• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Fisheries and Aquatic Sciences
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• v.26 no.3
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• pp.188-203
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• 2023

Lysozymes are well-known antibacterial enzymes that mainly target the peptidoglycan layer of the bacterial cell wall. Animal lysozymes are mainly categorized as g-type, c-type, and i-type based on protein sequence and structural differences. In this study, c-type (AcLysC) and g-like-type (AcLysG-like) lysozymes from Amphiprion clarkii were characterized in silico via expressional and functional approaches. According to in silico analysis, open reading frames of AcLysC and AcLysG-like were 429 bp and 570 bp, respectively, encoding the corresponding polypeptide chains with 142 and 189 amino acids. Elevated expression levels of AcLysC and AcLysG-like were observed in the liver and the heart tissues, respectively, as evidenced by quantitative real-time polymerase chain reaction assays. AcLysC and AcLysG-like transcript levels were upregulated in gills, head kidney, and blood cells following experimental immune stimulation. Recombinant AcLysC exhibited potent lytic activity against Vibrio anguillarum, whereas recombinant AcLysG-like showed remarkable antibacterial activity against Vibrio harveyi and Streptococcus parauberis, which was further evidenced by scanning electron microscopic imaging of destructed bacterial cell walls. The findings of this study collectively suggest the potential roles of AcLysC and AcLysG-like in host immune defense.

• Journal of Marine Life Science
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• v.1 no.2
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• pp.88-94
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• 2016

Determining the infection history of living organisms is essential for understanding the evolution of infection agents with their host, particularly for key aspects such as immunity. Viruses, which can spread between individuals and often cause disease, have been widely examined. The increasing availability of fish genome sequences has provided specific insights into the diversity and host distribution of retroviruses in fish. The shortspine spurdog (Squalus mitsukurii) is an important elasmobranch species; this medium-sized dogfish typically lives at depths of 100~500 m. However, the retroviral envelope polyprotein in dogfish has not been examined. Thus, the aim of the present study was to identify and analyze the retroviral envelope polyprotein in various tissues of dogfish. The 1334-base pair full-length novel cDNA of dogfish envelope polyprotein (dEnv) was obtained by 3' and 5'-rapid amplification of cDNA end analysis from S. mitsukurii. The open reading frame showed a complete coding sequence of 815 base pairs with a deduced peptide sequence of 183 amino acids that exhibited 34~50% identity with other fish and bird species. It was also expressed according to reverse transcription and real-time polymerase chain reaction in the kidney, liver, intestine, and lung, but not in the gill. This distribution can be assessed by identifying and analyzing endogenous retroviruses in fish, which consists of three main genes: gag, pol and env. Dogfish envelope polyprotein sequence is likely important in evolution and induces rearrangements, altering the regulatory and coding sequences. This is the first report of the identification and molecular characterization of retroviral envelope polyprotein in various tissues of S. mitsukurii.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1794-1799
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. therefore we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Also, we propose an integrated system for tracking the detected vehicles. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data.

• Journal of Korea Trade
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• v.27 no.2
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• pp.96-114
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• 2023

Purpose - This paper elucidates a nexus between the occurrence of rare disaster events and the volatility of economic growth by distinguishing the likelihood of rare events from stochastic volatility. We provide new empirical facts based on a quarterly time series. In particular, we focus on the role of financial liberalization in spreading the economic crisis in developing countries. Design/methodology - We use quarterly data on consumption expenditure (real per capita consumption) from 44 countries, including advanced and developing countries, ending in the fourth quarter of 2020. We estimate the likelihood of rare event occurrences and stochastic volatility for countries using the Bayesian Markov chain Monte Carlo (MCMC) method developed by Barro and Jin (2021). We present our estimation results for the relationship between rare disaster events, stochastic volatility, and growth volatility. Findings - We find the global common disaster event, the COVID-19 pandemic, and thirteen country-specific disaster events. Consumption falls by about 7% on average in the first quarter of a disaster and by 4% in the long run. The occurrence of rare disaster events and the volatility of gross domestic product (GDP) growth are positively correlated (4.8%), whereas the rare events and GDP growth rate are negatively correlated (-12.1%). In particular, financial liberalization has played an important role in exacerbating the adverse impact of both rare disasters and financial market instability on growth volatility. Several case studies, including the case of South Korea, provide insights into the cause of major financial crises in small open developing countries, including the Asian currency crisis of 1998. Originality/value - This paper presents new empirical facts on the relationship between the occurrence of rare disaster events (or stochastic volatility) and growth volatility. Increasing data frequency allows for greater accuracy in assessing a country's specific risk. Our findings suggest that financial market and institutional stability can be vital for buffering against rare disaster shocks. It is necessary to preemptively strengthen the foundation for financial stability in developing countries and increase the quality of the information provided to markets.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.128-132
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• 2023

Investment authorities are broad financial institutions that carefully manage investments on behalf of the national government using a long-term value development approach. To provide a stronger structure or framework for In-vestment Authorities to govern the distribution of funds to public and private markets, we've started research to create a blockchain-based prototype for managing and tracking numerous finances of such authorities. We have taken the case study of Oman Investment Authority (OIA) of Sultanate of Oman. Oman's wealth is held in OIA. It is an organization that oversees and utilizes the additional capital generated by oil and gas profits in public and private markets. Unlike other Omani funds, this one focus primarily on assets outside the Sultanate. The operation of the OIA entails a huge number of transactions, necessitating a high level of transparency and administration among the parties involved. Currently, OIA relies on various manuals to achieve its goals, such as the Authorities and Responsibilities manual, the In-vestment Manual, and the Code of Business Conduct, among others. In this paper, we propose a Blockchain based framework to manage the operations of OIA. Blockchain is a part of the Fourth Industrial Revolution, and it is re-shaping every industry. The main components of every blockchain are assets and participants. The funds are the major assets in the proposed study, and the participants are the various fund shareholders/recipients. The block-chain's transactions are all safe, secure, and immutable, and it's part of a trustless network. The transactions are simple to follow and verify. By replacing intermediary firms with smart contracts, blockchain-based solutions eliminate any middlemen in the fund allocation process.

• IMMUNE NETWORK
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• v.24 no.1
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• pp.1.1-1.6
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• 2024

IL-18 binding protein (IL-18BP) was originally discovered in 1999 while attempting to identify an IL-18 receptor ligand binding chain (also known as IL-18Rα) by subjecting concentrated human urine to an IL-18 ligand affinity column. The IL-18 ligand chromatography purified molecule was analyzed by protein microsequencing. The result revealed a novel 40 amino acid polypeptide. To isolate the complete open reading frame (ORF), various human and mouse cDNA libraries were screened using cDNA probe derived from the novel IL-18 affinity column bound molecule. The identified entire ORF gene was thought to be an IL-18Rα gene. However, IL-18BP has been proven to be a unique soluble antagonist that shares homology with a variety of viral proteins that are distinct from the IL-18Rα and IL-18Rβ chains. The IL-18BP cDNA was used to generate recombinant IL-18BP (rIL-18BP), which was indispensable for characterizing the role of IL-18BP in vitro and in vivo. Mammalian cell lines were used to produce rIL-18BP due to its glycosylation-dependent activity of IL-18BP (approximately 20 kDa). Various forms of rIL-18BP, intact, C-terminal his-tag, and Fc fusion proteins were produced for in vitro and in vivo experiments. Data showed potent neutralization of IL-18 activity, which seems promising for clinical application in immune diseases involving IL-18. However, it was a long journey from discovery to clinical use although there have been various clinical trials since IL-18BP was discovered in 1999. This review primarily covers the discovery of IL-18BP along with how basic research influences the clinical development of IL-18BP.

• Research in Plant Disease
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• v.30 no.2
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• pp.157-164
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• 2024

Jack bean (Canavalia ensiformis) is one of healthy products for fermented or functional food in Korea and is widely distributed and cultivated worldwide. During August 2022, Jack bean plants showing symptoms of yellow flecks, chlorosis, necrotic spots and mosaic were observed in Jangheung-gun, South Korea. By transmission electron microscopy, flexuous filamentous virus particles of approximately 750×13 nm in size were observed in the symptomatic leaf samples. The infection of a Korean isolate of clover yellow vein virus (ClYVV-Ce-JH) was confirmed using double antibody sandwich enzyme-linked sorbent assay, reverse transcription polymerase chain reaction and high-throughput sequencing. The complete genome sequence of ClYVV-Ce-JH consists of 9,549 nucleotides (nt) excluding the poly (A) tail and encodes 3,072 amino acids (aa), with an AUG start and UAG stop codon, containing one open reading frame that is typical of a potyvirus polyprotein. The polyprotein of ClYVV-Ce-JH was divided into ten proteins and each protein's cleavage sites were determined. The coat protein (CP) and polyprotein of ClYVV-Ce-JH were compared at the nt and aa levels with those of the previously reported 14 ClYVV isolates. ClYVV-Ce-JH shared 92.62% to 99.63% and 93.39% to 98.05% at the CP and polyprotein homology. To our knowledge, this is the first report of a Korean isolate of ClYVV from Jack bean plants and the complete genome sequence of a ClYVV Jack bean isolate in the world.

• Journal of Microbiology and Biotechnology
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• v.34 no.2
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• pp.436-456
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• 2024

Several thermostable proteases have been identified, yet only a handful have undergone the processes of cloning, comprehensive characterization, and full exploitation in various industrial applications. Our primary aim in this study was to clone a thermostable alkaline protease from a thermophilic bacterium and assess its potential for use in various industries. The research involved the amplification of the SpSKF4 protease gene, a thermostable alkaline serine protease obtained from the Geobacillus thermoglucosidasius SKF4 bacterium through polymerase chain reaction (PCR). The purified recombinant SpSKF4 protease was characterized, followed by evaluation of its possible industrial applications. The analysis of the gene sequence revealed an open reading frame (ORF) consisting of 1,206 bp, coding for a protein containing 401 amino acids. The cloned gene was expressed in Escherichia coli. The molecular weight of the enzyme was measured at 28 kDa using sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). The partially purified enzyme has its highest activity at a pH of 10 and a temperature of 80℃. In addition, the enzyme showed a half-life of 15 h at 80℃, and there was a 60% increase in its activity at 10 mM Ca²⁺ concentration. The activity of the protease was completely inhibited (100%) by phenylmethylsulfonyl fluoride (PMSF); however, the addition of sodium dodecyl sulfate (SDS) resulted in a 20% increase in activity. The enzyme was also stable in various organic solvents and in certain commercial detergents. Furthermore, the enzyme exhibited strong potential for industrial use, particularly as a detergent additive and for facilitating the recovery of silver from X-ray film.