Location-allocation problem for banking correspondent services : the colombian urban market case

Banking correspondents are a channel through which third parties operate on behalf of a bank, under a contract authorising the provision of some banking services. This model has been implemented extensively in developing countries, as a channel to increase financial inclusion by bringing financial products and services closer to marginalised populations. However, there is a lack of studies on the criteria employed by banks when selecting retailers to turn into banking correspondents (BC), in turn preventing the channel from offering a service portfolio adequate to the capacities of the retailers providing this kind of services, affecting the profitability and sustainability of the channel. The current research parted from the agency theory, which allowed to understand the relationship between the parties involved in the delivery of BC services, seeking to boost financial inclusion in Colombia through the development of the BC channel by solving the problem of location and portfolio allocation for retailers acting as banking correspondents in Colombian urban zones. It parted from the case of Bogota, where improvements were achieved in the selection of retailers and portfolio allocation, thus enhancing the relationship between agents, allowing banks to select banking correspondents and allocating them a particular service portfolio, while transaction volumes and channel profits are maximised. This was done through the development of a methodology comprising five stages, namely: (a) the development of a taxonomy on network integration models and financial services; (b) the development of a taxonomy on the strategies of small and medium retailers that could be selected as banking correspondents; (c) the validation of both taxonomies through cluster analyses; (d) validation of the resulting classifications through an ANOVA and a Kruskal-Wallis H test; and (e) the elaboration of a chance-constrained programming model that uses the elements built and validated in the formers stages. A classification of retailers was obtained from factors related to their operational and business strategies, as well as a classification of banking correspondents based on their service portfolios. It was also noted there is a significant relationship between the groups from both classifications, which led to the chance-constrained programming model being run on a sample of retailers in Bogotá, located at the borough of Suba. The model enabled to select those retailers best suited to become banking correspondents, determining the number of transactions according to their constraints in terms of retailer capabilities, banks and the environment, while estimating the expected income from these banking correspondent operationsTesi

How Artificial Intelligence Will Affect Law Firms?

This paper aims to give a fundamental understanding of how artificial intelligence will affect the legal industry. The overall purpose of the study is to explore a wide range of topics that ultimately show how AI can be the most effective in the workforce. Currently, AI has many setbacks in terms of its logical-decision making, meaning, when AI is used in our legal system, there is always legal precedence that should never be overlooked. The basic design of the study is discussing the innovative AI programs, the legal profession v. human intelligence, ethical aspects, Law School curriculum, and the global job market at law firms. I focused more on the domestic legal system in my argument, but I also touched on international statistics to help shape a broader macro outlook. As a result, we will see a multifaceted legal industry that is interdependent in many ways. Furthermore, the U.S. legal system is looking to continually improve the way AI is implemented at law firms and in court while learning how it reacts in a different geopolitical environment. In summary, my research analysis will explain how AI is useful to many law firms, but more importantly, recognize the fact that it lacks the “human individuality” in the way information is being presented

Emotion Model for Sociable Agent and Its Verification Based on Social Role

随着以智能体技术为依托的产品在教育、医疗、娱乐、交通和通信等领域得到越来越广泛的应用，人类对智能体本身的可交互性提出了更高的要求。和谐的人机交互体验对于加速智能体进一步融入到人类的社会生活中具有重要的意义。面向增强人机交互功能这一目标，社交型智能体的概念被提出并迅速成为智能体技术的研究热点，交互体系和情感等社交元素的重要性在这一概念中得到了强化。本文的研究工作便是围绕着提高智能体在社交过程中情感表现的合理性而展开。立足于已有的人工情感理论，本文将基于社会角色和关系的社会认知模型融入到情感的产生过程中，提高了情感与社交行为之间的耦合度。论文的主要研究工作如下： （1）吸收PSI理论中关于情感与...As agent-based products have been more and more widely used in the fields of education, health care, entertainment, transportation, communication and so on, human beings place higher demands on better interactive functionality of agents. Experience from human-machine interaction is significant for accelerating integration of agents into our social life. To achieve the goal of enhancing human-machi...学位：工学硕士院系专业：信息科学与技术学院计算机科学系_计算机应用技术学号：2302009115273

Accounting and Accountability in the Field of Social Services - A Multi-level Investigation

Roberts (1991, p.355) remarked that the analysis of accounting in systems of accountability (also) offers alternative ways to conceive of the transformation of accounting. This dissertation aims to improve multi-level understanding of accounting and accountability within the field of social services. Focusing on Indias Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS), one of the worlds largest social services programs, I examine the role of accounting in accountability practices and change processes at macro, meso and micro levels. Current social services literature, straddling public, private and third sectors, reveals accounting-accountability research to be underexplored (Bracci & Llewellyn, 2012) and conspicuously lacking in diversity of research sites, yet undergoing significant change (Ebrahim, 2003; Brinkerhoff & Brinkerhoff, 2004; Llewellyn, 1997; Walz & Ramachandran, 2011). This facilitates a unique set of observations and understandings as program delivery and implementation evolve. This dissertation specifically uses Bourdieus notions of field, habitus and capitals, also linking to literatures on management control systems, budgeting, routines and sense-making. Following the unfolding of MGNREGS over eight years, I raise two main research questions: How are accounting practices and artifacts intentionally enlisted in MGNERGS towards notions of accountability across multiple levels of program governance? What role do accounting practices in MGNREGS play in larger organizational and social change processes? I examine accountings enlistment in an enabling role to frame and diffuse accountability and program structure on a macro level; in a strategical role, to construct accountability at the meso level; and in a learning and sense-making role to implement accountability at the micro level, where the programs accounting and accountability practices intersect with rural villages. My analysis argues that accounting can be mobilized towards emergent change processes both within public organizations and wider social practices to impact the daily lives of underprivileged rural citizens. In MGNREGS, accounting as an organizational and social practice is not only shaped by organizational objectives but also in turn shapes these objectives and the fields material structure, players, powers, logics and habitus. Accounting practices are, thus, an important part of the ordering, (re)organizing and multi-level change processes in the field of social services in India

Corporate Smart Content Evaluation

Nowadays, a wide range of information sources are available due to the evolution of web and collection of data. Plenty of these information are consumable and usable by humans but not understandable and processable by machines. Some data may be directly accessible in web pages or via data feeds, but most of the meaningful existing data is hidden within deep web databases and enterprise information systems. Besides the inability to access a wide range of data, manual processing by humans is effortful, error-prone and not contemporary any more. Semantic web technologies deliver capabilities for machine-readable, exchangeable content and metadata for automatic processing of content. The enrichment of heterogeneous data with background knowledge described in ontologies induces re-usability and supports automatic processing of data. The establishment of “Corporate Smart Content” (CSC) - semantically enriched data with high information content with sufficient benefits in economic areas - is the main focus of this study. We describe three actual research areas in the field of CSC concerning scenarios and datasets applicable for corporate applications, algorithms and research. Aspect- oriented Ontology Development advances modular ontology development and partial reuse of existing ontological knowledge. Complex Entity Recognition enhances traditional entity recognition techniques to recognize clusters of related textual information about entities. Semantic Pattern Mining combines semantic web technologies with pattern learning to mine for complex models by attaching background knowledge. This study introduces the afore-mentioned topics by analyzing applicable scenarios with economic and industrial focus, as well as research emphasis. Furthermore, a collection of existing datasets for the given areas of interest is presented and evaluated. The target audience includes researchers and developers of CSC technologies - people interested in semantic web features, ontology development, automation, extracting and mining valuable information in corporate environments. The aim of this study is to provide a comprehensive and broad overview over the three topics, give assistance for decision making in interesting scenarios and choosing practical datasets for evaluating custom problem statements. Detailed descriptions about attributes and metadata of the datasets should serve as starting point for individual ideas and approaches

Providing awareness, explanation and control of personalized filtering in a social networking site

Social networking sites (SNSs) have applied personalized filtering to deal with overwhelmingly irrelevant social data. However, due to the focus of accuracy, the personalized filtering often leads to “the filter bubble” problem where the users can only receive information that matches their pre-stated preferences but fail to be exposed to new topics. Moreover, these SNSs are black boxes, providing no transparency for the user about how the filtering mechanism decides what is to be shown in the activity stream. As a result, the user’s usage experience and trust in the system can decline. This paper presents an interactive method to visualize the personalized filtering in SNSs. The proposed visualization helps to create awareness, explanation, and control of personalized filtering to alleviate the “filter bubble” problem and increase the users’ trust in the system. Three user evaluations are presented. The results show that users have a good understanding about the filter bubble visualization, and the visualization can increase users’ awareness of the filter bubble, understandability of the filtering mechanism and to a feeling of control over the data stream they are seeing. The intuitiveness of the design is overall good, but a context sensitive help is also preferred. Moreover, the visualization can provide users with better usage experience and increase users’ trust in the system

A Modeling, Optimization, and Analysis Framework for Designing Multi-Product Lignocellulosic Biorefineries

The objective of this research is to propose a methodology to develop modular decision analysis frameworks to design value chains for enterprises in the renewable fuels and chemicals sector. The decision support framework focuses on providing strategic decision support to startup and new product ventures. The tasks that are embedded in the framework include process and systems design, technology and product selection, forecasting cost and market variables, designing network capacities, and analysis of risks. The Decision support system (DSS) proposed is based on optimization modeling; systems design are carried out using integer programming with multiple sets of process and network configurations utilized as inputs. Uncertainty is incorporated using real options, which are utilized to design network processing capacity for the conversion of biomass resources. Risk analysis is carried out using Monte Carlo methods. The DSS framework is exemplified using a lignocellulosic biorefinery case study that is assumed to be located in Louisiana. The biorefinery utilizes energy crops as feedstocks and processes them into cellulosic biofuels and biobased chemicals. Optimization modeling is utilized to select an optimal network, a fractionation technology, a fermentation configuration, and optimal product recovery and purification unit operations. A decision tree is then used to design incremental capacity under uncertain market parameters. The valuation methodology proposed stresses flexibility in decision making in the face of market uncertainties as is the case with renewable fuels and chemicals. The value of flexibility, termed as “Option Value” is shown to significantly improve the net present value of the proposed biorefinery. Monte Carlo simulations are utilized to develop risk curves for alternate capacity design plans. Risk curves show a favorable risk reward ratio for the case of incremental capacity design with embedded decision options. The framework proposed here can be used by enterprises, government entities and decision makers in general to test, validate, and design technological superstructures and network processing capacities, conduct scenario analyses, and quantify the financial impacts and risks of their representative designs. We plan to further add functionality to the DSS framework and make available the tools developed to wide audience through an “open-source” software distribution model

Generative network models for simulating urban networks, the case of inter-city transport network in Southeast Asia

This paper examines the driving forces of urban network formation through the simulation of inter-city transportation networks in Southeast Asia. We present a generative network model (GNM) considering geographical and topological effects, thus combining factors commonly analysed through traditional spatial simulation models (e.g., gravity models) and topological simulation models (e.g., actor-oriented stochastic models)in a single framework. In our GNM, it is assumed that the probability of connections between cities emerges from competing forces. Stimulating factors are a measure of city size (i.e., population) and a topological rule favouring the formation of connections between cities sharing nearest neighbours (i.e., transitive effects). The hampering factors are physical distance between two cities as well as institutional distance (i.e., border effects). We discuss the model in the context of on-going engagements between urban-geographical research and the network science literature, and validate the credence of the model against empirical data on the transport networks connecting 51 major cities in Southeast Asia. Our results show that (1) the generated networks approximate the observed ones in terms of average path length, clustering, modularity, efficiency and quadratic assignment procedure (QAP) correlation between the observed composite network and the generated one, and that (2) GNM performs best when topographical and topological factors are considered simultaneously. Each factor contributes differently to network formation, with transitive effects playing the most important role

Renewable 1,4-Butanediol

The purpose of this project is to design a commercial-scale facility to produce 50 million pounds per year of 1,4-butanediol (BDO) from a renewable feedstock. A genetically engineered strain of Escherichia coli developed by Genomatica, Inc. will metabolize a molasses feed, delivered from an adjacent sugar and ethanol facility, into BDO. The BDO product purity and quality must meet or exceed current commercial requirements for polymer-grade material to be acceptable to prospective customers. The innovative technology to produce environmentally-friendly BDO will convert biomass-derived and renewable feedstocks in fewer steps than traditional petrochemical routes, with no toxic byproducts and minimal greenhouse gas emissions. Our BDO plant will be built in São Paulo, Brazil. This location was chosen due to its proximity to our sister sugar and ethanol facility. Despite the need to stop production for three months in mid-December through mid-March during the rainy season, when our sister plant will cease its molasses production, we determined that the low cost per amount of sugar from the Brazilian molasses will outweigh the ability to run year-round in a corn-based facility in the Midwestern United States. To account for the downtime associated with the rainy season, our facility has included extra molasses storage capacity to extend production for an additional month after our sister facility shuts down. We also anticipate 10 days of downtime due to maintenance and cleaning, which will result in about 290 days of full-scale facility operation. An economic analysis of our design demonstrated profitability after the first year of operation. Our feed materials, corn steep liquor, oleic acid, process water, and molasses, will cost us a total of about $200 per ton of BDO produced. But our vinasse co-product, which will be sold back to our sister sugar and ethanol plant for fertilizer, will result in additional revenues of$190 per ton of BDO produced. The selling price of the vinasse is discounted by 70% of the current fertilizer market price since we are selling it back to our sister facility, in return for discounted molasses and electricity. The direct permanent investment of the plant will be about $10.5 million and startup costs will be about$1.5 million, which results in a total permanent investment of $13.5 million. The net present value (NPV) of our facility with 15 years of production is$283 million and the internal rate of return (IRR) is 157%. We intend to sell our 99% pure BDO at $2,420 per ton produced, which will result in revenues of$72.5 million per year based on our commercial-scale production of 8,600 pounds of BDO per hour. Due to the profitability of our design, we will be able to sell our BDO at the low end of the U.S. market price range of $2,420 –$2,840 per ton, as was reported in the third quarter of 2010. Future research may need to be conducted to find out if additional equipment is needed in the actual plant, or if we were too optimistic on our pricing for the raw materials and utilities