scholarly journals Multi-Level Optimization Process for Rationalizing the Distribution Logistics Process of Companies Selling Dietary Supplements

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1480
Author(s):  
Szabolcs Szentesi ◽  
Béla Illés ◽  
Ákos Cservenák ◽  
Róbert Skapinyecz ◽  
Péter Tamás
Keyword(s):  
Dietary Supplements ◽  
Food Supplements ◽  
Optimization Process ◽  
Distribution Logistics ◽  
Multi Level ◽  
High Level ◽  
Logistics Process

The commission sales form is a very significant channel of sales today, which is especially true in the field of dietary supplements. In parallel, the prevailing digitalization trends have opened up further new opportunities for this form of distribution. The multi-level optimization process presented in the publication makes it possible to optimize the distribution logistics processes of companies producing food supplements at a high level by exploiting these new possibilities. The operation of the procedure is also illustrated through a practical example.

scholarly journals Dietary and Ergogenic Supplementation to Improve Elite Soccer Players’ Performance

2021 ◽  
pp. 1-7
Author(s):  
Haniel Fernandes
Keyword(s):  
Dietary Supplements ◽  
High Performance ◽  
Scientific Literature ◽  
Soccer Players ◽  
Competitive Sport ◽  
Beetroot Juice ◽  
Improve Performance ◽  
Sustained Release Tablets ◽  
High Level ◽  
Ergogenic Supplements

<b><i>Background:</i></b> Soccer is an extremely competitive sport, where the most match important moments can be defined in detail. Use of ergogenic supplements can be crucial to improve the performance of a high-performance athlete. Therefore, knowing which ergogenic supplements are important for soccer players can be an interesting strategy to maintain high level in this sport until final and decisive moments of the match. In addition, other supplements, such as dietary supplements, have been studied and increasingly referenced in the scientific literature. But, what if ergogenic supplements were combined with dietary supplements? This review brings some recommendations to improve performance of soccer athletes on the field through dietary and/or ergogenic supplements that can be used simultaneously. <b><i>Summary:</i></b> Soccer is a competitive sport, where the match important moments can be defined in detail. Thus, use of ergogenic supplements covered in this review can improve performance of elite soccer players maintaining high level in the match until final moments, such as creatine 3–5 g day<sup>−1</sup>, caffeine 3–6 mg kg<sup>−1</sup> BW around 60 min before the match, sodium bicarbonate 0.1–0.4 g kg<sup>−1</sup> BW starting from 30 to 180 min before the match, β-alanine 3.2 and 6.4 g day<sup>−1</sup> provided in the sustained-release tablets divided into 4 times a day, and nitrate-rich beetroot juice 60 g in 200 mL of water (6 mmol of NO3<sup>−</sup> L) around 120 min before match or training, including a combination possible with taurine 50 mg kg<sup>−1</sup> BW day<sup>−1</sup>, citrulline 1.2–3.4 g day<sup>−1</sup>, and arginine 1.2–6 g day<sup>−1</sup>. <b><i>Key Messages:</i></b> Soccer athletes can combine ergogenic and dietary supplements to improve their performance on the field. The ergogenic and dietary supplements used in a scientifically recommended dose did not demonstrate relevant side effects. The use of various evidence-based supplements can add up to further improvement in the performance of the elite soccer players.

scholarly journals An Efficient Module for Instance Segmentation Based on Multi-Level Features and Attention Mechanisms

2021 ◽  
Vol 11 (3) ◽  
pp. 968
Author(s):  
Yingchun Sun ◽  
Wang Gao ◽  
Shuguo Pan ◽  
Tao Zhao ◽  
Yahui Peng
Keyword(s):  
Feature Extraction ◽  
Spatial Structure ◽  
Semantic Feature ◽  
Semantic Features ◽  
Segmentation Method ◽  
Spatial Dimensions ◽  
Feature Pyramid ◽  
Multi Level ◽  
High Level ◽  
Instance Segmentation

Recently, multi-level feature networks have been extensively used in instance segmentation. However, because not all features are beneficial to instance segmentation tasks, the performance of networks cannot be adequately improved by synthesizing multi-level convolutional features indiscriminately. In order to solve the problem, an attention-based feature pyramid module (AFPM) is proposed, which integrates the attention mechanism on the basis of a multi-level feature pyramid network to efficiently and pertinently extract the high-level semantic features and low-level spatial structure features; for instance, segmentation. Firstly, we adopt a convolutional block attention module (CBAM) into feature extraction, and sequentially generate attention maps which focus on instance-related features along the channel and spatial dimensions. Secondly, we build inter-dimensional dependencies through a convolutional triplet attention module (CTAM) in lateral attention connections, which is used to propagate a helpful semantic feature map and filter redundant informative features irrelevant to instance objects. Finally, we construct branches for feature enhancement to strengthen detailed information to boost the entire feature hierarchy of the network. The experimental results on the Cityscapes dataset manifest that the proposed module outperforms other excellent methods under different evaluation metrics and effectively upgrades the performance of the instance segmentation method.

scholarly journals MATHEMATICAL DESCRIPTION OF THE DISTRIBUTION LOGISTICS PROCESSES OF CONSIGNMENT SELLER DIETARY SUPPLEMENT MANUFACTURING COMPANIES

2021 ◽  
Vol 24 (1) ◽  
pp. 39-42
Author(s):  
Szabolcs Szentesi ◽  
◽  
Béla Illés ◽  
Peter Tamas ◽  
◽  
...  
Keyword(s):  
Supply Chains ◽  
Shelf Life ◽  
Dietary Supplement ◽  
Mathematical Description ◽  
Food Supplements ◽  
Manufacturing Companies ◽  
Stock Level ◽  
Logistics Network ◽  
Distribution Logistics ◽  
Special Case

Consignment sales are a special case of supply chains, as the products are never the property of the seller, so the distribution logistics network and the logic of the structure of the products are radically different from those of normal supply chains. Another problem is that the products have a shelf life. The inventory mechanism (qmin; qmax) is most often used for the commissioned stocks of companies producing food supplements and when checking the central inventory, i.e. replenishment to the maximum stock level when a certain stock level is reached. There are several factors to consider when filling up commission stocks and reviewing them by period. With the mathematical correlations of these factors, it is possible to distribute the commissioned finished products optimally. The paper deals with this problem.

Y-Net: Dual-branch Joint Network for Semantic Segmentation

2021 ◽  
Vol 17 (4) ◽  
pp. 1-22
Author(s):  
Yizhen Chen ◽  
Haifeng Hu
Keyword(s):  
Feature Vector ◽  
State Of The Art ◽  
Computational Cost ◽  
Receptive Fields ◽  
Semantic Segmentation ◽  
Global Context ◽  
Multi Level ◽  
The One ◽  
Public Datasets ◽  
High Level

Most existing segmentation networks are built upon a “ U -shaped” encoder–decoder structure, where the multi-level features extracted by the encoder are gradually aggregated by the decoder. Although this structure has been proven to be effective in improving segmentation performance, there are two main drawbacks. On the one hand, the introduction of low-level features brings a significant increase in calculations without an obvious performance gain. On the other hand, general strategies of feature aggregation such as addition and concatenation fuse features without considering the usefulness of each feature vector, which mixes the useful information with massive noises. In this article, we abandon the traditional “ U -shaped” architecture and propose Y-Net, a dual-branch joint network for accurate semantic segmentation. Specifically, it only aggregates the high-level features with low-resolution and utilizes the global context guidance generated by the first branch to refine the second branch. The dual branches are effectively connected through a Semantic Enhancing Module, which can be regarded as the combination of spatial attention and channel attention. We also design a novel Channel-Selective Decoder (CSD) to adaptively integrate features from different receptive fields by assigning specific channelwise weights, where the weights are input-dependent. Our Y-Net is capable of breaking through the limit of singe-branch network and attaining higher performance with less computational cost than “ U -shaped” structure. The proposed CSD can better integrate useful information and suppress interference noises. Comprehensive experiments are carried out on three public datasets to evaluate the effectiveness of our method. Eventually, our Y-Net achieves state-of-the-art performance on PASCAL VOC 2012, PASCAL Person-Part, and ADE20K dataset without pre-training on extra datasets.

scholarly journals Natural Language Understanding for Multi-Level Distributed Intelligent Virtual Sensors

IoT ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 494-505
Author(s):  
Radu-Casian Mihailescu ◽  
Georgios Kyriakou ◽  
Angelos Papangelis
Keyword(s):  
Deep Learning ◽  
User Interface ◽  
Natural Language ◽  
Learning Approach ◽  
Language Understanding ◽  
Virtual Sensor ◽  
Virtual Sensors ◽  
Multi Level ◽  
High Level ◽  
User Friendly

In this paper we address the problem of automatic sensor composition for servicing human-interpretable high-level tasks. To this end, we introduce multi-level distributed intelligent virtual sensors (multi-level DIVS) as an overlay framework for a given mesh of physical and/or virtual sensors already deployed in the environment. The goal for multi-level DIVS is two-fold: (i) to provide a convenient way for the user to specify high-level sensing tasks; (ii) to construct the computational graph that provides the correct output given a specific sensing task. For (i) we resort to a conversational user interface, which is an intuitive and user-friendly manner in which the user can express the sensing problem, i.e., natural language queries, while for (ii) we propose a deep learning approach that establishes the correspondence between the natural language queries and their virtual sensor representation. Finally, we evaluate and demonstrate the feasibility of our approach in the context of a smart city setup.

scholarly journals Adaptive Model for Credit Card Fraud Detection

2020 ◽  
Vol 14 (03) ◽  
pp. 54
Author(s):  
Imane Sadgali ◽  
Naoual Sael ◽  
Faouzia Benabbou
Keyword(s):  
Credit Card ◽  
Fraud Detection ◽  
Machine Learning Techniques ◽  
Adaptive Model ◽  
Credit Card Fraud ◽  
New Methods ◽  
Learning Techniques ◽  
Multi Level ◽  
Set Up ◽  
High Level

<p>While the flow of banking transactions is increasing, the risk of credit card fraud is becoming greater particularly with the technological revolution that we know, fraudulent are improve and always find new methods to deal with the preventive measures that financial systems set up. Several studies have proposed predictive models for credit card fraud detection based on different machine learning techniques. In this paper, we present an adaptive approach to credit card fraud detection that exploits the performance of the techniques that have given high level of accuracy and consider the type of transaction and the client's profile. Our proposition is a multi-level framework, which encompasses the banking security aspect, the customer profile and the profile of the transaction itself.</p>

scholarly journals A new joint CTC-attention-based speech recognition model with multi-level multi-head attention

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Chu-Xiong Qin ◽  
Wen-Lin Zhang ◽  
Dan Qu
Keyword(s):  
Speech Recognition ◽  
Nonnegative Matrix Factorization ◽  
State Of The Art ◽  
Nonnegative Matrix ◽  
Attention Mechanism ◽  
Word Error Rate ◽  
Absolute Value ◽  
Multi Level ◽  
End To End ◽  
High Level

Abstract A method called joint connectionist temporal classification (CTC)-attention-based speech recognition has recently received increasing focus and has achieved impressive performance. A hybrid end-to-end architecture that adds an extra CTC loss to the attention-based model could force extra restrictions on alignments. To explore better the end-to-end models, we propose improvements to the feature extraction and attention mechanism. First, we introduce a joint model trained with nonnegative matrix factorization (NMF)-based high-level features. Then, we put forward a hybrid attention mechanism by incorporating multi-head attentions and calculating attention scores over multi-level outputs. Experiments on TIMIT indicate that the new method achieves state-of-the-art performance with our best model. Experiments on WSJ show that our method exhibits a word error rate (WER) that is only 0.2% worse in absolute value than the best referenced method, which is trained on a much larger dataset, and it beats all present end-to-end methods. Further experiments on LibriSpeech show that our method is also comparable to the state-of-the-art end-to-end system in WER.

scholarly journals Development of Dietary Supplement Label Database in Italy: Focus of FoodEx2 Coding

Nutrients ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 89 ◽  
Author(s):  
Alessandra Durazzo ◽  
Emanuela Camilli ◽  
Laura D’Addezio ◽  
Raffaela Piccinelli ◽  
Angelika Mantur-Vierendeel ◽  
...  
Keyword(s):  
Dietary Supplements ◽  
Dietary Supplement ◽  
Marketing Strategies ◽  
Food Supplements ◽  
Context Information ◽  
Dietary Survey ◽  
New Production ◽  
Drug Companies ◽  
Italian Market ◽  
Production Technologies

The sector of food supplements is certainly varied and growing: an ever wider offer of new products is launched on the market every year. This is reflected in new reorganization of drug companies and new marketing strategies, in the adoption of new production technologies with resulting changes in dietary supplements regulation. In this context, information on composition reported in labels of selected dietary supplements was collected and updated for the development of a Dietary Supplement Label Database according to products’ availability on the Italian market and also including items consumed in the last Italian Dietary Survey. For each item, a code was assigned following the food classification and description system FoodEx2, revision 2. A total of 558 products have been entered into the database at present, trying to give a uniform image and representation of the major classes of food supplements, and 82 descriptors have been compiled. Various suggestions on how the number of FoodEx2 system descriptors could be expanded were noted during the compilation of the database and the coding procedure, which are presented in this article. Limits encountered in compiling the database are represented by the changes in the formulation of products on the market and therefore by the need for a constant database update. The database here presented can be a useful tool in clinical trials, dietary plans, and pharmacological programs.

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