This study investigates key components of supply chain collaboration that contribute to firm performance in vertical collaboration with supply chain partners based on supply chain management experiences on the leather and footwear companies in Vietnam as well as how they work in these companies from an extended resource-based perspective. The mix-research methods employed, include prior studies’ review, in-depth interviews with seven experts in the leather and footwear industry, and case studies at three typical companies in this sector. The results indicate that seven critical supply chain collaboration components, such as information sharing, goal congruence, decision synchronization, joint knowledge creation, incentive alignment, resource sharing, and collaborative communication, have a significant impact on firm performance in the context of leather and footwear companies in Vietnam. Accordingly, companies in the leather and footwear industry, as well as those struggling to maximize firm performance can use it as a reference and put it into action.

/pdf/can-supply-chain-collaboration-improve-firm-performance-23q9bpac.pdf

Can Supply Chain Collaboration Improve Firm Performance? Experiences on Leather and Footwear Companies in Vietnam

On the Internet, "fake news" is a common phenomenon that frequently disturbs society because it contains intentionally false information. The issue has been actively researched using supervised learning for automatic fake news detection. Although accuracy is increasing, it is still limited to identifying fake information through channels on social platforms. This study aims to improve the reliability of fake news detection on social networking platforms by examining news from unknown domains. Especially, information on social networks in Vietnam is difficult to detect and prevent because everyone has equal rights to use the Internet for different purposes. These individuals have access to several social media platforms. Any user can post or spread the news through online platforms. These platforms do not attempt to verify users or the content of their locations. As a result, some users try to spread fake news through these platforms to propagate against an individual, a society, an organization, or a political party. In this paper, we proposed analyzing and designing a model for fake news recognition using Deep learning (called AAFNDL). The method to do the work is: 1) First, we analyze the existing techniques such as Bidirectional Encoder Representation from Transformer (BERT); 2) We proceed to build the model for evaluation; and finally, 3) We approach some Modern techniques to apply to the model, such as the Deep Learning technique, classifier technique and so on to classify fake information. Experiments show that our method can improve by up to 8.72% compared to other methods.

/pdf/aafndl-an-accurate-fake-information-recognition-model-using-2o2bfjgp.pdf

AAFNDL - An Accurate Fake Information Recognition Model Using Deep Learning for the Vietnamese Language

Automatic syntactic analysis of a sentence is an important computational linguistics task. At present, there are no syntactic structure parsers for Russian that are publicly available and suitable for practical applications. Ground-up creation of such parsers requires building of a treebank annotated according to a given formal grammar, which is quite a cumbersome task. However, since there are several syntactic dependency parsers for Russian, it seems reasonable to employ dependency parsing results for syntactic structure analysis. The article introduces an algorithm that allows to construct the constituency tree of a Russian sentence by a syntactic dependency tree. The formal grammar used by the algorithm is based on the D.E. Rosenthal’s classic reference. The algorithm was evaluated on 300 Russian-language sentences. 200 of them were selected from the aforementioned reference, and 100 from OpenCorpora, an open corpus of sentences extracted from Russian news and periodicals. During the evaluation, the sentences were passed to syntactic dependency parsers from Stanza, SpaCy, and Natasha packages, then the resulted dependency trees were processed by the proposed algorithm. The obtained constituency trees were compared with the trees manually annotated by experts in linguistics. The best performance was achieved using the Stanza parser: the constituency parsing F1–score was 0.85, and the sentence parts tagging accuracy was 0.93, that would be sufficient for many practical applications, such as event extraction, information retrieval and sentiment analysis.

Алгоритм построения дерева синтаксических единиц русскоязычного предложения по дереву синтаксических связей

/pdf/covid-19-supply-chain-and-profitability-a-survey-based-on-an-3o1k1cu1.pdf

COVID-19, Supply Chain, and Profitability: A Survey Based on an Emerging Economy

This manuscript reviews and synthesizes the product service system, lifecycle affordability, and performance-based contracting (PBC) literature across supply chain management, operations management, and logistics discipline.The study is based on 128 peer-reviewed articles published between 2005 and 2023 in journals related to the supply chain, logistics, and operations management field.The study proposes a framework for lifecycle affordability (LCA) and key aspects of performancebased strategy design and management, performance specification, and cost elements.This study is a robust literature review of lifecycle affordability and performance-based contracting.It proposes a framework to assist in business-tobusiness managerial decision-making to reduce lifecycle costs. 

pdf/lifecycle-cost-affordability-and-performance-based-3v2x1kyi1v.pdf

Lifecycle Cost Affordability and Performance-Based Contracting – A Managerial Decision Framework Based on Literature Review

Chatbot research has advanced significantly over the years. Enterprises have been investigating how to improve these tools’ performance, adoption, and implementation to communicate with customers or internal teams through social media. Besides, businesses also want to pay attention to quality reviews from customers via social networks about products available in the market. From there, please select a new method to improve the service quality of their products and then send it to publishing agencies to publish based on the needs and evaluation of society. Although there have been numerous recent studies, not all of them address the issue of opinion evaluation on the chatbot system. The primary goal of this paper’s research is to evaluate human comments in English via the chatbot system. The system’s documents are preprocessed and opinion-matched to provide opinion judgments based on English comments. Based on practical needs and social conditions, this methodology aims to evolve chatbot content based on user inter-actions, allowing for a cyclic and human-supervised process with the following steps to evaluate comments in English. First, we preprocess the input data by collecting social media comments, and then our system parses those comments according to the rating views for each topic covered. Finally, our system will give a rating and comment result for each comment entered into the system. Experiments show that our method can improve accuracy better than the referenced methods by 78.53%.

Building a Chatbot System to Analyze Opinions of English Comments

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Can Supply Chain Collaboration Improve Firm Performance? Experiences on Leather and Footwear Companies in Vietnam

Building a Chatbot System to Analyze Opinions of English Comments