A Comparative Study of Survival, Metabolism, Immune Indicators, and Proteomics, in Five Batches of Japanese Scallop Mizuhopecten yessoensis under Short-Term High Temperature Stress

Five batches of the Japanese scallop Mizuhopecten pyessoensis were tested for survival rate, oxygen consumption, catalase (CAT) and superoxide dismutase (SOD) activities, total antioxidant capacities (T-AOC) contents, and proteomics under short-term high temperature conditions. The five batches, (W1, W2, W3, W4, W5) selected from the established 21 ‘ivory white’ M. yessoensis batches, had higher survival rates than the other batches after one year of culture. Initial rearing water temperature of 15°C was increased by 1°C per day with a cooling and heating system. The temperature was raised until over 50% of the scallops from 3 batches died. This occurred at 30°C. The higher than normal culture temperature conditions showed significant or highly significant differences in the responses of some of the batches. Some showed significantly higher survival rates and significantly different rates of oxygen consumption. CAT activity, SOD activity and T-AOC content was similar in the five batches, and all three indices were significantly lower in W3 and W5 than in the other batches (P<0.01). Expression patterns of MDA content were opposite to those of CAT activity, SOD activity and T-AOC content. Protein profiles of all five batches were similar; the sizes of the predominant bands ranged from 20-110 kDa. We identified twenty-eight proteins with high scores in the database. These included heat shock proteins (HSPs), glucose-regulated protein 94, and arginine kinase

A Study on the Effects of Knowledge Management on Innovation Strategies and Competitive Advantages

The 21st century is a knowledge economic era when a person who could master knowledge and technologies could master the competitive future. The knowledge and technology competition and the emergence of information technology and the Internet in the future have innovation strategies enter a new era. Knowledge management and share as well as innovation strategies of a business present the importance on the enhancement of competitive advantages. Effective knowledge management and innovation strategies become the key in the success. Aiming at Kunshan German Industrial Park, the executives and employees in 6 of top 500 businesses are distributed 300 copies of questionnaires, among which 218 valid copies are retrieved, with the retrieval rate 73%. The research results show the significant correlations between 1. innovation strategies and competitive advantages, 2. knowledge management and innovative strategies, and 3. knowledge management and competitive advantages. It is expected to assist businesses in constructing knowledge management

Low-carbon optimal dispatch of integrated energy system considering demand response under the tiered carbon trading mechanism

In the operation of the integrated energy system (IES), considering further reducing carbon emissions, improving its energy utilization rate, and optimizing and improving the overall operation of IES, an optimal dispatching strategy of integrated energy system considering demand response under the stepped carbon trading mechanism is proposed. Firstly, from the perspective of demand response (DR), considering the synergistic complementarity and flexible conversion ability of multiple energy sources, the lateral time-shifting and vertical complementary alternative strategies of electricity-gas-heat are introduced and the DR model is constructed. Secondly, from the perspective of life cycle assessment, the initial quota model of carbon emission allowances is elaborated and revised. Then introduce a tiered carbon trading mechanism, which has a certain degree of constraint on the carbon emissions of IES. Finally, the sum of energy purchase cost, carbon emission transaction cost, equipment maintenance cost and demand response cost is minimized, and a low-carbon optimal scheduling model is constructed under the consideration of safety constraints. This model transforms the original problem into a mixed integer linear problem using Matlab software, and optimizes the model using the CPLEX solver. The example results show that considering the carbon trading cost and demand response under the tiered carbon trading mechanism, the total operating cost of IES is reduced by 5.69% and the carbon emission is reduced by 17.06%, which significantly improves the reliability, economy and low carbon performance of IES.Comment: Accepted by Electric Power Construction [in Chinese

MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices

We present MobileVLM, a competent multimodal vision language model (MMVLM) targeted to run on mobile devices. It is an amalgamation of a myriad of architectural designs and techniques that are mobile-oriented, which comprises a set of language models at the scale of 1.4B and 2.7B parameters, trained from scratch, a multimodal vision model that is pre-trained in the CLIP fashion, cross-modality interaction via an efficient projector. We evaluate MobileVLM on several typical VLM benchmarks. Our models demonstrate on par performance compared with a few much larger models. More importantly, we measure the inference speed on both a Qualcomm Snapdragon 888 CPU and an NVIDIA Jeston Orin GPU, and we obtain state-of-the-art performance of 21.5 tokens and 65.3 tokens per second, respectively. Our code will be made available at: https://github.com/Meituan-AutoML/MobileVLM.Comment: Tech Repor

BionoiNet: Ligand-binding site classification with off-the-shelf deep neural network

© The 2020 Author(s). Published by Oxford University Press. All rights reserved. Motivation: Fast and accurate classification of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures but also to projects in protein evolution, protein engineering and drug development. Deep learning techniques, which have already been successfully applied to address challenging problems across various fields, are inherently suitable to classify ligand-binding pockets. Our goal is to demonstrate that off-the-shelf deep learning models can be employed with minimum development effort to recognize nucleotide-and heme-binding sites with a comparable accuracy to highly specialized, voxel-based methods. Results: We developed BionoiNet, a new deep learning-based framework implementing a popular ResNet model for image classification. BionoiNet first transforms the molecular structures of ligand-binding sites to 2D Voronoi diagrams, which are then used as the input to a pretrained convolutional neural network classifier. The ResNet model generalizes well to unseen data achieving the accuracy of 85.6% for nucleotide-and 91.3% for heme-binding pockets. BionoiNet also computes significance scores of pocket atoms, called BionoiScores, to provide meaningful insights into their interactions with ligand molecules. BionoiNet is a lightweight alternative to computationally expensive 3D architectures

Exploring Query Understanding for Amazon Product Search

Online shopping platforms, such as Amazon, offer services to billions of people worldwide. Unlike web search or other search engines, product search engines have their unique characteristics, primarily featuring short queries which are mostly a combination of product attributes and structured product search space. The uniqueness of product search underscores the crucial importance of the query understanding component. However, there are limited studies focusing on exploring this impact within real-world product search engines. In this work, we aim to bridge this gap by conducting a comprehensive study and sharing our year-long journey investigating how the query understanding service impacts Amazon Product Search. Firstly, we explore how query understanding-based ranking features influence the ranking process. Next, we delve into how the query understanding system contributes to understanding the performance of a ranking model. Building on the insights gained from our study on the evaluation of the query understanding-based ranking model, we propose a query understanding-based multi-task learning framework for ranking. We present our studies and investigations using the real-world system on Amazon Search

Exploring the effects of lysozyme dietary supplementation on laying hens: performance, egg quality, and immune response

An experiment was conducted to evaluate the dietary supplementation with lysozyme's impacts on laying performance, egg quality, biochemical analysis, body immunity, and intestinal morphology. A total of 720 Jingfen No. 1 laying hens (53 weeks old) were randomly assigned into five groups, with six replicates in each group and 24 hens per replicate. The basal diet was administered to the laying hens in the control group, and it was supplemented with 100, 200, 300, or 400 mg/kg of lysozyme (purity of 10% and an enzyme activity of 3,110 U/mg) for other groups. The preliminary observation of the laying rate lasted for 4 weeks, and the experimental period lasted for 8 weeks. The findings demonstrated that lysozyme might enhance production performance by lowering the rate of sand-shelled eggs (P &lt; 0.05), particularly 200 and 300 mg/kg compared with the control group. Lysozyme did not show any negative effect on egg quality or the health of laying hens (P &gt; 0.05). Lysozyme administration in the diet could improve intestinal morphology, immune efficiency, and nutritional digestibility in laying hens when compared with the control group (P &lt; 0.05). These observations showed that lysozyme is safe to use as a feed supplement for the production of laying hens. Dietary supplementation with 200 to 300 mg/kg lysozyme should be suggested to farmers as a proper level of feed additive in laying hens breeding