Advancements, Challenges, and Future Directions in Rainfall-Induced Landslide Prediction: A Comprehensive Review

Rainfall-induced landslides threaten lives and properties globally. To address this, researchers have developed various methods and models that forecast the likelihood and behavior of rainfall-induced landslides. These methodologies and models can be broadly classified into three categories: empirical, physical-based, and machine-learning approaches. However, these methods have limitations in terms of data availability, accuracy, and applicability. This paper reviews the current state-of-the-art of rainfall-induced landslide prediction methods, focusing on the methods, models, and challenges involved. The novelty of this study lies in its comprehensive analysis of existing prediction techniques and the identification of their limitations. By synthesizing a vast body of research, it highlights emerging trends and advancements, providing a holistic perspective on the subject matter. The analysis points out that future research opportunities lie in interdisciplinary collaborations, advanced data integration, remote sensing, climate change impact analysis, numerical modeling, real-time monitoring, and machine learning improvements. In conclusion, the prediction of rainfall-induced landslides is a complex and multifaceted challenge, and no single approach is universally superior. Integrating different methods and leveraging emerging technologies offer the best way forward for improving accuracy and reliability in landslide prediction, ultimately enhancing our ability to manage and mitigate this geohazard

Advancements, Challenges, and Future Directions in Rainfall-Induced Landslide Prediction: A Comprehensive Review

Rainfall-induced landslides threaten lives and properties globally. To address this, researchers have developed various methods and models that forecast the likelihood and behavior of rainfall-induced landslides. These methodologies and models can be broadly classified into three categories: empirical, physical-based, and machine-learning approaches. However, these methods have limitations in terms of data availability, accuracy, and applicability. This paper reviews the current state-of-the-art of rainfall-induced landslide prediction methods, focusing on the methods, models, and challenges involved. The novelty of this study lies in its comprehensive analysis of existing prediction techniques and the identification of their limitations. By synthesizing a vast body of research, it highlights emerging trends and advancements, providing a holistic perspective on the subject matter. The analysis points out that future research opportunities lie in interdisciplinary collaborations, advanced data integration, remote sensing, climate change impact analysis, numerical modeling, real-time monitoring, and machine learning improvements. In conclusion, the prediction of rainfall-induced landslides is a complex and multifaceted challenge, and no single approach is universally superior. Integrating different methods and leveraging emerging technologies offer the best way forward for improving accuracy and reliability in landslide prediction, ultimately enhancing our ability to manage and mitigate this geohazard

Application of PCR-DGGE method for identification of nematode communities in pepper growing soil: Ứng dụng phương pháp PCR-DGGE để định danh cộng đồng tuyến trùng trong đất trồng hồ tiêu

Soil nematodes play an important role in indication for assessing soil environments and ecosystems. Previous studies of nematode community analyses based on molecular identification have shown to be useful for assessing soil environments. Here we applied PCR-DGGE method for molecular analysis of five soil nematode communities (designed as S1 to S5) collected from four provinces in Southeastern Vietnam (Binh Duong, Ba Ria Vung Tau, Binh Phuoc and Dong Nai) based on SSU gene. By sequencing DNA bands derived from S5 community sample, our data show 15 species containing soil nematode, other nematode and non-nematode (fungi) species. Genus Meloidogyne was found as abundant one. The genetic relationship of soil nematode species in S5 community were determined by Maximum Likelihood tree re-construction based on SSU gene. This molecular approach is applied for the first time in Vietnam for identification of soil nematode communities.Tuyến trùng đất đóng vai trò chỉ thị quan trọng trong công tác đánh giá môi trường và hệ sinh thái đất. Các nghiên cứu trước đây đã cho thấy lợi ích của việc phân tích cộng đồng tuyến trùng đất bằng định danh sinh học phân tử đối với việc đánh giá môi trường đất. Ở đây, chúng tôi ứng dụng phương pháp PCR-DGGE dựa trên gene SSU để phân tích năm (ký hiệu từ S1 đến S5) cộng đồng tuyến trùng đất thuộc các vùng trồng chuyên canh cây hồ tiêu ở miền nam Việt Nam (Bình Dương, Bà Rịa Vũng Tàu, Bình Phước và Đồng Nai). Bằng cách giải trình tự các vạch của mẫu tuyến trùng S5, kết quả cho thấy cộng đồng tuyến trùng này có 15 loài gồm nhóm tuyến trùng đất, nhóm các loại tuyến trùng khác và nhóm không phải tuyến trùng (nấm) và trong đó Meloidogyne là giống ưu thế. Mối quan hệ di truyền của các các loài tuyến trùng đất thuộc cộng đồng S5 được xác định bằng việc thiết lập cây phát sinh loài Maximum Likelihood dựa trên gene SSU. Đây là nghiên cứu đầu tiên ở Việt Nam sử dụng kỹ thuật PCR-DGGE để phân tích các cộng đồng tuyến trùng đất trồng hồ tiêu

RESEARCH ON FACTORS INFLUENCING THE INTENT TO USE NETFLIX MOVIES IN VIETNAM

Abstract The Netflix movies market is steadily growing, especially during the complex COVID-19 pandemic. Consumers, instead of opting for free movie streaming services with potential risks and copyright violations, are choosing to pay for a better experience while emphasizing responsibility for protecting copyrights and supporting authors and producers. This research aims to examine the factors influencing the intent to use Netflix movie streaming services among surveyed individuals, primarily focusing on employees aged 18 to 22 in Vietnam. Participants were surveyed through online and offline questionnaires. The author conducted logistic regression analysis, treating the use of Netflix movies as the dependent variable, with five independent variables sourced from a literature review. Through online and offline survey questionnaires and multivariate regression models, the study identified and concluded the factors influencing employees' intent to use Netflix movie streaming services in Vietnam. Data were quantitatively analyzed using IBM SPSS 20.0. The research results identified five positively influencing factors on the intent to use Netflix movie streaming services: Price perception, Risk perception, Attitude, Ethical awareness, Subjective norms. Among these factors, Price perception had the strongest influence on the intent to use Netflix movies, while the Subjective norms factor was found to be insignificant. Consequently, the article suggests managerial implications for businesses to attract customers and promote the Netflix movies market

Positioning the Adjacent Buried Objects Using UWB Technology Combine with Levenberg Marquardt Algorithm

The determination of the buried objects and cracks in building structures is an important is- sue in real-life. In this paper, we propose a new method called Correlation Function Separation Tech- nique (CFST) combine with the Lervenberg-Marquardt Algorithm (LMA) using the Impulse Radio Ultra-Wide Band (IR-UWB) penetrating system to improve the accuracy in detecting and positioning of the adjacent buried objects in building structures. Based on the UWB signal processing, the proposed method can be used to determine both the relative permittivity of the environment and the position of the buried objects, especially the adjacent objects. The analytical method is validated by mathematical proofs and Matlab simula- tions, and the position errors are used to assess the per- formance of proposed method. The numerical results shown that the proposed method can be used for posi- tioning the adjacent buried objects in the homogeneous environment which has an average positioning error of 3.52 cm, which is smaller than that of the conventional method based on B-canned radar images processing

Super cavity model with the coupling reaction of slender body motion and water flow

 On the imperfect water entry, a high-speed slender body moving in the forward direction, rotates inside the cavity. The body's motion makes super cavity phenomena in the water flow. The water velocity and pressure fields interact during the body's motion. In this paper, the coupling simulation model is a combination of two sub-models: In the first sub-model, the motion of slender body running very fast underwater is simulated. The equation system of this sub-model is solved by Runge-Kutta method; In the second sub-model, the water flow and pressure field under reaction of very fast slender body motion are simulated by CFD model. The simulation results of this coupled model are compared with experiments based on magnitudes of velocity U by x0 direction and error percents for cavity diameter and length

A Novel Parallel Hardware Architecture for Inter Motion Estimation in HEVC

High Efficiency Video Coding (HEVC) standard, generated by ITU, can provide compression ratio twice more than current H.264/ MPEG-4. To date, only a few hardware have been implementated for Integer Motion Estimation (IME) to date. In this paper, a parallel hardware architecture for IME in HEVC encoder is proposed. This design uses Rot-WDiamond (RWD) algorithm to reduce computational load and parallelism to improve processing speed. Therefore, this design can reach 4K (4096×2160) video in real time at 60 frames per second (fps) and achieve the frequency of 125MHz

Understanding and tackling meat reduction in different cultural contexts: a segmentation study of Swiss and Vietnamese consumers

Objective: This study aims to disclose and compare meat consumer segments in Switzerland and Vietnam, which differ in terms of their socioeconomic and cultural settings (the former is a developed country, and the latter is an emerging one) to develop a set of segment-specific recommendations that might be applied to consumption in comparable contexts, that is, in other developed countries and other emerging economies. Methods: Data were collected through two online surveys: one for Swiss residents from randomly selected households and one for Vietnamese urban residents recruited via snowball sampling. The final sample size was N = 643 for Switzerland and N = 616 for Vietnam. Hierarchical cluster analyses followed by K-means cluster analyses revealed five distinct clusters in both countries. Results: Three clusters were common to both countries: meat lovers (21% in Switzerland and 19% in Vietnam), proactive consumers (22% in Switzerland and 14% in Vietnam) and suggestible consumers (19% in Switzerland and 25% in Vietnam). Two were specific to each country, namely traditional (19%) and basic (21%) consumers in Switzerland and confident (16%) and anxious (26%) consumers in Vietnam. Conclusion: Relying on voluntary actions, nudging techniques, private initiatives and consumers’ sense of responsibility will certainly be useful but will nevertheless be insufficient to achieve a planetary health diet within the given timeframe (the 2030 Agenda for Sustainable Development). Governments will have no choice but to activate all levers within their sphere of influence – including regulatory measures – and oblige private sector actors to commit to the measures imposed on them. A binding international agenda with common objectives and measures is a judicious approach. Unlike most previous studies, which focused on meat consumption intensity and frequency or diet type to segment consumers, our approach, based on psychographic profiles, allows the identification of segments that share common drivers and barriers and thus the development of better-targeted measures to reduce meat consumption

Healthy or Environmentally Friendly? Meat Consumption Practices of Green Consumers in Vietnam and Switzerland

High meat consumption is a phenomenon in both developed countries such as Switzerland and emerging countries such as Vietnam. This high meat consumption is associated with environmental, social, and health consequences. Drawing upon social practice theory, this study explores the influence of social practices on the meat consumption of green consumers, as a growing number of consumers in both countries want to eat healthy and sustainably but still have different needs and face different barriers. Data were collected from online group discussions. For green consumers, meat consumption was found to convey certain meanings and depends, among other things, on the information available. The consumption decision in Vietnam is strongly influenced by health and food safety, whereas negative environmental consequences are important in Switzerland. Social and cultural aspects also play a major part in the decision to eat or abstain from meat in both countries. Meat is a non-negotiable part of any special occasion meal in Vietnam and is often eaten at social gatherings in Switzerland. We argue that meat consumption is linked to social status in both countries, but family influence is stronger in Vietnam than in Switzerland. Interventions, such as policy measures that are adapted to regional, cultural, and consumer group specificities and focus on social practices rather than individual behavior, are a promising means to promote meat reduction

COVID-19 Stressors on Migrant Workers in Vietnam: Cumulative Risk Consideration.

This study explored the impact of COVID-19 on migrant workers in Vietnam, using a cumulative risk assessment (CRA) framework which comprises four domains (workplace, environment, individual and community). A cross-sectional study was conducted. Data were collected in 2020 through a self-administered questionnaire with 445 domestic migrant workers in two industrial zones in two northern provinces (Bac Ninh and Ninh Binh) in Vietnam. The majority of migrant workers were female (65.2%), aged between 18 and 29 years old (66.8%), and had high school or higher education level qualifications. Most migrant workers had good knowledge about preventive measures (>90%) and correct practices on COVID-19 prevention (81.1%). Three health risk behaviors were reported: 10% of participants smoked, 25% consumed alcohol and 23.1% were engaged in online gaming. In terms of workplace, occupational working conditions were good. Noise was the most commonly reported hazard (29%). Regarding environment, about two-thirds of migrant workers lived in a small house (<36 m2). Most participants (80.4%) lived with their families. About community domain, many reported low salary or losing their job during January-July, 2020. Most migrants received information about COVID-19. The migrant workers suffered from poor health and low occupational safety, fear of job loss and income cut, poor housing and living conditions and limited access to public services. The holistic approach to address stressors is recommended to improve health and safety of migrant workers