• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.129-164
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• 2023

In a situation where the use and introduction of Large Language Models (LLMs) is expanding due to recent developments in generative AI technology, it is difficult to find actual application cases or implementation methods for the use of internal company data in existing studies. Accordingly, this study presents a method of implementing generative AI services using the LLM application architecture using the most widely used LangChain framework. To this end, we reviewed various ways to overcome the problem of lack of information, focusing on the use of LLM, and presented specific solutions. To this end, we analyze methods of fine-tuning or direct use of document information and look in detail at the main steps of information storage and retrieval methods using the retrieval augmented generation (RAG) model to solve these problems. In particular, similar context recommendation and Question-Answering (QA) systems were utilized as a method to store and search information in a vector store using the RAG model. In addition, the specific operation method, major implementation steps and cases, including implementation source and user interface were presented to enhance understanding of generative AI technology. This has meaning and value in enabling LLM to be actively utilized in implementing services within companies.

• Industrial Engineering and Management Systems
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• v.11 no.2
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• pp.165-169
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• 2012

There has been a growing interest in firms' environmental sustainability activities to improve environmental practices in their supply chain. This study aims to deal with supplier evaluation of firm's green supply chain management (GSCM) criteria with incomplete information. Nevertheless, the suitable supplier is a key strategic direction in eliminating environmental impact on supply chain management for manufacturing firms. The firm's GSCM criteria and supplier selection need to be unified as a system to improve the firm's performance.

• Journal of Applied Reliability
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• v.17 no.3
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• pp.188-196
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• 2017

Purpose: The purpose of this research is to determine optimal replacement age using non-informative prior information and Bayesian method. Methods: We propose a novel approach using Bayesian method to determine the optimal replacement age in block replacement policy by defining the prior probability with data on failure time and repair time. The Marcov Chain Monte Carlo simulation is used to investigate the asymptotic distribution of posterior parameters. Results: An optimal replacement age of block replacement policy is determined which minimizes cost and nonoperating time when no information on prior distribution of parameters is given. Conclusion: We find the posterior distribution of parameters when lack of information on prior distribution, so that the optimal replacement age which minimizes the total cost and maximizes the total values is determined.

• Industrial Engineering and Management Systems
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• v.13 no.1
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• pp.15-28
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• 2014

Evaluation of the implementation and performance of a firm's green supply chain management (GSCM) is an ongoing process. Balanced scorecard is a multi-criteria evaluation concept that highlights implementation and performance measures. The literature on the framework is abundant literature but scarce on how to build a hierarchical framework under uncertainty with dependence relations. Hence, this study proposes a hybrid approach, which includes applied interpretive structural modeling to build a hierarchical structure and uses the analytic network process to analyze the dependence relations. Additionally, this study applies the fuzzy set theory to determine linguistic preferences. Twenty dependence criteria are evaluated for a GSCM implemented firm in Taiwan. The result shows that the financial aspect and life cycle assessment are the most important performance and weighted criteria.

• Journal of Korean Society of Archives and Records Management
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• v.24 no.2
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• pp.89-112
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• 2024

Metadata is a crucial component of record management, playing a vital role in properly managing and understanding the record. In cases where automatic metadata assignment is not feasible, manual input by records professionals becomes necessary. This study aims to alleviate the challenges associated with manual entry by proposing a method that harnesses ChatGPT technology for extracting records management metadata elements. To employ ChatGPT technology, a Python program utilizing the LangChain library was developed. This program was designed to analyze PDF documents and extract metadata from records through questions, both with a locally installed instance of ChatGPT and the ChatGPT online service. Multiple PDF documents were subjected to this process to test the effectiveness of metadata extraction. The results revealed that while using LangChain with ChatGPT-3.5 turbo provided a secure environment, it exhibited some limitations in accurately retrieving metadata elements. Conversely, the ChatGPT-4 online service yielded relatively accurate results despite being unable to handle sensitive documents for security reasons. This exploration underscores the potential of utilizing ChatGPT technology to extract metadata in records management. With advancements in ChatGPT-related technologies, safer and more accurate results are expected to be achieved. Leveraging these advantages can significantly enhance the efficiency and productivity of tasks associated with managing records and metadata in archives.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.92-96
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• 2000

In this paper, we propose a call admission control(CAC) scheme fer the mixed voice/data DS-CDMA systems and analyze the Er-lang capacity under the proposed CAC scheme. Voice and data traffics require different system resources based oil their Quality of Service(QoS) requirements. In the proposed CAC scheme, some system resources are reserved exclusively for handoff calls to have high priority Over new calls. Additionally the queueing of both new and handoff data traffics that are not sensitive to delay is allowed. Ar a performance measure for the suggested CAC scheme. Erlang capacity is utilized. For the performance analysis, a four-dimensional Markov chain model is developed. Erlang capacity of a practical IS-95B type system depicts, and optimum values of system parameters such as the number of reservation channels and queue lengths are found with respect to Erlang capacity. Finally, it is observed that Erlang capacity is improved more than two times by properly selecting the system parameters with the proposed CAC scheme.

• Journal of Microbiology and Biotechnology
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• v.31 no.6
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• pp.784-793
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• 2021

Previous studies have uncovered the role of circ_0000144 in various tumors. Here, we investigated the function and mechanism of circ_0000144 in gastric cancer (GC) progression. The expression of circ_0000144 in GC tissues and cells was detected through quantitative real-time polymerase chain reaction (qRT-PCR) method. Gain- and loss-of-function experiments including colony formation, wound healing and transwell assays were performed to examine the role of circ_0000144 in GC cells. Furthermore, western blot was conducted to determine the expressions of epithelial mesenchymal transition (EMT)-related proteins. The interaction between circ_0000144 and miR-217 was analyzed by bioinformatic analysis and luciferase reporter assays. The circ_0000144 expression was obviously upregulated in GC tissues and cells. Silencing of circ_0000144 inhibited cell proliferation, migration and invasion of GC cells, but ectopic expression of circ_0000144 showed the opposite results. Moreover, circ_0000144 sponged miR-217, and rescue assays revealed that silencing miR-217 expression reversed the inhibitory effect of circ_0000144 knockdown on the progress of GC. Our findings reveal that circ_0000144 inhibition suppresses GC cell proliferation, migration and invasion via absorbing miR-217, providing a new biomarker and potential therapeutic target for treatment of GC.

• Journal of Distribution Science
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• v.20 no.8
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• pp.9-20
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• 2022

Purpose: Distribution competence plays a key role in supply chain management. A successful distribution system will bring high values and profit for company. Omni-channel is a modern type of distribution which is used by many companies to access customers more effectively. This research is about the influence of distribution competence on customer value creation, competitive advantage and business performance of retailers in omni-channel. Research design, data and methodology: Questionnaires were issued to 200 managers who have experiences in using an Omni-channel to provide consumer goods. PLS-SEM is used to check the relationship among these above factors in omni-channel in Vietnam context. Results: Distribution competence has positive direct effect on business performance. At the same time, it has positive indirect effect on business performance through the mediating role of customer value creation and competitive advantage. Moreover, the role of mediating variables make contribution to strengthen the relationship of distribution competence and business performance in omni-channel. Conclusions: Through distribution competence, retailers can get competitive advantage, create more values for customers and increase business performance in omni channel in Vietnam.

• Journal of Microbiology and Biotechnology
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• v.20 no.4
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• pp.670-677
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• 2010

An alkaline pectate lyase, Bsp165PelA, was purified to homogeneity from the culture broth of alkaliphilic Bacillus sp. N16-5. The enzyme showed a specific activity as high as 1,000 U/mg and had optimum activity at pH 11.5 and $50^{\circ}C$. It was composed of a single polypeptide chain with a molecular mass of 42 kDa deduced from SDS-PAGE, and its isoelectric point was around pH 6.0. It could efficiently depolymerize polygalacturonate and pectin. Characterization of product formation revealed unsaturated digalacturonate and trigalacturonate as the main products. The pectate lyase gene (pelA) contained an open reading frame (ORF) of 1,089 bp, encoding a 36-amino acids signal peptide and a mature protein of 326 amino acids with a calculated molecular mass of 35.943 Da. The deduced amino acid sequence from the pelA ORF exhibited significant homology to those of known pectate lyases in polysaccharide lyase family 1. Some conserved active-site amino acids were found in the deduced amino acid sequence of Bsp165PelA. $Ca^{2+}$ was not required for activity on pectic substrates.

• Journal of Forest and Environmental Science
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• v.33 no.2
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• pp.113-121
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• 2017

In flight particulate matter particularly emissions generated by incomplete combustion processes has become a subject of global concern due to the health problems and environmental impacts associated with them. This has compelled most countries to set standards for coarse and fine particles due to their conspicuous impacts on environment and public health. This contribution therefore explores forest fire emissions and how its particulates affects air quality, damage to vegetation, water bodies and biological functions as architects for lung diseases and other degenerative illnesses such as oxidative stress and aging. Soot was collected from simulated forest fire using a clean glass surface and carefully transferred into amber vials for analysis. Volatile components of soot were collected over 10 mL dichloromethane and analyzed using a QTOF Premier-Water Corp Liquid Chromatography hyphenated to a mass selective detector (MSD), and Gas Chromatograph coupled to a mass spectrometer (GC-MS). To characterize the size and surface morphology of soot, a scanning electron microscope (SEM) was used. The characterization of molecular volatiles from simulated forest fire emissions revealed long chain compounds including octadec-9-enoic acid, octadec-6-enoic acid, cyclotetracosane, cyclotetradecane, and a few aromatic hydrocarbons (benzene and naphthalene). Special classes of organics (dibenzo-p-dioxin and 2H-benzopyran) were also detected as minor products. Dibenzo-p-dioxin for instance in chlorinated form is one of the deadliest environmental organic toxins. The average particulate size of emissions using SEM was found to be $11.51{\pm}4.91{\mu}m$. This study has shown that most of the emissions from simulated forest fire fall within $PM_{10}$ particulate size. The molecular by-products of forest fire and particulate emissions may be toxic to both human and natural ecosystems, and are possible precursors for various respiratory ailments and cancers. The burning of a forest by natural disasters or man-made fires results in the destruction of natural habitats and serious air pollution.