Intelligent prediction of the stress-strain response of intact and jointed rocks

An application of Artificial Neural Networks for predicting the stress-strain response of jointed rocks under different confining pressures is presented in this paper. Rocks of different compressive strength with different joint properties (frequency, orientation and strength of joints) are considered in this study. The database for training the neural network is formed from the results of triaxial compression tests on different intact and jointed rocks with different joint properties tested at different confining pressures reported by various researchers in the literature. The network was trained using a three-layered network with the feed-forward back propagation algorithm.About 85% of the data was used for training and the remaining 15% was used for testing the network. Results from the analyses demonstrated that the neural network approach is effective in capturing the stress-strain behaviour of intact rocks and the complex stress-strain behaviour of jointed rocks. A single neural network is demonstrated to be capable of predicting the stress-strain response of different jointed rocks, whose intact strength varies from 11.32 MPa to 123 MPa, spacing of joints varies from 10 cm to 100 cm. and confining pressures range from 0 to 13.8 MPa. (C) 2010 Elsevier Ltd. All rights reserved.

Microfinance, Efficiency and Agricultural Production in Bangladesh

The objectives of this study were to make a detailed and systematic empirical analysis of microfinance borrowers and non-borrowers in Bangladesh and also examine how efficiency measures are influenced by the access to agricultural microfinance. In the empirical analysis, this study used both parametric and non-parametric frontier approaches to investigate differences in efficiency estimates between microfinance borrowers and non-borrowers. This thesis, based on five articles, applied data obtained from a survey of 360 farm households from north-central and north-western regions in Bangladesh. The methods used in this investigation involve stochastic frontier (SFA) and data envelopment analysis (DEA) in addition to sample selectivity and limited dependent variable models. In article I, technical efficiency (TE) estimation and identification of its determinants were performed by applying an extended Cobb-Douglas stochastic frontier production function. The results show that farm households had a mean TE of 83% with lower TE scores for the non-borrowers of agricultural microfinance. Addressing institutional policies regarding the consolidation of individual plots into farm units, ensuring access to microfinance, extension education for the farmers with longer farming experience are suggested to improve the TE of the farmers. In article II, the objective was to assess the effects of access to microfinance on household production and cost efficiency (CE) and to determine the efficiency differences between the microfinance participating and non-participating farms. In addition, a non-discretionary DEA model was applied to capture directly the influence of microfinance on farm households production and CE. The results suggested that under both pooled DEA models and non-discretionary DEA models, farmers with access to microfinance were significantly more efficient than their non-borrowing counterparts. Results also revealed that land fragmentation, family size, household wealth, on farm-training and off farm income share are the main determinants of inefficiency after effectively correcting for sample selection bias. In article III, the TE of traditional variety (TV) and high-yielding-variety (HYV) rice producers were estimated in addition to investigating the determinants of adoption rate of HYV rice. Furthermore, the role of TE as a potential determinant to explain the differences of adoption rate of HYV rice among the farmers was assessed. The results indicated that in spite of its much higher yield potential, HYV rice production was associated with lower TE and had a greater variability in yield. It was also found that TE had a significant positive influence on the adoption rates of HYV rice. In article IV, we estimated profit efficiency (PE) and profit-loss between microfinance borrowers and non-borrowers by a sample selection framework, which provided a general framework for testing and taking into account the sample selection in the stochastic (profit) frontier function analysis. After effectively correcting for selectivity bias, the mean PE of the microfinance borrowers and non-borrowers were estimated at 68% and 52% respectively. This suggested that a considerable share of profits were lost due to profit inefficiencies in rice production. The results also demonstrated that access to microfinance contributes significantly to increasing PE and reducing profit-loss per hectare land. In article V, the effects of credit constraints on TE, allocative efficiency (AE) and CE were assessed while adequately controlling for sample selection bias. The confidence intervals were determined by the bootstrap method for both samples. The results indicated that differences in average efficiency scores of credit constrained and unconstrained farms were not statistically significant although the average efficiencies tended to be higher in the group of unconstrained farms. After effectively correcting for selectivity bias, household experience, number of dependents, off-farm income, farm size, access to on farm training and yearly savings were found to be the main determinants of inefficiencies. In general, the results of the study revealed the existence substantial technical, allocative, economic inefficiencies and also considerable profit inefficiencies. The results of the study suggested the need to streamline agricultural microfinance by the microfinance institutions (MFIs), donor agencies and government at all tiers. Moreover, formulating policies that ensure greater access to agricultural microfinance to the smallholder farmers on a sustainable basis in the study areas to enhance productivity and efficiency has been recommended. Key Words: Technical, allocative, economic efficiency, DEA, Non-discretionary DEA, selection bias, bootstrapping, microfinance, Bangladesh.

Recommendations for driver licensing and traffic law enforcement in India aiming to improve road safety

During the last decade, developing countries such as India have been exhibiting rapid increase in human population and vehicles, and increase in road accidents. Inappropriate driving behaviour is considered one of the major causes of road accidents in India as compared to defective geometric design of pavement or mechanical defects in vehicles. It can result in conditions such as lack of lane discipline, disregard to traffic laws, frequent traffic violations, increase in crashes due to self-centred driving, etc. It also demotivates educated drivers from following good driving practices. Hence, improved driver behaviour can be an effective countermeasure to reduce the vulnerability of road users and inhibit crash risks. This article highlights improved driver behaviour through better driver education, driver training and licensing procedures along with good on-road enforcement; as an effective countermeasure to ensure road safety in India. Based on the review and analysis, the article also recommends certain measures pertaining to driver licensing and traffic law enforcement in India aimed at improving road safety.

Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources

Ground management problems are typically solved by the simulation-optimization approach where complex numerical models are used to simulate the groundwater flow and/or contamination transport. These numerical models take a lot of time to solve the management problems and hence become computationally expensive. In this study, Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) models were developed and coupled for the management of groundwater of Dore river basin in France. The Analytic Element Method (AEM) based flow model was developed and used to generate the dataset for the training and testing of the ANN model. This developed ANN-PSO model was applied to minimize the pumping cost of the wells, including cost of the pipe line. The discharge and location of the pumping wells were taken as the decision variable and the ANN-PSO model was applied to find out the optimal location of the wells. The results of the ANN-PSO model are found similar to the results obtained by AEM-PSO model. The results show that the ANN model can reduce the computational burden significantly as it is able to analyze different scenarios, and the ANN-PSO model is capable of identifying the optimal location of wells efficiently.

Subject independent human action recognition using spatio-depth information and meta-cognitive RBF network

In this paper, we present a machine learning approach for subject independent human action recognition using depth camera, emphasizing the importance of depth in recognition of actions. The proposed approach uses the flow information of all 3 dimensions to classify an action. In our approach, we have obtained the 2-D optical flow and used it along with the depth image to obtain the depth flow (Z motion vectors). The obtained flow captures the dynamics of the actions in space time. Feature vectors are obtained by averaging the 3-D motion over a grid laid over the silhouette in a hierarchical fashion. These hierarchical fine to coarse windows capture the motion dynamics of the object at various scales. The extracted features are used to train a Meta-cognitive Radial Basis Function Network (McRBFN) that uses a Projection Based Learning (PBL) algorithm, referred to as PBL-McRBFN, henceforth. PBL-McRBFN begins with zero hidden neurons and builds the network based on the best human learning strategy, namely, self-regulated learning in a meta-cognitive environment. When a sample is used for learning, PBLMcRBFN uses the sample overlapping conditions, and a projection based learning algorithm to estimate the parameters of the network. The performance of PBL-McRBFN is compared to that of a Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers with representation of every person and action in the training and testing datasets. Performance study shows that PBL-McRBFN outperforms these classifiers in recognizing actions in 3-D. Further, a subject-independent study is conducted by leave-one-subject-out strategy and its generalization performance is tested. It is observed from the subject-independent study that McRBFN is capable of generalizing actions accurately. The performance of the proposed approach is benchmarked with Video Analytics Lab (VAL) dataset and Berkeley Multimodal Human Action Database (MHAD). (C) 2013 Elsevier Ltd. All rights reserved.

Detection of the closure-burst transitions of stops and affricates in continuous speech using the plosion index

Automatic and accurate detection of the closure-burst transition events of stops and affricates serves many applications in speech processing. A temporal measure named the plosion index is proposed to detect such events, which are characterized by an abrupt increase in energy. Using the maxima of the pitch-synchronous normalized cross correlation as an additional temporal feature, a rule-based algorithm is designed that aims at selecting only those events associated with the closure-burst transitions of stops and affricates. The performance of the algorithm, characterized by receiver operating characteristic curves and temporal accuracy, is evaluated using the labeled closure-burst transitions of stops and affricates of the entire TIMIT test and training databases. The robustness of the algorithm is studied with respect to global white and babble noise as well as local noise using the TIMIT test set and on telephone quality speech using the NTIMIT test set. For these experiments, the proposed algorithm, which does not require explicit statistical training and is based on two one-dimensional temporal measures, gives a performance comparable to or better than the state-of-the-art methods. In addition, to test the scalability, the algorithm is applied on the Buckeye conversational speech corpus and databases of two Indian languages. (C) 2014 Acoustical Society of America.

A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation: Application to Image Denoising

In big data image/video analytics, we encounter the problem of learning an over-complete dictionary for sparse representation from a large training dataset, which cannot be processed at once because of storage and computational constraints. To tackle the problem of dictionary learning in such scenarios, we propose an algorithm that exploits the inherent clustered structure of the training data and make use of a divide-and-conquer approach. The fundamental idea behind the algorithm is to partition the training dataset into smaller clusters, and learn local dictionaries for each cluster. Subsequently, the local dictionaries are merged to form a global dictionary. Merging is done by solving another dictionary learning problem on the atoms of the locally trained dictionaries. This algorithm is referred to as the split-and-merge algorithm. We show that the proposed algorithm is efficient in its usage of memory and computational complexity, and performs on par with the standard learning strategy, which operates on the entire data at a time. As an application, we consider the problem of image denoising. We present a comparative analysis of our algorithm with the standard learning techniques that use the entire database at a time, in terms of training and denoising performance. We observe that the split-and-merge algorithm results in a remarkable reduction of training time, without significantly affecting the denoising performance.

Language model combination and adaptation using weighted finite state transducers

In speech recognition systems language model (LMs) are often constructed by training and combining multiple n-gram models. They can be either used to represent different genres or tasks found in diverse text sources, or capture stochastic properties of different linguistic symbol sequences, for example, syllables and words. Unsupervised LM adaptation may also be used to further improve robustness to varying styles or tasks. When using these techniques, extensive software changes are often required. In this paper an alternative and more general approach based on weighted finite state transducers (WFSTs) is investigated for LM combination and adaptation. As it is entirely based on well-defined WFST operations, minimum change to decoding tools is needed. A wide range of LM combination configurations can be flexibly supported. An efficient on-the-fly WFST decoding algorithm is also proposed. Significant error rate gains of 7.3% relative were obtained on a state-of-the-art broadcast audio recognition task using a history dependently adapted multi-level LM modelling both syllable and word sequences. ©2010 IEEE.

Assistance in poverty alleviation through improved aquatic resources management in Asia-Pacific: DoF/NACA-STREAM/FAO Workshop on livelihoods approaches and analysis. Yangon, Union of Myanmar, 11-15 May 2004

This is the report of the “DoF/NACA-STREAM/FAO Workshop on Livelihoods Approaches and Analysis” that was conducted in Yangon, Union of Myanmar from 11-15 May 2004. The purpose of the workshop was to develop and document mechanisms for training in livelihoods approaches and analysis, and to build national capacity to conduct livelihoods studies. The workshop in Yangon was the first STREAM event in Myanmar, with colleagues coming to participate from Yangon and many Divisions and States throughout the country. The workshop in Yangon was the fourth in a series, the first of which was held in Iloilo City, Philippines, in November 2003, the second in Ranchi, India, in February 2004, and the third in Vientiane, Lao PDR in March 2004. A subsequent workshop will take place in Yunnan, China. The objectives of the workshop were to: Understand issues of interest to people whose livelihoods include aquatic resources management, especially those with limited resources Build “(national) livelihoods teams” to do livelihoods analyses and training, and share their experiences with communities and other stakeholders Share understandings of livelihoods approaches and analysis using participatory methods Review current NACA-STREAM livelihoods analysis documentation, adapt and supplement, towards the drafting of a Guide for Livelihoods Analysis Experience the use of participatory tools for livelihoods analysis Plan activities for carrying out livelihoods analyses, and Consider how to build capacity in monitoring and evaluation (M&E) and “significant change”. (Pdf contains 56 pages).

Research Data Management — Activities and Challenges Report on the Knowledge Exchange Workshop

Workshop Research Data Management – Activities and Challenges 14-15 November 2011, Bonn The Knowledge Exchange initiative organised a workshop to highlight current activities and challenges with respect to research data management in the Knowledge Exchange partner countries and beyond. The workshop brought together experts from data centres, libraries, computational centres, funding organisations, publishing services and other institutions in the field of research and higher education who are working to improve research data management and encourage effective reuse of research data. A considerable part of the programme was dedicated to sharing perspectives from these communities, leading to the development of a roadmap of practical actions for the Knowledge Exchange initiative, partner organisations and other stakeholders to progress over the next two years. On the first day, principal investigators and project managers from a great variety of recent projects shared their insights on objectives and methods for improving data management ranging from discipline-specific to more general approaches. A series of short presentations of selected projects was followed by an extensive poster session that functioned as a “trade fair” of current trends and activities in the field of research data management. Moreover, the poster session offered ample network opportunities for participants. The second day was dedicated to intensive group discussions looking at a number of data management challenges. First the most important findings from the "Surfboard for 'Riding the Wave'" report were presented. This included the state of the art on activities and challenges in the field of research data management. The subgroups will concentrate on the following key themes: funding, incentives, training and technical infrastructure. These discussions culminated in the identification of practical recommendations for future cooperation on practical as well as on strategic levels that should be taken forward by the KE partner organisations and beyond. These activities aim to improve the sustainability of services and infrastructures at both national and international levels.

Knowledge Exchange RDM Training & Skills Workshop, 9-10 Feb 2016, London

In today’s changing research environment, RDM is important in all stages of research. The skills and know-how in RDM that researchers and research support staff need, should be nurtured all though their career. At the end of 2015, KE initiated a project to compare approaches in RDM training within the partnership’s five member countries. The project was structured around two strands of activity: In the last months of 2015 a survey was conducted to collect information on current practice around RDM training, in order to provide an overview of the RDM training landscape. In February 2016 a workshop was held to share successful approaches to RDM training and capacity building provided within institutions and by infrastructure. The report describes the outputs of both the analysis of the survey and the outcomes of the workshop. The document provides an evidence base and informed suggestions to help improve RDM training practices in KE partner countries and beyond.

A study of women's participation in fish food security and poverty alleviation in Lagos and Ogun States

Women, all over the world have contributed in various ways to the social, political and economic development of the Society. In fact, the World Resource Institute recognizes that "women have profound and preserve effect onn the well-being of their families, communities and local ecosystems" (Gamble and Well 1997:211). Women constitute more than 50 percent of the Agricultural (Fisheries being a sub sector), labour force. A study on Women in Fisheries showed that they participate in all aspects of the sector (capture, culture, processing, marketing research, training and Extension services). This paper reports the result of the study on women's contributions in the development of the Fisheries Industry particularly their roles in Fish Food Security, Poverty Alleviation and high rates of women's adoption of Fisheries technologies. The Case-study research methodology is used to study the "How" and "Why" Women's Contribution in Fish Food Security and Poverty Alleviation is at the index level recorded for the gender. The study made use of "Case Study" Research Instrument; documents, interview, artefacts, direct observation and archival records. The sampling techniques were purposive for research audiences and simple random for fisher-folks in the chosen locations. Analysed data showed among others that in Fisheries Research women occupy very important positions as Heads of Division/Section, Fisheries Liasion/Extension Officers and Fisheries Laboratory Chiefs etc. The paper also gave results of women production, processing, marketing and other services statistics; it also discusses the "whys" of women's low capacity in fisheries development of the nation and finally suggested ways in improving women's optimal capacity utilization in fisheries development

Initiatives for the improvement of continuous management training

[EN] Purpose. This work aims to present, from the company viewpoint, a structured account of management proposals and practices directed toward improving the intensity and effectiveness of continuous management training (CMT). Design/methodology/approach. The article takes as its main theoretical referents the Theory of Human Capital, the Resource-Based Vision and the contributions made via the new institutional economy with regard to the problems of information asymmetry between companies, employees and training providers and completes the proposals that derive from this theoretical approach. To do this, experience-based contributions are collected from a selection of company training and HR managers from twelve Basque companies characterised by their strong investment in management training. The methodology used was qualitative and obtained by different qualitative techniques: Focus Groups, Nominal Groups and the Delphi Method, which make up the so-called Hybrid Delphi. Findings and implications. The proposals are aimed at the main agents in training activity: training providers, associations and public agents engaged in management training and, particularly, companies themselves. The initiatives seek above all to increase training market transparency, to improve mutual commitments between companies and managers, and to link training and development with culture and strategic management, so that firms make optimal investment in management training. Originality/value. The methodology used is original, and the contributions are consistent with the theory, have a proven practical utility, and are presented in a hierarchy, which facilitates decision making.

A Delphi Process to Optimize Quality and Performance of Drug Evaluation in Neonates

Background: Neonatal trials remain difficult to conduct for several reasons: in particular the need for study sites to have an existing infrastructure in place, with trained investigators and validated quality procedures to ensure good clinical, laboratory practices and a respect for high ethical standards. The objective of this work was to identify the major criteria considered necessary for selecting neonatal intensive care units that are able to perform drug evaluations competently. Methodology and Main Findings: This Delphi process was conducted with an international multidisciplinary panel of 25 experts from 13 countries, selected to be part of two committees (a scientific committee and an expert committee), in order to validate criteria required to perform drug evaluation in neonates. Eighty six items were initially selected and classified under 7 headings: "NICUs description - Level of care'' (21), "Ability to perform drug trials: NICU organization and processes (15), "Research Experience'' (12), "Scientific competencies and area of expertise'' (8), "Quality Management'' (16), "Training and educational capacity'' (8) and "Public involvement'' (6). Sixty-one items were retained and headings were rearranged after the first round, 34 were selected after the second round. A third round was required to validate 13 additional items. The final set includes 47 items divided under 5 headings. Conclusion: A set of 47 relevant criteria will help to NICUs that want to implement, conduct or participate in drug trials within a neonatal network identify important issues to be aware of. Summary Points: 1) Neonatal trials remain difficult to conduct for several reasons: in particular the need for study sites to have an existing infrastructure in place, with trained investigators and validated quality procedures to ensure good clinical, laboratory practices and a respect for high ethical standards. 2) The present Delphi study was conducted with an international multidisciplinary panel of 25 experts from 13 countries and aims to identify the major criteria considered necessary for selecting neonatal intensive care units (NICUs) that are able to perform drug evaluations competently. 3) Of the 86 items initially selected and classified under 7 headings - "NICUs description - Level of care'' (21), "Ability to perform drug trials: NICU organization and processes (15), "Research Experience'' (12), "Scientific competencies and area of expertise'' (8), "Quality Management'' (16), "Training and educational capacity'' (8) and "Public involvement'' (6) - 47 items were selected following a three rounds Delphi process. 4) The present consensus will help NICUs to implement, conduct or participate in drug trials within a neonatal network.

Prediction of Multi-Target Networks of Neuroprotective Compounds with Entropy Indices and Synthesis, Assay, and Theoretical Study of New Asymmetric 1,2-Rasagiline Carbamates

In a multi-target complex network, the links (L-ij) represent the interactions between the drug (d(i)) and the target (t(j)), characterized by different experimental measures (K-i, K-m, IC50, etc.) obtained in pharmacological assays under diverse boundary conditions (c(j)). In this work, we handle Shannon entropy measures for developing a model encompassing a multi-target network of neuroprotective/neurotoxic compounds reported in the CHEMBL database. The model predicts correctly >8300 experimental outcomes with Accuracy, Specificity, and Sensitivity above 80%-90% on training and external validation series. Indeed, the model can calculate different outcomes for >30 experimental measures in >400 different experimental protocolsin relation with >150 molecular and cellular targets on 11 different organisms (including human). Hereafter, we reported by the first time the synthesis, characterization, and experimental assays of a new series of chiral 1,2-rasagiline carbamate derivatives not reported in previous works. The experimental tests included: (1) assay in absence of neurotoxic agents; (2) in the presence of glutamate; and (3) in the presence of H2O2. Lastly, we used the new Assessing Links with Moving Averages (ALMA)-entropy model to predict possible outcomes for the new compounds in a high number of pharmacological tests not carried out experimentally.