Alltech (United States)

Abstract The aim of this study was to evaluate Viligen® NE (Alltech®, USA) as a feed additive for Penaeus vannamei reared in a biofloc system during the nursery phase, focusing on growth performance, intestinal microbiological analysis, and resistance tocold challenge. Shrimp post-larvae (41 mg) were fed for 6 weeks with four experimental diets: (a) 0.5 g Viligen® NE kg⁻ 1 , (b) 1 g Viligen® NE kg⁻ 1 , (c) 2 g Viligen® NE kg⁻ 1 , and (d) a control diet (0 g Viligen® NE kg⁻ 1 ). The experiment was conducted in quadruplicate, totaling 16 experimental tanks, with a stocking density of 2000 post-larvae m⁻ 3 . Shrimp growth performance parameters were positively influenced by the inclusion of Viligen® NE in the diet. Polynomial regression indicated an optimal inclusion level of 1.17 g kg⁻ 1 for maximizing specific growth rate and weight gain. Additionally, supplementation at 1 g kg⁻ 1 enhanced shrimp resistance to cold challenge. However, no significant differences were observed in intestinal counts of total heterotrophic bacteria or Vibrio spp. among treatments. In conclusion, dietary supplementation with Viligen® NE at an optimal level of 1.17 g kg⁻ 1 improved growth performance, and 1 g kg⁻ 1 enhanced cold challenge resistance of nursery-phase shrimp reared in a biofloc system.

Synthetic face datasets for training Face Recognition (FR) models have gained significant interest in recent years due to privacy concerns associated with real facial data. Recent advances in generative techniques, based mainly on Generative Adversarial Networks (GANs) and Diffusion Models, have led to the proliferation of various synthetic datasets. Although the performance of FR models trained on synthetic data is becoming similar to those of models trained on real data when evaluated on standard benchmarks such as LFW, CFP-FP, AgeDB, and CALFW, little is known about their effectiveness in more challenging scenarios involving individuals with high facial similarity, such as doppelgängers (lookalikes) and identical twins. This work addresses this gap by evaluating state-of-the-art FR models trained on real or synthetic datasets across four testing datasets that feature high facial similarity: HDA Doppelgänger, DoppelVer, 3D-TEC, and ND-Twins-2009-2010. We analyze verification performances, including subgroup analysis by ethnicity, gender, and age, to better understand demographic disparities. Our results show that the gap between synthetic and real face datasets is larger in these challenging scenarios than on typical benchmarks. We also observe similar biases in FR models trained on synthetic or real data, suggesting that current synthetic datasets may inherit biases through the use of real data at some stage in their pipeline. Reproducibility code, trained models, and instructions are available at.

Transparent conductive materials (TCMs) are essential for optoelectrical devices ranging from smart windows and defogging films to soft sensors, display technologies, and flexible electronics. Materials, such as indium tin oxide (ITO) and silver nanowires (AgNWs), are commonly used and offer high optical transmittance and electrical conductivity, but suffer from brittleness, oxidation susceptibility, and require high-cost materials, greatly limiting their use. Carbon nanotube (CNT) networks provide a promising alternative, featuring mechanical compliance, chemical robustness, and scalable processing. This study reports an aqueous ink formulation composed of ultra-long mix-walled carbon nanotubes (UL-CNTs), compatible with the flow coating process, yielding uniform transparent conductive films (TCFs) on polyethylene terephthalate (PET), glass, and polycarbonate (PC). The resulting films exhibit tunable transmittance (85%–88% for single layers; ~57% for three layers at 550 nm) and sheet resistance of 7.5 kΩ/□ to 1.5 kΩ/□ accordingly. These TCFs maintain stable sheet resistance for over 5000 bending cycles and show excellent mechanical durability with negligible effects on heating performance. Post-deposition treatments, including nitric acid vapor doping or flash photonic heating (FPH), further reduce sheet resistance by up to 80% (7.5 kΩ/□ to 1.2 kΩ/□). X-ray photoelectron spectroscopy (XPS) results in reduced surface oxygen content after FPH. The photonic-treated heaters attain ~100 °C within 20 s at 100 V. This scalable, water-based process provides a pathway toward low-cost, flexible, and stretchable devices in a variety of fields, including printed electronics, optoelectronics, and thermal actuators.

Modern machine learning workloads face scalability challenges due to increasing data volumes and model complexity. While centralized cloud platforms reduce infrastructure overhead but incur recurring costs, proprietary solutions require capital invest

Abstract Thin‐walled cold‐formed members are mainly used in rack structures due to their light weight and the variety of cross‐sections of different shapes that can optimize the design process. However, in cases where bigger span distances or higher resistances are required, the use of single members is not adequate and built‐up sections deem to be necessary. To form such sections, single sections are connected with intermediate fasteners, with or without packing plates. Although the effect of these intermediate connections is considered in some international standards such as the American and the Australian ones, the latter is totally neglected in EN 1993‐1‐3. Rules are only found in EN 1993‐1‐1 related to closely‐spaced battened members, but their possible extension to cold‐formed thin‐walled sections could be questionable and needs further validations. This paper presents numerical and analytical studies aiming at investigating the validity and applicability of these design rules for predicting the buckling resistance under pure compression of a battened built‐up member consisting of two back‐to‐back thin‐walled U profiles. In this framework, a specific attention is paid to the effects of the shear stiffness associated to the batten plates. Finally, design recommendations are provided.

Aim of the Study: Conventional concrete, though widely used, is brittle and prone to cracking due to its low tensile strength. Glass Fiber Reinforced Concrete (GFRC) offers a promising alternative because of its high tensile capacity, crack resistance, and corrosion-free behavior. Methods: This study investigated the effect of adding alkali-resistant glass fibers (0.1%–1.0% by weight of cement) to Ordinary Portland Cement (OPC, 43 grade). Cube specimens (150 × 150 × 150 mm) were prepared, cured for 7, 14, and 28 days, and tested for compressive strength using a Universal Testing Machine (UTM). Workability was evaluated with a slump test, and durability was assessed through water absorption. Conventional concrete served as the control. Results: The addition of glass fibers improved compressive strength by up to 12% compared to control samples after 28 days (25.1 MPa vs. 22.4 MPa). GFRC also exhibited better crack resistance and lower water absorption (8.2 kg at 28 days vs. 7.1 kg in the control). However, higher fiber contents reduced workability. Conclusion: Results confirm that glass fibers act as micro-reinforcements, bridging cracks and redistributing stresses effectively. Optimal performance was observed at around 1.0% fiber content, while higher dosages caused mixing difficulties. GFRC demonstrates strong potential for prefabricated elements, pavements, and repair applications, contributing to sustainable construction by utilizing glass waste. Future research on hybrid fibers and long-term durability is recommended.

IntroductionThis study evaluated the effects of contamination of the beef cattle diet with mycotoxins and the use of yeast cell wall extract based mycotoxin adsorbent(YCWE) on the ruminal and fecal microbial communities.MethodsEight rumen-cannulated Nellore steers [initial body weight (BW) = 417 ± 42 kg; ± 36 month of age] were used in a 4 × 4 replicated Latin square design. A 2 × 2 factorial treatment structure was used to investigate the effects of mycotoxin contamination of the diet, the addition of YCWE and their interactions. The dietary treatments consisted of: (1) diet without mycotoxins (CTRL), and (2) control diet with added mycotoxins(MYCOT). The second factor was: (1) absence (YCWE−) or (2) presence (YCWE+)of YCWE. The addition of YCWE to the diets was 1 g/kg of dry matter (DM).ResultsIn the rumen, MYCOT increased microbial richness and diversity indices (p < 0.01), whereas YCWE decreased richness but increased diversity (p < 0.01). MYCOT contamination also increased the relative abundance of taxa associated with inefficient nitrogen utilization (p < 0.08). YCWE supplementation affected several microbial groups, reducing the abundance of methanogenic archaea and acetateproducing bacteria (p < 0.02). Predicted metabolic pathways indicated that MYCOT impaired several functions related to microbial growth and protein synthesis, while YCWE supplementation in contaminated diets partially restored pathways such aspurine and pyrimidine metabolism (p < 0.05). However, YCWE supplementation inuncontaminated diets reduced pathways linked to protein synthesis (p < 0.05). In feces, MYCOT and YCWE had no effects on richness (p > 0.10), although MYCOT increased diversity (p = 0.01). Treatment effects on predicted metabolic pathways of fecal microbiota were minimal, suggesting a low impact of MYCOT on fecal microorganisms (p > 0.10).ConclusionContamination of beef cattle diets with multiple mycotoxins altered ruminal and fecal microbial richness, diversity, and metabolic pathways, potentially reducing microbial growth and protein synthesis. YCWE mitigated several of these adverse effects, contributing to partial recovery of disrupted metabolic pathways. This study provides evidence that YCWE counteracts the antimicrobial effects of mycotoxins, offering a practical nutritional strategy to preserve rumen functionality.

We consider stochastic inverse problems with expensive forward models (e.g., PDE/ODE solvers). We propose a transformer-parameterized Conditional Flow Matching (CFM) that learns a time-dependent vector field to transport the prior to the posterior via an ODE. Under mild assumptions and accurate vector-field regression, the learned sampler converges to the true posterior. We validate the method on a numerical example, demonstrating efficient posterior sampling.