Search Results (925)

The increasing diffusion of plant-based milk alternatives poses new challenges at the intersection of food safety and consumer experience, particularly regarding allergen cross-contamination and beverage performance during preparation. Traditional quality control strategies are typically confined to upstream production stages and are unable to address individualized risks and sensory variability at the point of consumption. In this study, we propose an embedded volatilomic sensing approach that combines metal oxide semiconductor (MOX) sensor arrays with lightweight artificial intelligence algorithms to enable real-time, on-device decision-making. The volatilome of four commercially available plant-based milk beverages (oat, almond, soy, and coconut) was characterized using GC–MS/SPME as a reference method, while a MOX-based electronic nose provided rapid, non-destructive sensing of volatile fingerprints. Linear Discriminant Analysis demonstrated clear discrimination among beverage types based on their volatile signatures, supporting the use of MOX sensor arrays as functional descriptors of compositional identity and process-related variability. Beyond beverage classification, the proposed framework is designed to support future implementation of (i) screening for anomalous volatilomic patterns potentially compatible with accidental cow’s milk carryover in shared preparation settings and (ii) adaptive tuning of preparation parameters (e.g., foaming-related settings) in smart beverage systems. The results highlight the role of embedded volatilomic intelligence as a unifying layer between personalized risk-aware screening and sensory-oriented process control, paving the way for intelligent food-processing appliances capable of autonomous, real-time adaptation at the point of consumption. Full article

The increasing popularity of carnivore and animal-based diets among athletes has generated substantial interest, despite limited direct scientific evidence supporting their efficacy and safety in sport-specific contexts. This narrative review critically evaluates the current evidence and examines the physiological, performance, and health-related implications of these dietary models in athletic populations. These dietary models, characterized by the partial or complete exclusion of plant-derived foods, are often promoted on the basis of mechanistic arguments, anecdotal reports, and extrapolations from research on ketogenic and very low-carbohydrate diets. However, their physiological relevance, long-term health implications, and compatibility with the demands of athletic training remain poorly defined. This narrative review provides a critical perspective on the current evidence related to carnivore and animal-based diets in sport, integrating findings from studies on low-carbohydrate, ketogenic, high-protein, and elimination-based dietary patterns. The analysis focuses on metabolic adaptations, body composition, exercise performance, gastrointestinal function, micronutrient adequacy, hormonal responses, and potential long-term health risks. Particular attention is given to the distinction between metabolic adaptations and functional performance outcomes, as well as to the high interindividual variability in dietary responses. The available evidence suggests that while carbohydrate restriction may induce specific metabolic adaptations, such as increased fat oxidation, these changes do not consistently translate into improved performance, particularly in high-intensity or high-volume training contexts. Moreover, the highly restrictive nature of carnivore and animal-based diets raises concerns about micronutrient deficiencies, alterations in the gut microbiota, changes in the lipid profile, and potential effects on eating behaviours, particularly in competitive athletic populations. Given the absence of well-controlled, long-term intervention studies in athletes, carnivore and animal-based diets cannot currently be recommended as safe or optimal nutritional strategies for sports performance. Rather than representing viable alternatives to established sports nutrition guidelines, these dietary models may be better understood as experimental or short-term tools within highly controlled research or diagnostic frameworks. Future research should prioritize rigorous, sport-specific study designs, long-term safety outcomes, and personalized approaches that account for individual metabolic and physiological variability. Full article

This study evaluated the growth performance, carcass characteristics, and meat quality of male Alpine and Saanen goat kids raised under standardized fattening conditions to inform breed-specific strategies for meat production. The study included 36 single-born male kids (18 Alpine and 18 Saanen purebreds) of similar age and live weight. The animals were allocated by breed and randomly assigned to three replicates per breed, with six animals in each replicate. After a two-week adaptation period, the kids were fattened for 12 weeks on an 80:20 concentrate–roughage diet. At the end of the fattening period, all animals were slaughtered, and physical, sensory, and chemical analyses of the meat were performed on the Longissimus lumborum (LL) muscle. Final live weights did not differ significantly between Alpine and Saanen kids, nor did average daily gains. However, the feed conversion ratio favored the Saanen breed, indicating superior feed efficiency. Alpine kids had significantly higher internal fat content, while Saanen kids had a higher proportion of full intestines. Meat pH, color, and texture were similar between breeds. Fatty acid analysis showed that Alpine meat had higher palmitic and palmitoleic acid content, whereas Saanen meat contained more calcium and sodium. These results suggest that both breeds are suitable for high-quality meat production under controlled feeding conditions, but breed-specific differences in fat composition and mineral content may affect nutritional value and market positioning. These findings are valuable for optimizing selection and marketing strategies in goat meat production systems targeting diverse consumer demands. Full article

Cognitive impairments are a core feature of psychotic disorders and are strongly associated with long-term functional disability. Although Cognitive Remediation Therapy (CRT) is an evidence-based intervention for improving cognition in psychosis, its feasibility and preliminary effects in acute inpatient settings—particularly using web-based platforms—remain underexplored. This single-arm, pre–post pilot study evaluated the feasibility of delivering a web-based CRT program and examined preliminary cognitive outcomes in a secure psychiatric inpatient facility. Thirteen inpatients with psychotic and non-psychotic diagnoses completed a 15-week intervention comprising twice-weekly sessions that included adaptive computerized CRT exercises (Happy Neuron Pro) and therapist-led bridging discussions focused on metacognitive reflection and functional application. Cognitive performance was assessed pre- and post-intervention using the MATRICS Consensus Cognitive Battery. All participants completed the study with no withdrawals or adverse events, attending a mean of 27.77 of 30 sessions (93.0%). Pre–post improvements were observed in processing speed, verbal learning, and overall composite cognition, with large within-sample effect sizes that remained robust in sensitivity analyses. Exploratory analyses suggested potential associations between sex, history of self-harm, and cognitive change, though these findings require cautious interpretation. Findings support the feasibility of inpatient web-based CRT and provide preliminary cognitive effect-size estimates. Given the single-arm design and absence of systematic medication monitoring, results should be interpreted as exploratory signals warranting controlled validation. Overall, findings support the feasibility of inpatient web-based CRT and provide preliminary signals of cognitive benefit, warranting evaluation in larger controlled studies. Full article

Unsaturated polyester resin (UPR)–quartz composites have become increasingly important in structural, sanitary, and architectural applications. However, their manufacturing processes still rely heavily on empirical knowledge. This review compiles recent developments in materials science, curing kinetics, and digital manufacturing, outlining a pathway toward data-driven, adaptive production of quartz-filled thermosets. The chemical and physical fundamentals of UPR polymerization are summarized, including the influence of initiator systems, filler characteristics, and thermal management on network formation. Challenges associated with highly filled formulations—such as viscosity control, dispersion, shrinkage, and exothermic peak prediction—are discussed in detail. Recent advances in digital twins (DTs) and artificial intelligence (AI) are reviewed, demonstrating how physics-based simulations, machine learning models, and hybrid mechanistic–data-driven approaches improve the prediction of rheology, curing behavior, and quality outcomes in thermoset polymer processes. A practical application example demonstrates the prediction of peak time in quartz–UPR composites using Random Forest and Gradient Boosting ensemble models. Two prediction scenarios are evaluated: Scenario A with gel time by Leave-One-Out cross-validation, and Scenario B without gel time, representing post-mixing and pre-process prediction contexts, respectively. Stratified bootstrap augmentation improves Gradient Boosting in both scenarios. Principal component analysis confirms that the curing process is governed by three independent physical dimensions: curing reactivity, thermal environment and resin thermal state. Full article

Accurate thermal conductivity measurement requires temperature control systems to establish stable operating conditions within a limited time. In practical thermal conductivity performance testing systems, large thermal inertia, complex heat transfer paths, and input time delays arising from thermal propagation and sensor placement often degrade dynamic response and control accuracy. To overcome these limitations, a composite PI–RLS–MPC control strategy is proposed for thermal systems with inertia and time delay. A proportional–integral (PI) controller serves as the baseline stabilizing controller, while model predictive control (MPC) is utilized to optimize the control input by explicitly considering system delay and input constraints. To enhance robustness against model uncertainty and parameter variations, recursive least squares (RLS) is adopted for online parameter identification and adaptive PI tuning, and a steady-state parameter freezing mechanism is introduced to suppress unnecessary parameter updates after convergence. Simulation studies are performed on an identified thermal process model with a 20 s input time delay. The results indicate that the proposed strategy reduces overshoot, shortens settling time, and improves disturbance rejection compared with conventional controllers. Overall, the proposed PI–RLS–MPC approach provides a practical solution for improving temperature control performance in thermal conductivity testing systems. Full article

No. 0 diesel oil may pose a serious threat to sea cucumber (Apostichopus japonicus) aquaculture by inducing skin ulceration. This study aimed to evaluate the protective efficacy and mechanism of a previously developed inhibitor composition against diesel-induced injury. The inhibitor composition significantly alleviated skin ulceration in the experimental group (Eg), reducing the lesion area to 14.44 ± 1.79% after 96 h, compared to 33.19 ± 2.94% in the diesel-exposed control group (Cg) (p < 0.05). It effectively suppressed the overactivation of autolytic enzymes (cathepsin L and B) while enhancing the activities of acetylcholinesterase, superoxide dismutase, and catalase. Transcriptomic profiling revealed 3137 differentially expressed genes, with functional enrichment in pathways related to Notch signaling, ECM–receptor interaction, glycosaminoglycan biosynthesis, and detoxification. The upregulation of genes such as HES-C, CYP1A1, GST, and UGT may be linked to the regulation of apoptosis inhibition, xenobiotic metabolism, and antioxidant defense. Furthermore, enhanced expression of NAD kinase and PNLIPRP may indicate a potential strengthening of energy metabolism and lipid utilization during stress adaptation. This study suggests that the inhibitor composition may exert a multi-level protective effect against diesel-induced injury by coordinating tissue repair, oxidative balance, and detoxification processes, offering a potential strategy to mitigate pollution impacts in sea cucumber aquaculture. Full article

Urban traffic congestion leads to longer travel times, economic losses, and increased pollution. Recent advances in the Internet of Things (IoT) provide detailed real-time traffic data, yet testing adaptive control strategies directly on live networks remains costly and risky. To address this challenge, we propose an IoT-driven digital twin framework for the design and evaluation of AI-based traffic management systems. The framework is implemented in the Simulation of Urban MObility (SUMO) and uses its Python 3.14.2 API to emulate a dense network of IoT sensors that stream real-time information on vehicle density, queue lengths, and waiting times. This simulated IoT data feeds an AI agent that adapts traffic signal control in real time. The agent is trained with a composite reward function to jointly minimise vehicle waiting times and emissions. Its performance is compared with fixed-time and vehicle-actuated control under varying traffic demand scenarios. Results demonstrate the effectiveness of combining IoT-based simulation with AI control, providing a safe and scalable pathway towards the real-world deployment of intelligent traffic management systems. Full article

Ultrasound patches represent a transformative advancement beyond conventional ultrasonography, evolving into intelligent theranostic systems for personalized healthcare. This evolution is propelled by synergistic innovations in flexible piezoelectric materials and integrated designs. The development of piezoelectric polymers, lead-free ceramics, and bio-composite materials has laid the foundation for long-term, conformal, and biosafe interfacing with the human body. Structurally, miniaturized transducer arrays (e.g., CMOS-integrated arrays achieving ~200 μm focal spots and 100 kPa focal pressure), multimodal integration, and bioinspired interfaces have enabled high-precision deep-tissue sensing and spatiotemporally controlled energy delivery—exemplified by strain-sensing feedback improving the signal-to-noise ratio by 5 dB for precise neuromodulation. These capabilities are converging to create closed-loop platforms, as demonstrated in continuous cardiovascular monitoring (up to 164 mm depth for 12 h), image-guided neuromodulation for neurological disorders, on-demand drug delivery (achieving 100% higher plasma concentration than ultrasound alone), and integrated tumor therapy with real-time feedback. Despite persistent challenges in material biocompatibility, energy efficiency, and clinical standardization, the future of ultrasound patches lies in their deep integration with multimodal sensing, machine learning, and adaptive control algorithms. This path will ultimately realize their potential for intelligent, closed-loop theranostics in chronic disease management, telemedicine, and personalized therapy. Full article

Building lighting has a significant impact on occupant health and well-being, energy efficiency, spatial perception, and visual comfort. Many current building lighting systems, however, continue to be insufficiently responsive to changing environmental conditions and human-centric demands, leading to ineffective energy use, poor visual quality, and disruption of the circadian rhythm. This disparity highlights the need for modern buildings to incorporate integrated, intelligent, and sustainable lighting design strategies. This review offers a methodical examination of current, emerging and future developments in building lighting research in six related fields within an architectural scope of building design and performance. To assess lighting effectiveness, it first examines both qualitative and quantitative performance metrics, including illuminance, luminance distribution, glare, color quality, and user comfort. Second, it examines lighting control systems that use tunable light sources that can dynamically change the spectral composition and intensity in response to task demands, occupancy patterns, and daylight availability. Third, the study examines circadian-centric lighting strategies, focusing on digital modeling and simulation approaches that capture real-world lighting conditions and biological reactions. Fourth, the function of virtual reality and sophisticated visualization tools is examined, emphasizing their role in design decision-making and pre-implementation assessment. Fifth, a critical evaluation is conducted of the expanding use of machine learning and data-driven techniques in adaptive lighting control, prediction, and optimization. Limited real-time adaptability, inadequate personalization, disjointed simulation frameworks, and poor integration of human-centric metrics with intelligent control systems are some of the major research gaps. Sustainable Development Goal (SDG) 7, SDG 11, and SDG 3 are in line with the review, which ends with a summary of future paths toward intelligent, energy-efficient, and human-centered building lighting systems. Full article

Yeasts such as Cutaneotrichosporon oleaginosum can convert low-value side streams into single-cell oils with fatty acid profiles comparable to vegetable oils. Crude glycerol (CG), a byproduct of biodiesel production, offers a cost-effective substrate, but its variable impurity load often causes strong growth inhibition. In this study, two untreated industrial CG batches were characterized and evaluated in 2.5 L and 19 L stirred-tank fermentations. Direct batch cultivation on CG resulted in no measurable growth, whereas an adapted stepwise feeding strategy effectively mitigated early inhibition and restored biomass formation, metabolic activity, and lipid accumulation. In 2.5 L cultivations, apparent growth rates up to 0.51 h⁻¹ and volumetric productivities up to 0.22 g L⁻¹ h⁻¹ were achieved, with lipid contents of ~30% and oleate-dominated fatty acid profiles. Fatty acid profiles remained oleate-dominated (~53–55% C18:1). Transition-scale (19 L) repeated-batch fermentations confirmed process robustness across > 640 h of operation, during which lipid content (~30–36%) and fatty acid composition (oleate ~51–53%) remained stable despite pronounced substrate-batch variability and increasing nitrogen limitation. These results demonstrate that untreated CG can be reliably valorized for lipid production using scalable feeding strategies without prior detoxification. This closes a gap between laboratory-scale feasibility studies and process-oriented, multi-cycle operation on industrial-grade feedstocks, confirming that feeding-driven inhibition control can ensure robust performance without substrate purification. Full article

Generative Artificial Intelligence has shown immense potential in industrial design. However, applying Diffusion Transformers to precision manufacturing faces a critical bottleneck: the trade-off between flexible multi-task editing and high-fidelity texture preservation. Existing methods often suffer from “texture collapse” when merging multiple adapters, failing to maintain the intricate topological structures required for industrial standards. To address this, we present Knit-Edit, a unified framework for high-precision knitted garment editing. Our core contribution is EditLoRI, a novel task decoupling mechanism utilizing orthogonal Low-Rank Adaptation. By projecting task-specific gradients into orthogonal subspaces, EditLoRI enables the interference-free composition of multiple editing capabilities within a single lightweight model. Furthermore, we introduce a structure-preserving spatial guidance strategy using Bounding Boxes to resolve the localization ambiguity of text prompts. Validated on our constructed KnitEdit dataset, the proposed method significantly outperforms state-of-the-art baselines in controllability and structural fidelity, offering a robust solution for intelligent generative manufacturing. Full article

Low altitude logistics and aerial transport increasingly rely on multirotor unmanned aerial vehicles (UAVs) carrying slung payloads, where cable flexibility and load swing can degrade safety and delivery accuracy. This paper studies payload trajectory tracking for a dual-quadrotor cooperative slung-load system, targeting accurate tracking with swing suppression under thrust, attitude, and cable-tension limits. First, a payload-layer dynamic model is derived from d’Alembert’s principle with geometric cable constraints, and explicit tension reconstruction formulas are provided to enable direct enforcement of tension bounds. Building on this model, a payload-layer DEA nominal tracking controller is designed by applying dynamic extension to the tension-scalar channels and enforcing output-level linear error dynamics. To ensure real-time feasibility, a convex quadratic-programming (QP) projection layer minimally corrects the nominal command to satisfy thrust saturation, attitude-cone constraints, and cable-tension bounds. Moreover, an adaptive tuning control layer updates the DEA feedback gain and the projection weighting matrix within preset constraint limits based on energy residual and constraint-activation information, improving robustness and reducing manual tuning. Input-to-state stability is established under bounded disturbances and constraint-activation switching via a composite Lyapunov analysis. ROS–PX4–Gazebo simulations show low tracking error, suppressed swing, and sustained tension-limit compliance, validating the fusion controller. Full article

This study aimed to investigate the effects of dietary NFC/NDF ratio on nutrient apparent digestibility, fecal fermentation parameters, microbial diversity, and plasma metabolomics in Yili horses. Twenty-four healthy Yili horses with similar body weights (406 ± 22.73 kg) were divided into four groups, each with six replicates: the Control Group (CG), Low-NFC Group (LG), Medium-NFC Group (MG), and High-NFC Group (HG). The experiment lasted 52 d, comprising a 7-day adaptation period and a 45-day experimental period. Total fecal collection was conducted from days 41 to 45 to calculate nutrient apparent digestibility. On the final day, rectal fecal samples and blood samples were collected for full-length 16S rRNA gene sequencing and plasma metabolomics analysis. The results revealed the following findings: (1) The apparent digestibility of crude protein (CP) in the MG and HG groups was significantly higher than in the CG (p < 0.01), and significantly higher in the LG group compared to the CG (p < 0.05). (2) Significant differences were observed in fecal pH, propionate concentration, and the acetate-to-propionate ratio between the CG and the experimental groups (p < 0.05). (3) At the phylum level, Firmicutes, Bacteroidota, and Verrucomicrobiota were dominant in the fecal microbiota of all groups. PICRUSt2 prediction indicated that the MG and HG groups primarily enhanced energy conversion efficiency through amino acid metabolism and pantothenate and CoA biosynthesis metabolic pathways. (4) A total of 204 differential metabolites were identified between the CG and MG groups, with 98 upregulated and 106 downregulated in the MG group compared to the CG. These metabolites were mainly enriched in pantothenate and CoA biosynthesis, fructose and mannose metabolism, pyruvate metabolism, and starch and sucrose metabolism pathways. In summary, appropriately increasing NFC/NDF content influences the gut microbiota composition and energy metabolism of Yili horses, thereby effectively improving their digestion and absorption of dietary nutrients. Full article

The ability of Rhodotorula toruloides DSM 4444 to metabolize low-cost carbon sources such as fatty acids was comprehensively studied. This organism is shown, for the first time, to simultaneously accumulate microbial oils (biofuel precursors) and carotenoids from acetic acid obtained from CO₂ fermentation. This fatty acid is typically the single end product of acetogenic bioconversion of one-carbon gas pollutants (e.g., CO₂ and CO). In the first set of experiments, different aerobic fermentations were carried out in automated bioreactors, with acetic acid in one case and with glucose, a more conventional carbon source, as a control, in another bioreactor. R. toruloides consumed around 80 g/L substrate under both conditions. Maximum lipid content (27.2% g/g dry weight) was reached from 38 g/L glucose, while carotenoid content was higher with acetic acid (1.4 mg/g cell after 54.1 g/L acetic acid consumed), representing a 40% increase compared to glucose (1.0 mg/g cell after 64.2 g/L glucose consumed). Additionally, in the second set of assays, a fermented broth produced by Acetobacterium woodii from CO₂ fermentation, containing residual nutrients and metabolites, was tested. Despite its complex composition, R. toruloides grew and produced carotenoids (up to 0.141 mg/g), showing potential adaptability. To the best of our knowledge, this is the first report on a greenhouse gas-based biotechnological process as a promising sustainable alternative for the valorization of pollutants, e.g., gas emissions, their bioconversion to VFAs, such as acetic acid, and subsequent fermentation of the carboxylic acid into microbial oils, as a source of renewable energy, as well as carotenoids as a high-value nutraceutical product. Full article