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Presently, the traditional methods employed for detecting livestock and poultry meat predominantly involve sensory evaluation conducted by humans, chemical index detection, and microbial detection. While these methods demonstrate commendable accuracy in detection, their application becomes more challenging when applied to large-scale production by enterprises. Compared with traditional detection methods, machine vision and hyperspectral technology can realize real-time online detection of large throughput because of their advantages of high efficiency, accuracy, and non-contact measurement, so they have been widely concerned by researchers. Based on this, in order to further enhance the accuracy of online quality detection for livestock and poultry meat, this article presents a comprehensive overview of methods based on machine vision, hyperspectral, and multi-sensor information fusion technologies. This review encompasses an examination of the current research status and the latest advancements in these methodologies while also deliberating on potential future development trends. The ultimate objective is to provide pertinent information and serve as a valuable research resource for the non-destructive online quality detection of livestock and poultry meat. [ABSTRACT FROM AUTHOR]

Underwater object detection plays a significant role in marine ecosystem research and marine species conservation. The improvement of related technologies holds practical significance. Although existing object-detection algorithms have achieved an excellent performance on land, they are not satisfactory in underwater scenarios due to two limitations: the underwater objects are often small, densely distributed, and prone to occlusion characteristics, and underwater embedded devices have limited storage and computational capabilities. In this paper, we propose a high-precision, lightweight underwater detector specifically optimizing for underwater scenarios based on the You Only Look Once Version 8 (YOLOv8) model. Firstly, we replace the Darknet-53 backbone of YOLOv8s with FasterNet-T0, reducing model parameters by 22.52%, FLOPS by 23.59%, and model size by 22.73%, achieving model lightweighting. Secondly, we add a Prediction Head for Small Objects, increase the number of channels for high-resolution feature map detection heads, and decrease the number of channels for low-resolution feature map detection heads. This results in a 1.2% improvement in small-object detection accuracy, while the remaining model parameters and memory consumption are nearly unchanged. Thirdly, we use Deformable ConvNets and Coordinate Attention in the neck part to enhance the accuracy in the detection of irregularly shaped and densely occluded small targets. This is achieved by learning convolution offsets from feature maps and emphasizing the regions of interest (RoIs). Our method achieves 52.12% AP on the underwater dataset UTDAC2020, with only 8.5 M parameters, 25.5 B FLOPS, and 17 MB model size. It surpasses the performance of large model YOLOv8l, at 51.69% AP, with 43.6 M parameters, 164.8 B FLOPS, and 84 MB model size. Furthermore, by increasing the input image resolution to 1280 × 1280 pixels, our model achieves 53.18% AP, making it the state-of-the-art (SOTA) model for the UTDAC2020 underwater dataset. Additionally, we achieve 84.4% mAP on the Pascal VOC dataset, with a substantial reduction in model parameters compared to previous, well-established detectors. The experimental results demonstrate that our proposed lightweight method retains effectiveness on underwater datasets and can be generalized to common datasets. [ABSTRACT FROM AUTHOR]

Background: Cashew (Anacardium occidentale L.) is a commercially important plant. Cashew nuts are a popular food source that belong to the tree nut family. Tree nuts are one of the eight major food allergens identified by the Food and Drug Administration in the USA. Allergies to cashew nuts cause severe and systemic immune reactions. Tree nut allergies are frequently fatal and are becoming more common. Aim: We aimed to identify the key allergenic epitopes of cashew nut proteins by correlating the phage display epitope prediction results with bioinformatics analysis. Design: We predicted and experimentally confirmed cashew nut allergen antigenic peptides, which we named Ana o 2 (cupin superfamily) and Ana o 3 (prolamin superfamily). The Ana o 2 and Ana o 3 epitopes were predicted using DNAstar and PyMoL (incorporated in the Swiss-model package). The predicted weak and strong epitopes were synthesized as peptides. The related phage library was built. The peptides were also tested using phage display technology. The expressed antigens were tested and confirmed using microtiter plates coated with pooled human sera from patients with cashew nut allergies or healthy controls. Results: The Ana o 2 epitopes were represented by four linear peptides, with the epitopes corresponding to amino acids 108–111, 113–119, 181–186, and 218–224. Furthermore, the identified Ana o 3 epitopes corresponding to amino acids 10–24, 13–27, 39–49, 66–70, 101–106, 107–114, and 115–122 were also screened out and chosen as the key allergenic epitopes. Discussion: The Ana o 3 epitopes accounted for more than 40% of the total amino acid sequence of the protein; thus, Ana o 3 is potentially more allergenic than Ana o 2. Conclusions: The bioinformatic epitope prediction produced subpar results in this study. Furthermore, the phage display method was extremely effective in identifying the allergenic epitopes of cashew nut proteins. The key allergenic epitopes were chosen, providing important information for the study of cashew nut allergens. [ABSTRACT FROM AUTHOR]

Low carbon has become a highly relevant topic in today's society, particularly for manufacturing enterprises. To gain insight into how manufacturing enterprises embedded in the industrial internet platform make decisions regarding low-carbon technology innovation, this article examines the service quality of the platform, the low-carbon preferences of the manufacturing enterprises, and government subsidy factors. A platform ecological system game model, comprised of a single manufacturing enterprise and an industrial internet platform, is then established. The results indicate that, under the model's assumptions, the decarbonization of production can only occur when the cost of low-carbon innovation is below a specific threshold. Decentralized decision making is more effective in promoting low-carbon innovation by the manufacturing enterprises when the cost of low-carbon technology innovation is low. The greater the service quality of the industrial internet platform, the stronger the positive influence of the low-carbon preferences of users and government subsidies on the low-carbon innovation level of the manufacturing enterprises. This study offers useful decision-making advice for both the industrial internet platform and the manufacturing enterprises. [ABSTRACT FROM AUTHOR]

The deep integration of digital technologies has given rise to the development of new industries and models in various sectors, as well as new opportunities and challenges. Whether digital transformation can drive collaborative green innovation in the supply chain has also become an important topic of great interest, which has not yet been resolved. In this paper, we study the impact of digital enablement on collaborative green innovation in supply chain enterprises in order to assist in reasonable strategic decision making. An evolutionary game model is constructed for both upstream and downstream supply chain companies under digital enablement, following which the model is solved and systematically simulated. Our main findings are as follows: The influencing factors of collaborative green innovation in the supply chain can be divided into driving factors, blocking factors, and regulating factors. After digital enablement, the effect of the drivers of collaborative green innovation is more obvious, the side-effects of the deterrents are weakened, and the threshold of the positive effect of the moderators is expanded. Overall, digital enablement helps to promote collaborative green innovation in the supply chain, and companies should apply digital technology to enable collaborative green innovation. [ABSTRACT FROM AUTHOR]

Over the last few decades, biosensors have made significant advances in detecting non-invasive biomarkers of disease-related body fluid substances with high sensitivity, high accuracy, low cost and ease in operation. Among various two-dimensional (2D) materials, MXenes have attracted widespread interest due to their unique surface properties, as well as mechanical, optical, electrical and biocompatible properties, and have been applied in various fields, particularly in the preparation of biosensors, which play a critical role. Here, we systematically introduce the application of MXenes in electrochemical, optical and other bioanalytical methods in recent years. Finally, we summarise and discuss problems in the field of biosensing and possible future directions of MXenes. We hope to provide an outlook on MXenes applications in biosensing and to stimulate broader interests and research in MXenes across different disciplines. [ABSTRACT FROM AUTHOR]

In this paper, we provide theoretical and policy support for quota-allocation strategies based on a national unified renewable energy (RE) power market. Renewable portfolio standards (RPSs) are of great significance in promoting the stable development of renewable energy and improving power market decision making in China's power industry. To resolve the geographical, resource allocation, and power-grid problems of multi-regional RE power generation, we constructed a regional distribution optimization model with the lowest cost under the RPS policy and designed a set of dynamic distribution mechanisms based on the renewable energy power quota index. The results show that it is necessary to prioritize development of wind-generated power on the North China and Northeast Power Grids, solar energy on the Northwest Power Grid, and biomass energy generation on grids in other regions to plan specific task undertakings and allocate RE power generation to each grid. We propose a multi-regional power distribution model at the lowest cost under the RPS policy to provide solutions and references for renewable energy power market quota allocation. [ABSTRACT FROM AUTHOR]

In this paper, the filtering problem of nonlinear networked systems with event-triggered data transmission tasks is studied. To reduce the transmission of excessive measurement data in the bandwidth-limited network, a data transmission mechanism with event trigger is introduced to analyze the error behavior of the extended Kalman filter. We prove that the real estimation error and error covariance matrices can be determined by restricting the initial conditions appropriately. Finally, the effectiveness of the filtering algorithm is verified by simulation. [ABSTRACT FROM AUTHOR]

Lactiplantibacillus plantarum could regulate certain physiological functions through the AI-2/LuxS-mediated quorum sensing (QS) system. To explore the regulation mechanism on the growth characteristics and bacteriostatic ability of L. plantarum SS-128, a luxS mutant was constructed by a two-step homologous recombination. Compared with ΔluxS/SS-128, the metabolites of SS-128 had stronger bacteriostatic ability. The combined analysis of transcriptomics and metabolomics data showed that SS-128 exhibited higher pyruvate metabolic efficiency and energy input, followed by higher LDH level and metabolite overflow compared to ΔluxS/SS-128, resulting in stronger bacteriostatic ability. The absence of luxS induces a regulatory pathway that burdens the cysteine cycle by quantitatively drawing off central metabolic intermediaries. To accommodate this mutations, ΔluxS/SS-128 exhibited lower metabolite overflow and abnormal proliferation. These results demonstrate that the growth characteristic and metabolism of L. plantarum SS-128 are mediated by the AI-2/LuxS QS system, which is a positive regulator involved in food safety. It would be helpful to investigate more bio-preservation control potential of L. plantarum, especially when applied in food industrial biotechnology. [ABSTRACT FROM AUTHOR]

The growth of renewable energy sources presents a pressing challenge to the operation and maintenance of existing fossil fuel power plants, given that fossil fuel remains the predominant fuel source, responsible for over 60% of electricity generation in the United States. One of the main concerns within these fossil fuel power plants is the unpredictable failure of boiler tubes, resulting in emergency maintenance with significant economic and societal consequences. A reliable high-temperature sensor is necessary for in situ monitoring of boiler tubes and the safety of fossil fuel power plants. In this study, a comprehensive four-stage multi-physics computational framework is developed to assist the design, optimization installation, and operation of the high-temperature stainless-steel and quartz coaxial cable sensor (SSQ-CCS) for coal-fired boiler applications. With the consideration of various operation conditions, we predict the distributions of flue gas temperatures within coal-fired boilers, the temperature correlation between the boiler tube and SSQ-CCS, and the safety of SSQ-CCS. With the simulation-guided sensor installation plan, the newly designed SSQ-CCSs have been employed for field testing for more than 430 days. The computational framework developed in this work can guide the future operation of coal-fired plants and other power plants for the safety prediction of boiler operations.

First principles calculations have been performed to energetically investigate the helium cluster nucleation, formation and growth behavior in the nano-structured ferritic alloy 14YWT. The helium displays strong affinity to the oxygen:vacancy (O:Vac) pair. By investigating various local environments of the vacancy, we find that the energy cost for He cluster growth increases with the appearance of solutes in the reference unit. He atom tends to join the He cluster in the directions away from the solute atoms. Meanwhile, the He cluster tends to expand in the directions away from the solute atoms. A growth criterion is proposed based on the elastic instability strain of the perfect iron lattice in order to determine the maximum number of He atoms at the vacancy site. We find that up to seven He atoms can be trapped at a single vacancy. However, it is reduced to five if the vacancy is pre-occupied by an oxygen atom. Furthermore, the solute atoms within nanoclusters, such as Ti and Y, will greatly limit the growth of the He cluster. A migration energy barrier study is performed to discuss the reduced mobility of the He atom/He cluster in 14YWT.