A critical analysis of poverty reduction initiatives in North West Vietnam: A case study of Son La province

This thesis focuses upon poverty and poverty reduction programmes within Son La province, North-West Vietnam. Ethnic minority groups constitute 86 per cent of the total population of this province and are disproportionately subjected to poverty, both within the province and the nation. A critical analysis of current poverty reduction programmes and their sustainability, with particular reference to ethnic minority groups in Son La province, is undertaken. The thesis then proposes a new, sustainable approach to poverty alleviation for ethnic minority groups in Son La province. Drawing upon original, primary, qualitative research conducted by the author, it is argued that the current poverty reduction policies in Son La province specifically, and Vietnam more widely, with particular reference to ethnic minority groups, have many limitations. It is contended here that asset based approaches are most suitable for the sustainable activation of growth and the reduction of poverty within Son La province, and the reduction of poverty amongst ethnic minority groups within Vietnam generally. The nation should formulate a holistic programme for improving grassroots governance in ethnic minority communities in order to improve the accountability of local authorities, based on voice enhancement for, and the empowerment of, local people and village institutions

The efficient computation of the nonlinear dynamic response of a foil-air bearing rotor system

AbstractThe foil–air bearing (FAB) enables the emergence of oil-free turbomachinery. However, its potential to introduce undesirable nonlinear effects necessitates a reliable means for calculating the dynamic response. The computational burden has hitherto been alleviated by simplifications that compromised the true nature of the dynamic interaction between the rotor, air film and foil structure, introducing the potential for significant error. The overall novel contribution of this research is the development of efficient algorithms for the simultaneous solution of the state equations. The equations are extracted using two alternative transformations: (i) Finite Difference (FD); and (ii) a novel arbitrary-order Galerkin Reduction (GR) which does not use a grid, considerably reducing the number of state variables. A vectorized formulation facilitates the solution in two alternative ways: (i) in the time domain for arbitrary response via implicit integration using readily available routines; and (ii) in the frequency domain for the direct computation of self-excited periodic response via a novel Harmonic Balance (HB) method. GR and FD are cross-verified by time domain simulations which confirm that GR significantly reduces the computation time. Simulations also cross-verify the time and frequency domain solutions applied to the reference FD model and demonstrate the unique ability of HB to correctly accommodate structural damping

Vers un modèle proactif pour identifier des risques de trafic

La sécurité routière prend de plus en plus d’importance dans de nombreux pays. En mettant l'accent sur la prévention des accidents sur les autoroutes, nous proposons une méthodologie pour identifier les risques en utilisant des modèles de régression logistique. La caractéristique novatrice de cette méthodologie réside dans l’évaluation des risques de trafic par rapport à différentes régimes de trafic obtenues par des Cartes Auto-Organisatrices (Self-Organizing Maps - SOM). Nous agrégeons les données de trafic et les données météorologiques pour produire des états de trafic pour des intervalles de 5 minutes, que nous appelons des situations de trafic. En utilisant l'analyse en composantes principales (ACP), qui permet de réduire le nombre de dimensions et de supprimer le bruit aléatoire des données, nous transformons les situations de trafic pour le processus de regroupement par les SOM. Ce processus de regroupement produit des groupes de situations de trafic similaires que l'on appelle des régimes de trafic. De la base de données des accidents, nous déterminons les situations de trafic qui précèdent les collisions, appelées situations pré-accidentelles. Avec les résultats de regroupement obtenus, nous classons les situations pré-accidentelles dans des régimes de trafic. Pour chaque régime de trafic, nous développons un modèle de régression logistique pour identifier les situations à risque. Les résultats montrent que les modèles peuvent identifier correctement un pourcentage élevé de situations à risque, tandis que le taux de fausse détection est faible

Motorway Traffic Risks Identification Model - MyTRIM Methodology and Application

Road traffic crashes are becoming increasing concerns in many countries. In Europe, many efforts have been devoted to improve road traffic safety yet the important target of halving the number of yearly road deaths in 2010 could not be achieved in many European countries. Among different road types, motorways are safe by design yet crashes if occur would be severe due to high speed practiced. If motorway traffic crash risk could be identified, lives could be saved and severity could be reduced. For this objective, the current thesis aims to establish a methodology for developing models capable of identifying real-time traffic crash risk on motorways. A real-time MotorwaY Traffic Risk Identification Model (MyTRIM) is developed for a study site on motorway A1 in Switzerland. MyTRIM is tested, validated with real data. Three types of historical data altogether available at the study site are used for developing MyTRIM. The data include individual vehicle traffic data collected from double loop traffic detectors, meteorological data from meteorological station located near the study site, and a crash database containing crashes recorded by the police. Based on crash time, pre-crash data representing traffic and meteorological conditions leading to crashes are extracted. Similarly, non-crash data representing traffic and meteorological conditions that are unrelated with crashes are also extracted. As crashes are rare events, a methodology for sampling non-crash data comparable with pre-crash data is developed using clustering – classification basis: non-crash data are clustered into groups; pre-crash data are classified into obtained groups; pre-crash and non-crash data within one group are similar and therefore, comparable. Each group is called a traffic regime. Under each traffic regime, a regime-based Risk Identification Model (RIM) is developed to differentiate pre-crash and non-crash data. Given a new datum, regime-based RIM must be able to classify the datum into pre-crash or non-crash. As a result of the model development, variables which are important for the differentiation are also identified. These important variables can be potential for implementing countermeasures to prevent the risk from ending up with a crash. MyTRIM is developed based on the outputs from regime-based RIM. MyTRIM memorizes the latest risk evolution to predict whether there is crash risk in the coming time interval. Regime-based RIM and MyTRIM are tested and validated using real data. Results show that regime-based RIM and MyTRIM perform with high accuracy. The output of MyTRIM can be useful for traffic operators as an input for actively managing the traffic. The developed methodology can be applied for any motorway traffic sections where similar data are available

RMDM: A Multilabel Fakenews Dataset for Vietnamese Evidence Verification

In this study, we present a novel and challenging multilabel Vietnamese dataset (RMDM) designed to assess the performance of large language models (LLMs), in verifying electronic information related to legal contexts, focusing on fake news as potential input for electronic evidence. The RMDM dataset comprises four labels: real, mis, dis, and mal, representing real information, misinformation, disinformation, and mal-information, respectively. By including these diverse labels, RMDM captures the complexities of differing fake news categories and offers insights into the abilities of different language models to handle various types of information that could be part of electronic evidence. The dataset consists of a total of 1,556 samples, with 389 samples for each label. Preliminary tests on the dataset using GPT-based and BERT-based models reveal variations in the models' performance across different labels, indicating that the dataset effectively challenges the ability of various language models to verify the authenticity of such information. Our findings suggest that verifying electronic information related to legal contexts, including fake news, remains a difficult problem for language models, warranting further attention from the research community to advance toward more reliable AI models for potential legal applications.Comment: ISAILD@KSE 202

Bacteria associated with soft coral from Mot island - Nha Trang bay and their antimicrobial activities

Microbial communities associated with invertebrates had been considered as a new source of bioactive compounds. The soft coral associated bacteria in Mot island, Nha Trang bay were isolated, extracted and assessed for antagonistic activity against human and coral pathogens, the strongly active strains were identified by 16S rRNA analysis. The soft coral associated bacterium SCN10 had abcd antibacterial pattern which was named for inhibition towards Bacillus subtilis (pattern a), Escherichia coli (pattern b), Salmonella typhimurium (pattern c) and Serratia marcescens (pattern d). It was the nearest strain to the well-known antibiotic producer Bacillus amyloliquefaciens with 99% sequence similarity. Whereas strain SCL19 had abde pattern which means inhibition of the growth of B. subtilis, E. coli, S. marcescens and Vibrio parahaemolyticus (pattern e). This strain SCL19 affiliated with Bacillus sp. strain A-3-23B with 99.8% identity. In addition to antimicrobial activity to the aforementioned tested bacteria, the isolate SCX15 also inhibited Vibrio alginolyticus (pattern f) and Candida albicans (pattern g), so this isolate possessed abcdefg antimicrobial pattern. The coral associated isolate SCX15 was identified as Bacillus velezensis with 99% sequence similarity. Among the 78 screened strains, 25 isolates possessed antibacterial activity against at least one of seven tested microorganisms and exhibited 12 different types of antimicrobial activities, suggesting that they can produce many different natural substances with antibacterial activity

Micro-simulation Modeling of Coordination of Automated Guided Vehicles at Intersection

One of the challenging problems with autonomous vehicles is their performance at intersections. This paper shows an alternative control method for the coordination of autonomous vehicles at intersections. The proposed approach is grounded in multi-robot coordination and it also takes into account vehicle dynamics as well as realistic communication constraints. The existing concept of decentralized navigation functions is combined with a sensing model and a crossing strategy is developed. It is shown that, thanks to the proposed approach, vehicles have smoother trajectories when crossing at a four-way intersection. The proposed method is compared to adaptive traffic lights and roundabouts in terms of throughput. Results show that using a decentralized navigation function for the coordination of autonomous vehicles improves the performance by reducing energy consumption and pollution emission

An efficient adaptive fuzzy hierarchical sliding mode control strategy for 6 degrees of freedom overhead crane

The paper proposes a new approach to efficiently control a three-dimensional overhead crane with 6 degrees of freedom (DoF). Most of the works proposing a control law for a gantry crane assume that it has five output variables, including three positions of the trolley, bridge, and pulley and two swing angles of the hoisting cable. In fact, the elasticity of the hoisting cable, which causes oscillation in the cable direction, is not fully incorporated into the model yet. Therefore, our work considers that six under-actuated outputs exist in a crane system. To design an efficient controller for the 6 DoF crane, it first employs the hierarchical sliding mode control approach, which not only guarantees stability but also minimizes the sway and oscillation of the overhead crane when it transports a payload to a desired location. Moreover, the unknown and uncertain parameters of the system caused by its actuator nonlinearity and external disturbances are adaptively estimated and inferred by utilizing the fuzzy inference rule mechanism, which results in efficient operations of the crane in real time. More importantly, stabilization of the crane controlled by the proposed algorithm is theoretically proved by the use of the Lyapunov function. The proposed control approach was implemented in a synthetic environment for the extensive evaluation, where the obtained results demonstrate its effectiveness. © 2022 by the authors. Licensee MDPI, Basel, Switzerland