• 공업화학
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• 제33권3호
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• pp.242-257
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• 2022

Microalgae is becoming a vital component for a circular economy and ultimately for sustainable development. Herein, recent developments in different outcomes of microalgae for wastewater treatment and biorefinery were reviewed. From its primary function as a third-generation resource of biofuel, the usage of microalgae has been diversified as an integral element for the CO₂ sequestration and production of economically valuable products (e.g., pharmaceuticals, animal feeds, biofertilizer, biochar, etc.). Principles and recent challenges for each microalgae application were presented to suggest a motivation for future research and the direction of development. The integration of microalgae within the concept of the circular economy was also discussed with various routes of microalgae-based biorefinery.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.1-10
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• 2024

In the era of big data, the growth of e-commerce transactions brings forth both opportunities and risks, including the threat of data theft and fraud. To address these challenges, an automated real-time fraud detection system leveraging machine learning was developed. Four algorithms (Decision Tree, Naïve Bayes, XGBoost, and Neural Network) underwent comparison using a dataset from a clothing website that encompassed both legitimate and fraudulent transactions. The dataset exhibited an imbalance, with 9.3% representing fraud and 90.07% legitimate transactions. Performance evaluation metrics, including Recall, Precision, F1 Score, and AUC ROC, were employed to assess the effectiveness of each algorithm. XGBoost emerged as the top-performing model, achieving an impressive accuracy score of 95.85%. The proposed system proves to be a robust defense mechanism against fraudulent activities in e-commerce, thereby enhancing security and instilling trust in online transactions.

• 무역상무연구
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• 제61권
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• pp.327-358
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• 2014

During the past decade after entering the World Trade Organization (WTO), China has quickened its integration into the global economy while its foreign trade has been further invigorated. On the 10th anniversary of China's accession to the WTO, the Chinese government issues White Paper to give a comprehensive introduction to China's foreign trade development. Through this paper, the Chinese government introduces I. Historic Progress in China's Foreign Trade II. Reform of and Improvements to China's Foreign Trade System III. The Development of China's Foreign Trade Contributes to the World Economy IV. Promoting Basically Balanced Growth of Foreign Trade V. Constructing All-round Economic and Trade Partnerships with Mutually Beneficial Cooperation VI. Realizing Sustainable Development of Foreign Trade. At present, the underlying impact of the international financial crisis, the protracted, arduous and complicated nature of the world economic recovery is manifesting itself, and the global economic structure and trade layout face in-depth readjustment. China will make new adjustments to its foreign trade, in an effort to turn foreign trade from scale expansion to quality and profit improvement, and from mainly relying on its low-cost advantage to enhancing its comprehensive competitive edge, thereby turning China from a big trading country to a strong trading power. China's foreign trade is still hampered by many uncertainties and is bound to meet new difficulties and challenges. During the 12th Five-year Plan period China will open itself wider to the outside world as a driver for further reform, development and innovation, make full use of its advantages, strengthen international cooperation in all respects. And at the same time China integrate itself into the world economy on a wider scale and at a higher level. China is willing to work with its trading partners to cope with the various challenges facing the world economy and trade, and promote its foreign trade to realize a more balanced, coordinated and sustainable development, and share prosperity and mutually beneficial results with its trading partners.

• 대한원격탐사학회지
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• 제40권1호
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• pp.93-101
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• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.

• 셀메드
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• 제7권1호
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• pp.2.1-2.3
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• 2017

World health organization has recently published a strategic plan for the development and promotion of traditional system of medicine. Herbal pathies especially Unani and Ayurvedic systems of medicines have great scope in this aspect. But, there are several problems with these pathies in the current era, as the way of research and identification is still on classical system. The correct identification of medicinal plant is one of the major problems in both the system. This should be corrected with the modern tools and techniques. The various types of data including recent discoveries, economical growth, ethnobotanical literature and extremely rapid increase in herbal journals and books have emerged great scope for these pathies. At the same time several challenges and threats are present including herb-drug interaction, false reports, toxicity studies etc. In this review paper, opportunities, threats, and researches to be focused will be discussed.

• 스마트미디어저널
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• 제11권11호
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• pp.63-74
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• 2022

Nowadays, lithium-ion battery has become more popular around the world. Knowing when batteries reach their end of life (EOL) is crucial. Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is needed for battery health management systems and to avoid unexpected accidents. It gives information about the battery status and when we should replace the battery. With the rapid growth of machine learning and deep learning, data-driven approaches are proposed to address this problem. Extracting aging information from battery charge/discharge records, including voltage, current, and temperature, can determine the battery state and predict battery RUL. In this work, we first outlined the charging and discharging processes of lithium-ion batteries. We then summarize the proposed techniques and achievements in all published data-driven RUL prediction studies. From that, we give a discussion about the accomplishments and remaining works with the corresponding challenges in order to provide a direction for further research in this area.

• International Journal of Internet, Broadcasting and Communication
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• 제16권3호
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• pp.236-241
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• 2024

Due to the recent emphasis on carbon neutrality and environmental regulations, the global electric vehicle (EV) market is experiencing rapid growth. This surge has raised concerns about the recycling and disposal methods for EV batteries. Unlike traditional internal combustion engine vehicles, EVs require unique and safe methods for the recovery and disposal of their batteries. In this process, predicting the lifespan of the battery is essential. Impedance and State of Charge (SOC) analysis are commonly used methods for this purpose. However, predicting the lifespan of batteries with complex chemical characteristics through electrical measurements presents significant challenges. To enhance the accuracy and precision of existing measurement methods, this paper proposes using a Long Short-Term Memory (LSTM) model, a type of deep learning-based recurrent neural network, to diagnose battery performance. The goal is to achieve safe classification through this model. The designed structure was evaluated, yielding results with a Mean Absolute Error (MAE) of 0.8451, a Root Mean Square Error (RMSE) of 1.3448, and an accuracy of 0.984, demonstrating excellent performance.

• Fisheries and Aquatic Sciences
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• 제17권1호
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• pp.1-11
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• 2014

The Kenyan aquaculture sector is broadly categorized into freshwater aquaculture and mariculture. Whereas freshwater aquaculture has recorded significant progress over the last decade, the mariculture sector has yet to be fully exploited. The Kenyan aquaculture industry has seen slow growth for decades until recently, when the government-funded Economic Stimulus Program increased fish farming nationwide. Thus far, the program has facilitated the alleviation of poverty, spurred regional development, and led to increased commercial thinking among Kenyan fish farmers. Indeed, national aquaculture production grew from 1,000 MT/y in 2000 (equivalent to 1% of national fish production) to 12,000 MT/y, representing 7% of the national harvest, in 2010. The production is projected to hit 20,000 MT/y, representing 10% of total production and valued at USD 22.5 million over the next 5 years. The dominant aquaculture systems in Kenya include earthen and lined ponds, dams, and tanks distributed across the country. The most commonly farmed fish species are Nile tilapia Oreochromis niloticus, which accounts for about 75% of production, followed by African catfish Clarias gariepinus, which contributes about 21% of aquaculture production. Other species include common carp Cyprinus carpio, rainbow trout Oncorhynchus mykiss, koi carp Cyprinus carpio carpio, and goldfish Carassius auratus. Recently, Kenyan researchers have begun culturing native fish species such as Labeo victorianus and Labeo cylindricus at the National Aquaculture Research Development and Training Centre in Sagana. Apart from limited knowledge of modern aquaculture technology, the Kenyan aquaculture sector still suffers from an inadequate supply of certified quality seed fish and feed, incomprehensive aquaculture policy, and low funding for research. Glaring opportunities in the Kenyan aquaculture industry include the production of live fish food, e.g., Artemia, daphnia and rotifers, marine fish and shellfish larviculture; seaweed farming; cage culture; integrated fish farming; culture of indigenous fish species; and investment in the fish feed industry.

• Tribology and Lubricants
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• 제35권5호
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• pp.274-285
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• 2019

Chemical mechanical polishing (CMP), which is a material removal process involving chemical surface reactions and mechanical abrasive action, is an essential manufacturing process for obtaining high-quality semiconductor surfaces with ultrahigh precision features. Recent rapid growth in the industries of digital devices and semiconductors has accelerated the demands for processing of various substrate and film materials. In addition, to solve many issues and challenges related to high integration such as micro-defects, non-uniformity, and post-process cleaning, it has become increasingly necessary to approach and understand the processing mechanisms for various substrate materials and abrasive particle behaviors from a tribological point of view. Based on these backgrounds, we review recent CMP R&D trends in this study. We examine experimental and analytical studies with a focus on substrate materials and abrasive particles. For the reduction of micro-scratch generation, understanding the correlation between friction and the generation mechanism by abrasive particle behaviors is critical. Furthermore, the contact stiffness at the wafer-particle (slurry)-pad interface should be carefully considered. Regarding substrate materials, recent research trends and technologies have been introduced that focus on sapphire (${\alpha}$-alumina, $Al_2O_3$), silicon carbide (SiC), and gallium nitride (GaN), which are used for organic light emitting devices. High-speed processing technology that does not generate surface defects should be developed for low-cost production of various substrates. For this purpose, effective methods for reducing and removing surface residues and deformed layers should be explored through tribological approaches. Finally, we present future challenges and issues related to the CMP process from a tribological perspective.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권7호
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• pp.1986-2009
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• 2024

The integration of blockchain technology with the rapid growth of Internet of Things (IoT) devices has enabled secure and decentralised data exchange. However, security vulnerabilities and performance limitations remain significant challenges in IoT blockchain networks. This work proposes a novel approach that combines transaction representation and machine learning techniques to address these challenges. Various clustering techniques, including k-means, DBSCAN, Gaussian Mixture Models (GMM), and Hierarchical clustering, were employed to effectively group unlabelled transaction data based on their intrinsic characteristics. Anomaly transaction prediction models based on classifiers were then developed using the labelled data. Performance metrics such as accuracy, precision, recall, and F1-measure were used to identify the minority class representing specious transactions or security threats. The classifiers were also evaluated on their performance using balanced and unbalanced data. Compared to unbalanced data, balanced data resulted in an overall average improvement of approximately 15.85% in accuracy, 88.76% in precision, 60% in recall, and 74.36% in F1-score. This demonstrates the effectiveness of each classifier as a robust classifier with consistently better predictive performance across various evaluation metrics. Moreover, the k-means and GMM clustering techniques outperformed other techniques in identifying security threats, underscoring the importance of appropriate feature selection and clustering methods. The findings have practical implications for reinforcing security and efficiency in real-world IoT blockchain networks, paving the way for future investigations and advancements.