Determining customer limits by data mining methods in credit allocation process

The demand for credit is increasing constantly. Banks are looking for various methods of credit evaluation that provide the most accurate results in a shorter period in order to minimize their rising risks. This study focuses on various methods that enable the banks to increase their asset quality without market loss regarding the credit allocation process. These methods enable the automatic evaluation of loan applications in line with the sector practices, and enable determination of credit policies/strategies based on actual needs. Within the scope of this study, the relationship between the predetermined attributes and the credit limit outputs are analyzed by using a sample data set of consumer loans. Random forest (RF), sequential minimal optimization (SMO), PART, decision table (DT), J48, multilayer perceptron(MP), JRip, naïve Bayes (NB), one rule (OneR) and zero rule (ZeroR) algorithms were used in this process. As a result of this analysis, SMO, PART and random forest algorithms are the top three approaches for determining customer credit limits.

Analytical Models to Characterize Trade-Offs between Technological Upgrading and Innovation

We propose two analytical models to characterize the relationship between technological upgrading and innovation in the oil & gas industry. The first one is an “optimization model” which focuses on the trade-offs between profit maximization and environmental compliance cost. The other has been developed based on “predator-prey” model which captures the dynamics of biological systems. Our study contributes to the strategic planning process for sustainable development by providing the insight that optimal allocation process is determined by multiple operational factors, including a firm’s competitive ranking among its industrial competitors, industrial consent on the concurrent rate of return on capital investment, the projected demand of oil & gas in future, and a change in environmental compliance cost. Further, we add to the robustness of the optimal allocation process by providing binding conditions of the set of solutions.

A Culturally Adapted Cognitive Behavioral Therapy Group: A Randomized Controlled Trial

Objective This study aimed to evaluate a culturally adapted cognitive behavior therapy (CBT) group for Chinese people with depression that aimed to alter participants’ negative beliefs of themselves, which were influenced by traditional Chinese cultural values. Method In this randomized controlled trial, 43 participants diagnosed with depression were randomly allocated to a 10-session culturally adapted CBT group or treatment as usual (TAU). A research assistant, who was blinded to the group allocation process, used the Chinese Beck Depression Inventory-II to assess participants’ depressive symptoms during the pre-treatment and post-treatment periods. Results The results of the mixed linear model for repeated measures showed that the CBT group had significantly greater improvement in depressive symptoms than treatment as usual, with a medium effect size. Conclusion This study supports the efficacy and effectiveness of the culturally adapted CBT group in facilitating clinically significant improvement in Chinese people with depression.

Distributing the European structural and investment funds from a conflicting claims approach

In order to support economic development across all European Union regions, €351.8 billion –almost a third of the total EU budget– has been set aside for the Cohesion Policy during the 2014–2020 period. The distribution of this budget is made through five main structural and investment funds, after long and difficult negotiations among the EU member states. This paper analyzes the problem of allocating the limited resources of the European Regional Development Fund as a conflicting claims problem. Specifically, we attempt to show how the conflicting claims approach fits this actual problem, and we propose alternative ways of distributing the budget via (i) claims solutions or (ii) the imposition of bounds (guarantees) to each of the regions. By applying this approach we also show that there is a claims solution that performs better than the others by reducing inequality and promoting convergence to a greater degree. It is clear that political bargaining will always be part of the allocation process. However, having an intuitive initial proposal may help politicians to find the best agreement. To that effect, we propose the use of a claims solution as a way to find an initial proposal for future policy changes concerning the allocations of the EU structural funds.

Optimal Allocation of Shared Manufacturing Resources Based on Bilevel Programming

In the shared manufacturing environment, on the basis of in-depth analysis of the shared manufacturing process and the allocation process of manufacturing resources, a bilevel programming model for the optimal allocation of manufacturing resources considering the benefits of the shared manufacturing platform and the rights of consumers is established. In the bilevel programming model, the flexible indicators representing the interests of the platform are the upper-level optimization target of the model and the Quality of Service (QoS) indicators representing the interests of consumers are the lower-level optimization goal. The weights of the upper indicators are determined by Analytic Hierarchy Process (AHP) and Improved Order Relation Analysis (Improved G1) combination weighting method and the bilevel programming model is solved by the Improved Fast Elitist Non-Dominated Sorting Genetic Algorithm (Improved NSGA-II). Finally, the effectiveness of the model is validated by a numerical example.

Novel Data-Driven Methodology for Production Allocation Calculation Utilizing Physics-Based Models Coupled and Integrated Within a Digital Framework

Determining the production from each well is crucial for financial and technical purposes. Moreover, this production can be anticipated using several different techniques. This paper describes the procedures to calculate the production allocated to each well in a giant gas-producing field by utilizing physics-based models that are orchestrated in a dynamic digital platform to provide a robust and efficient solution. The cases for this study of allocating gas rates to individual wells were performed using a digital platform as the primary tool utilized to account for the main productional location factors such as well tests and events that are used to estimate actual production volumes. Subsequently, relevant data is extracted, filtered, and loaded into the system in a dynamic interaction with fewer human interventions. The methodology for calculating the production allocated followed these main steps: a) Determine production per well under existing possible measures, b) Determine well contribution factors, c) Distribute actual rates and production according to allocation factors. By using polynomial equations where the inflow performance of the gas wells was verified, the allocation rates were calculated at every desired point of the network. Having an integrated platform proved to be advantageous since it provided a seamless link between different relevant manual and real-time databases and well / network models bringing unique capabilities and benefits. While comparing this integrated and holistic approach versus the previously established one, it was highlighted that production allocation using mainly choke sizes and well test as a sole source for well production can bring significant variations. This creates production mismatches at the well level; therefore, it portrays a misrepresentation of the actual field conditions. Numerous challenges, which are usually faced while calculating the production allocation process, were overcome during the development of this study, such as frequent surface network changes, lack of databases communication, and daily variations on the on/off wells’ status. Furthermore, the data management capabilities of the framework allowed data to be quickly accessible by the users whenever needed allowing them to visualize across the different teams and departments, taking actions when and where required. This standardized methodology provided consistency, reliability, and accuracy, which can be replicated on oil-producing fields and networks; it can be enhanced and scaled in order to incorporate other business processes such as well allowable calculation and voidage monitoring.

Does Inter-Border Conflict Influence the Views of Task Sharing Among Community Health Volunteers in Nigeria? a Qualitative Study

Background: Volunteer community health workers are increasingly being engaged in Nigeria, through the World Health Organization’s task sharing strategy. This strategy aims to address gaps in human resources for health, including inequitable distribution of health workers. Recent conflicts in rural and fragile border communities in northcentral Nigeria create challenges for volunteer community health workers to meet their communities increasing health needs. This study aimed to explore the perception of volunteers involved in task sharing to understand factors affecting performance and delivery in such contexts.Methods: Eighteen audio recorded, semi-structured interviews with volunteers and supervisors were conducted. Their perceptions on on how task sharing and allocation affect performance and delivery were elucidated. The transactional social framework was applied during the thematic analysis process to generate an explanatory account of the research data.Results: Promotive and preventive tasks were shared among the predominantly agrarian respondents. There was a structured task allocation process that linked the community with the health system and mainly cordial relationships were in place. However, there were barriers related to ethnoreligious crises and current conflict, timing of task allocations, gender inequities in volunteerism, shortage of commodities, inadequate incentives, dwindling community support and negative attitudes of some volunteers.Conclusion: The perception of task sharing was mainly positive, despite the challenges, especially the current conflict. In this fragile context, reconsideration of non-seasonal task allocations within improved community-driven selection and security systems should be encouraged. Supportive supervision and providing adequate and timely renumerations will also be beneficial in this fragile setting.

PP118 A Survival Analysis Of The Lag Times In The Publication Of Network Meta-Analyses

IntroductionThe use of inconsistent and outdated information may significantly compromise healthcare decision-making. We aimed to assess the extent of lag times in the publication and indexing of network meta-analyses (NMAs).MethodsSearches for NMAs on drug interventions were performed in PubMed (May 2020). Lag times were measured as the time between the last systematic search and the date of the article's submission, acceptance, online publication, indexing, and Medical Subject Heading (MeSH) allocation. Correlations between lag times and time trends were calculated by means of Spearman's rank correlation coefficient. Time-to-event analyses were performed considering independent variables such as geographical origin, journal impact factor, Scopus CiteScore, and open access status.ResultsWe included 1,245 NMAs. The median time from last search to article submission and publication was 6.8 months and 11.6 months, respectively. Only five percent of authors updated their literature searches after submission. There was a very slight decreasing historical trend for acceptance (r =−0.087; p = 0.01), online publication (r =−0.08; p = 0.008), and indexing lag times (r =−0.080; p = 0.007). Journal impact factor influenced the MeSH allocation process (log-rank p = 0.02). Slight differences were observed for acceptance, online publication, and indexing lag times when comparing open access and subscription journals.ConclusionsAuthors need to update their literature searches before submission to reduce evidence production time. Peer reviewers and editors should ensure that authors comply with NMA standards and encourage the development of living meta-analyses.

Urban search and rescue (USAR) simulation system: spatial strategies for agent task allocation under uncertain conditions

Task allocation under uncertain conditions is a key problem for agents attempting to achieve harmony in disaster environments. This paper presents an agent-based simulation to investigate task allocation considering appropriate spatial strategies to manage uncertainty in urban search and rescue (USAR) operations. The proposed method is based on the contract net protocol (CNP) and implemented over five phases: ordering existing tasks considering intrinsic interval uncertainty, finding a coordinating agent, holding an auction, applying allocation strategies (four strategies), and implementing and observing the real environment. Applying allocation strategies is the main innovation of the method. The methodology was evaluated in Tehran's District 1 for 6.6, 6.9, and 7.2 magnitude earthquakes. The simulation began by calculating the numbers of injured individuals, which were 28 856, 73 195, and 111 463 people for each earthquake, respectively. Simulations were performed for each scenario for a variety of rescuers (1000, 1500, and 2000 rescuers). In comparison with the CNP, the standard duration of rescue operations with the proposed approach exhibited at least 13 % improvement, with a maximal improvement of 21 %. Interval uncertainty analysis and comparison of the proposed strategies showed that increased uncertainty led to increased rescue time for the CNP and strategies 1 to 4. The time increase was less with the uniform distribution strategy (strategy 4) than with the other strategies. The consideration of strategies in the task allocation process, especially spatial strategies, facilitated both optimization and increased flexibility of the allocation. It also improved conditions for fault tolerance and agent-based cooperation stability in the USAR simulation system.

A Decentralized Framework for Managing Task Allocation in Distributed Software Engineering

In distributed software development, planning and managing fair and transparent task allocation is both critical and challenging. The objective of this paper is to propose a decentralized blockchain-oriented, transparent task allocation framework to improve the quality of the task allocation process. It addresses the concerns of (i) enhancing collaboration, (ii) inhibiting knowledge vaporization, and (iii) reducing documentation problems. The proposed method is a novel two-fold process: First, it identifies and categorizes tasks exhibiting different dependencies and complexities to create equal task clusters based on their dependency type, difficulty, cost, and time. Second, it uses a blockchain-oriented framework to broadcast, check bid validity, allow developers to bid on tasks matching their roles and expertise, evaluate, and announce the winner for task allocation using smart contracts. Results of experimentation, surveys, and interviews with software practitioners conclude that the proposed solution is transparent and effective in allocating tasks (with Cranach’s alpha of 0.894) at a low cost of contract execution in a distributed software development environment. Overall, the proposed approach will have a positive and significant impact in industrial settings.