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
Influence of high-strength geotextiles on load distribution in pile systems embedded in soft soil
The application of small cross-sectional concrete piles for ground improvement in soft soil areas—particularly in warehouses and factory buildings—has long been applied in construction practice in Vietnam. The effectiveness of this technique is significantly enhanced by the inclusion of high-strength geotextiles positioned atop the pile heads, facilitating improved load transfer and stress distribution. Despite its practical advantages, a comprehensive understanding of the working mechanism, load-transfer behavior, and optimal design of this combined system remains limited in the current literature. This paper presents a detailed analysis based on existing analytical formulas and employs the finite element method for validation. The relationships between applied load and variables such as cushion thickness, pile spacing, and pile cap dimensions will be investigated to provide a foundation for similar future applications.
Enhancing tomato leaf disease detection with contrast-limited adaptive histogram equalization and image blending for improved machine learning accuracy
This study examines the application of Contrast Limited Adaptive Histogram Equalization (CLAHE), an advanced version of Adaptive Histogram Equalization (AHE), and image blending as a preprocessing technique for tomato leaf images to improve the accuracy of machine learning models in agricultural applications, particularly in the context of disease detection. We implemented CLAHE to normalize the contrast in tomato images and the image blending technique, thereby enhancing the visibility of key features critical for accurate analysis. The experimental results demonstrate a significant increase in the accuracy of the machine learning algorithms, with improvements of up to 4.02% compared to baseline models using standard unprocessed images. When compared to existing methods that rely solely on traditional image enhancement techniques, the CLAHE method does not involve image blending. CLAHE, combined with image blending, showed superior performance in highlighting disease symptoms, thereby leading to more accurate predictions. These findings highlight the crucial role of effective disease detection in tomato crops, as timely identification of health issues can lead to more informed management decisions and enhanced yield. By facilitating higher accuracy rates in disease detection, this research underscores the importance of advanced image preprocessing methods in developing robust machine learning solutions, ultimately enhancing decision-making processes in crop management and improving production efficiency. In this research, we conduct numerous experiments on various machine learning algorithms to identify and evaluate the algorithm that performs best in predicting diseases in tomatoes based on the provided image of a tomato leaf.
Bioinspired synthesis of Ag/AgCl nanoparticles from Aganonerion Polymorphum leaves: Boosted antibacterial potential
The use of plant extracts (mainly polyphenols) as both reducing and stabilizing agents has opened up new scopes in AgNPs biosynthesis. A lack of comprehensive studies on the possibility of Aganonerion Polymorphum (AP) leaves extract prompts an updated synthesis of Ag/AgCl nanoparticles, thanks to the rich source of chlorine in the AP leaves extract, to illuminate the positive effect of AgCl in antibacterial systems. Ag/AgCl nanoparticles were efficiently synthesized using AP leaf extract, serving as both a natural reducing and stabilizing agent. The highest concentrations of polyphenols (2.55 mg/g) and ascorbic acid (2.89 µg/mL) were extracted under optimal conditions (at 70°C for 1.5 hours), acting as the phytochemicals for metal ion reduction to form Ag/AgCl nanoparticles under sunlight, observed by absorbance bands at the wavelength of 400 - 500nm. XRD showed a highly crystalline face-centered cubic structure of Ag and AgCl, and EDX indicated the presence of Ag and Cl. TEM revealed spheres with a nanosize of 15 - 20nm. The zeta potential confirmed the long-term stability of the Ag/AgCl in aqueous solution (-8.7mV). As-prepared Ag/AgCl demonstrated an excellent antibacterial ability against five bacteria, as indicated by their inhibition zone (average diameter > 9.3mm) and minimum inhibitory concentration (MIC, 16.6 μg/mL). This study proposes a green and sustainable strategy for synthesizing Ag/AgCl nanoparticles, employing AP leaf extract as a natural reducing and stabilizing agent under ambient temperature conditions. The synthesized Ag/AgCl nanoparticles exhibited potent antibacterial activity against both Gram-positive and Gram-negative bacteria. Notably, the presence of AgCl further enhanced their efficacy, expanding their potential for advanced antibacterial applications.
Mitigating output DC current ripple in a three-phase current source rectifier using an optimized space vector modulation
The mitigation of output DC ripple in three-phase current source rectifiers represents a significant design challenge, directly impacting the quality of the delivered power. To address this issue, this paper introduces a novel control framework based on the concept of Virtual Space Vector (VSV) modulation. The fundamental operating principle of the proposed strategy is to systematically reduce the dwell time of the active vector with the largest magnitude within each control sector. This principle is realized through a two-fold process. First, a set of virtual vectors is constructed, with each being a linear combination of two adjacent active switching vectors. Second, within every switching period, the desired current reference vector is synthesized not by the original active vectors, but by a combination of two neighboring virtual vectors and a zero vector. Comprehensive simulation results corroborate the effectiveness of this approach in attenuating DC-link current ripple.
Optimization of red yeast rice fermentation for enhanced Monacolin K accumulation and assessment of α-amylase inhibitory activity for antihyperglycemic potential
Monascus sp. can break down starch-based substrates into bioactive metabolites with antibacterial, anticancer, antidiabetic, and anti-obesity effects. Combining Monacolin K (MK) and other compounds produced by Red Yeast Rice products reduces inflammation and insulin resistance, thereby minimizing the adverse effects of Advanced Glycation End products (AGEs) on pancreatic function, which is associated with various chronic diseases, including diabetes and atherosclerosis. This study aimed to determine the optimal fermentation conditions for Monascus purpureus to produce RYR with high red pigment intensity, Monacolin K, and low citrinin content, and to assess the inhibition of α-amylase activity. The study applied Response Surface Methodology with a Box-Behnken design to optimize fermentation parameters. The experimental matrix was generated by JMP software, allowing systematic analysis of variable interactions and identification of optimal conditions. The LC-MS/MS method was used to analyze the citrinin and Monacolin K content. Additionally, the α-amylase inhibitory activity of the extract was evaluated. The optimal fermentation conditions for the rice substrate are a temperature of 24°C, with a fungal biomass inoculum size of 8% and an incubation period of 12 days. Triplicate analyses showed that the concentration of Monacolin K was 288.44 ± 3.88ppm without citrinin. The α-amylase inhibitory activity of the RYR extract varied with concentration, which ranged from 5.39% to 79.44%. The IC₅₀ value was calculated to be 119.36 μg/mL. These results are essential for evaluating RYR extract α-amylase inhibitory capacity and suggest that it can be used to lower cholesterol and support the prevention of diseases related to obesity.
Antioxidant, antibacterial, anti-inflammatory, and anticancer activities of the methanol extract from Manilkara zapota leaves collected in Vietnam
Manilkara zapota, a member of the Sapotaceae family, is a well-known and widely distributed plant with various applications in traditional medicine. However, the number of studies about the bioactivities of the leaf extract from this species in Vietnam is limited. This study showed that the methanol leaf extract of M. zapota contained relatively high contents of antioxidant compounds, including polyphenols and flavonoids (197.48 ± 2.08mg GAE/g extract and 39.04 ± 0.36mg QE/g extract, respectively). The extract exhibited high antioxidant properties, demonstrated by its potent free radical scavenging activity (IC50 of DPPH assay: 2.71 ± 0.11 μg/mL) and its high total antioxidant capacity (218.91 ± 7.84mg AAE/g extract). Additionally, the extract showed a practical anti-bacterial effect against Bacillus cereus, Staphylococcus aureus, and Shigella boydii, with their inhibitory halos ranging from 8.00 - 11.33mm. The extract also exhibited a significant anti-inflammatory potential (IC50: 0.107 ± 0.008 mg/mL). Furthermore, its anti-cancer activities against two cancer cell lines representing soft-tissue sarcoma (RD) and liver (HepG2) cancers were also demonstrated (IC50: 260.27 ± 3.17 and 164.41 ± 1.83 μg/mL, respectively). This is the first study to report the inhibitory effect on protein denaturation and anticancer ability on soft-tissue sarcoma cells of the methanol leaf extract of M. zapota. These findings highlight M. zapota as a potential candidate for pharmaceutical research and development, particularly for its antioxidant, anti-inflammatory, and anticancer properties.
Integrating multiscale features for robust breast cancer histopathology image classification
Breast cancer is a prominent contributor to cancer-related fatalities among women globally, and early detection via histopathological examination, despite being regarded as the benchmark method, is frequently time-consuming and prone to subjectivity. To overcome these challenges, we introduce an automated deep learning framework that categorizes Hematoxylin and Eosin (H&E)-stained breast tissue samples into four groups: normal tissue, benign lesions, localized malignant tumors, and invasive carcinoma. Our system utilizes intermediate feature embeddings from the Xception (Extreme Inception) architecture to derive discriminative features, attaining 98% precision, a 0.969 kappa metric, and exceptional AUC-ROC 0.998 and AUC-PR 0.995 values on the raw (non-normalized) dataset, with heightened detection rates for localized malignant tumors 96% and invasive carcinoma, 99%. We also assessed four stain standardization methods (Reinhard, Ruifrok, Macenko, and Vahadane) and found that Macenko normalization yielded the strongest results: 97.79% accuracy, kappa = 0.965, AUC-ROC = 0.997, AUC-PR = 0.991. However, unprocessed images still outperformed all normalized versions. A comparative study revealed that our method exceeds baseline AlexNet and state-of-the-art deep learning architectures (VGG16, VGG19, Inception-v3, and conventional Xception), highlighting its capability to improve diagnostic reliability while confirming that stain normalization, though viable, does not surpass the effectiveness of the unaltered dataset.
Assessing the adoption and impact of construction automation technologies on project management efficiency: A case study of Cebu City
This study rigorously assessed the adoption and impact of construction automation technologies on project management efficiency within the urban infrastructure context of Cebu City, Philippines. Grounded in the Technology Acceptance Model (TAM), the research examined the influence of three key constructs; Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and Behavioral Intention to Use (BIU) on the integration of automation technologies, including Building Information Modeling (BIM), Artificial Intelligence (AI), and the Internet of Things (IoT). Utilizing a quantitative-descriptive and correlational design, data were collected from 40 purposively selected construction professionals through validated survey instruments. Statistical analyses included descriptive statistics, Pearson’s correlation, and internal consistency testing via Cronbach’s Alpha. The findings revealed that only PEOU exhibited a statistically significant relationship with the level of influence of automation adoption. At the same time, PU and BIU did not demonstrate significant associations with project management efficiency or procedural effectiveness. This outcome suggests that external structural barriers, such as regulatory gaps, limited financial capacity, and inadequate workforce readiness, remain significant impediments to the implementation of automation. Despite favorable perceptions of automation’s utility in enhancing coordination, cost estimation, and real-time monitoring, actual usage remained suboptimal. The study concluded that construction automation can substantially contribute to improved project management efficiency if institutionalized through policy incentives, standardized digital frameworks, and comprehensive workforce upskilling programs. These findings offered critical implications for stakeholders seeking to accelerate digital transformation in the Philippine construction sector. They proposed directions for future research focused on automation, scalability, and BIM compliance enforcement across varying firm sizes.
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.