The Experts below are selected from a list of 4020192 Experts worldwide ranked by ideXlab platform
Gertrud Buchenrieder - One of the best experts on this subject based on the ideXlab platform.
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Do Connections Matter? Individual Social Capital and Credit Constraints in Vietnam
The European Journal of Development Research, 2012Co-Authors: Quoc Hoang Dinh, Thomas Bernhard Dufhues, Gertrud BuchenriederAbstract:We analyze the effects of network-based social capital on easing the credit constraints of rural households, using zero-inflated negative binomial regression analysis. In the context of development economics, the data collection Approach used, which originates from the field of sociology, is innovative, insofar as a personal network survey was carried out to measure the individual social capital of rural households. We define four social capital variables according to tie strength and social distance between the respondent and his/her network members, resulting in four different social capital variables: (i) bonding (strong ties to persons of similar social standing); (ii) bridging (weak ties to persons of similar social standing); (iii) bonding-link (strong ties to persons of higher social standing); and (iv) bridging-link (weak ties to persons of higher social standing). Econometric analysis suggests that strong ties to persons of higher social standing can reduce the magnitude of credit constraints. Nous analysons, à l’aide d’un modèle de régression binomiale négative à inflation de zéros, en quoi avoir un capital social de réseaux peut aider les ménages ruraux à atténuer les contraintes de crédit. Dans le contexte de l’économie du développement, la méthode de recueil de données utilisée, qui trouve ses origines dans la sociologie, est innovatrice, dans la mesure où nous avons réalisé une enquête sur les réseaux personnels afin de mesurer le capital social individuel des ménages ruraux. Nous définissons quatre variables de capital social en fonction de la solidité des liens et de la distance sociale entre les personnes interrogées et les membres de leur réseau. Nous obtenons ainsi les quatre variables de capital social suivantes: 1. bonding (liens forts avec des personnes occupant des positions sociales similaires); 2. bridging (liens faibles avec des personnes occupant des positions sociales similaires); 3. bonding-link (liens forts avec des personnes occupant des positions sociales supérieures); et 4. bridging-link (liens faibles avec des personnes occupant des positions sociales supérieures). Notre analyse économétrique suggère que des liens forts avec des personnes de positions sociales supérieures peuvent réduire l’impact des contraintes de crédit.
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Network based social capital and individual loan repayment performance
The Journal of Development Studies, 2011Co-Authors: Thomas Bernhard Dufhues, Gertrud Buchenrieder, Dirk G. Euler, Nuchanata MungkungAbstract:This study analyses the effects of social capital on the repayment behaviour of borrowers in Thailand. In the context of agricultural economics, an innovative data collection Approach is used that originates from the field of sociology. A personal network survey is carried out to measure the individual social capital of borrowers. Social capital variables are defined according to: tie strength (bonding/bridging) and social distance (linking) between the respondent and his/her network member. Bonding social capital has a significant and positive influence on repayment performance. However, we find no significant evidence for an effect of bridging and linking social capital.
Thomas Bernhard Dufhues - One of the best experts on this subject based on the ideXlab platform.
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Do Connections Matter? Individual Social Capital and Credit Constraints in Vietnam
The European Journal of Development Research, 2012Co-Authors: Quoc Hoang Dinh, Thomas Bernhard Dufhues, Gertrud BuchenriederAbstract:We analyze the effects of network-based social capital on easing the credit constraints of rural households, using zero-inflated negative binomial regression analysis. In the context of development economics, the data collection Approach used, which originates from the field of sociology, is innovative, insofar as a personal network survey was carried out to measure the individual social capital of rural households. We define four social capital variables according to tie strength and social distance between the respondent and his/her network members, resulting in four different social capital variables: (i) bonding (strong ties to persons of similar social standing); (ii) bridging (weak ties to persons of similar social standing); (iii) bonding-link (strong ties to persons of higher social standing); and (iv) bridging-link (weak ties to persons of higher social standing). Econometric analysis suggests that strong ties to persons of higher social standing can reduce the magnitude of credit constraints. Nous analysons, à l’aide d’un modèle de régression binomiale négative à inflation de zéros, en quoi avoir un capital social de réseaux peut aider les ménages ruraux à atténuer les contraintes de crédit. Dans le contexte de l’économie du développement, la méthode de recueil de données utilisée, qui trouve ses origines dans la sociologie, est innovatrice, dans la mesure où nous avons réalisé une enquête sur les réseaux personnels afin de mesurer le capital social individuel des ménages ruraux. Nous définissons quatre variables de capital social en fonction de la solidité des liens et de la distance sociale entre les personnes interrogées et les membres de leur réseau. Nous obtenons ainsi les quatre variables de capital social suivantes: 1. bonding (liens forts avec des personnes occupant des positions sociales similaires); 2. bridging (liens faibles avec des personnes occupant des positions sociales similaires); 3. bonding-link (liens forts avec des personnes occupant des positions sociales supérieures); et 4. bridging-link (liens faibles avec des personnes occupant des positions sociales supérieures). Notre analyse économétrique suggère que des liens forts avec des personnes de positions sociales supérieures peuvent réduire l’impact des contraintes de crédit.
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Network based social capital and individual loan repayment performance
The Journal of Development Studies, 2011Co-Authors: Thomas Bernhard Dufhues, Gertrud Buchenrieder, Dirk G. Euler, Nuchanata MungkungAbstract:This study analyses the effects of social capital on the repayment behaviour of borrowers in Thailand. In the context of agricultural economics, an innovative data collection Approach is used that originates from the field of sociology. A personal network survey is carried out to measure the individual social capital of borrowers. Social capital variables are defined according to: tie strength (bonding/bridging) and social distance (linking) between the respondent and his/her network member. Bonding social capital has a significant and positive influence on repayment performance. However, we find no significant evidence for an effect of bridging and linking social capital.
C Bandaragoda - One of the best experts on this subject based on the ideXlab platform.
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data collection methodology for dynamic temperature model testing and corroboration
Hydrological Processes, 2009Co-Authors: Bethany T Neilson, David K. Stevens, Steven C. Chapra, C BandaragodaAbstract:This article describes a data collection Approach for determining the significance of individual heat fluxes within streams with an emphasis on testing (i.e. identification of possible missing heat fluxes), development, calibration and corroboration of a dynamic temperature model. The basis for developing this Approach was a preliminary temperature modelling effort on the Virgin River in southwestern Utah during a low-flow period that suggested important components of the energy balance might be missing in the original standard surface-flux temperature model. Possible missing heat fluxes were identified as bed conduction, hyporheic exchange, dead zone warming and exchange and poor representation of the amount of solar radiation entering the water column. To identify and estimate the relative importance of the missing components, a comprehensive data collection effort was developed and implemented. In particular, a method for measuring shortwave radiation behaviour in the water column and an in situ method for separating out bed conduction and hyporheic influences were established. The resulting data and subsequent modelling effort indicate that hyporheic and dead zone heat fluxes are important, whereas solar radiation reflection at the water surface was found to be insignificant. Although bed conduction can be significant in certain rivers, it was found to have little effect on the overall heat budget for this section of the Virgin River. Copyright © 2009 John Wiley & Sons, Ltd.
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data collection methodology for dynamic temperature model testing and corroboration
Hydrological Processes, 2009Co-Authors: Bethany T Neilson, David K. Stevens, Steven C. Chapra, C BandaragodaAbstract:This article describes a data collection Approach for determining the significance of individual heat fluxes within streams with an emphasis on testing (i.e. identification of possible missing heat fluxes), development, calibration and corroboration of a dynamic temperature model. The basis for developing this Approach was a preliminary temperature modelling effort on the Virgin River in southwestern Utah during a low-flow period that suggested important components of the energy balance might be missing in the original standard surface-flux temperature model. Possible missing heat fluxes were identified as bed conduction, hyporheic exchange, dead zone warming and exchange and poor representation of the amount of solar radiation entering the water column. To identify and estimate the relative importance of the missing components, a comprehensive data collection effort was developed and implemented. In particular, a method for measuring shortwave radiation behaviour in the water column and an in situ method for separating out bed conduction and hyporheic influences were established. The resulting data and subsequent modelling effort indicate that hyporheic and dead zone heat fluxes are important, whereas solar radiation reflection at the water surface was found to be insignificant. Although bed conduction can be significant in certain rivers, it was found to have little effect on the overall heat budget for this section of the Virgin River.
Nuchanata Mungkung - One of the best experts on this subject based on the ideXlab platform.
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Network based social capital and individual loan repayment performance
The Journal of Development Studies, 2011Co-Authors: Thomas Bernhard Dufhues, Gertrud Buchenrieder, Dirk G. Euler, Nuchanata MungkungAbstract:This study analyses the effects of social capital on the repayment behaviour of borrowers in Thailand. In the context of agricultural economics, an innovative data collection Approach is used that originates from the field of sociology. A personal network survey is carried out to measure the individual social capital of borrowers. Social capital variables are defined according to: tie strength (bonding/bridging) and social distance (linking) between the respondent and his/her network member. Bonding social capital has a significant and positive influence on repayment performance. However, we find no significant evidence for an effect of bridging and linking social capital.
Abdallah Makhoul - One of the best experts on this subject based on the ideXlab platform.
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energy efficient sensor data collection Approach for industrial process monitoring
IEEE Transactions on Industrial Informatics, 2018Co-Authors: Hassan Harb, Abdallah MakhoulAbstract:The use of wireless sensor network for industrial applications has attracted much attention from both academic and industrial sectors. It enables a continuous monitoring, controlling, and analyzing of the industrial processes, and contributes significantly to finding the best performance of operations. Sensors are typically deployed to gather data from the industrial environment and to transmit it periodically to the end user. Since the sensors are resource constrained, effective energy management should include new data collection techniques for an efficient utilization of the sensors. In this paper, we propose adaptive data collection mechanisms that allow each sensor node to adjust its sampling rate to the variation of its environment, while at the same time optimizing its energy consumption. We provide and compare three different data collection techniques. The first one uses the analysis of data variances via statistical tests to adapt the sampling rate, whereas the second one is based on the set-similarity functions, and the third one on the distance functions. Both simulation and real experimentations on telosB motes were performed in order to evaluate the performance of our techniques. The obtained results proved that our proposed adaptive data collection methods can reduce the number of acquired samples up to 80% with respect to a traditional fixed-rate technique. Furthermore, our experimental results showed significant energy savings and high accurate data collection compared to existing Approaches.
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self adaptive data collection and fusion for health monitoring based on body sensor networks
IEEE Transactions on Industrial Informatics, 2016Co-Authors: Carol Habib, Abdallah Makhoul, Rony Darazi, Christian SalimAbstract:In the past few years, wireless body sensor networks (WBSNs) emerged as a low-cost solution for healthcare applications. In WBSNs, biosensors collect periodically physiological measurement and send them to the coordinator where the data fusion process takes place. However, processing the huge amount of data captured by the limited lifetime biosensors and taking the right decisions when there is an emergency are major challenges in WBSNs. In this paper, we introduce a biosensor data management framework, starting from data collection to decision making. First, we propose an adaptive data collection Approach on the biosensor node level. This Approach uses an early warning score system to optimize data transmission and estimates in real time the sensing frequency. Second, we present a data fusion model on the coordinator level using a decision matrix and fuzzy set theory. To evaluate our Approach, we conducted multiple series of simulations on real sensor data. The results show that our Approach reduces the amount of collected data, while maintaining data integrity. In addition, we show the impact of sampling and filtering data on the accuracy of the taken decisions and compare our data fusion Approach with a basic decision tree algorithm.
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Adaptive data collection Approach based on sets similarity function for saving energy in periodic sensor networks
International Journal of Information Technology and Management, 2016Co-Authors: Hassan Harb, Abdallah Makhoul, Ali Jaber, Rami Tawil, Oussama BazziAbstract:Disaster monitoring becomes a requirement for collecting and analysing data in order to offer a better disaster management situation. Periodic sensor networks PSNs are usually used in disaster monitoring and are characterised by the acquisition of sensor data from remote sensor nodes before being forwarded to the sink in a periodic basis. The major challenges in PSN are energy saving and collected data reduction in order to increase the sensor network lifetime and to ensure a long-time monitoring for disasters. In this paper, we propose an adaptive sampling Approach for energy-efficient periodic data collection in sensor networks. Our proposed Approach provides each sensor node the ability to identify redundancy between collected data over time, by using similarity functions, and allowing for sampling adaptive rate. Experiments on real sensors data show that our Approach can be effectively used to conserve energy in the sensor network and to increase its lifetime, while still keeping a high quality of the collected data.
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Self-Adaptive Data Collection and Fusion for Health Monitoring Based on Body Sensor Networks
IEEE Transactions on Industrial Informatics, 2016Co-Authors: Carol Habib, Abdallah Makhoul, Rony Darazi, Christian SalimAbstract:In the past few years, wireless body sensor networks (WBSNs) emerged as a low cost solution for healthcare applications. In WBSNs, biosensors collect periodically physiological measures and send them to the coordinator where the data fusion process takes place. However, processing the huge amount of data captured by the limited lifetime biosensors and taking the right decisions when there is an emergency are major challenges in WBSNs. In this paper, we introduce a bio-sensor data management framework, starting from data collection to decision making. First, we propose an adaptive data collection Approach on the biosensor node level. This Approach uses an early warning score system to optimize data transmission and estimates in real-time the sensing frequency. Second, we present a data fusion model on the coordinator level using a decision matrix and fuzzy set theory. To evaluate our Approach, we conducted multiple series of simulations on real sensor data. The results show that our Approach reduces the amount of collected data while maintaining data integrity. In addition, we show the impact of sampling and filtering data on the accuracy of the taken decisions and compare our data fusion Approach to a basic decision tree algorithm.
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Residual energy-based adaptive data collection Approach for periodic sensor networks
Ad Hoc Networks, 2015Co-Authors: Abdallah Makhoul, Hassan Harb, David LaiymaniAbstract:Due to its potential applications and the density of the deployed sensors, distributed wireless sensor networks are one of the highly anticipated key contributors of the big data in the future. Consequently, massive data collected by the sensors beside the limited battery power are the main limitations imposed by such networks. In this paper, we consider a periodic sensor networks (PSNs) where sensors transmit their data to the sink on a periodic basis. We propose an efficient adaptive model of data collection dedicated to PSN, in order to increase the network lifetime and to reduce the huge amount of the collected data. The main idea behind this Approach is to allow each sensor node to adapt its sampling rate to the physical changing dynamics. In this way, the oversampling can be minimized and the power efficiency of the overall network system can be further improved. The proposed method is based on the dependence of measurements variance while taking into account the residual energy that varies over time. We study three well known statistical tests based on One-Way Anova model. Then, we propose a multiple levels activity model that uses behavior functions modeled by modified Bezier curves to define application classes and allow for sampling adaptive rate. Experiments on real sensors data show that our Approach can be effectively used to minimize the amount of data retrieved by the network and conserve energy of the sensors, without loss of fidelity/accuracy.
