Prominent Variables Research Articles

Identifying spatial data availability and spatial data needs for Chagas disease mitigation in South America

The objective of this paper on Chagas disease is to determine the availability and spatial resolution of existing data that can be used to address Chagas disease transmission risk in South America. A literature review was conducted to determine prominent variables that models utilize to assist with efforts to mitigate Chagas disease. Next, a Web search was performed to collect publicly available spatial data pertaining to these variables for the countries in South America. The data were classified based on type and spatial extent, which were then used to create maps of data availability of variables related to Chagas disease transmission. Governments can use this information to better direct their resources to collect data and control the spread of triatomines and Chagas more effectively, and potentially identify more cost-effective strategies for eliminating triatomine vectors.

Using a stochastic gradient boosting algorithm to analyse the effectiveness of Landsat 8 data for montado land cover mapping: Application in southern Portugal

This study aims to develop and propose a methodological approach for montado ecosystem mapping using Landsat 8 multi-spectral data, vegetation indices, and the Stochastic Gradient Boosting (SGB) algorithm. Two Landsat 8 scenes (images from spring and summer 2014) of the same area in southern Portugal were acquired. Six vegetation indices were calculated for each scene: the Enhanced Vegetation Index (EVI), the Short-Wave Infrared Ratio (SWIR32), the Carotenoid Reflectance Index 1 (CRI1), the Green Chlorophyll Index (CIgreen), the Normalised Multi-band Drought Index (NMDI), and the Soil-Adjusted Total Vegetation Index (SATVI). Based on this information, two datasets were prepared: (i) Dataset I only included multi-temporal Landsat 8 spectral bands (LS8), and (ii) Dataset II included the same information as Dataset I plus vegetation indices (LS8+VIs). The integration of the vegetation indices into the classification scheme resulted in a significant improvement in the accuracy of Dataset II’s classifications when compared to Dataset I (McNemar test: Z-value=4.50), leading to a difference of 4.90% in overall accuracy and 0.06 in the Kappa value. For the montado ecosystem, adding vegetation indices in the classification process showed a relevant increment in producer and user accuracies of 3.64% and 6.26%, respectively. By using the variable importance function from the SGB algorithm, it was found that the six most prominent variables (from a total of 24 tested variables) were the following: EVI_summer; CRI1_spring; SWIR32_spring; B6_summer; B5_summer; and CIgreen_summer.

Disclosure Attributes Impact of Corporate Governance Auditing Quality and Voluntary Disclosures on the Financial Performance of Banks Listed in the Egyptian Stock Exchange Market

Banks in Egypt have unique characteristics that makes its micro economic role vital in the macro economy throughout their financial performance impact on the investors’ decisions in the capital exchange market. The research investigates assesses, and discusses the appropriate disclosure attributes of banks, and disclosure attributes impact of corporate governance, audit quality, along with the voluntary disclosure attributes on the financial performance of the Islamic and non-Islamic banks in the Egyptian exchange market through an empirical research applied on banks listed in the stock exchange market. The corporate governance disclosure attributes presented by the research included Audit committee dimensions: Audit committee size, AC autonomy, AC meetings, Board size, Board independence, Managerial ownership, and other prominent variables. The audit quality disclosure attributes addressed by the research divided into two subcategories, Auditing firms determinates and Audit process determinates. The attributes used to measure the level of voluntary disclosure includes, Internet reporting, brief history of the bank, directors skills and experiences, environment protection programs and other variables. The financial performance of banks are measured through the price earnings ratio, return on total assets, total asset growth, net profit growth and EPS ratio calculated based on the annals of the annual reports of the selected banks. The research uses the secondary data from the annual reports of the banks, and the disclosure books brought from the websites of the banks or purchased through Egypt for Information Dissemination Company. The research will cover a period starting from 2004 until 2014 (according to the availability of the data and the date of registration of the banks in the stock market) along 12 banks listed in the Egyptian stock market. The sample of the 12 banks included 3 Islamic banks and 9 non-Islamic banks The research results supported the hypothesis of the research that the corporate governance, audit quality and voluntary disclosure have an impact on the financial performance of the Islamic and the non-lslamic banks with different level of significance. The empirical study of this research has been conducted using the SPSS package along with other statistical techniques to verify the hypotheses. The results of the research may have some important implications for enhancing the disclosure attributes of the Islamic banks and non-lslamic banks, which will lead to better financial performance for both categories of banks.

Systems Thinking in 49 Communities Related to Healthy Eating, Active Living, and Childhood Obesity

Community partnerships to promote healthy eating and active living in order to prevent childhood obesity face a number of challenges. Systems science tools combined with group model-building techniques offer promising methods that use transdisciplinary team-based approaches to improve understanding of the complexity of the obesity epidemic. This article presents evaluation methods and findings from 49 Healthy Kids, Healthy Communities sites funded to implement policy, system, and environmental changes from 2008 to 2014. Through half-day group model-building sessions conducted as part of evaluation site visits to each community between 2010 and 2013, a total of 50 causal loop diagrams were produced for 49 communities (1 community had 2 causal loop diagrams representing different geographic regions). The analysis focused on the following evaluation questions: (1) What were the most prominent variables in the causal loop diagrams across communities? (2) What were the major feedback structures across communities? (3) What implications from the synthesized causal loop diagram can be translated to policy makers, practitioners, evaluators, funders, and other community representatives? A total of 590 individuals participated with an average of 12 participants per session. Participants' causal loop diagrams included a total of 227 unique variables in the following major subsystems: healthy eating policies and environments, active living policies and environments, health and health behaviors, partnership and community capacity, and social determinants. In a synthesized causal loop diagram representing variables identified by at least 20% of the communities, many feedback structures emerged and several themes are highlighted with respect to implications for policy and practice as well as assessment and evaluation. The application of systems thinking tools combined with group model-building techniques creates opportunities to define and characterize complex systems in a manner that draws on the authentic voice of residents and community partners.

Partitioning of eggplant cultivars in north central Nigeria using gross vegetative and reproductive characters

Eggplants emanating from a gene bank were studied. The aim was to partition and separate the cultivars into species level on the basis of their variable characters. Seeds were obtained from the seed gene bank of the University of Agriculture, Makurdi, Nigeria, and planted in the university research farm. Forty nine (49) cultivars at full maturity were examined using thirty (30) vegetative and thirty three (33 reproductive characters. Among the cultivars, the following characters remained unvaried: bud position (terminal), leaf type (simple), leaf arrangement (alternate), pubescence type (puberulent), venation type (pinnately netted), petal cohesion (fused), ovary position (superior), corolla shape (companulate), anther type (basifixed), filament adhesion (free) and symmetry of flower (actinomorphic). Some characters were moderately varied but not important to separate the cultivars. The prominent variables that formed the basis of partitioning the species are: leaf sizes, leaf shapes, leaf apices, number of leaf produced, plant height, number of days to produce flower, number of days to yield fruit, quantity of fruit produced (yield), fruit sizes, fruit colour, fruit shape, fruit stripe, fruit groove, fruit taste, fruit position, colour of petal and influorescence type. Based on these features, the cultivars are separated into eight (8) different taxonomic groups that may be assigned specific circumscription and named accordingly by taxonomists.

Forecasting the Commercial Attractiveness of User‐Generated Designs Using Online Data: An Empirical Study within theLEGOUser Community

Web 2.0 technologies and the rapid emergence of virtual user communities have created new challenges and opportunities for producer firms. The challenges concern the problem of idea overload when a large number of users are empowered to develop their own design creations. At the same time, opportunities arise because firm‐hosted user communities offer a promising source of creativity outside the firms' boundaries. In this paper, we study which data present in firm‐hosted online communities on user‐generated designs and user‐designers can be used to help a focal producer firm to reduce its workload in the selection phase by predicting which user‐generated designs it would most likely perceive as commercially attractive. Prior research emphasizes that among the vast amount of ideas generated in online user communities, it is the lead users' ideas that tend to stick out in terms of commercial attractiveness. Our paper aims to provide the next step by developing a heuristic for filtering commercially attractive ideas that are generated in online user communities. Therefore, prior lead user research is used as a point of reference for our study. This research stream has produced rich insights into the characteristics of users who are capable of developing new products that are commercially attractive from the perspective of a focal producer firm, as well as the characteristics of artifacts that such users tend to develop. Based on prior lead user research, we use theories on problem solving, creativity, and new product adoption to develop hypotheses on the factors that might influence the attractiveness of user‐generated designs from the focal producer firm's perspective in such a setting. Applying multilevel generalized linear modeling, 1799 designs from 116 user‐designers in theLEGOuser community are analyzed. Our findings show that three prominent variables, the complexity of a given design, positive feedback from the community on specific designs, and the intensity of design activity by a user‐designer, can be used by a focal producer firm as filtering heuristics for the selection of promising user‐generated designs. We find an invertedU‐shaped relationship between the complexity of a user‐generated design and its perceived commercial attractiveness. Furthermore, we find a positive relationship between the positive feedback received by a given user‐generated design within the peer community and its perceived commercial attractiveness, as well as aU‐shaped relationship between the intensity of a certain user‐designer's activities and the likelihood that a given design by that user will be perceived as commercially attractive. The study is a first step toward a new Web‐based marketing research approach that can enable firms to filter vast numbers of user‐generated designs more effectively and efficiently.

Formulation of Mathematical Modeling to Characterize The Aluminium Metals Using Ultrasonic Non-Destructive Techniques

Predicting the type of aluminium metals and composition of elements present in the aluminium samples through Nondestructive testing (NDT) is a matter of very importance for aluminium Industry. The unique method to determine grade of the aluminium sample is required to characterize the aluminium metals. The Nondestructive Technique (NDT) and determination of characteristics and mechanical properties of aluminium metals are used to identify the grade of aluminium metals so that accordingly it can be used for the specific applications. Therefore a technique is required to predict the percentage of aluminium, Iron, Copper, Manganese of aluminium metals so as to categorize into different grades and applications. In Aluminium samples percentage of Aluminium plays very important role which may help to decide the grade of the aluminium metals hence its applications. The present work is focused on how the percentage of aluminium in aluminium samples can be calculated by adopting the mathematical modeling technique. Â 
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 There are various parameters which generally affect the percentage of aluminium in aluminium samples, and play a very major role. Therefore through this investigation an attempt is being made to formulate an approximate mathematical model which will certainly predict the percentage of aluminium in aluminium samples. In advent of this a dimensionless pie terms of various prominent parameters or variables have been taken to form a mathematical model. Some of these variables used to formulate this model are given as follows (i) physical properties of the aluminium samples like hardness, density, modulus of elasticity etc (ii) Signal analysis properties like Peak amplitude of Time signal, FFT, PSD and (iii) both the properties. The data of such types of variables have been recorded and calculated and thus the formulation of model is being done by multiple regression analysis. The model is then optimized and the reliability of the model has also been estimated. In fact this type of model will be helpful to estimate the aluminium percentage.

Interval type-2 fuzzy kernel based support vector machine algorithm for scene classification of humanoid robot

This paper proposed an Interval Type-2 Fuzzy Kernel based Support Vector Machine (IT2FK-SVM) for scene classification of humanoid robot. Type-2 fuzzy sets have been shown to be a more promising method to manifest the uncertainties. Kernel design is a key component for many kernel-based methods. By integrating the kernel design with type-2 fuzzy sets, a systematic design methodology of IT2FK-SVM classification for scene images is presented to improve robustness and selectivity in the humanoid robot vision, which involves feature extraction, dimensionality reduction and classifier learning. Firstly, scene images are represented as high dimensional vector extracted from intensity, edge and orientation feature maps by biological-vision feature extraction method. Furthermore, a novel three-domain Fuzzy Kernel-based Principal Component Analysis (3DFK-PCA) method is proposed to select the prominent variables from the high-dimensional scene image representation. Finally, an IT2FM SVM classifier is developed for the comprehensive learning of scene images in complex environment. Different noisy, different view angle, and variations in lighting condition can be taken as the uncertainties in scene images. Compare to the traditional SVM classifier with RBF kernel, MLP kernel, and the Weighted Kernel (WK), respectively, the proposed method performs much better than conventional WK method due to its integration of IT2FK, and WK method performs better than the single kernel methods (SVM classifier with RBF kernel or MLP kernel). IT2FK-SVM is able to deal with uncertainties when scene images are corrupted by various noises and captured by different view angles. The proposed IT2FK-SVM method yields over $$92~\% $$ 92 % classification rates for all cases. Moreover, it even achieves $$98~\% $$ 98 % classification rate on the newly built dataset with common light case.

Study of ECG process while machining Al2O3/Al – IPC using grey-Taguchi methodology

A B S T R A C T A R T I C L E I N F O The present paper attempts to optimize machining parameters of e lectr ochemical grinding (ECG) while machining alumina-aluminum interpenetrating phase composites by Grey-Taguchi methodology. Control parameters like electrolyte concentration, voltage, depth of cut and electrolyte flow rate have been considered to ensure two conflicting responses – higher material removal rate and lower surface roughness simultaneously by a single parametric combination. The L9 orthogonal array design is followed for the purpose of experimentation. The well-known S/N ratio analysis is performed along with ANOVA to establish the prominent variables that govern the responses separately. Finally Grey Relational Analysis is performed to optimize multiple performances in which different levels combinations of the factors are ranked based on grey relational grade. Surface roughness is given more importance than the MRR considering basic objective of the process. The analysis reveals that substantial improvement in machining performance takes place following this technique. The experimental investigation approach for evaluating the optimum ECG parametric combination during machining of composites materials can act as useful and an efficient guideline for manufacturing of products using such material. The study highlights the effects of different process variables on multiple performances for complex process like ECG.

Determinants of mediclaim policyholder satisfaction

Insurance sector in India has undergone a sea-change, specifically after liberalisation. InIndia, before opening up of the insurance sector, public sector general insurance companies alone offeredhealth insurance products and thereby enjoyed the monopoly status in the country. After liberalisation,more number of private players with foreign alliances have entered into the Indian health insurancemarket and offers variety of policies, which results in increased competition. As a result, public playersare slowly loosing their market share to private insurers in the country. In this backdrop, it is imperativeto find out the variables that determine the policyholder satisfaction on service quality of public generalinsurers. An attempt has been made in this paper to ascertain the variables that determine policyholdersatisfaction on service quality of public sector general insurers in Coimbatore District. Step-wiseregression analysis, used to ascertain the prominent variables that contribute for the piling up of servicequality satisfaction, shows that, „Awareness on Mediclaim Insurance‟, „Premium Amount‟, „Period ofAwareness‟, „Gender‟, „Sources of Awareness on Mediclaim Policy‟, „Occupation – Professional‟ and„Level of Education – Illiterate‟ are the prominent variables that determine policyholder satisfaction. Theresults of the study reveal that Public Sector General Insures have to concentrate more on spreading awarenessas high level of satisfaction is associated with awareness on mediclaim insurance.

An integrated evolutionary approach for modelling and optimization of laser beam cutting process

This paper presents a new integrated methodology based on evolutionary algorithms (EAs) to model and optimize the laser beam cutting process. The proposed study is divided into two parts. Firstly, genetic programming (GP) approach is used for empirical modelling of kerf width (Kw) and material removal rate (MRR) which are the important performance measures of the laser beam cutting process. GP, being an extension of the more familiar genetic algorithms, recently has evolved as a powerful optimization tool for nonlinear modelling resulting in credible and accurate models. Design of experiments is used to conduct the experiments. Four prominent variables such as pulse frequency, pulse width, cutting speed and pulse energy are taken into consideration. The developed models are used to study the effect of laser cutting parameters on the chosen process performances. As the output parameters Kw and MRR are mutually conflicting in nature, in the second part of the study, they are simultaneously optimized by using a multi-objective evolutionary algorithm called non-dominated sorting genetic algorithm II. The Pareto optimal solutions of parameter settings have been reported that provide the decision maker an elaborate picture for making the optimal decisions. The work presents a full-fledged evolutionary approach for optimization of the process.

Assessing the relevance of legal and extralegal factors in felony drug sentencing: the case of Michigan

Previous research has identified several prominent legal and extralegal variables and their effect on sentencing outcomes. However, only a few states have been subjected to critical or scholarly analysis. With the focal concerns perspective in mind, the current study examines 399 male defendants convicted on federal drug trafficking charges in the state of Michigan during 2006. Findings reveal that legally relevant factors are the strongest predictor of sentence length, offenders who are detained prior to trial face disparate sentences, and district in which the defendant was adjudicated significantly impacts sentence outcome. These results confirm that case processing strategies are an important source of sentencing disparities.

DIESEL FUEL ADDITIVES: WORTH CONSIDERING? LOCAL EXHAUST EMISSIONS AND FUEL CONSUMPTION TEST RESULTS

Pollutant regulations are not a matter of concern only for engine manufacturers but they also require policy implementation, commitment and a dedicated effort by industry. In particular, some in the mining industries are expanding their efforts to provide environmentally friendly working procedures and conditions. Use of fuel enhancershas been suggested. The Letšeng Diamond Mine, in Lesotho has taken the lead and commissioned a study on three different fuel additives. For all three fuel additives, three prominent variables were measured, namely diesel fuel consumption, diesel exhaust gases and smoke emission levels. All instruments used were EPA certified andcalibrated or verified according to the specification of the manufacturer and or international best practice to ensure accurate readings. This paper discusses the results obtained on site over a thirteen month study period involving different classes of machinery and under different weather conditions. Product C improved fuel consumption by 9.9% and reduced black smoke by 32%. Pitfalls have been identified with product A and product B that can help to avoid costly trials on other sites, such as rusted and blown injectors, engine failure and corroded seals, valves and cylinders. Further research is required however to determine if these additives will become major role players toreduce emissions and improve efficiency. In conclusion, we cannot ignore the use of fuel additives when talking about diesel pollution and the fact that the additives cou ld assist in reducing harmful emissions even if only one additive appears to be performing positively in terms of consumption and emissions, such as product C. Three strategies have been identified for reducing emissions on diesel earth moving equipment currently in use: engine modifications, exhaust gas treatment and fuel composition modifications. Engine modifications and exhaust gas treatment are considered costly alternatives to achieve reduced exhaust emissions plus these strategies are proven to have little or no positive impact on fuel economy performance. The study has shown one factor clearly – we cannot ignore the effects of additives both with regard to emissions and combustion efficiency.

Identifying Prominent Explanatory Variables for Water Demand Prediction Using Artificial Neural Networks: A Case Study of Bangkok

The water demand of a city is a complex and non linear function of climatic, socioeconomic, institutional and management variables. Identifying the prominent variables among these is essential in order to adequately predict water demand, and to plan and manage water resources and the supply systems. Further, the need for such identification becomes more pronounced when data constraints arise. The objective of this study was to establish, using correlation and sensitivity analyses, a minimum set of variables required to predict water demand with significant accuracy. Artificial Neural Networks (ANN) models were developed to predict short-term (daily) and medium-term (monthly) demands for Bangkok. Using meteorological and water utility variables for short-term prediction, and different ANN architecture, 16 sets of models with a 1-, 2- and 3-day lead period were developed. Although the best fit models for the three lead periods used different input variables, prediction accuracies over 98% were achieved by using only the historic daily demand (HDD) as the explanatory variable. Similarly, for medium-term prediction, 11 sets of models with lead periods of 1-, 2- and 6-months were developed, using meteorological, water utility and socioeconomic variables. The best fit models for the three lead periods used all explanatory variables but prediction accuracies of more than 98% were obtained by downsizing the variable set. The meteorological variables have a greater influence on medium-term prediction as compared to short-term prediction, suggesting that future water demand in Bangkok could be significantly affected by climate change.

Exploring linkages between people and rural landscapes at broad ecological scales

Effective conservation and management of natural resources requires integrating knowledge of ecological systems with an understanding of the cultural, social and economic characteristics of people who live and work in ecological landscapes. This study explores how people with distinctive social attributes are situated spatially across the landscape at the level of ecological section and subsection in Missouri. We identify prominent (statistically significant) social variables that may have implications for policy making at these two levels. We also examine the composition and spatial distribution of social groups within each level. To do so, we link non-urban, statewide U.S. Census data with units of the Missouri Ecological Classification System. Results show that: (1) education trumps other social variables such as income and employment as the most prominent variable differentiating people by ecological section in Missouri; (2) housing and income also emerge as prominent partitioning variables for people across ecological sections; and (3) social groups tend to be more spatially fragmented in ecological subsections with low physiographic gradients. The findings suggest a variety of research questions for addressing regional policies relative to education and homestead tenure as well as the ecological effects of spatial distributions of people with different social attributes. The findings establish a broad socio-ecological context for natural resource studies at more magnified levels of ecological scale at which more specific hypotheses can be designed and tested. We discuss possible research questions and hypotheses underling each finding as well as management implications.

Factors Affecting the Length of Responses in Field Investigative Interviews of Children (FIIC) in Child Sexual Abuse Cases

In this study some of the most prominent variables considered to facilitate interviewee responses in the literature of field investigative interviews of children (FIIC) are reviewed and included in the analysis of 100 FIICs. Analysis of variance (ANOVA) showed that of all the variables, the interviewer's utterances had the most impact on the children's responses, with the open questions eliciting the longest answers. The variable to follow was the children's age, with the oldest children yielding longer responses than the younger children to the open questions. Contrary to the hypothesis, the interviewer's competence, children's gender, and time of interview had little impact on the length of the children's responses. Possible explanations for the results are discussed.

Why some rural people become members of agricultural cooperatives while others do not

Though agricultural cooperatives are considered as a basis for household food security, there are many rural people in Tigray (Ethiopia) who do not so far become members. The study explores why some rural people become member of cooperatives while others do not using group discussion, household survey and probit model. The result of the study shows the prominent variables that strongly and significantly induce rural people to join agricultural cooperatives are being male household head, member in rural associations, attending public meeting and/or workshop; membership in tabia/woreda administrating committees; accessibility to credit services, exposure visits and training access; number of family sizes, family members in secondary school, and information access. Therefore, intensive awareness enhancement initiatives should be done in rural areas so that more people become members of agricultural cooperatives and ensure their sustainable food security, and contributes their parts in the development endeavor of the region. Key words: Agricultural cooperative, membership and participation.

Sensitivity of profitability to working capital management: a study of Indian corporate hospitals

Working capital management almost always determines the ability of a firm to earn profit. Efficiency with which a firm handles working capital ensures prosperity while neglect would spell danger for the very survival of the firm. The study focuses on the impact of working capital management on profitability of selected Indian corporate hospitals. The time span is ten years from 1996 to 2006. The results of regression analysis point out that the current ratio, cash turnover ratio, proportion of current assets to operating income and leverage have a negative influence on profitability. Stepwise regression analysis has identified seven prominent variables that significantly influence profitability. Path analysis reveals that 'quick ratio' has the highest direct effect on profitability, while 'current ratio' has the least direct effect.

GC Content and Recombination: Reassessing the Causal Effects for the Saccharomyces cerevisiae Genome

Recombination plays a crucial role in the evolution of genomes. Among many chromosomal features, GC content is one of the most prominent variables that appear to be highly correlated with recombination. However, it is not yet clear (1) whether recombination drives GC content (as proposed, for example, in the biased gene conversion model) or the converse and (2) what are the length scales for mutual influences between GC content and recombination. Here we have reassessed these questions for the model genome Saccharomyces cerevisiae, for which the most refined recombination data are available. First, we confirmed a strong correlation between recombination rate and GC content at local scales (a few kilobases). Second, on the basis of alignments between S. cerevisiae, S. paradoxus, and S. mikatae sequences, we showed that the inferred AT/GC substitution patterns are not correlated with recombination, indicating that GC content is not driven by recombination in yeast. These results thus suggest that, in S. cerevisiae, recombination is determined either by the GC content or by a third parameter, also affecting the GC content. Third, we observed long-range correlations between GC and recombination for chromosome III (for which such correlations were reported experimentally and were the model for many structural studies). However, similar correlations were not detected in the other chromosomes, restraining thus the generality of the phenomenon. These results pave the way for further analyses aimed at the detailed untangling of drives involved in the evolutionary shaping of the yeast genome.

Politics of Formalized River Cooperation

While much of the extant literature has focused on the potential of international rivers to generate militarized conflict, this study builds on more recent works that examine the politics of river cooperation. The article focuses on the efforts to regulate the use of international rivers formally by the means of treaties. The theoretical framework incorporates prominent variables from the (neo)realist and neo-liberal schools of thought as well as the need for potable water and river-related geographic factors. The framework is used to generate expectations about whether riparian countries will enter into the treaties dealing in particular with the issues of water quantity and quality. Systematic empirical evaluations covering the entire world in the 1948—2000 time period confirm some while challenging much of the conventional wisdom on the topic. Specifically, preponderant power distribution, economic interdependence, democratic governance, and water scarcity all increase the chances for formalized river cooperation between contiguous riparian states. In contrast, the findings suggest that the roles of allegedly important and problematic factors such as the upstream/downstream relationship and recent militarized conflict have been exaggerated in earlier research. Cumulatively, the findings sound a cautiously optimistic note for the prospects of the spread of formal river cooperation in the less developed parts of the world.