Search Results (76)

Proteomics combined with peptidomics approaches were used to analyze the protein degradation and the release pattern of umami peptides in stewed chicken. The results showed that a total of 422 proteins were identified, of which 273 proteins consistently existed in samples stewed for 0–5 h. Myosin heavy chain exhibited the highest abundance (26.29–30.26%) throughout the stewing process. The proportion of proteins under 20 kDa increased progressively with the duration of stewing and reached 61% at 4–5 h of stewing. A total of 8018 peptides were detected in the soup samples, and 2323 umami peptides were identified using the prediction platforms iUmami-SCM, UMPred-FRL, Umami_YYDS, and TastePertides-DM. Umami peptides derived from titin (accession number A0A8V0ZZ81) were determined to be the most abundant, accounting for 24% of the total umami peptides, and Val534 and Lys33639 were the key N-terminal and C-terminal amino acids of these umami peptides. Abundance analysis showed that the umami peptides KK16 and SK18 ranked among the top 5 in the samples stewed for 0–5 h, and they were most abundant in the 3 h stewed samples. The results obtained will provide data support for promoting the industrialization of high-quality chicken soup products. Full article

This paper introduces a new class of Newton-type inequalities (NTIs) within the framework of extended fractional integral operators. This study begins by establishing a fundamental identity for generalized fractional Riemann–Liouville (FR-L) operators, which forms the basis for deriving various inequalities under different assumptions on the integrand. In particular, fractional counterparts of the classical $1/3$ and $3/8$ Simpson rules are obtained when the modulus of the first derivative is convex. The analysis is further extended to include functions that satisfy a Lipschitz condition or have bounded first derivatives. Moreover, an additional NTI is presented for functions of bounded variation, expressed in terms of their total variation. In all scenarios, the proposed results reduce to classical inequalities when the fractional parameters are specified accordingly, thus offering a unified perspective on numerical integration through fractional operators. Full article

The response of the desert cyanobacterium Chroococcidiopsis sp. CCMEE 010 was tested in Mars simulations to investigate the possibility of photosynthesis in near-surface protected niches. This cyanobacterium colonizes lithic niches enriched in far-red light (FRL) and depleted in visible light (VL) and is capable of far-red light photoacclimation (FaRLiP). Biofilms were grown under FRL and VL and exposed in a hydrated state to a low-pressure atmosphere, variable humidity, and UV irradiation, as occur on the Martian surface. VL biofilms showed a maximum quantum efficiency that dropped after 1 h, whereas a slow reduction occurred in FRL biofilms up to undetectable after 8 h, indicating that UV irradiation was the primary cause of photoinhibition. Post-exposure analyses showed that VL and FRL biofilms were dehydrated, suggesting that they entered a dried, dormant state and that top-layer cells shielded bottom-layer cells from UV radiation. After Mars simulations, the survivors (12% in VL biofilms and few cells in FRL biofilms) suggested that, during the evolution of Mars habitability, near-surface niches could have been colonized by phototrophs utilizing low-energy light. The biofilm UV resistance suggests that, during the loss of surface habitability on Mars, microbial life-forms might have survived surface conditions by taking refuge in near-surface protected niches. Full article

This study investigates the influence of fabric weave design and adhesive type on the adhesion quality and mechanical properties of Kevlar woven fabric-reinforced laminates (FRLs). Three adhesives (EVA, EVOH, and TPU) and three weave structures (plain, 2/2 twill, and crowfoot) were analyzed while keeping other fabric parameters constant. Both weave structure and adhesive type, as well as their interactions, significantly influenced adhesion and mechanical performance. Combinations like the crowfoot weave with EVOH adhesive enhanced adhesion due to increased surface contact, while the 2/2 twill weave with EVA adhesive improved tear strength but resulted in weaker adhesion, highlighting the trade-offs in material design. A negative correlation between yarn pullout force and tear resistance was observed, particularly for EVA and EVOH adhesives, where improved adhesion often coincided with reduced tear resistance. Tensile strength varied significantly across weaves, with twill exhibiting the highest strength, followed by plain and crowfoot weaves. This study highlights the critical role of weave design and adhesive choice in FRLs, providing valuable insights for optimizing material selection to meet specific industrial performance criteria. Full article

This study investigates the impact of mechanical and chemical surface treatments on the interfacial adhesion and mechanical properties of Kevlar and ultra-high molecular weight polyethylene (UHMWPE) fiber-reinforced laminates (FRLs). Various treatments, including surface roughening, plasma exposure, NaOH and silane coupling, and graphene nanoparticle (NP) incorporation, were conducted to enhance the fiber–matrix bonding within thermoplastic polyurethane (TPU) and ethylene-vinyl acetate (EVA) matrices. Results demonstrated that treatment efficacy highly depends on fiber type and matrix material, with chemical modifications generally outperforming the physical treatment (surface roughness). Plasma treatment significantly enhanced adhesion for UHMWPE, increasing yarn pullout force by 188.1% with TPU. While combining plasma with graphene slightly improved performance, it did not exceed plasma-only results due to potential surface functionalization losses during wet graphene application. For Kevlar, the combination of NaOH, silane, and graphene NP (NSG) treatment yielded the highest adhesion, showing increases of 76.6% with TPU and 95.4% with EVA, underscoring the synergy between chemical coupling and nanomaterial reinforcement. This study’s insights align with previous research, expanding the knowledge base by investigating graphene’s role independently and alongside established methods. Full article

This paper investigates how the present paths support massive manufacturing by evaluating the existing state of sustainable aviation fuel (SAF) technologies, examining technology readiness levels (TRL), fuel readiness levels (FRL), costs, economic conditions, emissions, etc. This assessment summarizes major conclusions about bio-jet replacements for conventional jet fuels. In order for SAF to play a sustainable role, a full life cycle emissions assessment, techno-economic analysis (TEA), and discussions about the sustainability of SAF materials are required. The life cycle assessment (LCA) discusses the capability of SAF in cutting down emissions, TEA argues for its economic viability, and the sustainable supply of SAF feedstock is a third critical factor determining the sustainability of the industry. With all the analyses, this overview provides recommendations for the sustainable development of the SAF industry and calls on industry stakeholders to enhance cooperation. Full article

This experiment explores the regulatory mechanisms of various light qualities on the phenotypic plasticity of Quercus variabilis seedlings during their growth. The light conditions included blue light (BL), red light (RL), far-red light (FrL), a blend of RL and FrL with a ratio of 1:1 (RFr1:1L), and a blend of RL and FrL with a ratio of 1:2 (RFr1:2L), alongside a broad-spectrum white light (WL) as the control. Each treatment was maintained at a consistent photosynthetic photon flux density of 400 µmol·m⁻²·s⁻¹. Results indicate significant morphological variations in Q. variabilis seedlings under different light qualities. Compared to white light treatment, all light quality treatments enhance seedling height, with the FrL treatment exhibiting the most pronounced effect. Seedling ground diameter elongation is stimulated by all light quality treatments, except for the BL treatment. Although the BL treatment promotes leaf morphology in Q. variabilis seedlings, it inhibits root growth, leading to reduced biomass accumulation and a lower root-to-shoot ratio. FrL can mitigate the effects of RL. Under the FrL treatment, Q. variabilis seedlings exhibit a greater increase in plant height and a higher height-to-diameter ratio. While the leaf morphology of RFr1:1L treatment does not show significant advantages, it demonstrates substantial root growth, resulting in the highest biomass accumulation. Quercus variabilis displays the strongest morphological plasticity in its root system, showing greater sensitivity to variations in light quality compared to leaf morphology and biomass accumulation. Strategically optimizing light spectrum and wavelength can significantly boost economic yields and improve the quality of forestry products. Full article

This study investigated the adhesion and mechanical properties of woven fabric-reinforced laminates (FRLs) made with four distinct Kevlar fabrics of varying areal densities (36 g/m², 60 g/m², 140 g/m², and 170 g/m²) under different fabric-to-adhesive weight ratios (1:0.5, 1:1, and 1:1.5) in both the warp and weft directions. A novel aspect of this research lies in our systematic study of the effect of adhesive quantity on FRLs, a topic that has received limited attention despite its critical role in laminate performance. Additionally, the application of a newly developed yarn pullout test alongside the standard T-peel test provides unique insights into the interfacial behavior of laminates. The results show that in lower areal density fabrics (36 g/m² and 60 g/m²), adhesive quantity minimally affects the pullout and T-peel forces or tear strength, indicating that structural integrity can be maintained with reduced adhesive application. In contrast, higher areal density fabrics (140 g/m² and 170 g/m²) benefit from an increased adhesive ratio, with a transition from 1:0.5 to 1:1 significantly enhancing the pullout resistance, while further increases to 1:1.5 yielded diminishing returns. Tensile strength remained consistent across all samples, highlighting that it is largely dictated by the inherent properties of the fibers and fabric structure rather than the adhesive. This study concludes that a 1:1 fiber-to-adhesive ratio offers an optimal balance of adhesion quality and mechanical performance for FRLs. By addressing the understudied impact of adhesive quantity on FRLs and introducing the yarn pullout test, this research provides novel and practical guidelines for optimizing FRLs in applications demanding high structural integrity and adaptability under challenging conditions. Full article

The hybrid flocculant P(aluminum chloride-co-diallyldimethylammonium chloride) was synthesized in this study. Diallyldimethylammonium chloride monomers were used and ammonium persulfate served as the initiator. The structure of P(aluminum chloride-co-diallyldimethylammonium chloride) was characterized using Fourier-transform infrared spectroscopy, scanning electron microscopy, an electrical conductivity test, and thermogravimetric analysis. Single-factor experiments were conducted to optimize the synthetic conditions of the hybrid flocculant. An optimized product with an intrinsic viscosity of 926.36 mL/g and a flocculation decolorization rate of 99% was obtained under the following reaction conditions: the total monomer concentration was 30%, the initiator concentration was 0.7%, the reaction temperature was 60 °C, and the reaction time was 3 h. The results demonstrated that the PAC-PDMDAAC hybrid flocculant exhibited covalent bonding between its organic–inorganic components and displayed enhanced stability properties due to its high intrinsic viscosity and spatial structure. Moreover, this hybrid flocculant showed superior decolorization performance in disperse-violet-H-FRL-dye wastewater. Full article

A self-driving vehicle can navigate autonomously in smart cities without the need for human intervention. The emergence of Autonomous Connected Vehicles (ACVs) poses a substantial threat to public and passenger safety due to the possibility of cyber-attacks, which encompass remote hacking, manipulation of sensor data, and probable disablement or accidents. The sensors collect data to facilitate the network’s recognition of local landmarks, such as trees, curbs, pedestrians, signs, and traffic lights. ACVs gather vast amounts of data, encompassing the exact geographical coordinates of the vehicle, captured images, and signals received from various sensors. To create a fully autonomous system, it is imperative to intelligently integrate several technologies, such as sensors, communication, computation, machine learning (ML), data analytics, and other technologies. The primary issues in ACVs involve data privacy and security when instantaneously exchanging substantial volumes of data. This study investigates related data security and privacy research in ACVs using the Blockchain-enabled Federated Reinforcement Learning (BFRL) framework. This paper provides a literature review examining data security and privacy in ACVs and the BFRL framework that can be used to protect ACVs. This study presents the integration of FRL and Blockchain (BC) in the context of smart cities. Furthermore, the challenges and opportunities for future research on ACVs utilising BFRL frameworks are discussed. Full article

This paper applies federated reinforcement learning (FRL) in cellular vehicle-to-everything (C-V2X) communication to enable vehicles to learn communication parameters in collaboration with a parameter server that is embedded in an unmanned aerial vehicle (UAV). Different sensors in vehicles capture different types of data, contributing to data heterogeneity. C-V2X communication networks impose additional communication overhead in order to converge to a global model when the sensor data are not independent-and-identically-distributed (non-i.i.d.). Consequently, the training time for local model updates also varies considerably. Using FRL, we accelerated this convergence by minimizing communication rounds, and we delayed it by exploring the correlation between the data captured by various vehicles in subsequent time steps. Additionally, as UAVs have limited battery power, processing of the collected information locally at the vehicles and then transmitting the model hyper-parameters to the UAVs can optimize the available power consumption pattern. The proposed FRL algorithm updates the global model through adaptive weighing of Q-values at each training round. By measuring the local gradients at the vehicle and the global gradient at the UAV, the contribution of the local models is determined. We quantify these Q-values using nonlinear mappings to reinforce positive rewards such that the contribution of local models is dynamically measured. Moreover, minimizing the number of communication rounds between the UAVs and vehicles is investigated as a viable approach for minimizing delay. A performance evaluation revealed that the FRL approach can yield up to a 40% reduction in the number of communication rounds between vehicles and UAVs when compared to gross data offloading. Full article

Fiber-reinforced laminates with flexibility (FRLs) are becoming increasingly crucial across diverse sectors due to their adaptability and outstanding mechanical attributes. Their ability to deliver high performance relative to their weight makes them indispensable in lighter-than-air (LTA) applications, such as aerostats, inflatable antennas, surge bladders, gas storage balloons, life rafts, and other related uses. This research delved into employing woven fabrics as the reinforcement material and explored how their specific parameters, like fiber type, fabric count (warp thread density × weft thread density), fabric areal density, and fabric cover influence the bonding and mechanical properties of laminates. A thorough analysis encompassing standard T-peel (ASTM standard D1876) and a newly proposed yarn pullout in laminate test were conducted on laminates fabricated with various woven reinforcements, each with its unique specifications. The T-peel test was utilized to gauge the adhesive strength between FRL components, offering crucial insights into interfacial bonding within the laminates. Nevertheless, challenges exist with the T-peel test, including instances where the adherents lack the strength to withstand rupture, resulting in unsuccessful peel propagation and numerous outliers that necessitate costly additional trials. Thus, our research group introduced a novel yarn pullout in laminate test to accurately assess adhesion in FRLs. This study uncovered correlations between both adhesion tests (T-peel and yarn pullout in laminate), indicating that the innovative yarn pullout in laminate test could effectively substitute for characterizing adhesion in FRLs. Furthermore, the findings unveiled a complex relationship between woven fabric specifications and laminate properties. We noted that variations in fiber type, yarn linear density, and adhesive type significantly impacted adhesion strength. For instance, Kevlar exhibited markedly superior adhesion compared to Ultra-High Molecular Weight Polyethylene (UHMWPE) when paired with Thermoplastic Polyurethane (TPU) adhesive, whereas UHMWPE demonstrated better adhesion with Ethylene Vinyl Acetate (EVA). Moreover, the adhesion quality lessened as fabric count increased for the same adhesive quantity. These discoveries carry practical implications for material selection and design across industries, from automotive to aerospace, offering avenues to enhance FRL performance. Full article

YOLOv5 is currently one of the mainstream algorithms for object detection. In this paper, we propose the FRL-YOLO model specifically for river floating object detection. The algorithm integrates the Fasternet block into the C3 module, conducting convolutions only on a subset of input channels to reduce computational load. Simultaneously, it effectively captures spatial features, incorporates reparameterization techniques into the feature extraction network, and introduces the RepConv design to enhance model training efficiency. To further optimize network performance, the ACON-C activation function is employed. Finally, by employing a structured non-destructive pruning approach, redundant channels in the model are trimmed, significantly reducing the model’s volume. Experimental results indicate that the algorithm achieves an average precision value (mAP) of 79.3%, a 0.4% improvement compared to yolov5s. The detection speed on the NVIDIA GeForce RTX 4070 graphics card reaches 623.5 fps/s, a 22.8% increase over yolov5s. The improved model is compressed to a volume of 2 MB, representing only 14.7% of yolov5s. Full article

Background: Liver venous deprivation (LVD) is a recent radiological technique that has shown promising results on Future Remnant Liver (FRL) hypertrophy. The aim of this retrospective study is to compare the segmentary hypertrophy of the FRL after LVD and after portal vein embolization (PVE). Methods: Patients undergoing PVE or LVD between April 2015 and April 2020 were included. The segmentary volumes (seg 4, seg2+3 and seg1) were assessed before and after the radiological procedure. Results: Forty-four patients were included: 26 undergoing PVE, 10 LVD and 8 eLVD. Volume gain of both segment 1 and segments 2+3 was significantly higher after LVD and eLVD than after PVE (segment 1: 27.33 ± 35.37 after PVE vs. 38.73% ± 13.47 after LVD and 79.13% ± 41.23 after eLVD, p = 0.0080; segments 2+3: 40.73% ± 40.53 after PVE vs. 45.02% ± 21.53 after LVD and 85.49% ± 45.51 after eLVD, p = 0.0137), while this was not true for segment 4. FRL hypertrophy was confirmed to be higher after LVD and eLVD than after PVE (33.53% ± 21.22 vs. 68.63% ± 42.03 vs. 28.11% ± 28.33, respectively, p = 0.0280). Conclusions: LVD and eLVD may induce greater hypertrophy of segment 1 and segments 2+3 when compared to PVE. Full article

Swarm control of unmanned aerial vehicles (UAV) has emerged as a challenging research area, primarily attributed to the presence of conflicting behaviors among individual UAVs and the influence of external movement disturbances of UAV swarms. However, limited attention has been drawn to addressing the fission–fusion motion of UAV swarms for unknown dynamic obstacles, as opposed to static ones. A Bio-inspired Fission–Fusion control and planning via Reinforcement Learning (BiFRL) algorithm for the UAV swarm system is presented, which tackles the problem of fission–fusion behavior in the presence of dynamic obstacles with homing capabilities. Firstly, we found the kinematics models for the UAV and swarm controller, and then we proposed a probabilistic starling-inspired topological interaction that achieves reduced overhead communication and faster local convergence. Next, we develop a self-organized fission–fusion control framework and a fission decision algorithm. When dealing with various situations, the swarm can autonomously re-configure itself by fissioning an optimal number of agents to fulfill the corresponding tasks. Finally, we design a sub-swarm confrontation algorithm for path planning optimized by reinforcement learning, where the sub-swarm can engage in encounters with dynamic obstacles while minimizing energy expenditure. Simulation experiments demonstrate the capability of the UAV swarm system to accomplish self-organized fission–fusion control and planning under different interference scenarios. Moreover, the proposed BiFRL algorithm successfully handles adversarial motion with dynamic obstacles and effectively safeguards the parent swarm. Full article