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Purpose – This paper is presented through the lens of Social Cognitive Theory (SCCT) (Lent et al., 1994), and seeks to examine the role of parental influence and internship programs on Job Pursuit Intentions (JPI) in the Vietnamese Hospitality and Tourism industry. Methodology/Design/Approach – Data were collected through a survey of fourth-year Hospitality and Tourism students at six universities in Ho Chi Minh City, Vietnam. Student responses to a series of prepared questions allowed structural relationships to be developed between key variables, which were illuminated through the deployment of Structural Equation Modeling (SEM) using Smart-PLS. Findings – The study found that parental support and internship programs significantly affect JPI. This research also revealed the potential for using SCCT in JPI examinations. Further, it indicated that the relationship between parental support and JPI and the relationship between internship programs and JPI are moderated by External Locus of Control (ELOC). Originality of the research - This research deploys a novel application of SCCT within a collectivist society with Confucian values in the context of JPI amongst Vietnamese hospitality and tourism students. It further gives insight for human resources departments of H&T foreign brands and universities on how to better target student and graduate recruitment.

We present a fully autonomous self-recharging drone system capable of long-duration sustained operations near powerlines. The drone is equipped with a robust onboard perception and navigation system that enables it to locate powerlines and approach them for landing. A passively actuated gripping mechanism grasps the powerline cable during landing after which a control circuit regulates the magnetic field inside a split-core current transformer to provide sufficient holding force as well as battery recharging. The system is evaluated in an active outdoor three-phase powerline environment. We demonstrate multiple contiguous hours of fully autonomous uninterrupted drone operations composed of several cycles of flying, landing, recharging, and takeoff, validating the capability of extended, essentially unlimited, operational endurance., Comment: Accepted for publication at the 2024 IEEE International Conference on Robotics and Automation (ICRA). Video at https://www.youtube.com/watch?v=C-uekD6VTIQ

In this paper, we propose a radar odometry structure that directly utilizes radar velocity measurements for dead reckoning while maintaining its ability to update estimations within the Kalman filter framework. Specifically, we employ the Doppler velocity obtained by a 4D Frequency Modulated Continuous Wave (FMCW) radar in conjunction with gyroscope data to calculate poses. This approach helps mitigate high drift resulting from accelerometer biases and double integration. Instead, tilt angles measured by gravitational force are utilized alongside relative distance measurements from radar scan matching for the filter's measurement update. Additionally, to further enhance the system's accuracy, we estimate and compensate for the radar velocity scale factor. The performance of the proposed method is verified through five real-world open-source datasets. The results demonstrate that our approach reduces position error by 47% and rotation error by 52% on average compared to the state-of-the-art radar-inertial fusion method in terms of absolute trajectory error., Comment: 9 pages, 5 figures, 1 table, conference

We propose a multilevel Monte Carlo-FEM algorithm to solve elliptic Bayesian inverse problems with "Besov random tree prior". These priors are given by a wavelet series with stochastic coefficients, and certain terms in the expansion vanishing at random, according to the law of so-called Galton-Watson trees. This allows to incorporate random fractal structures and large deviations in the log-diffusion, which occur naturally in many applications from geophysics or medical imaging. This framework entails two main difficulties: First, the associated diffusion coefficient does not satisfy a uniform ellipticity condition, which leads to non-integrable terms and thus divergence of standard multilevel estimators. Secondly, the associated space of parameters is Polish, but not a normed linear space. We address the first point by introducing cut-off functions in the estimator to compensate for the non-integrable terms, while the second issue is resolved by employing an independence Metropolis-Hastings sampler. The resulting algorithm converges in the mean-square sense with essentially optimal asymptotic complexity, and dimension-independent acceptance probabilities., Comment: 31 pages. arXiv admin note: text overlap with arXiv:2302.00522

This paper describes the NOWJ1 Team's approach for the Automated Legal Question Answering Competition (ALQAC) 2023, which focuses on enhancing legal task performance by integrating classical statistical models and Pre-trained Language Models (PLMs). For the document retrieval task, we implement a pre-processing step to overcome input limitations and apply learning-to-rank methods to consolidate features from various models. The question-answering task is split into two sub-tasks: sentence classification and answer extraction. We incorporate state-of-the-art models to develop distinct systems for each sub-task, utilizing both classic statistical models and pre-trained Language Models. Experimental results demonstrate the promising potential of our proposed methodology in the competition., Comment: ISAILD@KSE 2023

Innovative enhancement in embedded system platforms, specifically hardware accelerations, significantly influence the application of deep learning in real-world scenarios. These innovations translate human labor efforts into automated intelligent systems employed in various areas such as autonomous driving, robotics, Internet-of-Things (IoT), and numerous other impactful applications. NVIDIA's Jetson platform is one of the pioneers in offering optimal performance regarding energy efficiency and throughput in the execution of deep learning algorithms. Previously, most benchmarking analysis was based on 2D images with a single deep learning model for each comparison result. In this paper, we implement an end-to-end video-based crime-scene anomaly detection system inputting from surveillance videos and the system is deployed and completely operates on multiple Jetson edge devices (Nano, AGX Xavier, Orin Nano). The comparison analysis includes the integration of Torch-TensorRT as a software developer kit from NVIDIA for the model performance optimisation. The system is built based on the PySlowfast open-source project from Facebook as the coding template. The end-to-end system process comprises the videos from camera, data preprocessing pipeline, feature extractor and the anomaly detection. We provide the experience of an AI-based system deployment on various Jetson Edge devices with Docker technology. Regarding anomaly detectors, a weakly supervised video-based deep learning model called Robust Temporal Feature Magnitude Learning (RTFM) is applied in the system. The approach system reaches 47.56 frames per second (FPS) inference speed on a Jetson edge device with only 3.11 GB RAM usage total. We also discover the promising Jetson device that the AI system achieves 15% better performance than the previous version of Jetson devices while consuming 50% less energy power., Comment: Accepted in Future of Information and Communication Conference (FICC) 2024

We study the Electrical Impedance Tomography Bayesian inverse problem for recovering the conductivity given noisy measurements of the voltage on some boundary surface electrodes. The uncertain conductivity depends linearly on a countable number of uniformly distributed random parameters in a compact interval, with the coefficient functions in the linear expansion decaying at an algebraic rate. We analyze the surrogate Markov Chain Monte Carlo (MCMC) approach for sampling the posterior probability measure, where the multivariate sparse adaptive interpolation, with interpolating points chosen according to a lower index set, is used for approximating the forward map. The forward equation is approximated once before running the MCMC for all the realizations, using interpolation on the finite element (FE) approximation at the parametric interpolating points. When evaluation of the solution is needed for a realization, we only need to compute a polynomial, thus cutting drastically the computation time. We contribute a rigorous error estimate for the MCMC convergence. In particular, we show that there is a nested sequence of interpolating lower index sets for which we can derive an interpolation error estimate in terms of the cardinality of these sets, uniformly for all the parameter realizations. An explicit convergence rate for the MCMC sampling of the posterior expectation of the conductivity is rigorously derived, in terms of the interpolating point number, the accuracy of the FE approximation of the forward equation, and the MCMC sample number. We perform numerical experiments using an adaptive greedy approach to construct the sets of interpolation points. We show the benefits of this approach over the simple MCMC where the forward equation is repeatedly solved for all the samples and the non-adaptive surrogate MCMC with an isotropic index set treating all the random parameters equally.

To identify whether EU certified food – here organic and geographical indications – is more sustainable than a conventional reference, we developed 25 indicators covering the three sustainability pillars. Original data was collected on 52 products at farm, processing and retail levels, allowing the estimation of circa 2000 indicator values. Most strikingly, we show that, in our sample, certified food outperforms its non-certified reference on most economic and social indicators. On major environmental indicators – carbon and water footprint – their performance is similar. Although certified food is 61% more expensive, the extra-performance per euro is similar to classical policy interventions to improve diet sustainability such as subsidies or taxes. Cumulatively, our findings legitimate the recent initiatives by standards to cover broader sustainability aspects.

This paper is an extension of the current literature on the impact of climate induced natural disasters on the agricultural sector and the first empirical study that investigates the impact of natural disasters on technical efficiency (TE) of rice (also known as paddy) production in Sri Lanka and elsewhere. Severity and intensity of disasters were measured using data on a number of different natural disasters. Data for 22 districts in Sri Lanka representing two rice growing seasons and three agro-ecological regions for the 18 year period from 2000 were collected. The study employed a stochastic production function approach to examine technical efficiencies of different districts and different agro-ecological regions. The results reveal that drought and flood have a significant negative impact on the TE of rice production and that droughts have had the most severe impact. The disasters have decreased the TE of rice production by 2–5% on average. This study highlights the need to change the policies and resource allocation currently based on the traditional knowledge and beliefs about production efficiency during different seasons and in different agro-ecological regions. Such changes need to reflect the global trend of greater climatic variations resulting from extreme weather conditions.

Improving the productive efficiency (also called technical efficiency) of real estate and construction firms (RECFs) is essential to tackle the sustained shortage of housing supply and the increasing unaffordability of housing in developing countries. However, there are few studies that focus on this issue in the context of developing countries; and available literature focuses mainly on firms listed on stock markets. Our study employs the meta-frontier framework to measure the efficiency of 832 small, medium, and large RECFs across Indonesia from 2012 to 2016, using a data envelopment analysis technique. The meta-frontier framework allows for different production technologies and different business environments operated by firms of different sizes. Under the specification of group-specific production frontiers, large firms obtain the highest average efficiency scores (0.694), followed by medium firms (0.529), and small firms (0.479). The technology gaps between the meta-frontier and the group-frontiers for small and medium firms are relatively large, suggesting that the overall industry would be able to achieve remarkable efficiency improvement if firms could access technologies used by more efficient firms. Our results also show that determinants of the efficiency vary across firm groups, suggesting that policy and managerial interventions tailored to each group would have more impact on the overall productive efficiency of the entire industry.

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.

This study empirically estimates the impact of clean and non-clean energy consumption on economic growth and carbon dioxide emissions within the framework of the environmental Kuznets curve and pollution haven hypothesis in the case of PIMC countries from 1980 to 2019. The results of the panel cointegration test proposed by Westerlund (2007) show a long-term equilibrium relationship among the variables of each designated model. The long-term elasticities of economic growth and carbon emission estimated by AMG, CCEMG, and MG estimators indicate that both clean and non-clean energy consumption has a significant impact on economic growth, while carbon emission hinders growth. The results also reveal that economic growth, non-clean energy consumption, and interaction between trade openness and non-clean energy consumption have a driving effect on carbon dioxide emission; however, clean energy consumption is found to reduce carbon emission. In addition, the analysis confirms the existence of the inverted U-shaped environmental Kuznets curve and pollution haven hypothesis in the panel of PIMC economies. Finally, there is a one-way causality from non-clean energy consumption to economic growth, but no such causation exists between clean energy consumption and economic growth. The objective of sustained economic growth with a safe environment may be achieved by encouraging clean energy consumption in the PIMC economies.

The unsustainable use of irrigation water is one of the most serious environmental problems in coffee farming. Rapid expansion of sustainability certifications and adoption of advanced irrigation technologies are expected to promote more sustainable water use through changes in water management practices in coffee farming. However, there lacks empirical evidence on the effects of those certification schemes on water efficiency. This study examines the effect of sustainability certification on the water efficiency of coffee growers, considering distinct irrigation technologies used by farmers - overhead sprinkler and micro-basin irrigation technologies. The meta-frontier framework is used in our empirical analysis of 896 Vietnamese coffee farms over three crop years. Results confirm that irrigation water efficiency in coffee farming is substantially low. There is no evidence supporting the effect of sustainability certification on water efficiency, but advanced irrigation technologies could improve water efficiency. Coffee farms using the sprinkler system are more efficient than those using the micro-basin irrigation technology. Our results also support environmentally friendly farming practices, i.e., having wind-break trees on coffee farms. It also suggests that improving the quality of sustainability certification schemes towards sustainable use of water requires water-saving technologies.

Drone autonomous operations near power lines are growing steadily and require innovative techniques to keep them on air. This paper presents a novel electromechanical recharging station that can be mounted on energized AC power line to charge the drone battery wirelessly without a need to modify the electrical infrastructure. The work shows a thorough analysis of the electrical and mechanical core components to build a flexible, lightweight and efficient recharging station that can be attached to a robotic arm. The work also discusses the recharging station design and its special robot end effector that mechanically couples the station with an aerial manipulator. Finally, the recharging station has been tested in the lab and in a real power line setup to validate its design and efficiency. The total achieved mass is 2300 grams with a harvesting efficiency of 77% at 250 A primary current.

Drone autonomous operations near power lines are growing steadily and require innovative techniques to keep them on air. This paper presents a novel electromechanical recharging station that can be mounted on energized AC power line to charge the drone battery wirelessly without a need to modify the electrical infrastructure. The work shows a thorough analysis of the electrical and mechanical core components to build a flexible, lightweight and efficient recharging station that can be attached to a robotic arm. The work also discusses the recharging station design and its special robot end effector that mechanically couples the station with an aerial manipulator. Finally, the recharging station has been tested in the lab and in a real power line setup to validate its design and efficiency. The total achieved mass is 2300 grams with a harvesting efficiency of 77% at 250 A primary current.

Sheaf theory, which is a complex but powerful tool supported by topological theory, offers more flexibility and precision than traditional graph theory when it comes to modeling relationships between multiple features. In the realm of air quality monitoring, this can be incredibly useful in detecting sudden changes in local dust particle density, which can be difficult to accurately measure using commercial instruments. Traditional methods for air quality measurement often rely on calibrating the measurement with public standard instruments or calculating the measurements moving average over a constant period. However, this can lead to an incorrect index at the measurement location, as well as an oversmoothing effect on the signal. In this study, we propose a compact device that uses sheaf theory to detect and count vehicles as a local air quality change-causing factor. By inferring the number of vehicles into the PM2.5 index and propagating it into the recorded PM2.5 index from low-cost air monitoring sensors such as PMS7003 and BME280, we can achieve self-correction in real-time. Plus, the sheaf-theoretic method allows for easy scaling to multiple nodes for further filtering effects. By implementing sheaf theory in air quality monitoring, we can overcome the limitations of traditional methods and provide more accurate and reliable results.

Vietnam requires a sustainable urbanization, for which city sensing is used in planning and de-cision-making. Large cities need portable, scalable, and inexpensive digital technology for this purpose. End-to-end air quality monitoring companies such as AirVisual and Plume Air have shown their reliability with portable devices outfitted with superior air sensors. They are pricey, yet homeowners use them to get local air data without evaluating the causal effect. Our air quality inspection system is scalable, reasonably priced, and flexible. Minicomputer of the sys-tem remotely monitors PMS7003 and BME280 sensor data through a microcontroller processor. The 5-megapixel camera module enables researchers to infer the causal relationship between traffic intensity and dust concentration. The design enables inexpensive, commercial-grade hardware, with Azure Blob storing air pollution data and surrounding-area imagery and pre-venting the system from physically expanding. In addition, by including an air channel that re-plenishes and distributes temperature, the design improves ventilation and safeguards electrical components. The gadget allows for the analysis of the correlation between traffic and air quali-ty data, which might aid in the establishment of sustainable urban development plans and poli-cies.

We develop an essentially optimal finite element approach for solving ergodic stochastic two-scale elliptic equations whose two-scale coefficient may depend also on the slow variable. We solve the limiting stochastic two-scale homogenized equation obtained from the stochastic two-scale convergence in the mean (A. Bourgeat, A. Mikelic and S. Wright, J. reine angew. Math, Vol. 456, 1994), whose solution comprises of the solution to the homogenized equation and the corrector, by truncating the infinite domain of the fast variable and using the sparse tensor product finite elements. We show that the convergence rate in terms of the truncation level is equivalent to that for solving the cell problems in the same truncated domain. Solving this equation, we obtain the solution to the homogenized equation and the corrector at the same time, using only a number of degrees of freedom that is essentially equivalent to that required for solving one cell problem. Optimal complexity is obtained when the corrector possesses sufficient regularity with respect to both the fast and the slow variables. Although the regularity norm of the corrector depends on the size of the truncated domain, we show that the convergence rate of the approximation for the solution to the homogenized equation is independent of the size of the truncated domain. With the availability of an analytic corrector, we construct a numerical corrector for the solution of the original stochastic two-scale equation from the finite element solution to the truncated stochastic two-scale homogenized equation. Numerical examples of quasi-periodic two-scale equations, and a stochastic two-scale equation of the checker board type, whose coefficient is discontinuous, confirm the theoretical results.

The Vietnamese agricultural sector has experienced a dramatic structural change based on increased specialization in rice cultivation. However, small-scale rice-farmers have continued to grow multiple crops, especially in less developed provinces. While the literature advocates crop diversification for reasons of both economic and ecological sustainability, there lacks empirical evidence as to whether crop diversification brings efficiency and productivity gains to small farms. The present study is the first applications of the input-oriented stochastic distance function approach in estimating scale and scope economies using data of multi-crop farming households in Vietnam. We find strong evidence of product-specific economies of scale. Scope economies are also present for rice, vegetable, and other annual crop production. This suggests that crop diversification enhances efficiency and productivity. However, there still exists significant technical inefficiency in crop production, indicating opportunities to expand farm output at the existing level of inputs and technologies. More specifically, our empirical results indicate that it is desirable to expand vegetable and other annual crop production in mountainous areas while rice cultivation can be further expanded in delta and coastal regions.

Welfare and poverty are multidimensional in nature. However, empirical research on the impacts of trade liberalisation on multidimensional welfare is rare. This paper provides one of the first empirical analyses of the impact of trade liberalisation on multidimensional deprivation of rice farmers utilising data from the six waves of the Vietnam Household Living Standard Surveys. Our empirical model captures the direct effects of incomes from farm and non-farm activities and indirect effects of employment, rice price levels, rice production and domestic trade. Results unveil several important new findings which have policy implications. A higher level of export openness is correlated with higher levels of income from farming and non-farm activities. Higher income levels appear to reduce multidimensional deprivation but working in export-oriented industries increases it. Farmers selling rice to domestic private traders generate higher income than those selling to government-owned trading companies. Regional farming productivity, the scale of rice production, crop diversification, and education help increase income and reduce deprivation.

The paper presents one of the first economic analyses of residents’ choice of different coastal erosion control measures in a developing country - Vietnam. Hoi An, a UNESCO World Heritage Site, was selected given the frequency of coastal erosion events which have caused increasing damages to property, tourism activities and the livelihood of local people in an iconic tourist destination. We designed a discrete choice experiment based on responses from 399 households in order to estimate the willingness to pay (WTP) for differing coastal erosion control measures. Using the generalized multinomial logit model, empirical results yield five important findings. First, residents prefer wider, more public beaches having both trees and restaurants and are willing to accept visible structures such as groynes and stair revetment to prevent further erosion. Second, residents place a higher value on a beach that is protected by robust permanent structures. In particular, residents have the highest WTP for groynes. Third, there exists preference and scale heterogeneity across respondents which are driven by level of education, knowledge of the problem, and the stated level of choice certainty. Fourth, knowledge and experience of coastal erosion are shown to have a strong influence on the valuation residents place on the choice of protective structures. Finally, on average, a resident is willing to pay USD 1.7 per year for a coastal erosion management program that increases beach width by additional 50 meters, beach access by additional 25%, restaurants and trees on the beach and groynes as the erosion protection structure.

This paper examines the economic impact of climate induced disasters on Sri Lanka's agricultural, industrial and services sectors and their subsectors. In doing so, it seeks to explain a central paradox – that there are winners and losers in terms of the effect of the disasters on sectoral growth. This poses problems for many developing countries which are seeking to aggressively raise economic growth targets. These targets typically do not adequately take into account the impact of climate change on growth nor that climate change likely is to have a different effect on different economic sectors. Using cross-provincial panel datasets for Sri Lanka for the period, 1997–2018, we show that the agricultural sector is the most affected by climate induced disasters, although not all agricultural sub-sectors are equally vulnerable. Similarly, the industrial sector is shown to suffer a significant negative impact due to strong winds and landslide events. The textiles and garment sub-sectors are negatively impacted while the machinery sub sector shows a positive impact. This indicates the effect of higher demand for new machinery and equipment employed in disaster reconstruction efforts. The study further reveals that the services sector derives a mostly positive impact following disasters, especially public administration and health subsectors. The study also indicates that for Sri Lanka during the current decade, there has been a considerably greater negative impact from climate induced disasters compared to the previous decade.

Literature has documented significant trade-offs between economic and environmental performance in many areas of agricultural production. However, while it has focused on the trade-off for technically efficient farms there are differing types of trade-offs that exist for differing types of farms. The present paper addresses these gaps in the literature by proposing a more generalised approach to analysing cost and environmental trade-offs. There is also a lack of research on the nature of the trade-off in coffee farming. This is especially so in Vietnam where there is both significant overuse of nutrients and increasing autonomous adaption through voluntary participation in sustainability certification schemes. The proposed framework is employed to investigate a data set of 679 Vietnamese coffee farms. This study delivers several important findings. First, the proposed approach indicates four distinct groups of farms facing differing types of trade-offs with some farms not facing any trade-offs. Second, empirical results show that the average levels of technical, cost and environmental efficiency are relatively low. Third, sustainability certified farms are found to perform better than non-certified farms although the differences in performance diminish over time. Overall, the empirical results favour a heterogeneous and more integrated policy approach to improving the technical, cost and environmental efficiency of coffee farmers in Vietnam.

The literature postulates that government subsidization of strategic R&D activities of profit maximising firms (PMFs) increases their shares in international markets. Will this also hold for labor-managed firms (LMFs) which are owned by employees and aim to maximize the profit per labor? This paper provides a theoretical model that examines how such governmental interventions can help LMFs in their home countries to compete better with PMFs in international markets. Our model shows that in most, but not all cases, investing more in R&D activities benefits LMFs by increasing their shares in international markets and decreasing the market share of their competitors. The optimal government R&D subsidy or tax for LMFs depends on the R&D elasticity of LMFs as well as how their competitors react to R&D investment. In contrast, the optimal government R&D subsidy for PMFs depends solely on the slope of the R&D reaction curve of their competitors. Our results present useful policy implications for those governments that seek ways to support LMFs - or more broadly cooperatives - to attain more sustained growth given their advantages over PMFs in the context of sustainable development.

Climate change is likely to worsen poverty, and agriculture-dependent groups and poorest countries are at the greatest risk. Farmers’ have begun developing and implementing climate change adaptations. This study investigates the extent to which climate change adaptations by smallholder farmers have the potential to contribute to the UN's sustainable development goals of no poverty (SDG 1) and zero hunger (SDG 2). To this end, the study measures the impact of such adaptations on food production using farm-level survey data from Nepal. We utilize a matching technique and stochastic production frontier model to examine the productivity and efficiency of farmers. Results reveal that the group of farmers adopting adaptations exhibit higher levels of productivity and technical efficiency in food production as compared to the non-adopters. It is evident from the results that policy makers should encourage farming households in climate change adaptations, which have the potential to enhance farmers’ productivity and efficiency in agriculture, thereby contributing to two of the United Nations Sustainable Development Goals (SDGs) of eradicating hunger and poverty (SDG's target indicators 2.3).

The popularity of nature-based tourism revolves around tourist experiences. Tourism, as an experiential good has the potential to impact on tourists’ awareness, appreciation, and actions concerning the environment and wildlife. User preferences and valuations of nature and wildlife may change because of the experience. Exploring user valuations of environmental goods before and after the experience is, however, an area that has been given little attention in the literature. We use discrete choice experiments (DCEs) to evaluate tourists’ preferences and valuations for nature, wildlife and related services before and after experiencing a nature tour. The results demonstrate how the unfamiliarity of the good consumed (in this case fauna and flora in a hitherto unseen natural environment) affect the non-use valuations of consumers. We find that tourists’ valuations for nature and wildlife significantly improve after experiencing the nature tour.

During and after the 2008–09 global financial crisis, the growth cycle of Vietnam's economy has shifted from an average annual growth rate of 7%–8% to an average annual growth rate of 5%-6% with a high level of macroeconomic instability and uncertainty from 2009 till 2016. Related studies have speculated that the operations of monetary policies during this period were not effective in recovering the economic growth and stabilising the overall price level and total output level. This paper provides the first empirical examination of this speculation using the Trilemma framework. Our empirical results show that the State Bank of Vietnam has had adopted a set of policies aiming at maintaining exchange rate stability and interest rate independence while easing the restrictions on capital inflows. The combination of these three monetary policy approaches is found to violate the rule of Trilemma. Consequently, exchange rate and interest rate policies became less effective and failed to stabilise the economy in response to the global economic recession.

Grain yield improvement is considered the key target for many cereal breeding programs. In barley, grain yield is closely associated with flowering time, which provides crops with the opportunity to avoid stress and to best utilise available environmental resources. In this study, major genes associated with flowering time were identified using a multiparent advanced generation intercross (MAGIC) population constructed from four commercial barley cultivars (Compass, GrangeR, La Trobe and Lockyer). Gene-specific markers were developed for fast-screening of phenology genes in 1,919 MAGIC recombinant inbred lines using Kompetitive allele-specific PCR (KASP) assays. Sequencing data revealed a novel mutation in the Gibberellin 20 oxidase 2 (HvGA20ox2) region in cv. Lockyer, which showed similar impacts on flowering time and plant height compared to the well-known semi-dwarf 1 (sdw1) gene. Analyses using gene-specific genotype data and phenotype data from field trials with differing sowing locations and times confirmed the association of the major genes HvGA20ox2, Photoperiod 1 (HvPPD-H1), Dense and erect panicle 1 (HvDEP1) and Flowering locus T 1 (HvFT1) with flowering time. Different combinations of these phenology genes showed a broader range in flowering time compared to the four parental lines. Novel gene combinations showed up to 7 days earlier in flowering time compared to the earliest parental cultivar (La Trobe), and 5 days later than the latest parental cultivar (Lockyer). The phenotype data from Corrigin and Esperance trials suggested that gene combinations for optimal flowering time might differ in these locations. Optimal gene combinations are estimated to result in up to 24% improved grain yield compared to the parental cultivars. The results from this study will benefit the development of higher-yielding and better-adapted barley cultivars for various growing conditions across Australia.

Grain yield improvement is considered the key target for many cereal breeding programs. In barley, grain yield is closely associated with flowering time, which provides crops with the opportunity to avoid stress and to best utilise available environmental resources. In this study, major genes associated with flowering time were identified using a multiparent advanced generation intercross (MAGIC) population constructed from four commercial barley cultivars (Compass, GrangeR, La Trobe and Lockyer). Gene-specific markers were developed for fast-screening of phenology genes in 1,919 MAGIC recombinant inbred lines using Kompetitive allele-specific PCR (KASP) assays. Sequencing data revealed a novel mutation in the Gibberellin 20 oxidase 2 (HvGA20ox2) region in cv. Lockyer, which showed similar impacts on flowering time and plant height compared to the well-known semi-dwarf 1 (sdw1) gene. Analyses using gene-specific genotype data and phenotype data from field trials with differing sowing locations and times confirmed the association of the major genes HvGA20ox2, Photoperiod 1 (HvPPD-H1), Dense and erect panicle 1 (HvDEP1) and Flowering locus T 1 (HvFT1) with flowering time. Different combinations of these phenology genes showed a broader range in flowering time compared to the four parental lines. Novel gene combinations showed up to 7 days earlier in flowering time compared to the earliest parental cultivar (La Trobe), and 5 days later than the latest parental cultivar (Lockyer). The phenotype data from Corrigin and Esperance trials suggested that gene combinations for optimal flowering time might differ in these locations. Optimal gene combinations are estimated to result in up to 24% improved grain yield compared to the parental cultivars. The results from this study will benefit the development of higher-yielding and better-adapted barley cultivars for various growing conditions across Australia.

The Vietnamese agricultural sector has experienced a dramatic structural change based on increased specialization in rice cultivation. However, small-scale rice-farmers have continued to grow multiple crops, especially in less developed provinces. While the literature advocates crop diversification for reasons of both economic and ecological sustainability, there lacks empirical evidence as to whether crop diversification brings efficiency and productivity gains to small farms. The present study is the first applications of the input-oriented stochastic distance function approach in estimating scale and scope economies using data of multi-crop farming households in Vietnam. We find strong evidence of product-specific economies of scale. Scope economies are also present for rice, vegetable, and other annual crop production. This suggests that crop diversification enhances efficiency and productivity. However, there still exists significant technical inefficiency in crop production, indicating opportunities to expand farm output at the existing level of inputs and technologies. More specifically, our empirical results indicate that it is desirable to expand vegetable and other annual crop production in mountainous areas while rice cultivation can be further expanded in delta and coastal regions.

We study the Bayesian inverse problem for inferring the log-normal slowness function of the eikonal equation given noisy observation data on its solution at a set of spatial points. We study approximation of the posterior probability measure by solving the truncated eikonal equation, which contains only a finite number of terms in the Karhunen-Loeve expansion of the slowness function, by the Fast Marching Method. The error of this approximation in the Hellinger metric is deduced in terms of the truncation level of the slowness and the grid size in the Fast Marching Method resolution. It is well known that the plain Markov Chain Monte Carlo procedure for sampling the posterior probability is highly expensive. We develop and justify the convergence of a Multilevel Markov Chain Monte Carlo method. Using the heap sort procedure in solving the forward eikonal equation by the Fast Marching Method, our Multilevel Markov Chain Monte Carlo method achieves a prescribed level of accuracy for approximating the posterior expectation of quantities of interest, requiring only an essentially optimal level of complexity. Numerical examples confirm the theoretical results., Comment: 23 pages, 10 figures. Updated to address reviewer notes on the theory presented, as well as expand the numerical example section

The overwhelming abundance of data has created a misinformation crisis. Unverified sensationalism that is designed to grab the readers' short attention span, when crafted with malice, has caused irreparable damage to our society's structure. As a result, determining the reliability of an article has become a crucial task. After various ablation studies, we propose a multi-input model that can effectively leverage both tabular metadata and post content for the task. Applying state-of-the-art finetuning techniques for the pretrained component and training strategies for our complete model, we have achieved a 0.9462 ROC-score on the VLSP private test set.

Space-based observations, emerged in the hydrology field in the last two decades, play a fundamental role in providing alternative information of hydrologic variables besides gauge measurements, especially in data scarce regions. Among all satellite products, products derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Satellite are popular due to the satellite's rapid re-visit time and adequate spatial resolutions. However, cloud obscuration limits the usage of products derived from MODIS because clouds block satellites from capturing the ground state of the earth surface. This dissertation aims to (1) recover two cloud-free MODIS datasets of snow and flood using a 3-D interpolation technique, namely, Variational Interpolation (VI) and (2) demonstrate their usefulness for hydrologic applications.In the first part of this dissertation, the computational stability of the existing VI method is improved, then, we apply the algorithm to produce a cloud-free snow dataset for CONUS from 2000 to 2017. Moreover, by taking into consideration specific assumptions about the water body characteristic, we implement VI algorithm to remove clouds from MODIS flood maps. Promising results from a validation period over the Mississippi River are presented. We also couple the elevation information to derive the cloud-free MODIS water depth maps from the MODIS water extent maps. Water level maps are important for hydrological studies and can also act as references when the future Surface Water and Ocean Topography (SWOT) direct observations of water elevation are available in 2021.In the second part of this dissertation, we use the resulting cloud-free MODIS flood and water depth maps to improve a hydrological model by reducing model errors via calibration and data assimilation. The calibrated output inundation maps accurately reflect flood events for the Upper Mississippi River Basin in 2013 and 2014. Also, the downstream discharge via the data assimilation scheme can co

Space-based observations, emerged in the hydrology field in the last two decades, play a fundamental role in providing alternative information of hydrologic variables besides gauge measurements, especially in data scarce regions. Among all satellite products, products derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Satellite are popular due to the satellite's rapid re-visit time and adequate spatial resolutions. However, cloud obscuration limits the usage of products derived from MODIS because clouds block satellites from capturing the ground state of the earth surface. This dissertation aims to (1) recover two cloud-free MODIS datasets of snow and flood using a 3-D interpolation technique, namely, Variational Interpolation (VI) and (2) demonstrate their usefulness for hydrologic applications.In the first part of this dissertation, the computational stability of the existing VI method is improved, then, we apply the algorithm to produce a cloud-free snow dataset for CONUS from 2000 to 2017. Moreover, by taking into consideration specific assumptions about the water body characteristic, we implement VI algorithm to remove clouds from MODIS flood maps. Promising results from a validation period over the Mississippi River are presented. We also couple the elevation information to derive the cloud-free MODIS water depth maps from the MODIS water extent maps. Water level maps are important for hydrological studies and can also act as references when the future Surface Water and Ocean Topography (SWOT) direct observations of water elevation are available in 2021.In the second part of this dissertation, we use the resulting cloud-free MODIS flood and water depth maps to improve a hydrological model by reducing model errors via calibration and data assimilation. The calibrated output inundation maps accurately reflect flood events for the Upper Mississippi River Basin in 2013 and 2014. Also, the downstream discharge via the data assimilation scheme can co

This research was carried out to test the potential of combining unmanned aerial vehicle (UAV) and remote sensing (RS) to support precision mapping of irrigation systems for paddy land. The study area is an urban/agricultural area of Central Vietnam. The Sentinel-2A imagery acquired on 30 June 2018 was interpreted according an object-based classification method aiming to map paddy land and irrigation systems for the Hoa Vang district; the total accuracy was 91.33% with a Kappa coefficient of 0.87. However, with the spatial resolution from the Sentinel-2A images (20 meters x 20 meters) it was difficult to classify paddy land and water from other objects within small and scattered parcel areas. This research was designed on five experimental flying zones, collecting 2,085 images by the UAV. With the very high spatial resolution data of the UAV, it was possible to clearly identify the boundaries of paddy land parcels, water sources such as rivers and lakes, and other objects such as canals and concrete irrigation systems. This classification derived from the orthogonal images from the five experimental zones using an object-based classification method, correcting the interpretation results of the Sentinel 2A images. Outcomes indicate that, the combination of UAV and RS can be applied to support precision mapping of irrigation systems for paddy land in urban agricultural areas., Nghiên cứu này được thực hiện nhằm thử nghiệm khả năng kết hợp giữa UAV với viễn thám trong hỗ trợ độ chính xác của bản đồ hệ thống nước tưới cho đất trồng lúa ở vùng nông nghiệp đô thị tại Miền trung Việt Nam. Ảnh viễn thám Sentinel- 2A thu nhận vào 30/6/2018 đã được giải đoán bằng phương pháp định hướng đối hướng để thành lập bản đồ hệ thống nguồn nước tưới cho huyện Hòa Vang vào năm 2018, với kết quả độ chính xác tổng số là 91,33% và hệ số kappa là 0,87. Mặc dù với kết quả giải đoán có độ chính xác cao nhưng với độ phân giải không gian của ảnh Sentinel-2A là 20m x 20m rất khó để phân loại được các vùng đất lúa có diện tích nhỏ và phân bố phân tán. Nghiên cứu này đã thiết kế 5 khu vực bay thử nghiệm với 2.085 ảnh để thu thập dữ liệu từ UAV. Có thể thấy rằng dữ liệu ảnh từ UAV với độ phân giải siêu cao có thể nhận diện và phân biệt được một cách rõ ràng không chỉ ranh giới của các thửa đất lúa, hệ thống nguồn nước như sông hồ, mà còn cả những đối tượng kênh mương thủy lợi nhỏ. Kết quả giải đoán các ảnh bay chụp bằng UAV sử dụng dụng phương pháp định hướng đối tượng, nghiên cứu này đã hiệu chỉnh được kết quả giải đoán ảnh Sentinel 2A. Kết quả cho thấy việc kết hợp dữ liệu viễn thám với UAV là hoàn toàn có khả năng sử dụng để hỗ trợ độ chính xác thành lập bản đồ hệ thống nguồn nước cho đất trồng lúa ở vùng nông nghiệp đô thị.

This research was carried out to test the potential of combining unmanned aerial vehicle (UAV) and remote sensing (RS) to support precision mapping of irrigation systems for paddy land. The study area is an urban/agricultural area of Central Vietnam. The Sentinel-2A imagery acquired on 30 June 2018 was interpreted according an object-based classification method aiming to map paddy land and irrigation systems for the Hoa Vang district; the total accuracy was 91.33% with a Kappa coefficient of 0.87. However, with the spatial resolution from the Sentinel-2A images (20 meters x 20 meters) it was difficult to classify paddy land and water from other objects within small and scattered parcel areas. This research was designed on five experimental flying zones, collecting 2,085 images by the UAV. With the very high spatial resolution data of the UAV, it was possible to clearly identify the boundaries of paddy land parcels, water sources such as rivers and lakes, and other objects such as canals and concrete irrigation systems. This classification derived from the orthogonal images from the five experimental zones using an object-based classification method, correcting the interpretation results of the Sentinel 2A images. Outcomes indicate that, the combination of UAV and RS can be applied to support precision mapping of irrigation systems for paddy land in urban agricultural areas., Nghiên cứu này được thực hiện nhằm thử nghiệm khả năng kết hợp giữa UAV với viễn thám trong hỗ trợ độ chính xác của bản đồ hệ thống nước tưới cho đất trồng lúa ở vùng nông nghiệp đô thị tại Miền trung Việt Nam. Ảnh viễn thám Sentinel- 2A thu nhận vào 30/6/2018 đã được giải đoán bằng phương pháp định hướng đối hướng để thành lập bản đồ hệ thống nguồn nước tưới cho huyện Hòa Vang vào năm 2018, với kết quả độ chính xác tổng số là 91,33% và hệ số kappa là 0,87. Mặc dù với kết quả giải đoán có độ chính xác cao nhưng với độ phân giải không gian của ảnh Sentinel-2A là 20m x 20m rất khó để phân loại được các vùng đất lúa có diện tích nhỏ và phân bố phân tán. Nghiên cứu này đã thiết kế 5 khu vực bay thử nghiệm với 2.085 ảnh để thu thập dữ liệu từ UAV. Có thể thấy rằng dữ liệu ảnh từ UAV với độ phân giải siêu cao có thể nhận diện và phân biệt được một cách rõ ràng không chỉ ranh giới của các thửa đất lúa, hệ thống nguồn nước như sông hồ, mà còn cả những đối tượng kênh mương thủy lợi nhỏ. Kết quả giải đoán các ảnh bay chụp bằng UAV sử dụng dụng phương pháp định hướng đối tượng, nghiên cứu này đã hiệu chỉnh được kết quả giải đoán ảnh Sentinel 2A. Kết quả cho thấy việc kết hợp dữ liệu viễn thám với UAV là hoàn toàn có khả năng sử dụng để hỗ trợ độ chính xác thành lập bản đồ hệ thống nguồn nước cho đất trồng lúa ở vùng nông nghiệp đô thị.

The demand for education is partly revealed by a higher demand for properties around good schools, which has been the basis for a number of empirical studies. This research attempts to explore the nexus of school quality and the property market when school ranking is clearly available to the public. The spatial hedonic property valuation method was employed to analyze selected Brisbane public (state) and private schools consisting of those with high, middle and low ranking in terms of average student academic school performance. The results imply the capitalization of school values in both property and rental prices. The research also demonstrates the spillover impacts of school development. Such impacts are higher in comparison to previous studies, which are supported by the average school performance considered in individual assessments. The results of this study can be a valuable input into school policy reforms. (JEL: H23, I20, I28, R21)

The net stable funding ratio (NSFR) was introduced under the Basel III accord to promote financial stability. Under this new international regulation, individual financial institutions are required to maintain a sustainable funding structure. Hence this new universal requirement is expected to materially affect bank operations. In this paper, we provide one of the first empirical examinations of the non-linear impact of the NSFR on profit (in)efficiency for commercial banks using two data sets from Bankscope (from 2000 to 2015) and the Federal Financial Institutions Examination Council call reports (between 2000–2013). Our results suggest that modest intensification in liquidity helps to reduce bank profit inefficiency (i.e. increase efficiency) but a too greater liquidity enlargement could increase inefficiency. This result is consistent with the trade-off hypothesis based on an assumption of a non-linear relationship between liquidity and bank performance

Purpose: There is ample empirical evidence to show that larger banks are more efficient than smaller banks in developed countries. However, there is very little empirical evidence to show that in small developing economies, such as Vietnam, bank size is associated with increased risk, especially credit risk. This paper aim to provide empirical evidence to fill in this gap. This paper employs a slack-based directional distance function using the intermediation approach in measuring the inefficiency of banks in Vietnam during the period 2006–2015. Non-performing loans are used as an undesirable output to capture credit risk. The results show that small banks are more efficient than large banks at the mean level and across the entire distributions of inefficiency of the two groups. Input waste, output shortage and risk surplus of big banks are nearly three times higher than those of small banks. The results are robust under constant and variable returns to scale for production technologies. The study’s empirical results contribute to the ongoing debate on the merits of enlarging bank size in a small transitional economy and suggest that policy makers should pay attention to the risk and inefficiency of large banks to enhance the performance of Vietnam's banking system as a whole. Design/methodology/approach: This paper uses the non-radial slack-based directional technology distance function developed by Färe and Grosskopf (2010) to estimate the efficiency of banks using the data envelopment analysis technique. Data for 44 commercial banks are used. Findings: The empirical results of the paper contribute to the ongoing debate on the merits of enlarging bank size in a small transitional economy and suggest that policy makers should pay attention to the risk and inefficiency of large banks to improve the performance of Vietnam's banking system as a whole. Originality/value: This paper extends the extant literature by examining wh

Consumers have responded negatively to agricultural products originating from the Fukushima prefecture after the 2011 Fukushima Daiichi nuclear power plant incident. These consumers’ behavioural changes have caused substantial economic losses for the producers located in contaminated regions through reductions in market prices, consumption quantities and market shares. Remarkably, these losses are observed also for uncontaminated products, posing an opportunity for research in reputation damage. However, existing literature is very rare on empirical modeling of such losses. We utilise the Dixit–Stiglitz demand framework to derive a simple but efficient empirical model that uses market data to quantify reputation damage. This model estimates changes in the perceived quality of a product originating in contaminated regions in relation to those products originating from other competing regions. The model also measures subsequent loss in market share for those products grown in contaminated regions. In our application to Fukushima, results suggest that consumers’ valuation of peaches from Fukushima decreased dramatically (between 22.5% and 23.6%) in the year the nuclear accident occurred, but rapidly recovered in the following years. The results also show that the degree of impact varies across wholesale markets. Fukushima farmers lost about 13.1–18.9% of sales due to reputation damage. Estimates from our proposed models deliver meaningful information in the context of policy interventions such as transfer programmes financed by gainers to compensate reputation losses.

Sea bass (Lates calcarifer) is a key aquaculture species in Malaysia, produced as part of a polyculture system in cages but predominantly as a single species in ponds. The production of sea bass in Malaysia has decreased substantially since 2015. Several studies suggest disease may be the main cause for such decline, but changing economic conditions may also have played a role in this decline. In this paper, we use Data Envelopment Analysis (DEA) to examine these issues as well as identify the key drivers of efficiency and capacity utilization in the grow-out finfish industry. Our results suggest that most farms are technically efficient, but many are operating at lower levels of capacity utilization. Socio-demographic factors appear to affect pond production but less so cage production. The results suggest that the reduced output of sea bass is largely in response to changed economic conditions (output and input prices).

In this paper, we test to what extent Food Quality Schemes (FQS, including Geographical Indications and organic products) contribute to the social and economic sustainability of farmers and regions through employment and education. Through employment, FQS may counter the urban migration trend affecting rural regions, and help retain economic and social capital in the local region. Indeed, as FQS are often small and specialised sectors, the economic inefficiency of such businesses may translated into greater employment and social sustainability. Separately , by requiring a higher-level of quality and hence skills, FQS may encourage greater local educational attainment or skilled immigration. To test these propositions , we analyse the employment and educational outcomes of 25 FQS. Our results show that the FQS products examined have a 13% higher labour usage (labour-to-production ratio) compared to reference products, indicating that they provide greater employment. Additionally, wage levels are 32% higher in FQS compared to references. Despite providing greater employment and higher wages, profitability of FQS (i.e. how much turnover/profit is generated per employee) is nevertheless 32% higher for FQS compared to reference products, due to the ability to attract higher product prices. Finally, there is no clear link between FQS and greater (or lower) education attainment in the supply chain. Overall, our results suggest that FQS can provide a strong contribution to local employment, employee income and business profits, strengthening the social and economic sustainability of producers and regions.

This research was carried out to test the potential of combining unmanned aerial vehicle (UAV) and remote sensing (RS) to support precision mapping of irrigation systems for paddy land. The study area is an urban/agricultural area of Central Vietnam. The Sentinel-2A imagery acquired on 30 June 2018 was interpreted according an object-based classification method aiming to map paddy land and irrigation systems for the Hoa Vang district; the total accuracy was 91.33% with a Kappa coefficient of 0.87. However, with the spatial resolution from the Sentinel-2A images (20 meters x 20 meters) it was difficult to classify paddy land and water from other objects within small and scattered parcel areas. This research was designed on five experimental flying zones, collecting 2,085 images by the UAV. With the very high spatial resolution data of the UAV, it was possible to clearly identify the boundaries of paddy land parcels, water sources such as rivers and lakes, and other objects such as canals and concrete irrigation systems. This classification derived from the orthogonal images from the five experimental zones using an object-based classification method, correcting the interpretation results of the Sentinel 2A images. Outcomes indicate that, the combination of UAV and RS can be applied to support precision mapping of irrigation systems for paddy land in urban agricultural areas., Nghiên cứu này được thực hiện nhằm thử nghiệm khả năng kết hợp giữa UAV với viễn thám trong hỗ trợ độ chính xác của bản đồ hệ thống nước tưới cho đất trồng lúa ở vùng nông nghiệp đô thị tại Miền trung Việt Nam. Ảnh viễn thám Sentinel- 2A thu nhận vào 30/6/2018 đã được giải đoán bằng phương pháp định hướng đối hướng để thành lập bản đồ hệ thống nguồn nước tưới cho huyện Hòa Vang vào năm 2018, với kết quả độ chính xác tổng số là 91,33% và hệ số kappa là 0,87. Mặc dù với kết quả giải đoán có độ chính xác cao nhưng với độ phân giải không gian của ảnh Sentinel-2A là 20m x 20m rất khó để phân loại được các vùng đất lúa có diện tích nhỏ và phân bố phân tán. Nghiên cứu này đã thiết kế 5 khu vực bay thử nghiệm với 2.085 ảnh để thu thập dữ liệu từ UAV. Có thể thấy rằng dữ liệu ảnh từ UAV với độ phân giải siêu cao có thể nhận diện và phân biệt được một cách rõ ràng không chỉ ranh giới của các thửa đất lúa, hệ thống nguồn nước như sông hồ, mà còn cả những đối tượng kênh mương thủy lợi nhỏ. Kết quả giải đoán các ảnh bay chụp bằng UAV sử dụng dụng phương pháp định hướng đối tượng, nghiên cứu này đã hiệu chỉnh được kết quả giải đoán ảnh Sentinel 2A. Kết quả cho thấy việc kết hợp dữ liệu viễn thám với UAV là hoàn toàn có khả năng sử dụng để hỗ trợ độ chính xác thành lập bản đồ hệ thống nguồn nước cho đất trồng lúa ở vùng nông nghiệp đô thị.

In this paper, we test to what extent Food Quality Schemes (FQS, including Geographical Indications and organic products) contribute to the social and economic sustainability of farmers and regions through employment and education. Through employment, FQS may counter the urban migration trend affecting rural regions, and help retain economic and social capital in the local region. Indeed, as FQS are often small and specialised sectors, the economic inefficiency of such businesses may translated into greater employment and social sustainability. Separately , by requiring a higher-level of quality and hence skills, FQS may encourage greater local educational attainment or skilled immigration. To test these propositions , we analyse the employment and educational outcomes of 25 FQS. Our results show that the FQS products examined have a 13% higher labour usage (labour-to-production ratio) compared to reference products, indicating that they provide greater employment. Additionally, wage levels are 32% higher in FQS compared to references. Despite providing greater employment and higher wages, profitability of FQS (i.e. how much turnover/profit is generated per employee) is nevertheless 32% higher for FQS compared to reference products, due to the ability to attract higher product prices. Finally, there is no clear link between FQS and greater (or lower) education attainment in the supply chain. Overall, our results suggest that FQS can provide a strong contribution to local employment, employee income and business profits, strengthening the social and economic sustainability of producers and regions.

In this paper, we test to what extent Food Quality Schemes (FQS, including Geographical Indications and organic products) contribute to the social and economic sustainability of farmers and regions through employment and education. Through employment, FQS may counter the urban migration trend affecting rural regions, and help retain economic and social capital in the local region. Indeed, as FQS are often small and specialised sectors, the economic inefficiency of such businesses may translated into greater employment and social sustainability. Separately , by requiring a higher-level of quality and hence skills, FQS may encourage greater local educational attainment or skilled immigration. To test these propositions , we analyse the employment and educational outcomes of 25 FQS. Our results show that the FQS products examined have a 13% higher labour usage (labour-to-production ratio) compared to reference products, indicating that they provide greater employment. Additionally, wage levels are 32% higher in FQS compared to references. Despite providing greater employment and higher wages, profitability of FQS (i.e. how much turnover/profit is generated per employee) is nevertheless 32% higher for FQS compared to reference products, due to the ability to attract higher product prices. Finally, there is no clear link between FQS and greater (or lower) education attainment in the supply chain. Overall, our results suggest that FQS can provide a strong contribution to local employment, employee income and business profits, strengthening the social and economic sustainability of producers and regions.

This research was carried out to test the potential of combining unmanned aerial vehicle (UAV) and remote sensing (RS) to support precision mapping of irrigation systems for paddy land. The study area is an urban/agricultural area of Central Vietnam. The Sentinel-2A imagery acquired on 30 June 2018 was interpreted according an object-based classification method aiming to map paddy land and irrigation systems for the Hoa Vang district; the total accuracy was 91.33% with a Kappa coefficient of 0.87. However, with the spatial resolution from the Sentinel-2A images (20 meters x 20 meters) it was difficult to classify paddy land and water from other objects within small and scattered parcel areas. This research was designed on five experimental flying zones, collecting 2,085 images by the UAV. With the very high spatial resolution data of the UAV, it was possible to clearly identify the boundaries of paddy land parcels, water sources such as rivers and lakes, and other objects such as canals and concrete irrigation systems. This classification derived from the orthogonal images from the five experimental zones using an object-based classification method, correcting the interpretation results of the Sentinel 2A images. Outcomes indicate that, the combination of UAV and RS can be applied to support precision mapping of irrigation systems for paddy land in urban agricultural areas., Nghiên cứu này được thực hiện nhằm thử nghiệm khả năng kết hợp giữa UAV với viễn thám trong hỗ trợ độ chính xác của bản đồ hệ thống nước tưới cho đất trồng lúa ở vùng nông nghiệp đô thị tại Miền trung Việt Nam. Ảnh viễn thám Sentinel- 2A thu nhận vào 30/6/2018 đã được giải đoán bằng phương pháp định hướng đối hướng để thành lập bản đồ hệ thống nguồn nước tưới cho huyện Hòa Vang vào năm 2018, với kết quả độ chính xác tổng số là 91,33% và hệ số kappa là 0,87. Mặc dù với kết quả giải đoán có độ chính xác cao nhưng với độ phân giải không gian của ảnh Sentinel-2A là 20m x 20m rất khó để phân loại được các vùng đất lúa có diện tích nhỏ và phân bố phân tán. Nghiên cứu này đã thiết kế 5 khu vực bay thử nghiệm với 2.085 ảnh để thu thập dữ liệu từ UAV. Có thể thấy rằng dữ liệu ảnh từ UAV với độ phân giải siêu cao có thể nhận diện và phân biệt được một cách rõ ràng không chỉ ranh giới của các thửa đất lúa, hệ thống nguồn nước như sông hồ, mà còn cả những đối tượng kênh mương thủy lợi nhỏ. Kết quả giải đoán các ảnh bay chụp bằng UAV sử dụng dụng phương pháp định hướng đối tượng, nghiên cứu này đã hiệu chỉnh được kết quả giải đoán ảnh Sentinel 2A. Kết quả cho thấy việc kết hợp dữ liệu viễn thám với UAV là hoàn toàn có khả năng sử dụng để hỗ trợ độ chính xác thành lập bản đồ hệ thống nguồn nước cho đất trồng lúa ở vùng nông nghiệp đô thị.

In order for High-Performance Computing (HPC) applications with data security requirements to execute in the public cloud, the cloud infrastructure must ensure the privacy and integrity of data. To meet this goal, we consider incorporating encryption in the Message Passing Interface (MPI) library. We empirically evaluate four contemporary cryptographic libraries, OpenSSL, BoringSSL, Libsodium, and CryptoPP using micro-benchmarks and NAS parallel benchmarks on two different networking technologies, 10Gbps Ethernet and 40Gbps InfiniBand. We also develop accurate models that allow us to reason about the performance of encrypted MPI communication in different situations and give guidance on how to improve encrypted MPI performance., Comment: Under review - IEEE Transactions on Dependable and Secure Computing (TDSC). 12 figures, 11 tables, and 16 pages

The cloud infrastructure must provide security for High-Performance Computing (HPC) applications of sensitive data to execute in such an environment. However, supporting security in the communication infrastructure of today's public cloud is challenging, because current networks for data centers are so fast that adding encryption can incur very significant overheads. In this work, we introduce CryptMPI, a high performance encrypted MPI library that supports communication with both integrity and privacy. We present the techniques in CryptMPI and report our benchmarking results using micro-benchmarks and NAS parallel benchmarks. The evaluation results indicate that the aforementioned techniques are effective in improving the performance of encrypted communication., Comment: Updated system description, format changed

In this paper, we test to what extent Food Quality Schemes (FQS, including Geographical Indications and organic products) contribute to the social and economic sustainability of farmers and regions through employment and education. Through employment, FQS may counter the urban migration trend affecting rural regions, and help retain economic and social capital in the local region. Indeed, as FQS are often small and specialised sectors, the economic inefficiency of such businesses may translated into greater employment and social sustainability. Separately , by requiring a higher-level of quality and hence skills, FQS may encourage greater local educational attainment or skilled immigration. To test these propositions , we analyse the employment and educational outcomes of 25 FQS. Our results show that the FQS products examined have a 13% higher labour usage (labour-to-production ratio) compared to reference products, indicating that they provide greater employment. Additionally, wage levels are 32% higher in FQS compared to references. Despite providing greater employment and higher wages, profitability of FQS (i.e. how much turnover/profit is generated per employee) is nevertheless 32% higher for FQS compared to reference products, due to the ability to attract higher product prices. Finally, there is no clear link between FQS and greater (or lower) education attainment in the supply chain. Overall, our results suggest that FQS can provide a strong contribution to local employment, employee income and business profits, strengthening the social and economic sustainability of producers and regions.