebral osteomyelitis. Crucially, the PA, urethritis, subcutaneous cysts, and potentially the vertebral osteomyelitis were either overlooked or not detected by initial CT imaging. DWIBS allows for simultaneous whole-body screening and serves as a useful adjunctive tool for identifying minute abscesses, which may assist in detecting inflammatory foci that are sometimes overlooked by conventional imaging. Unlike CT, DWIBS avoids radiation and contrast agents, and is significantly more cost-effective than positron emission tomography-CT (PET-CT). DWIBS can thus serve as a useful, non-invasive tool for the early detection and exclusion of abscesses in other organs when metastatic abscess formation is suspected or cultures are positive for microorganisms causing metastatic abscesses. Full article

The opto-mechanical automated manufacturing process, characterized by stringent process constraints, dynamic disturbances, and conflicting optimization objectives, presents significant control challenges for traditional scheduling and control approaches. We formulate the scheduling problem within a closed-loop control paradigm and propose a novel bi-level intelligent control framework integrating Deep Reinforcement Learning (DRL) and Bayesian Optimization (BO). The core of our approach is a bi-level intelligent control framework. An inner DRL agent acts as an adaptive controller, generating control actions (scheduling decisions) by perceiving the system state and learning a near-optimal policy through a carefully designed reward function, while an outer BO loop automatically tunes the DRL’s hyperparameters and reward weights for superior performance. This synergistic BO-DRL mechanism facilitates intelligent and adaptive decision-making. The proposed method is extensively evaluated against standard meta-heuristics, including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), on a complex 20-jobs × 20-machines flexible job shop scheduling benchmark specific to opto-mechanical automated manufacturing. The experimental results demonstrate that our BO-DRL algorithm significantly outperforms these benchmarks, achieving reductions in makespan of 13.37% and 25.51% compared to GA and PSO, respectively, alongside higher machine utilization and better on-time delivery. Furthermore, the algorithm exhibits enhanced convergence speed, superior robustness under dynamic disruptions (e.g., machine failures, urgent orders), and excellent scalability to larger problem instances. This study confirms that integrating DRL’s perceptual decision-making capability with BO’s efficient parameter optimization yields a powerful and effective solution for intelligent scheduling in high-precision manufacturing environments. Full article

Ovarian cancer (OC), the third most common gynecologic malignancy, is characterized by high mortality largely driven by chemotherapy resistance, leading to recurrence and metastasis. Using transcriptomic data from GSE73935, we constructed a weighted gene co-expression network and identified eight hub genes (IGF1R, CDH2, PDGFRA, CDKN1A, SHC1, SPP1, CAV1 and FGF18) associated with cisplatin resistance, among which CDH2 emerged as the most clinically relevant candidate. CDH2 demonstrated moderate diagnostic potential (AUC = 0.792) and was markedly upregulated in cisplatin-resistant A2780/CP70 cells. Independent validation using clinical single-cell RNA-seq data (GSE211956) confirmed its selective enrichment in resistant tumor cell subpopulations. Gene set enrichment analysis linked elevated CDH2 expression to p53 signaling, DNA replication, nucleotide excision repair, and Toll-like receptor pathways, with qPCR supporting upregulation of key downstream genes in resistant cells. Immune deconvolution further indicated that high CDH2 expression correlated with increased infiltration of NK cells, Tregs, macrophages, and neutrophils, and immunohistochemistry verified CDH2 overexpression in cisplatin-resistant tissues. In addition, virtual screening and drug sensitivity profiling identified several FDA-approved agents with potential relevance to CDH2-associated drug response. These findings indicate that CDH2 may serve as a candidate marker associated with cisplatin response in OC, and its association with immune cell infiltration provides further insight into mechanisms potentially underlying chemoresistance. Full article

This study investigates the influence of varying cold stratification durations (0–165 days) on the germination performance and early seedling development of Rudbeckia fulgida. Seeds were divided into 11 groups at 15-day intervals, using a total of 1320 seeds. For each stratification duration, an equivalent number of seeds stored at room temperature served as non-stratified controls. Results demonstrated a clear and significant increase in germination percentage with longer stratification periods (Kruskal–Wallis, H = 57.03, p < 0.001), with the highest germination observed at 135 and 165 days (96.7%). In contrast, seeds kept at room temperature exhibited low and inconsistent germination. Strong positive correlations were detected between stratification duration and both germination percentage (r = 0.914) and post-stratification seed weight (r = 0.419). Furthermore, a Response Surface Methodology (RSM) model was developed to predict germination behavior, achieving an exceptionally high 99% predictive accuracy. The RSM analysis confirmed that cold stratification duration is the dominant factor shaping germination responses in Rudbeckia fulgida Aiton. Overall, the study demonstrates that cold stratification is essential for breaking seed dormancy in R. fulgida, substantially improving propagation efficiency and offering valuable insights for nursery production, landscape practices, and restoration ecology. Full article

The development of cross-border hydrogen energy value chains involves complex interactions between technological, regulatory, and logistical subsystems. Static assessment models often fail to capture the dynamic response of these coupled systems to external perturbations. This study addresses this gap by proposing the Dual Carbon Cooperation Index (DCCI), a data-driven framework designed to quantify the synergy efficiency of the China–Korea hydrogen ecosystem. We construct a dynamic state estimation model integrating three coupled dimensions—Technology Synergy, Regulatory Alignment, and Supply Chain Resilience—utilizing an adaptive weighting algorithm (Triple Dynamic Response). Based on multi-source heterogeneous data (2020–2024), the model employs Natural Language Processing (NLP) for vectorizing unstructured regulatory texts and incorporates an exogenous signal detection mechanism (GPR). Empirical results reveal that the ecosystem’s composite synergy score recovered from 0.38 to 0.50, driven by robust supply chain resilience but constrained by high impedance in technological transfer protocols. Crucially, the novel dynamic weighting algorithm significantly reduces state estimation error during high-volatility periods compared to static linear models, as validated by bootstrapping analysis (1000 resamples). The study provides a quantitative engineering tool for monitoring ecosystem coupling stability and proposes a technical roadmap for reducing system constraints through secure IP data architectures and synchronized standard protocols. Full article

Background: Computerized Provider Order Entry (CPOE) systems are critical for medication safety, but their effectiveness relies heavily on the completeness of entered data. Incomplete clinical and anthropometric information can disable Clinical Decision Support Systems (CDSSs), compromising patient safety. Objective: This study aimed to assess the longitudinal evolution of CPOE data completeness, specifically focusing on “Breadth Completeness” (the presence of essential clinical variables), and to identify factors predicting data integrity in a tertiary care setting. Methods: A retrospective cross-sectional study was conducted at a 500-bed tertiary referral center in Riyadh. Data were extracted from the Cerner Millennium CPOE system for three “steady-state” years (2015, 2017, and 2019); years involving major system overhauls (2016 and 2018) were excluded to avoid structural bias. A total of 600 unique patient encounters (200 per year) were selected using systematic random sampling from a chronologically ordered sampling frame to minimize temporal bias. The primary outcome was “Breadth Completeness,” defined as the presence of eight key variables: age, gender, marital status, weight, height, diagnosis, vital signs, and allergies. Secondary outcomes included documentation consistency (daily notes). Multivariable logistic regression, adjusted for potential confounders, was used to determine predictors of completeness. Results: The rate of primary data completeness (Breadth) improved significantly over the study period, rising from 5.5% in 2015 to 26% in 2017 and 49.5% in 2019. In the multivariable analysis, the year of documentation (OR = 17.47 for 2019 vs. 2015, p < 0.0001) and length of hospitalization (OR = 1.04, p = 0.045) were significant predictors of completeness. Pharmacist-led medication reconciliation was associated with a 2.5-fold increase in data completeness in bivariate analysis (p < 0.0001). Conclusions: While system maturity has driven substantial improvements in CPOE documentation, critical gaps persist, particularly in anthropometric data required for safety alerts. The study underscores the necessity of mandating “hard stops” for core variables and formalizing pharmacist involvement in data reconciliation to ensure patient safety. Full article

The shale oil reservoir is characterized by ultra-low porosity and permeability and multi-scale strong heterogeneity. During the sampling process of downhole cores, the rocks can easily be affected by drilling fluid contamination, mechanical stress damage, and other factors, altering the original distribution of oil–water and the characteristics of pore structures. Oil removal and oil saturation are critical steps in core pre-treatment, yet the mechanism of its impact on cores has not been systematically studied. This research focuses on oil removal in six cores from the Jimsar shale oil reservoir with different oil saturations. The necessity and effectiveness of the oil removal saturation and its impact on the microstructure of the cores were systematically evaluated by employing nuclear magnetic resonance (NMR), CT scanning, and permeability testing methods. The results indicate that there are significant differences in fluid composition, pore structure, and wettability among downhole cores, making oil removal saturation treatment a necessary prerequisite for subsequent experiments. High-temperature and high-pressure oil removal shows significant effectiveness, with an average core weight reduction of 2.46% and average reduction in T₂ peak area of 73.75%. The efficacy of oil saturation is influenced by the initial pore-throat distribution in the cores. The oil removal process significantly alters petrophysical parameters, with an average increase in porosity of 3.21 times and permeability rising by an average of 2.16 times, although individual variations exist. Microstructural analysis demonstrates that the oil removal process preferentially removes crude oil from larger pores, while residual oil is mainly distributed in smaller pores, indicated by a left shift in T₂ peak values. Meanwhile, high-temperature and high-pressure conditions induce microfracture development, promoting the migration of crude oil into smaller pores. This research reveals the complex impact mechanism of the oil removal saturation process on shale cores, providing a theoretical basis for accurately evaluating shale reservoir characteristics and optimizing experimental design. Full article

Fractional-order time-delay systems boast better dynamic performance than integer-order ones in optimally controlling industrial design objects. However, in lack of commendable methodologies, designing proper controllers for these systems confronts a plurality of challenges. This study puts forth an innovative design approach that merges frequency-domain analysis with time-domain optimization concepts, so that fractional-order Tilt-Integral-Derivative (TID) controllers can be acquired. To pursue a stable control system loop, the tilted and integral gains of fractional-order TID controllers are identified as per frequency-domain specifications, including gain crossover frequency and phase margin. In light of these specifications (e.g., the integral of time-weighted absolute error (ITAE)), the differential gain and fractional-order operator λ of the controller are determined, which accomplishes a desirable dynamic performance in the time domain. This article expounds on the procedure of how to develop the proposed fractional-order TID controller and furnishes illustrative examples for the research steps. As manifested by the simulation results, the proposed controller dramatically upgrades the control performance of the system in contrast to conventional PID, FOPI, and FOPID controllers. Moreover, it outperforms PID and fuzzy PID in terms of responding to the demand variations in step signals. Full article

Background: Metabolic syndrome (MetS) is a multifactorial condition characterized by insulin resistance, dyslipidemia, hypertension, and central obesity, and is strongly influenced by lifestyle factors. Growing evidence highlights the gut microbiota as a key mediator linking diet and physical exercise to cardiometabolic health. Objective: This narrative review aims to qualitatively synthesize current evidence on the effects of physical exercise and major dietary patterns including the Mediterranean diet (MedDiet), Dietary Approaches to Stop Hypertension (DASH), and ketogenic/very-low-calorie ketogenic diets (KD/VLCKD) on gut microbiota composition and function, and their implications for metabolic health in MetS. Methods: A qualitative narrative synthesis of experimental, observational, and interventional human and animal studies was performed. The reviewed literature examined associations between structured physical exercise or dietary interventions and changes in gut microbiota diversity, key bacterial taxa, microbial metabolites, and cardiometabolic outcomes. Considerable heterogeneity across studies was noted, including differences in populations, intervention duration and intensity, dietary composition, and microbiota assessment methodologies. Results: Across human interventional studies, moderate-intensity physical exercise was most consistently associated with increased gut microbial diversity and enrichment of short-chain fatty acid (SCFA)-producing taxa, contributing to improved insulin sensitivity and reduced inflammation. MedDiet and DASH were generally linked to favorable microbiota profiles, including increased abundance of Faecalibacterium prausnitzii, Akkermansia muciniphila, and Bifidobacterium, alongside reductions in pro-inflammatory metabolites such as lipopolysaccharides and trimethylamine N-oxide. In contrast, KD and VLCKD were associated with rapid weight loss and glycemic improvements but frequently accompanied by reductions in SCFA-producing bacteria, depletion of Bifidobacterium, and markers of impaired gut barrier integrity, raising concerns regarding long-term microbiota resilience. Conclusions: Lifestyle-based interventions exert diet- and exercise-specific effects on the gut microbiota–metabolism axis. While MedDiet, DASH, and regular moderate physical activity appear to promote sustainable microbiota-mediated cardiometabolic benefits, ketogenic approaches require careful personalization, limited duration, and medical supervision. These findings support the integration of dietary quality, exercise prescription, and individual microbiota responsiveness into translational lifestyle strategies for MetS prevention and management. Full article

With the rapid development of urban rail transit, train operation control is required to meet increasingly stringent demands in terms of energy consumption, comfort, punctuality, and precise stopping. The optimization and tracking control of speed profiles are two critical issues in ensuring the performance of automatic train operation systems. However, conventional model predictive control (MPC) methods are highly dependent on parameter settings and show limited adaptability, while heuristic optimization approaches such as the whale optimization algorithm (WOA) often suffer from premature convergence and insufficient robustness. To overcome these limitations, this study proposes an optimized model predictive controller using the multi-objective whale optimization algorithm (MPC-MOWOA) for urban rail train tracking control. In the improved optimization algorithm, a nonlinear convergence mechanism and the Tchebycheff decomposition method are introduced to enhance convergence accuracy and population diversity, which enables effective optimization of the initial parameters of the MPC. During real-time operation, the MPC is further enhanced by integrating a fuzzy satisfaction function that adaptively adjusts the softening factor. In addition, the control coefficients are corrected online according to the speed error and its rate of change, thereby improving adaptability of the control system. Taking the section from Lvshun New Port to Tieshan Town on Dalian Metro Line 12 as the study case, the proposed control algorithm was deployed on a TMS320F28335 embedded processor platform, and hardware-in-the-loop simulation experiments (HILSEs) were conducted under the same simulation environment, a unified train dynamic model, consistent operating conditions, and an identical evaluation index system. The results indicate that, compared with the Fuzzy-PID control method, the proposed control strategy reduces the integral of time-weighted absolute error nearly by 39.6% and decreases energy consumption nearly by 5.9%, while punctuality, stopping accuracy, and comfort are improved nearly by 33.2%, 12.4%, and 7.1%, respectively. These results not only verify the superior performance of the proposed MPC-MOWOA, but also demonstrate its capability for real-time implementation on embedded processors, thereby overcoming the limitations of purely MATLAB-based offline simulations and exhibiting strong potential for practical engineering applications in urban rail transit. Full article