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Purpose – Over energy consumption is one of the causes of global warming and climate change. To deal with this issue, using energy-efficient appliances is strongly encouraged and cultivating consumer loyalty toward energy-efficient appliances is crucial for long-term sustainability. This study investigates the effects of multiple dimensions of perceived value on consumer satisfaction and three outcomes of consumer loyalty (i.e. willingness to pay a premium, repurchase intention and word-of-mouth intention), considering the moderating role of the product category. Design/methodology/approach – Quota sampling based on age and gender and snowball sampling methods were applied to recruit 423 participants for this study. A combination of partial least squares structural equation modeling (PLS-SEM), importance-performance map analysis (IPMA) and necessary condition analysis (NCA) was employed to examine the proposed model. Findings – Functional, price, emotional and environmental values positively impact consumer satisfaction, enhancing the three dimensions of consumer loyalty. The product category moderates the effects of perceived value dimensions on consumer satisfaction. Moreover, the IPMA results highlight that functional value and environmental value are the most essential but underperforming value attributes. The NCA results indicate that social value is a necessary condition for consumer satisfaction. Originality/value – This study is one of the pioneers in integrating PLS-SEM, IPMA and NCA approaches to comprehensively unpack the relationships between perceived value dimensions, consumer satisfaction and consumer loyalty in the context of energy-efficient appliances. The findings offer theoretical and practical importance for academics, retailers, producers and policymakers to encourage consumer loyalty toward energy-efficient appliances.

ABSTRACT Anterior spacing caused by missing upper lateral incisors and edge‐to‐edge position in the front teeth can negatively affect the appearance of a person's smile, leading to mental and social distress. A 29‐year‐old woman approached with the issue of edge‐to‐edge anterior occlusion, upper and lower spacing, missing lateral teeth, and improper occlusion. The treatment plan involved an interdisciplinary approach with orthodontics, implants, periodontics, and prosthodontics. After orthodontic treatment, third molar extraction and two buccal shelf mini screws were recommended to distalize the lower posterior teeth and retract the lower incisors using a power chain. Gingivoplasty and veneer restorations were performed on teeth 14–24 to improve the patient's smile aesthetics. The interdisciplinary approach used in this case involved fixed orthodontics, periodontal surgery, and prosthodontics to manage and improve the patient's smile and facial appearance.

This study utilizes machine learning models, including Logistic Regression, Support Vector Machine, Decision Tree, and Random Forest, in the early warning system for debt group migration in a Vietnamese commercial bank. In predicting customers’ overdue debt migration (B Score), the RF model achieves the highest accuracy of 81.84%. However, if the priority is to reduce Type I errors, SVM performs better with a recall of 91.48%, although the accuracy drops to 46.62%. When predicting customers’ debt group improvement (C Score), SVM proves to be the optimal model in terms of both accuracy and criteria based on Type II errors, with an accuracy of 71.6% and precision of 62.3%. When applied to new datasets, the evaluation criteria decrease, but SVM remains the most optimal model for both B Score and C Score. Additionally, the research results demonstrate that tuning the model parameters leads to a significant improvement in accuracy compared to the default parameters.

Introduction: Chronic venous insufficiency (CVI) is a condition that the function of venous system is impaired due to insufficiency of venous valves. The prolonged periods of standing or sitting is considered the most crucial risk factor of CVI. This study aims to determine the prevalence and associated factors of CVI among seafood processing workers. Methods: A cross-sectional study was conducted through clinical examination and face-to-face interviews with 1160 seafood processing workers in Hai Phong from March to November in 2023 to assess the prevalence and associated factors to CVI. Results: The prevalence of CVI was 51.9%; the prevalent symptom was restless legs (54.0%); nocturnal cramps (49.3%); tight feeling in calves (43.2%); pins and needles (36.4%); edema lower legs (28.6%). Of all participants 4.1% were C0, 82.9% were C1, 11.0% were C2 and 2.0% were C3. Some factors associated with CVI: female (OR = 2.51, p=0.002); advanced age; prolonged periods of standing or sitting (OR = 2.08, p=0.001); overweight and obesity (OR =1.82, p=0.037); Abdominal obesity (OR =2.11, p=0.025); diabetes (OR = 1.95, p=0.045); hypertension (OR =2.59, p=0.004); gave birth to 2 and 3 children (OR = 1.78 and 2.35, p=0.002); working time over 8 hours per day (OR =3.87, p

In this work, we study the security performance of a double intelligent reflecting surface non-orthogonal multiple access (DIRS-NOMA) wireless communication system supporting communication for a group of two NOMA users (UEs) at the edge, with the existence of an eavesdropping device (ED). We also assume that there is no direct connection between the BS and the UEs. From the proposed model, we compute closed-form expressions for the secrecy outage probability (SOP) and the average security rate (ASR) for each UE. After that, we discuss and analyze the system security performance according to the NOMA power allocation for each user and the number of IRS counter-emission elements. In addition, we analyze the SOP of both the considered DIRS-NOMA and conventional NOMA systems to demonstrate that DIRS-NOMA systems have much better security than conventional NOMA systems. Based on the analytical results, we develop an ASR optimization algorithm using the alternating optimization method, combining NOMA power allocation factor optimization and IRS passive beam optimization through the Lagrange double transform. The derived analytical expressions are validated through Monte Carlo simulations.

Purpose – Improving service quality, student satisfaction and student loyalty is important to higher education institutions’ sustainable growth. The objectives of this study are a twofold: first, the study seeks to determine the dimensions of higher education service quality with a specific focus on Vietnam. Second, it examines how the service quality dimensions impact student satisfaction and student loyalty, with the moderating role of the university image. Design/methodology/approach – This study followed a rigorous procedure, including interviews, a survey, exploratory factor analysis (EFA) and reliability analysis to identify higher education service quality dimensions and their measures. After that, using the data obtained from 1,550 university students in Vietnam, confirmatory factor analysis was used to validate the identified dimensions and structural equation modeling was used to test a proposed model explaining the outcomes of higher education service quality. Findings – The findings reveal five dimensions of higher education service quality: academic aspect, nonacademic aspect, programming issues, facilities and industry interaction. Most of these factors have a positive influence on student satisfaction. In addition, the university image moderates the positive relationship between student satisfaction and student loyalty. Practical implications – This study’s findings highlight the complexity of service quality in the higher education context and encourage higher education institutions to improve their service quality in image to enhance student satisfaction and loyalty. Originality/value – This study suggests a unique measure of higher education service quality dimensions and provides fresh insights into how they impact student satisfaction and loyalty in Vietnam.

Bài báo nghiên cứu lựa chọn các giải pháp đấu nối nhà máy điện gió ngoài khơi vào hệ thống điện Việt Nam, với hai giải pháp đấu nối hiện nay là truyền tải điện xoay chiều (HVAC) và truyền tải điện một chiều (HVDC). Bài báo đã thực hiện tính toán phân tích ảnh hưởng của nhà máy điện gió ngoài khơi đến hệ thống điện dựa vào các tiêu chuẩn kỹ thuật yêu cầu đảm bảo khi thực hiện đấu nối với hai giải pháp truyền tải trên. Ngoài các tính toán trào lưu công suất và phân tích ảnh hưởng đến hệ thống điện bằng hai giải pháp đấu nối, bài báo đã thực hiện phân tích kinh tế tổng mức đầu tư cho hai giải pháp đấu nối cho nhà máy Điện Gió Ngoài Khơi (ĐGNK) Trung Nam công suất 800MW đang đề xuất bổ sung Quy hoạch nhằm lựa chọn giải pháp đấu nối cho nhà máy điện gió ngoài khơi đảm bảo về mặt kỹ thuật và kinh tế cho dự án. Kết quả cho thấy, phương án đấu nối bằng HDVC đảm bảo được các yếu tố về kỹ thuật và kinh tế.

Recently, intelligent reflective surface (IRS)-aided systems are becoming a prospective technology in realizing for sixth generation (6G) wireless communication era because of extremely low power transmission, seamless coverage and their superiority. These network systems can allow many users and devices to connect to each other, extending the coverage. To empower IRS-aided systems, non-orthogonal multiple access (NOMA) can be leveraged to work with IRS technique enabling further benefits such as mass connectivity, flexible resource allocation and improved performance. Increasing connected devices and expanding coverage means devices have the potential to interfere with each other. Recent studies focusing on researching and analyzing the performance of the IRS-supported NOMA network have not taken into account or not fully calculated the impact of interference on system performance. In this study, we first analyze the effect of co-channel interference (CCI) at users in downlink IRS-NOMA systems. In particular, the CCIs generated by the terminals deployed randomly in the coverage area affect the signal reception at the user in the downlink. In this network model, the channel conditions that follow the Rayleigh distribution and the CCI statistical model are independent and identically distributed. We analyze and evaluate network performance by extracting closed-form expressions of outage probability, ergodic capacity, total achievable rate then highlighting the adverse effects of CCI on IRS-NOMA. In addition, to improve the performance of the IRS-NOMA downlink, we present a framework of theorical analysis to look more insights of users’ performance, i.e. diversity order. Our analytical derivatives are verified through computer simulations based on Monte-Carlo and intuitive comparisons with the benchmarks.

OBJECTIVES: To evaluate the effectiveness of mini-implant (MI) anchorage versus conventional anchorage for the treatment of skeletal class II malocclusion. MATERIALS AND METHODS: The study was conducted on 64 patients with skeletal class II malocclusion. The patients were divided into two groups: 1) 32 patients underwent conventional anchorage, and 2) 32 patients underwent MI anchorage. Cephalometric radiographs were taken pre-treatment, and the data were compared with post-treatment measurements. RESULTS: The age and gender distribution between the conventional and MI anchorage groups was similar, and the differences were statistically significant. The SNA and SNB angles post-treatment were reduced compared to pre-treatment measurements in both groups. The MI anchorage demonstrated a superior subsidence effect, reducing the level of forward displacement of the first molars in the maxilla. The MI group also exhibited less extrusion of the molars compared to the conventional group. In skeletal class II cases, especially in patients with a high mandibular angle using MI anchorage helped prevent the mandible from rotating counterclockwise, thus improving aesthetics rather than worsening them after treatment. In 75% of the cases in the MI anchorage group, the outcome was assessed as “greatly improved” based on the PAR index. CONCLUSIONS: MI anchorage was more effective than conventional anchorage. It allows for greater posterior movement and intrusion of the maxillary incisors while maintaining or rotating the mandibular plane counterclockwise.

There is an increasing demand to introduce Introductory Business Analytics (IBA) courses into undergraduate business education. Many real-world business contexts require predictive analytics to understand the determinants of a dichotomous outcome; hence, IBA courses should include binary logistic regression analysis. This article provides our reflective discussions on the design of learning activities and assessments to assist business students in learning binary logistic regression in an IBA course. Data on student engagement and learning outcomes are used to shed light on the impacts of teaching logistic regression on student learning and experience. Notably, students opt to focus their assessment work more on logistic regression than on multiple regression analysis, showing the potential attraction of students toward binary logistic regression analysis. We also observed several challenges, mainly related to the use of Excel, that require special attention from instructors.

Purpose: Co-composting of mango by-products and biogas solid residue eliminates some shortcomings of composting these wastes separately. Specifically, co-composing solves the problem of the low pH values in mango by-products while enhances biodegradable organic matter of biogas solid residues. However, no research report is available on co-composting of mango by-products (MB) and biogas solid residue (BR). Method: This study established three in-vessel lab-scale composting bins with 3 different C/N ratios, including Bin 1: 27.4/1 (156 kg MB + 144 kg BR); Bin 2: 30.23/1 (193 kg MB + 107 kg BR); and Bin 3: 37.7/1 (224 kg MB + 76 kg BR). The raw compost materials underwent 57 days of incubation, including 36 days of raw incubation and 21 days of mineralization. Results: Bin 3 containing larger amounts of mango by-products and less amounts of biogas residue showed a higher percentage of remaining carbon in the final products (17.97%), lower nitrogen loss (17%), and showed 0.5% increase in available P2O5 content, compared to the other bins. From 300 kg of initial raw material, the final compost mass in Bin 1, Bin 2, and Bin 3 were 26.2 kg, 32.7 kg, and 88.1 kg, respectively. Conclusion: Resultantly, an initial C/N ratio of 37.7/1 could be suggested in the aerobic co-composting of biogas residue with mango by-products. Research Highlights · Co-composting of mango by-products and biogas solid residue eliminates some of the weaknesses of composting these wastes separately. · The incubation period determined was 57 days of incubation, including 36 days of raw incubation and 21 days of mineralization. · Among three C/N ratios surveyed, the highest C/N in bin 3 (C/N=37.7/1) showed the best-favoured condition for mango by-products treatment and generate the highest amount of compost product. · Bin 3 showed the highest percentage of remaining carbon in compost products (17.97%), a lower in nitrogen loss (17%), and 0.5% increase in available P2O5 content.

Deep learning has been studied in recent years to identify periapical lesions- a significant indicator of periapical periodontitis in radiographs. An accurate dataset is essential for constructing an efficient learning model for detecting periapical lesions. In order to achieve this goal, we gathered and created a database of panoramic radiographs containing periapical lesions from the High-quality Dental Treatment Centre, School of Dentistry, Hanoi Medical University, between January 2016 and March 2021. Out of 16,519 radiographs, three experienced dentists identified 3,926 images of periapical lesions and annotated those lesions based on the Periapical Lesions Classification. By applying well-known data processing techniques (e.g. scaling, mirroring, and flipping), the amount of data is increased to 17,004 images through generating additional images for machine learning. The dataset has three folders: one for the original photos, one for the post-augmentation images, and the rest for the annotation of periapical lesions. The information could assist researchers in developing a predictive machine model for detecting periapical lesions in radiographs.

Key Clinical Message The primary cause of complex AOB malocclusion is typically a combination of dental, skeletal, functional, and habitual factors. Open bite correction is a challenging treatment due to its complexity and the requirement for long‐term stability, therefore, multidisciplinary treatment is often the best option for achieving stable esthetic outcomes.

Key Clinical Abstract Multidisciplinary treatment options can help provide good clinical outcomes if these are appropriately sequenced and carried out correctly. This case exemplifies interdisciplinary involvement to ensure the patient received an improved esthetic outcome. Abstract The presence of anterior diastemas may compromise the esthetics of a patient's smile, causing mental, and social trauma in many patients. After careful evaluation Periodontal, surgical, and prosthodontic treatments are sometimes required to ensure successful treatment outcomes using a multidisciplinary staged treatment approach. These approaches must be carefully planned to ensure timely treatment progress. Surgical intervention must be planned at the right point in treatment to ensure adequate healing before placement of esthetic restorations.