Impacts of farmer-based training in seed production in Vietnam

Key words: farmer seed production school, farm-saved seed, formal seed sector, impact assessment, improved practice, local practice, rice (Oryza sativa), seed production, seed quality, Vietnam Rice (Oryza sativa) is the most important food and cash crop of Vietnam. It is cultivated in all provinces of the country since ancient times. Farm-saved seed is the most important seed source covering more than 80% of the farmers’ seed needs. However, farmers not always use the best techniques of producing and selecting seeds. Inadequate seed quality is an important yield limiting factor in rice production. To improve the farmers’ capacity to produce, process, store and use good rice seed, the farmer seed production school (FSPS) training programme was conducted in seven provinces of Vietnam during the period 2003−2007. The study reported in this thesis took place in four out of those seven provinces, i.e. Nam Dinh, Nghe An, Binh Dinh and Dong Thap. The objective was to assess to what extent farmers’ knowledge in seed production practices and seed quality management had increased and whether that knowledge increase was reflected in an increase in potential rice yields and profits, and in diffusion of retained practices after training to other farmers in communities. A long seed production training programme with the farmer field school approach was combined with field demonstrations including plots with either local practices or improved practices which were conducted in each FSPS. We recorded and analysed data on on-farm demonstrations at 429 FSPSs and on ex-ante and ex-post tests of knowledge at the FSPSs. Moreover, we carried out a survey among 240 rural households. Results of the study indicate that some rice varieties were better adopted in the farming systems than other varieties: well adopted ones were KD18 in both Nam Dinh and Nghe An province and OM1490, Ai32 and MO2718 in Binh Dinh and Dong Thap. With local practices in the farm-saved seed system of the transplanted rice crop, farmers used old seedlings, planted many seedlings per hill, planted too many or too few plants per unit area and applied unbalanced quantities of fertilizers; for the directly sown crop farmers used high seed rates in the traditional system. Rice yields showed larger differences between local practices and improved practices in the dry season than in the wet season all across Vietnam. With improved practices at the FSPSs, rice yields were 8.5% higher in the wet season and 13.6% higher in the dry season; additional profits associated with the improved practice in both the dry and wet seasons averaged 212 US$ ha-1. The majority of the FSPS-farmers moved from food production to seed production, reduced seed rates by about 50%, and used high quality seed to produce seeds with much better quality. More important is that the FSPS-farmers diffused improved practices (79%) and shared good seeds (57% of respondents) with other farmers in their communities to help other rice growers to improve their productivity. A large proportion of non-FSPS farmers learned and applied improved practices for rice production through neighbouring FSPS-farmers within the community. Besides, evaluation in acquired knowledge during training showed that FSPS-farmers with lower scores (<20%) in the ex-ante test realized an enormous improvement of 55.4% points in the ex-post test. There was a clear trend: the higher the scores in the ex-ante test, the smaller the increase in the score, suggesting that the tests provided insight into the knowledge gaps for improvement in training programmes. The FSPS is considered as a good training model for farmers. The FSPS-farmers well retained the acquired knowledge and applied the improved practices to enhance the farm-saved seed system in the project provinces. The community capacity was strengthened through establishing seed clubs by FSPS-farmers. It created a seed supply and production network to ensure seed security for small farmer’s seed needs in the rural areas. Thus, it promoted seed policies to strengthen the informal seed system in Vietnam. Impacts of farmer-based training programme in seed production illustrate that in a country like Vietnam where more than seventy percent of the population live in rural areas and depend on agricultural production, farmer education is a very effective way for agricultural development

Extensions and Applications of Mean Length Mortality Estimators for Assessment of Data-Limited Fisheries

For data-limited fisheries, length-based mortality estimators are attractive as alternatives to age-structured models due to the simpler data requirements and ease of use of the former. This dissertation develops new extensions of mean length-based mortality estimators and applies them to federally-managed stocks in the southeastern U.S. and U.S. Caribbean. Chapter 1 presents a review of length-based methods from the literature. Common themes regarding the methodology, assumptions, and diagnostics in these length-based methods are discussed. In Chapter 2, a simulation study evaluates the performance of the length-converted catch curve (LCCC), Beverton-Holt equation (BHE), and Length Based-Spawner Potential Ratio (LB-SPR) over a range of scenarios. Although the LCCC and BHE are older methods than LB-SPR, the former outperformed LB-SPR in many scenarios in the simulation. Overall, it was found that the three length-based mortality estimators are less likely to perform well for low M/K stocks (M/K is the ratio of the natural mortality rate and the von Bertalanffy growth parameter; this ratio describes different life history strategies of exploited fish and invertebrate populations), while various decision rules for truncating the length data for the LCCC and BHE were less influential. In Chapter 3, a multi-stock model is developed for the non-equilibrium mean length-based mortality estimator and then applied to the deepwater snapper complex in Puerto Rico. The multispecies estimator evaluates synchrony in changes to the mean length of multiple species in a complex. Synchrony in mortality can reduce the number of estimated parameters and borrows information from more informative species to lesser sampled species in the model. In Chapter 4, a new method is developed to estimate mortality from both mean lengths and catch rates (MLCR), which is an extension of the mean length-only (ML) model. to do so, the corresponding behavior for the catch rate following step-wise changes in mortality is derived. Application of both models to Puerto Rico mutton snapper shows that the MLCR model can provide more information to support a more complex mortality history with the two data types compared to the ML model. In Chapter 5, a suite of mean length-based mortality estimators is applied to six stocks (four in the Gulf of Mexico and two in the U.S. Atlantic) recently assessed with age-structured models. There was general agreement in historical mortality trends between the age-structured models and the mean length-based methods, although there were some discrepancies which are discussed. All models also agreed on the overfishing status in the terminal year of the assessment of the six stocks considered here when the mortality rates were compared relative to reference points. This dissertation develops new length-based assessment methods which consider multiple sources of data. The review guides prospective users on potential choices for assessment with length-based methods. Issues and diagnostics associated with the methods are also discussed in the review and highlighted in the example applications

Extracting Symbolic Representations Learned by Neural Networks

Understanding what neural networks learn from training data is of great interest in data mining, data analysis, and critical applications, and in evaluating neural network models. Unfortunately, the product of neural network training is typically opaque matrices of floating point numbers that are not obviously understandable. This difficulty has inspired substantial past research on how to extract symbolic, human-readable representations from a trained neural network, but the results obtained so far are very limited (e.g., large rule sets produced). This problem occurs in part due to the distributed hidden layer representation created during learning. Most past symbolic knowledge extraction algorithms have focused on progressively more sophisticated ways to cluster this distributed representation. In contrast, in this dissertation, I take a different approach. I develop ways to alter the error backpropagation neural network training process itself so that it creates a representation of what has been learned in the hidden layer activation space that is more amenable to existing symbolic representation extraction methods. In this context, this dissertation research makes four main contributions. First, modifications to the backpropagation learning procedure are derived mathematically, and it is shown that these modifications can be accomplished as local computations. Second, the effectiveness of the modified learning procedure for feedforward networks is established by showing that, on a set of benchmark tasks, it produces rule sets that are substantially simpler than those produced by standard backpropagation learning. Third, this approach is extended to simple recurrent networks, and experimental evaluation shows remarkable reduction in the sizes of the finite state machines extracted from the recurrent networks trained using this approach. Finally, this method is further modified to work on echo state networks, and computational experiments again show significant improvement in finite state machine extraction from these networks. These results clearly establish that principled modification of error backpropagation so that it constructs a better separated hidden layer representation is an effective way to improve contemporary symbolic extraction methods

Developing skilled labour : an analysis of the major factors which enable and hinder employee training in construction companies in Vietnam

This thesis makes a contribution to the literature on training practices and attitudes, within the context of the Vietnamese construction industry. Drawing on the international scholarship in the area of training practices and attitudes, the thesis tests a series of hypotheses about the nature of training in Vietnam. The conceptual framework for the thesis utilised the established methods and insights from a range of studies on the relationship between training provision, company size, company ownership, and attitudes of managers. Within this scholarship, the nature of those relationships is still unclear and under-theorised in particular contexts. Specifically, the international research suggest particular patterns of training provision, namely that larger companies will be more likely to provide training than smaller companies and that larger companies will be more likely to provide off-the-job training. It was unclear, however, whether these findings, and the key theoretical premises that underpin them, had application to the Vietnamese construction industry. This thesis, therefore, makes a valuable contribution to this area of research in attempting to deal with these issues. In the context of the discussions and debates within the literature, the specific contribution of the thesis spans a number of dimensions. First, it has generated, presented, and analysed a new data set about the general characteristics of training practices in the Vietnamese construction industry. Based on a survey of 510 construction companies in Ho Chi Minh City, this is the largest database to analyse training within Vietnam, and the largest database to document the construction industry. Second, it has applied statistical analysis and methods consistent with examinations of training practices evident in the international scholarship, and thereby extended the study of the construction industry in Vietnam beyond anecdotal information and simple descriptive statistics. In this sense, the use of such a large database to focus on one industry is particularly valuable. Third, it has interpreted these results and findings both in the context of the international literature, but also noted the relevant features of the local context in which these results are observed, i.e., an economy that is in transition from high level of central control to one that is developing a market orientation. The specific findings of the thesis are interesting and extend across the range of issues emphasised in the international literature on training. The thesis affirms the role of training in developing skilled labour in the Vietnamese construction industry and indicates that training fulfills this function of developing human capital for different sized companies and companies with different ownership structures. The thesis demonstrates that company size has some correlation with training provision in the industry, although there is some divergence with the international literature in this regard. In the context of a transitioning economy and increased levels of foreign investment, the thesis makes a key contribution in understanding the pattern of training provision by company ownership type and examining in particular the impact of foreign capital in shaping training provision in Vietnam. Indeed, the results demonstrate that ownership type exercises a distinctive effect on training provision with a higher evidence of training in foreign invested companies than in other company types. In considering the attitudes of managers within the construction industry, the results show that managers of construction companies have positive attitudes towards training and the utility of training for a series of strategic purposes. These attitudes were evident despite the absence of a sophisticated training infrastructure. Company size was not a significant differentiating factor in the attitudes of managers. Company ownership was a greater predictor of the strength of positive attitudes by managers with the managers of foreign-invested companies demonstrating more positive attitudes towards training than managers in other company types. The findings of the thesis facilitate a more nuanced understanding of key premises in the research scholarship about training. The fact that some of the patterns commonly observed in the international literature are not entrenched in the Vietnamese construction industry identifies the benefits of industry-based research. However, these findings might also reflect the transition of the Vietnamese economy more generally from a centralised, controlled economy to a market economy, noting that this transition is unlikely to be uniform and that companies will move to adopt human resource development practices at different rates. One final issue identified and discussed by the thesis is that despite the affirmations of training demonstrated by manager attitudes, the Vietnamese construction industry in Ho Chi Minh City confronts persistent skill shortages. This dissonance contributes to the development of recommendations to policy makers, and to the industry leaders more generally, as to the potential to facilitate and support successful training policies and human resource functions for the range of construction companies in Vietnam

Mining Explicit and Implicit Relationships in Data Using Symbolic Regression

Identification of implicit and explicit relations within observed data is a generic problem commonly encountered in several domains including science, engineering, finance, and more. It forms the core component of data analytics, a process of discovering useful information from data sets that are potentially huge and otherwise incomprehensible. In industries, such information is often instrumental for profitable decision making, whereas in science and engineering it is used to build empirical models, propose new or verify existing theories and explain natural phenomena. In recent times, digital and internet based technologies have proliferated, making it viable to generate and collect large amount of data at low cost. This inturn has resulted in an ever growing need for methods to analyse and draw interpretations from such data quickly and reliably. With this overarching goal, this thesis attempts to make contributions towards developing accurate and efficient methods for discovering such relations through evolutionary search, a method commonly referred to as Symbolic Regression (SR). A data set of input variables x and a corresponding observed response y is given. The aim is to find an explicit function y = f (x) or an implicit function f (x, y) = 0, which represents the data set. While seemingly simple, the problem is challenging for several reasons. Some of the conventional regression methods try to “guess” a functional form such as linear/quadratic/polynomial, and attempt to do a curve-fitting of the data to the equation, which may limit the possibility of discovering more complex relations, if they exist. On the other hand, there are meta-modelling techniques such as response surface method, Kriging, etc., that model the given data accurately, but provide a “black-box” predictor instead of an expression. Such approximations convey little or no insights about how the variables and responses are dependent on each other, or their relative contribution to the output. SR attempts to alleviate the above two extremes by providing a structure which evolves mathematical expressions instead of assuming them. Thus, it is flexible enough to represent the data, but at the same time provides useful insights instead of a black-box predictor. SR can be categorized as part of Explainable Artificial Intelligence and can contribute to Trustworthy Artificial Intelligence. The works proposed in this thesis aims to integrate the concept of “semantics” deeper into Genetic Programming (GP) and Evolutionary Feature Synthesis, which are the two algorithms usually employed for conducting SR. The semantics will be integrated into well-known components of the algorithms such as compactness, diversity, recombination, constant optimization, etc. The main contribution of this thesis is the proposal of two novel operators to generate expressions based on Linear Programming and Mixed Integer Programming with the aim of controlling the length of the discovered expressions without compromising on the accuracy. In the experiments, these operators are proven to be able to discover expressions with better accuracy and interpretability on many explicit and implicit benchmarks. Moreover, some applications of SR on real-world data sets are shown to demonstrate the practicality of the proposed approaches. Besides, in related to practical problems, how GP can be applied to effectively solve the Resource Constrained Scheduling Problems is also presented

Can positron emission tomography - computed tomography imaging predict of metastases in patients with small cell lung cancer

Background: Small-cell lung cancer (SCLC) accounts for 15%-20% of all lung cancer cases. positron emission tomography - computed tomography (PET/CT) has become increasingly used as an initial staging tool in patients with SCLC. We aimed to explore the relationships between primary tumor 18F-FDG uptake measured as the maximum standardized uptake value (SUV max) and clinical stage at PET/CT for small cell lung cancer patients (SCLC).Methods: Patients with SCLC who underwent 18F-FDG PET/CT scans before the treatment were included in the study at Bach Mai hospital of Vietnam, from November 2014 to May 2018. The primary tumor and secondary lesion SUVmax was calculated; the tumor size was measured; the TNM status was determined mainly by FDG PET/CT imaging according to The 8th Edition of the TNM Classification for Lung Cancer were recorded. An evaluation was made of the linear relationship between tumor size, T stage, N stage, and M stages of the patients and their SUVmax using Spearman’s correlation.Results: Total 37 cases (34 men and 3 women; age range 38 - 81 years, median 64 years) were analyzed. The average of primary tumor size and SUVmax were 5.95±2.77 cm and 10.21±4.75, respectively. The SUVmax of primary tumor is significantly greater than that of nodal and distant organ metastasis (10.21±4.75 vs 8.20±4.35 and 6.44±3.17, p<0.01). There was a moderate correlation between SUVmax and tumor size (r =0.596, p<0.001), tumor stage (r = 0.502, p<0.01) but not significant with nodal stage (r =-0.218, p=0.194), metastasis stage (r = -0.055, p=0.747), and overall stage (r=-0.060, p=0.725).Conclusions: SUVmax was significantly correlated with tumor size, but not with distant metastases or lymph node involvement. Therefore, SUVmax on positron emission tomography is not predictive of the presence of metastases in patients with SCLC

Impact of 18F-FDG PET/CT on the staging of patients with non-small cell lung cancer

Background: Non-small cell lung cancer (NSCLC) accounts for approximately 80% of new diagnoses of pulmonary carcinoma. This study investigated the correlation between 18 F-fluorodeoxyglucose uptake in computerized tomography integrated positron emission tomography and tumor size, lymph node metastasis, and distant metastasis in patients with NSCLC.Methods: The records of 318 NSCLC patients (220 male, 98 females; mean age 60.94 years) were evaluated retrospectively.Results: 278 cases were adenocarcinomas; 28 squamous cell carcinomas; and 12 large cell carcinoma. When the cases were categorized according to tumor size (group 1, ≤3 cm; group 2, >3 and ≤5 cm; group 3, >5 cm), the maximum standardized uptake value (SUVmax) was significantly lower in groups 1 and 2 compared with group 3 (p<0,001 for each). Considering all cases, tumor SUVmax was not correlated with age, gender or histopathological type. Lymph node metastases were seen in 250 cases: 80.2% of these were adenocarcinomas, 71.4% squamous cell carcinomas, and 58.3% large cell carcinomas. Neither lymph node involvement nor distant metastases were correlated with tumor SUVmax, although lymph node size was positively correlated with lymph node SUVmax (r=0.758; p<0.001).Conclusions: SUVmax was significantly associated with tumor size, but not with distant metastases or lymph node involvement. Therefore, SUVmax on positron emission tomography is not predictive of the presence of metastases.

Using positron emission tomography - computed tomography imaging to distinguish of metastatic disease from second primary lung tumours in patients with non-small cell lung cancer

Background: In NSCLC patients with multiple lesions, the differentiation between metastases and second primary tumours has significant therapeutic and prognostic implications. The aim of this retrospective study was to investigate the potential of 18F-FDG PET to discriminate metastatic disease from second primary lung tumours.Methods: Of 318 NSCLC patients between November 2015 and October 2018 at Bach Mai hospital, patients with a synchronous second primary lung cancer were selected. Patients with metastatic disease involving the lungs served as the control group. Maximum standardized uptake values (SUVs) measured with 18F-FDG PET were determined for two tumours in each patient. The SUVmax was determined and compared between the second primary group and metastatic disease group. Receiver-operating characteristic (ROC) curve analysis was performed to determine the sensitivity and specificity of the SUVmax for an optimal cut-off value.Results: A total of 81 NSCLC patients (44 metastatic disease, 37 second primary cancer) were included for analysis. The SUVmax was significantly higher in patients with second primary cancer than in those with metastatic disease (7.53±4.33 vs 4.35±2.58, respectively, p<0.001). The area under the ROC curve was 0.81 and the odds ratio for the optimal cut-off was 7.52.Conclusions: SUVmax from 18F-FDG PET images can be helpful in differentiating metastatic disease from second primary tumours in patients with synchronous pulmonary lesions. Further studies are warranted to confirm the consistency of these results.

A Genetic Algorithm for Power-Aware Virtual Machine Allocation in Private Cloud

Energy efficiency has become an important measurement of scheduling algorithm for private cloud. The challenge is trade-off between minimizing of energy consumption and satisfying Quality of Service (QoS) (e.g. performance or resource availability on time for reservation request). We consider resource needs in context of a private cloud system to provide resources for applications in teaching and researching. In which users request computing resources for laboratory classes at start times and non-interrupted duration in some hours in prior. Many previous works are based on migrating techniques to move online virtual machines (VMs) from low utilization hosts and turn these hosts off to reduce energy consumption. However, the techniques for migration of VMs could not use in our case. In this paper, a genetic algorithm for power-aware in scheduling of resource allocation (GAPA) has been proposed to solve the static virtual machine allocation problem (SVMAP). Due to limited resources (i.e. memory) for executing simulation, we created a workload that contains a sample of one-day timetable of lab hours in our university. We evaluate the GAPA and a baseline scheduling algorithm (BFD), which sorts list of virtual machines in start time (i.e. earliest start time first) and using best-fit decreasing (i.e. least increased power consumption) algorithm, for solving the same SVMAP. As a result, the GAPA algorithm obtains total energy consumption is lower than the baseline algorithm on simulated experimentation.Comment: 10 page