Ho Chi Minh City Open University Journal of Science: Engineering and Technology
Tạp chí của Trường Đại học Mở Thành phố Hồ Chí Minh
Ho Chi Minh City Open University Journal of Science
An unsupervised approach for sentiment analysis via financial texts
The rapidly increasing volume of textual data has made manual labeling extremely costly and time-consuming. To address this limitation, researchers have gradually focused on unsupervised learning techniques that enable models to classify text without relying on labeled data. Among these, deep clustering has garnered significant interest. However, most existing deep clustering methods are primarily designed for computer vision tasks. In this paper, we propose modifications to two of the most powerful deep clustering methods, including DEKM and DeepCluster, by integrating transformer algorithms in the Natural Language Processing (NLP) domain, enabling these methods to handle textual data. With the proposed methods, we achieved the best results on the test set of the Financial Phrase Bank (FPB) dataset with an accuracy of 57.71% and on the test set of the Twitter Financial News (TFN) dataset with an accuracy of 65.58%. Although these results are still lower than those of traditional supervised deep learning methods, we have demonstrated that the performance of our proposed methods can be further improved when trained with more data. This highlights the promising potential of deep clustering methods for natural language processing tasks. Especially when addressing tasks where the data is either unlabeled or lacks sufficient labeling.
Machine learning techniques for cohesive soil classification in construction in Vietnam
Accurate soil classification is imperative for determining land suitability for various construction projects in construction and geotechnical engineering. The physical and mechanical properties of soil significantly influence the design of foundations, the assessment of landslide risks, and the overall stability of structures. Recognizing the limitations of traditional soil classification methods, which are often labor-intensive and time-consuming, this research introduces machine learning as a transformative tool for enhancing soil classification processes. Utilizing K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms, this study analyzes 5,869 soil samples collected from 39 construction projects in Ho Chi Minh City, Vietnam, to evaluate the efficacy of machine learning techniques in classifying construction soils. The study identifies optimal strategies that significantly improve classification accuracy through a methodical investigation that includes varying training set sizes and integrating directly obtained and indirectly derived soil features. The findings underscore the importance of incorporating liquid and plastic limits and their derived indices, with the KNN model demonstrating superior performance in specific scenarios. This research highlights the potential of machine learning to revolutionize traditional soil classification methods. It provides foundational insights for future advancements in geotechnical engineering, aiming to achieve safer, more efficient, and sustainable construction practices.
Optimization of indole-3-acetic acid production by Bacillus subtilis strain IA3
Indole-3-Acetic Acid (IAA) is a crucial plant growth-promoting hormone produced as a secondary metabolite by various microorganisms. The present study aims to isolate and screen IAA-producing endospore-forming bacteria from soil samples collected in Vietnam. A total of ten bacteria were isolated, and they were shown to have the ability to produce IAA, with strain IA3 exhibiting the highest production. Strain IA3 was identified as Bacillus subtilis based on the 16S rRNA sequencing and subsequently used to optimize conditions for IAA production. To maximize IAA yield, key factors influencing production were analyzed using Box-Behnken design. The optimized conditions led to a maximum IAA production of 47.67 ± 0.78 g/mL under the following conditions: 15.65 g/L molasses, 1.06 g/L (NH4)2SO4, and 0.52 g/L tryptophan. Therefore, Bacillus subtilis IA3 has potential applications for stimulating and enhancing plant growth or crop production.
Association of SNP Rs34678647 with breast cancer risk in the Vietnamese population: An initial study
Breast cancer remains a leading cause of mortality among women worldwide. In Vietnam, the rising incidence underscores the urgent need for early diagnostic markers. Genetic factors, particularly Single Nucleotide Polymorphisms (SNPs), play a critical role in breast cancer susceptibility. The SNP rs34678647, located downstream of the miR-221/222 cluster targeting Estrogen Receptor alpha (ERα), is hypothesized to influence breast cancer risk by modulating cancer-related pathways. This study investigates the association between rs34678647 and breast cancer risk in the Vietnamese population. A total of 234 DNA samples, comprising 131 breast cancer cases and 103 healthy controls, were genotyped using the Polymerase Chain Reaction-High Resolution Melting (PCR-HRM) technique, which is selected for its high sensitivity and specificity in SNP detection. Genotype frequencies were determined, and statistical analyses were conducted to evaluate associations. The PCR-HRM method successfully genotyped rs34678647 with high accuracy. The T allele was observed in 23% of cases and 19% of controls; however, no significant association with breast cancer risk was identified (OR = 1.19, 95% CI: 0.77-1.85, p = 0.43). Genotype distributions conformed to the Hardy-Weinberg equilibrium in both groups, supporting the representativeness of the sample. In conclusion, while the T allele of rs34678647 showed a nonsignificant trend toward increased breast cancer risk among Vietnamese women, the results were not statistically conclusive. These findings highlight the need for larger-scale studies to further explore the potential role of rs34678647 as a genetic risk factor and its applicability as an early diagnostic biomarker.
Automated customer consultation system for Pastry Shops
Automated customer consulting is a form of automated customer care and consulting that utilizes texting and chat functions to replace human interaction. This research improves the Bi-LSTM language model. We aim to enhance the accuracy and applicability of an automated customer consultation system, which may impact enterprises and traders. Our question-answer system uses querying the entity and model textual similarity to match models. Automated customer care systems utilize computers or other technologies to assist customers. It empowers clients to address problems without human assistance in customer care. Human resources can address complex requests or high-value consumers, as automation handles many repetitive and straightforward activities. Many firms utilize it, especially fast-growing ones that need to arrange support.
Comparative analysis of machine learning models for smart irrigation systems
Intelligent irrigation systems play a crucial role in addressing the global issues of water scarcity, climate variability, and sustainable agricultural production. These systems can help identify the efficient time and the exact quantity of irrigation through the use of data-driven ideas, which ensures maximum crop yield with minimal use of water. This paper provides a thorough comparative analysis of the four most commonly used Machine Learning (ML) models: Support Vector Machines (SVM), Gradient Boosting (GB), K-Nearest Neighbors (KNN), and Logistic Regression (LR), to predict the need of irrigation based on critical environmental and agronomic variables. The dataset features include soil moisture, air temperature, relative humidity, solar radiation, and crop types, among other features, obtained using sensor networks installed on farmland. We trained and tested each model before comparing its performance using standard evaluation metrics, which include accuracy, precision, recall, F1 Score, and the Area Under the Curve. These findings indicate that GB and KNN models performed better than SVM and LR. For instance, GB and KNN achieved precisions of 95.6% and 92.4%, respectively, compared to SVM and LR, which achieved precisions of 86.2% and 72.8%, respectively. In both accuracy and generalization, the GB model performs overall best. This study contributes a fair investigation of the suitability of well-known ML models in irrigation forecasting for smart farming in the south-western region of Nigeria. This study makes use of a region-specific dataset that is gathered by sensor networks, involving 100,000 records in two farming seasons.
Evaluation of the anti-inflammatory activity of the rhizome essential oil of Distichochlamys citrea
Distichochlamys citrea, an endemic plant native to central Vietnam, holds significant ethnomedicinal value in Vietnamese herbal medicine. Although the essential oil derived from the rhizome of D. citrea is known for its bioactivities, including antibacterial and antioxidant properties, its anti-inflammatory potential has not been extensively explored yet. In this study, the anti-inflammatory effect of the essential oil has been comprehensively evaluated from in silico to in vivo studies. A molecular docking study revealed that cineole, neral, and geranial, three main components of the essential oil, were capable of forming promising interactions with 5-lipoxygenase and inducible nitric oxide synthase (ranging from -4.7 to -6.3 kcal/mol). Furthermore, the essential oil exhibited a strong inhibitory effect against protein denaturation (86.67%, 100 µg/mL). In the carrageenan-induced paw edema model, the essential oil (4%) could inhibit 32.83% of paw edema as compared to the NaCl-treated group. Collectively, these findings position the essential oil extracted from rhizomes of D. citrea as a novel candidate for the development of anti-inflammatory medicine.
Enhancing face recognition in native low-resolution images using deep learning and bicubic interpolati
This paper addresses the challenge of face recognition in Low-Resolution (LR) images, mainly when the resolution is below 48x48 pixels, which is common in surveillance systems. Current face recognition algorithms struggle to deliver satisfactory results with such low-resolution images. This study utilizes over 16,000 face images with an average resolution of 20x20 pixels to improve recognition, applying deep learning and bicubic interpolation to enhance image resolution. Unlike traditional Super-Resolution (SR) methods that operate in the LR space, our approach introduces a novel data constraint that evaluates errors in the High-Resolution (HR) image domain. By leveraging the finer details in HR images, the reconstructed HR images significantly improve visual quality and recognition accuracy. This unique data constraint seamlessly incorporates discriminative features into the optimization process. Experimental results demonstrate that our method outperforms existing visual quality and recognition performance approaches.
An approach for traffic sign recognition
This article presents a model for detecting and recognizing traffic signs based on the YOLO (You Only Look Once) algorithm. Our system can detect traffic signs in real-world scenarios, including prohibitory, stop, no entry, speed limit, regulatory, and hazardous signs. However, there are still some cases where successful recognition is not achieved. Experiments were conducted on a dataset of 29,632 images, yielding % recognition accuracy of 86.8%. The system performs well in practical environments with relatively high accuracy, yet some errors persist during detection.
Kinship verification via ear images: A comparative study
Kinship verification is crucial in daily life, especially in the legal field. Nowadays, most kinship verification methods utilize the advantages of human DNA and facial features. However, these methods require a lot of complex procedures, so they are unsuitable for real-time application. Therefore, researchers started to propose other promising biometrics, and the human ear is one of the most potential. The human ear has long been recognized as a robust biometric trait, comparable to others, such as face, iris, and fingerprint. This paper proposes using ear images to identify human kinship based on several well-known deep-learning networks. Moreover, an ear image set is presented to tackle the lack of a kinship-annotated dataset.
Extraction process optimization for maximizing total polyphenol content in cashew nut testa (Anacardium occidentale L.) extract
Cashew nut testa (Anacardium occidentale L.) is a valuable source of polyphenols known for their strong bioactivities. This study optimized the extraction conditions to obtain polyphenol-rich extracts from cashew nut testa. The effects of three key factors, extraction time, material-to-solvent ratio, and temperature, on Total Polyphenol Content (TPC, mg GAE/g extract) were investigated. Response surface methodology, using a Box-Behnken design, was applied to design the experiments and optimize the extraction process. The results showed that the relationship between TPC and extraction conditions followed a second-order model with an R² value of 0.9999. All three factors significantly influenced TPC (p < 0.05), and their interactions were also significant. The model predicted optimal conditions for maximum TPC at an extraction time of 19.85h, a temperature of 58.80°C, and a material-to-solvent ratio of 1:16.39 (w/v). Validation experiments under the optimized conditions of 20h, 59°C, and a 1:16 ratio confirmed the model's accuracy, yielding a TPC of 534.67mg GAE/g extract, statistically equivalent to the predicted value. These findings demonstrate the efficacy of the optimized conditions in maximizing polyphenol extraction from cashew nut testa.
Exploring the anti-cancer potential of coumarin derivatives targeting COX-2 inhibitors
Overexpression of cyclooxygenase-2 (COX-2) plays a crucial role in cancer therapy. In this study, we investigated the role of two coumarin derivatives with the chlorobenzylamide group in inhibiting COX-2 activity and growth inhibition of cancer cells. The effect of synthesized 2a and 2b coumarin derivatives on COX-2 enzyme was investigated in vitro. Besides that, A549, HT-29, MDA-MMB-231, and HeLa tumor cell lines were used to evaluate potential inhibition growth using the MTT assay. According to the results, two synthesized compounds showed an inhibitory effect on COX-2 enzymes. In particular, 2a exhibited the best activity with an IC50 value of 49.54μM on COX-2. The 2a and 2b exhibited a noteworthy dose-dependent inhibitory growth activity against all the tested cancer cell lines, indicating their broad-spectrum anti-cancer properties. Based on the in vitro data, we strongly recommend this new coumarin-o-chlorobenzylamide as a preclinical development candidate for novel anti-cancer agents targeting COX-2 inhibition.
Anti-oxidant and anti-bacterial effects of Diospyros kaki leaf extract in Vietnam
Diospyros kaki is a famous commercial plant with its edible fruit (persimmon) and is widely used as a traditional remedy in Vietnam. However, the anti-oxidant and anti-bacterial effects against gastrointestinal microbes of the D. kaki leaf extract have not been elucidated yet. In this study, the methanol extract from D. kaki leaves contained some chemical groups, such as tannins, flavonoids, phenolic compounds, alkaloids, and saponins, mainly it was rich in polyphenols and flavonoids (528.11 ± 12.96 mg GAE/g and 44.49 ± 6.77 mg QE/g, respectively). The extract exhibited a strong anti-oxidant ability (DPPH scavenging activity of about 91.83 ± 1.02% and total anti-oxidant capacity of approximately 110.58 ± 2.65 mg AAE/g). Furthermore, the anti-bacterial activity of the extract against some gastrointestinal bacteria, including Staphylococcus aureus, Enterococcus durans, Enterococcus faecium, and Yersinia enterocolitica, has been reported for the first time (the zone of inhibition ranging from 14.17 - 24.67mm). MIC of the extract was 0.6125 - 1.25 mg/mL, and MBC was 2.5 - 5 mg/mL, which implied the extract exerted a bactericidal effect. These findings suggest D. kaki leaf extract is a promising source of extracting bioactive compounds, such as anti-oxidant and anti-microbial agents, and a potential medicine for the treatment of gastrointestinal infections.
Prospecting the levels of some macroelements (sodium, calcium, potassium, magnesium) and possible goitrogenic effects of calcium and magnesium on iodine in herbal snuff in Sokoto State, Nigeria
The elements sodium, calcium, potassium, iodine, and magnesium are essential in our diets for nutritional guidance and avoiding excessive intake and effects. Among other factors, healthcare and the high cost of conventional medicines compelled many people to use herbal snuffs for medicinal or psychoactive means. Likewise, sodium, potassium, calcium, and magnesium pollution levels could be exaggerated and harm humans. The objective of this work was to detect the levels of sodium, potassium, calcium, magnesium, and iodine present in four different types of herbal snuffs consumed in Sokoto and nearby in Nigeria by using atomic bsorption spectroscopy and prospect the effect of calcium and agnesium on iodine. Four brands of herbal snuffs bought in Sokoto were used for this work. The highest sodium was detected in herbal two and the lowest in herbal 1. The potassium was highest in herb 2 (39.41 ± 1.4ppm) and lowest in herbal snuffs 3 (6.61 + 1.4ppm). The lowest levels of Ca and Mg were determined in herbal snuff brands sold in Sokoto, Nigeria. The highest calcium was detected in herbal snuff 4 (35.51 ± 6.10ppm), and the lowest was in snuff 1 (31.00 ± 9.10ppm). The magnesium concentration was highest in herbal stock 4 (9.31 ± 1.11ppm) and lowest in herbal stuff 3 (3.05 ± 0.60ppm). Generally, the levels of the analyzed elements are in the order of sodium > calcium > potassium > magnesium. Thus, there are significant (p < 0.05) sodium, potassium, calcium, and magnesium levels in herbal snuffs in Sokoto, Nigeria, and may contribute to the daily elemental requirements of consumers in the state and nearby.
Performance comparison ensemble classifier’s performance in answering frequently asked questions about psychology
In today’s era of digital healthcare transformation, there is a growing demand for swift responses to mental health queries. To meet this need, we introduce an AI-driven chatbot system designed to automatically address frequently asked questions in psychology. Leveraging a range of classifiers including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes, our system extracts insights from expert data sources and employs natural language processing techniques like LDA Topic Modeling and Cosine similarity to generate contextually relevant responses. Through rigorous experimentation, we find that SVM surpasses Naïve Bayes and KNN in accuracy, precision, recall, and F1-score, making it our top choice for constructing the final response system. This research underscores the effectiveness of ensemble classifiers, particularly SVM, in providing accurate and valuable information to enhance mental health support in response to common psychological inquiries.
Integrating BIM and computer vision for preventing Hazards at construction sites
Construction safety monitoring is vital in enhancing site safety, such as tracking entering hazardous areas and the correlation between workers and other hazard entities. Therein, computer vision-based image/video processing, one of the emerging technologies, has been actively used to automatically identify and recognize unsafe conditions. However, the construction site has various potential hazard situations during the project. Due to the site’s complexity, many visual devices simultaneously participate in monitoring. It challenges developing and operating corresponding detection algorithms at specific workplaces and times. Besides, safety information detected by computer vision must be organized before being delivered to stakeholders. Hence, this study proposes an approach for construction safety monitoring using vision intelligence technology and BIM-cloud, called BMT. The BMT comprises two modules: (1) the virtual model based on the 4D BIM-cloud model, which provides the spatial-temporal information to decide computer vision algorithm adoptions; (2) the construction physical model built the vision intelligence technologies, which is supported by (1) and deliver safety status and update into the BIM-cloud model to visualize and deliver the risk level to related employees. The efficiency of the BMT approach is validated by testing with the preliminary implementation of a prototype.
Detecting spelling errors in Vietnamese administrative document using large language models
In the context of the emergence of more and more administrative documents, the need to ensure accuracy and improve the quality of these documents becomes increasingly important. This research focuses on applying advanced language models to detect spelling errors in administrative documents. Specifically, in this study, a new method using a language model based on the Transformers architecture is proposed to automatically detect and correct common spelling errors in administrative documents. This method combines the model’s ability to understand context and grammar to identify words or phrases that are likely to be misspelled. The proposed method is tested on a dataset containing real administrative documents, and the experimental results show that the proposed model is capable of detecting spelling errors with significant performance, helping to improve accuracy. and improve the quality of administrative documents. This research not only contributes to improving the quality of administrative documents but also opens up new research directions in applying language models to issues related to natural language processing in the field of administration.
Hybrid knowledge-infused collaborative filtering for enhanced movie clustering and recommendation
This article proposes an enhanced knowledge-based collaborative filtering model for movie recommendation services to address the limitations of collaborative filtering in capturing the diverse preferences and specific characteristics of movies. The proposed model integrates external knowledge sources, such as movie plots and reviews, to enrich the recommendation process. By leveraging this additional information, the model can better understand movies’ unique features and attributes, improving recommendation accuracy and relevance. The knowledge-based features are extracted and incorporated into the collaborative filtering framework, enhancing the model’s ability to match user preferences with movie characteristics. Experiments are conducted using the MovieLens dataset to evaluate the proposed model. The MAE and RMSE metrics are employed to assess the quality of recommendations. Comparative analyses are conducted against various baseline approaches, including popularity-based, CF-based, content-based, and hybrid recommendation models. The experimental results demonstrate the effectiveness of the proposed knowledge-based collaborative filtering model. The proposed model consistently outperforms the baselines, providing more accurate and personalized recommendations.
A study on constructing an efficient examination scheduling system
The objective of this study is to investigate the final exam scheduling process at the Ho Chi Minh City Open University and develop an automated exam scheduling application. Our primary objectives are to prevent students from having conflicting exam schedules and to ensure that no student has to take more than two exams on the same day. This research focuses on applying graph coloring algorithms to the problem of automatic exam scheduling. Our research findings indicate that the graph coloring algorithm is highly effective for automated exam scheduling. This study has the potential to expand and support the development of an automatic exam scheduling and management system, in line with our overall goals. We conduct the experiments on the practical data at HCMCOU and obtain promising results.
A sum rate maximization problem in uplink MIMO with RSMA systems
This study explores the problem of maximizing the sum rate in uplink multi-user Multiple-Input Multiple-Output (MIMO) using Rate-Splitting Multiple Access (RSMA) systems. The investigation revolves around the scenario where the Users (UEs) are single-antenna nodes transmitting data to a multi-antenna Base Station (BS) through the RSMA technique. The optimization process encompasses determining parameters such as UEs’ transmit powers, decoding order, and detection vector at the BS. An approach based on Deep Reinforcement Learning (DRL) is introduced to address this challenge. This DRL framework involves an action-refined stage and applies a Deep Deterministic Policy Gradient (DDPG)-based strategy. Simulation outcomes effectively demonstrate the convergence of the proposed DRL framework, where it converges after approximately 1,800 episodes. Also, the results prove the superior performance of the proposed method when compared to established benchmark strategies, where it is up to 45% and 86% higher than the local search and random schemes, respectively.
Dynamic matrix factorization-based collaborative filtering in movie recommendation services
Movies are a primary source of entertainment, but finding specific content can be challenging given the exponentially increasing number of movies produced each year. Recommendation systems are extremely useful for solving this problem. While various approaches exist, Collaborative Filtering (CF) is the most straightforward. CF leverages user input and historical preferences to determine user similarity and suggest movies. Matrix Factorization (MF) is one of the most popular Collaborative Filtering (CF) techniques. It maps users and items into a joint latent space, using a vector of latent features to represent each user or item. However, traditional MF techniques are static, while user cognition and product variety are constantly evolving. As a result, traditional MF approaches struggle to accommodate the dynamic nature of user-item interactions. To address this challenge, we propose a Dynamic Matrix Factorization CF model for movie recommendation systems (DMF-CF) that considers the dynamic changes in user interactions. To validate our approach, we conducted evaluations using the standard MovieLens dataset and compared it to state-of-the-art models. Our preliminary findings highlight the substantial benefits of DMF-CF, which outperforms recent models on the MovieLens-100K and MovieLens-1M datasets in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics.
Preliminary evaluation of human hair follicle-derived dermal papilla cell characteristics
Dermal Papilla Cells (DPCs) are mesenchymal cells residing at the base of the hair follicles. They play an important role in the hair cycle remodeling and hair follicle regeneration. In this study, DPCs were isolated, and cultured from human hair follicles and evaluated the expression of a-SMA, versican by immunocytochemistry, and alkaline phosphatase activity. The human fibroblasts were used as a control to confirm the non-fibroblast DPCs. The results showed that the cells grew from the hair bulb and had fibroblast morphology. These bulb cells strongly expressed alkaline phosphatase activity a-SMA, and versican while the human fibroblast lacked ALP activity and less expression of a-SMA and versican. In conclusion, the protocol is established to isolate and culture DPCs from hair follicle bulbs, and ALP activity is suggested as the major marker for distinguishing DPCs and human fibroblasts.
Preliminary survey and molecular identification of Geocoris ochropterus (Fieber) (Hemiptera: Geocoridae)
Big-eyed bugs Geocoris spp. are a natural enemy species presented in many parts of the world that have been extensively researched because of the benefits that they bring to plants. In Vietnam, big-eyed bugs are found in many places, especially in farming complexes or large farms. In Ho Chi Minh City, Geocoris sp. was found in Pham Van Coi Commune, Cu Chi District, Ho Chi Minh City, with bug density fluctuations across seasons at an average of 5.33 ± 2.67 individuals per point. The density of the big-eyed bug in Cu Chi District was 03 times higher than the density recorded in Ham Tan District, Binh Thuan Province. Morphological and molecular-based COI genes identified the speciments from Cu Chi, Ho Chi Minh City, and Ham Tan, Binh Thuan are Geocoris ochropterus. From the research results, these two sampling sites will be selected to further evaluate the rate of natural increase of the big-eyed bug Geocoris ochropterus.
Investigation of extraction conditions and evaluation of the ability of antioxidants and lowering blood glucose levels of the Ensete glaucum extract
Diabetes is a type of metabolic disorder, which is the cause of many dangerous complications. Ensete glaucum (Musaceae family) has active ingredients with antioxidant and hypoglycemic characteristics in some parts of the plant. This study investigates the ability to reduce blood sugar based on the inhibition of α-amylase and α-glucosidase enzymes of Ensete glaucum stem extract. The results indicated that 70% ethanol, a material ratio of 1/30, and a temperature of 65oC for 360 minutes are the optimal conditions for the extraction process. Sonication has an extraction efficiency that is 1.8 times greater than that of a thermostatic bath or shaker. With the above conditions, the total polyphenol contents of the upper and the lower stem samples combined with sonication are higher than those without sonication. The ability to capture 2,2-Diphenyl-1-picrylhydrazyl (DPPH) free radicals of the extract used with sonication in the upper stem is four times higher than without sonication. In addition, the presence of sonication increases the capacity to inhibit the α-amylase enzyme (IC50 of the upper stem is 29.94 g/mL, the lower stem is 16.42 g/mL), compared to the absence of sonication (IC50 of the upper stem is 36 g/mL, the lower stem is 26.72 g/mL). Similar to the ability to inhibit α-glucosidase enzyme when using sonication (IC50 of the upper stem is 71.04 µg/mL, the lower stem is 91.91 µg/mL) is higher than not using sonication (IC50 of the upper stem is 142.99 µg/mL, the lower stem is 129.88 µg/mL). In general, the inhibition of α-amylase enzyme was higher than α–glucosidase enzyme in all extracts. In addition, the presence of sonication increased the polyphenol content, DPPH free radical scavenging, and hypoglycemic capacity of Ensete glaucum stem extract.
Authentication test of seasoning food materials made from turmeric using real-time PCR
The authentication test for spices made from turmeric using real-time. This study aimed to determine whether the spice ingredients claimed to be made from turmeric actually contain turmeric, as asserted by the product. The sample set consisted of 10 types of spices based on turmeric, which were obtained from various supermarkets. The research employed real-time PCR analysis using the SYBR Green method as the chosen approach. Data analysis was performed based on Ct analysis and Tm analysis. The measurement of DNA purity and concentration resulted in a DNA concentration value of 56.205 and a DNA purity value of 2.302, measured at a wavelength of A260/A280. The real-time PCR analysis yielded Ct values ranging from 15.20 to 15.83 with an average of 15.50, and Tm values ranging from 78.70 to 89.00 with an average of 78.83. Based on these data, it can be concluded that the ready-to-use spice products, claimed to be made from genuine turmeric, indeed contain turmeric, as supported by the results of real-time PCR amplification.
Communicable disease surveillance through predictive analysis: A comparative analysis of prediction models
Effective prediction and surveillance of communicable diseases are vital for public health management. This study leveraged machine learning algorithms to predict disease occurrences in the Province of Marinduque, focusing on Hand Foot Mouth Disease, Dengue, Typhoid, Influenza, Chikungunya, Rabies, Measles, Meningitis, Hepatitis, and Acute Bloody Diarrhea using data from 2015 to 2019. The monthly morbidity rate served as the criterion variable. Machine learning models, including Random Forest, Logistic Regression, SVM, and k-Nearest Neighbors, were employed. Material and methods encompassed data collection, preprocessing, feature selection, and model evaluation. Results revealed Random Forest as the most accurate algorithm, with implications for proactive disease management and resource allocation. This research enhances disease prediction methodologies and contributes to public health surveillance.
Establishment of protocol to investigate the expression of Sucrose Phosphate Synthase 2 (SPS2) in Vietnamese golden melon (cucumis melo L.)
The accumulation of soluble sugars, especially sucrose, in mature Vietnamese golden melon, has been reported to be the determining factor of the sweetness of fruits. The gene of SPS2 has been shown to be mainly involved in the metabolic signal pathway as those for the synthesis of sucrose. The purpose of the current study is to establish a procedure to investigate the expression of the gene of SPS2 in Cucumis melo L. The materials of melon’s exocarp and mesocarp tissues were applied to isolate the total of RNAs. The procedure of Real-time PCR within the primers of SPS-F and SPS-R was successfully established as seen through the results. As a result, the cycle threshold (Ct) of 27.19, and 28.12, respectively, were observed in the sample of exocarp and mesocarp tissues. For authenticity, the products were sequenced, as determined as the gene of SPS by BLAST (NCBI). In conclusion, the procedure of investigation of SPSs gene expression was successfully established in Cucumis melo L.
Data Mining technique: Application of Apriori algorithm for road accident analysis
Road accidents can happen due to various factors. These factors that contribute to road accidents have cost damage to properties, injuries or deaths and most road accidents are attributable to the lack of knowledge on road safety. To provide safe driving and road safety plans, critical analysis of road accident data is needed, to identify the causes of road accidents. Annually, 1,250,000 people die and 50,000,000 are injured in road accidents worldwide, and fatal road accidents are caused by human error. Improving road conditions is not sufficient, but significantly understanding human errors that cause road accidents, and negligence of corrective and safety driving protocols provided by the concerned government agencies or private organizations. The study aimed to help get insights about the causes of road accidents, and to provide knowledge of road accidents for road safety using Association Rule Mining with the application of the Apriori Algorithm. Association rule mining using the Apriori Algorithm produces significant patterns and insights that help identify the causes of road accidents.
Latency-aware task offloading in UAV-assisted edge computing: Leveraging deep reinforcement learning
Due to the exponential expansion of the Internet of Things (IoT) technology, the increasing amount of data generated by numerous IoT devices has become a significant challenge for communication and computing networks. On the other hand, the concept of Mobile Edge Computing (MEC) presents a viable solution as it brings computing, storage, and networking capabilities in close proximity to the user, enabling the hosting of applications that require significant computation power and low latency right at the network’s edge. In addition, the maneuverability, cost-effectiveness, and ease of deployment make Unmanned Aerial Vehicles (UAVs) highly versatile wireless platforms. Capitalizing on the benefits of MEC and UAVs, this paper proposes a UAV-assisted MEC system that offloads services to reduce the computation burden in IoT. The objective is to maximize the task completion rate, considering factors such as user transmit power, task offloading rate, and UAV trajectory variables. To tackle this optimization problem, this paper devises a method based on the Deep Deterministic Policy Gradient (DDPG), an algorithm for continuous action spaces in deep reinforcement learning. The numerical simulations show the effectiveness of the proposed approach in comparison to other benchmarks, i.e., deep Q-network (DQN) and full offloading methods. In a simulation, the proposed DDPG method can achieve a fully optimized system with a 100% task success rate, while the DQN-based method achieves a lower task success rate of 95.93% and the full offloading method only obtains a task success rate of 82.73%.
An approach to automatic answering for English reading comprehension tests
This study focuses on the reading comprehension problem with multiple-choice answers, using the BERT model to achieve the highest performance. The ultimate goal is to create a solution to help solve reading comprehension problems without any reasoning or knowledge, suitable for the level of students in grades six and seven. The model will solve factoid questions from a given text. Our research topic will use a deep learning model-based approach to create a model that automatically answers the English reading comprehension question. We obtain promising results to give an accuracy of 78 percent.