Accounting conservatism and banking expertise on board of directors

Previous studies show mixed evidence of the role of banking expertise on the board of directors on accounting conservatism. In this paper, we add to this growing literature by providing an innovative way to measure banking expertise based on life-time working history in banks of all individual directors on the board. We find that accounting conservatism is negatively affected by banking expertise on the board. Also, the results indicate that banking expertise on the board has a more pronounced impact on accounting conservatism when firms have high bankruptcy risk and when firms have high financial leverage. The evidence has some implications for boards of directors

Using a system dynamics framework to assess disease risks of pig value chains in Vietnam

In Vietnam, there are more than 4 million households producing pigs and pork. This accounts for 57% of quantity of meat consumed. One of the most critical constraints to pig production is the presence of animal disease. Pig disease outbreaks are a regular occurrence in various parts of the country, with the industry affected by diseases such as foot and mouth disease, porcine reproductive and respiratory syndrome, classical swine fever, porcine high fever disease, and swine influenza. In addition, food safety issues related to pig diseases and pork-borne diseases have also increasingly become more important concerns for consumers. Recent studies have shown significant changes in consumption behaviour in response to disease outbreaks. For instance, at least half of urban consumers stop consuming pork in times of pig disease epidemics and/or shift consumption to other meat substitutes such as poultry or fish. Disease risks thus have both public health and livelihoods impacts that are important to understand for appropriate policy and practice response. A proposed methodology for investigating disease risks uses a system dynamics analysis framework. System dynamics models are particularly relevant in the study of livestock systems, as they capture the diverse actors and feedbacks present in value chains and their interface with disease risk and behaviour. A system dynamics model is developed that will describe different scenarios of disease risks and the consequences of different interventions to mitigate these risks. Data from a sample of 1000 farmers and value chain actors including all actors in the pig value chain in Vietnam was collected with support from an ACIAR-funded project on Reducing Disease Risks and Improving Food Safety in Smallholder Pig Value Chains in Vietnam. We propose to test the hypotheses that disease risk is affected by type of production system, feeding system and types of feed uses, access to inputs and services, and selected socio-demographic variables associated with farmers and location

Market-based approaches to food safety and animal health interventions: Lessons from smallholder pig value chains in Vietnam

Food safety and animal health issues are increasingly important constraints to smallholder pig production in Viet Nam. Recent studies have highlighted the significant prevalence of animal disease and food‐borne pathogens inherent within the Vietnamese pig sector. These in turn have important negative livelihoods effects on smallholder pig producers and other value chain actors, as well as important public health impacts. An important research gap is in identifying ex‐ante appropriate market‐based policy responses that take into account the tradeoffs between improved animal health and food safety outcomes and their associated costs for different value chain actors as a means of developing chain‐level solutions for their control. In this paper, we constructed a system dynamics model of the pig value chain that combines a detailed model of herd production and marketing with modules on short‐ and long‐term investment in pig capacity, and decisions by value chain actors to adopt different innovations. The model further highlights the feedbacks between different actors in the chain to identify both the potential entry points for upgrading food safety and animal health as well as potential areas of tension within the chain that may undermine uptake. Model results demonstrate that interventions at nodal levels (e.g. only at farm or slaughterhouse level) are less cost‐effective and sustainable than those that jointly enhance incentives for control across the value chain, as weak links downstream undermine the ability of producers to sustain good health practices

Harmonization of Landsat and Sentinel 2 for Crop Monitoring in Drought Prone Areas: Case Studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon)

Proper satellite-based crop monitoring applications at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions Sentinel 2 (ESA) and Landsat 7/8 (NASA) provides this unprecedented opportunity at a global scale; however, this is rarely implemented because these procedures are data demanding and computationally intensive. This study developed a robust stream processing for the harmonization of Landsat 7, Landsat 8 and Sentinel 2 in the Google Earth Engine cloud platform, connecting the benefit of coherent data structure, built-in functions and computational power in the Google Cloud. The harmonized surface reflectance images were generated for two agricultural schemes in Bekaa (Lebanon) and Ninh Thuan (Vietnam) during 2018–2019. We evaluated the performance of several pre-processing steps needed for the harmonization including the image co-registration, Bidirectional Reflectance Distribution Functions correction, topographic correction, and band adjustment. We found that the misregistration between Landsat 8 and Sentinel 2 images varied from 10 m in Ninh Thuan (Vietnam) to 32 m in Bekaa (Lebanon), and posed a great impact on the quality of the final harmonized data set if not treated. Analysis of a pair of overlapped L8-S2 images over the Bekaa region showed that, after the harmonization, all band-to-band spatial correlations were greatly improved. Finally, we demonstrated an application of the dense harmonized data set for crop mapping and monitoring. An harmonic (Fourier) analysis was applied to fit the detected unimodal, bimodal and trimodal shapes in the temporal NDVI patterns during one crop year in Ninh Thuan province. The derived phase and amplitude values of the crop cycles were combined with max-NDVI as an R-G-B false composite image. The final image was able to highlight croplands in bright colors (high phase and amplitude), while the non-crop areas were shown with grey/dark (low phase and amplitude). The harmonized data sets (with 30 m spatial resolution) along with the Google Earth Engine scripts used are provided for public use

Physical Layer Security: Detection of Active Eavesdropping Attacks by Support Vector Machines

This paper presents a framework for converting wireless signals into structured datasets, which can be fed into machine learning algorithms for the detection of active eavesdropping attacks at the physical layer. More specifically, a wireless communication system, which consists of K legal users, one access point (AP) and one active eavesdropper, is considered. To cope with the eavesdropper who breaks into the system during the uplink phase, we first build structured datasets based on several different features. We then apply support vector machine (SVM) classifiers and one-class SVM classifiers to those structured datasets for detecting the presence of eavesdropper. Regarding the data, we first process received signals at the AP and then define three different features (i.e., MEAN, RATIO and SUM) based on the post-processing signals. Noticeably, our three defined features are formulated such that they have relevant statistical properties. Enabling the AP to simulate the entire process of transmission, we form the so-called artificial training data (ATD) that is used for training SVM (or one-class SVM) models. While SVM is preferred in the case of having perfect channel state information (CSI) of all channels, one-class SVM is preferred in the case of having only the CSI of legal users. We also evaluate the accuracy of the trained models in relation to the choice of kernel functions, the choice of features, and the change of eavesdropper's power. Numerical results show that the accuracy is relatively sensitive to adjusting parameters. Under some settings, SVM classifiers (or even one-class SVM) can bring about the accuracy of over 90%.Comment: All versions on this site are withdrawn because of their serious mistakes. Moreover, the contributions of the co-authors were not considered carefully. Two co-authors have little contributions, which cannot constitute any main contribution. It was a mistake when the first author forgot to update the actual authors, and he hurried to upload the incomplete and flaw file

Participatory agro-climate information services: A key component in climate resilient agriculture

The brief promotes participatory agro-climate information services as a key component in achieving climate-smart agriculture. The brief emphasizes that actionable agro-climate information starts with—and responds to—gender-based needs of farmers, integrated at all stages of the value chain. Timely forecasts and accurate agroclimate advisories have been proven to provide farmers with production, adaptation, and mitigation benefits

Differentiable Physics-based Greenhouse Simulation

We present a differentiable greenhouse simulation model based on physical processes whose parameters can be obtained by training from real data. The physics-based simulation model is fully interpretable and is able to do state prediction for both climate and crop dynamics in the greenhouse over very a long time horizon. The model works by constructing a system of linear differential equations and solving them to obtain the next state. We propose a procedure to solve the differential equations, handle the problem of missing unobservable states in the data, and train the model efficiently. Our experiment shows the procedure is effective. The model improves significantly after training and can simulate a greenhouse that grows cucumbers accurately.Comment: Accepted at the Machine Learning and the Physical Sciences workshop, NeurIPS 2022. 7 pages, 2 figure