Search results for: decision model

Authors: Kotaro Sasai, Daijiro Mizutani, Kiyoyuki Kaito

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Expressways in Japan have been built in an accelerating manner since the 1960s with the aid of rapid economic growth. About 40 percent in length of expressways in Japan is now 30 years and older and has become superannuated. Time-related deterioration has therefore reached to a degree that administrators, from a standpoint of operation and maintenance, are forced to take prompt measures on a large scale aiming at repairing inner damage deep in pavements. These measures have already been performed for bridge management in Japan and are also expected to be embodied for pavement management. Thus, planning methods for the measures are increasingly demanded. Deterioration of layers around road surface such as surface course and binder course is brought about at the early stages of whole pavement deterioration process, around 10 to 30 years after construction. These layers have been repaired primarily because inner damage usually becomes significant after outer damage, and because surveys for measuring inner damage such as Falling Weight Deflectometer (FWD) survey and open-cut survey are costly and time-consuming process, which has made it difficult for administrators to focus on inner damage as much as they have been supposed to. As expressways today have serious time-related deterioration within them deriving from the long time span since they started to be used, it is obvious the idea of repairing layers deep in pavements such as base course and subgrade must be taken into consideration when planning maintenance on a large scale. This sort of maintenance requires precisely predicting degrees of deterioration as well as grasping the present situations of pavements. Methods for predicting deterioration are determined to be either mechanical or statistical. While few mechanical models have been presented, as far as the authors know of, previous studies have presented statistical methods for predicting deterioration in pavements. One describes deterioration process by estimating Markov deterioration hazard model, while another study illustrates it by estimating Proportional deterioration hazard model. Both of the studies analyze deflection data obtained from FWD surveys and present statistical methods for predicting deterioration process of layers around road surface. However, layers of base course and subgrade remain unanalyzed. In this study, data collected from FWD surveys are analyzed to predict deterioration process of layers deep in pavements in addition to surface layers by a means of estimating a deterioration hazard model using continuous indexes. This model can prevent the loss of information of data when setting rating categories in Markov deterioration hazard model when evaluating degrees of deterioration in roadbeds and subgrades. As a result of portraying continuous indexes, the model can predict deterioration in each layer of pavements and evaluate it quantitatively. Additionally, as the model can also depict probability distribution of the indexes at an arbitrary point and establish a risk control level arbitrarily, it is expected that this study will provide knowledge like life cycle cost and informative content during decision making process referring to where to do maintenance on as well as when.

Keywords: deterioration hazard model, falling weight deflectometer, inner damage, load bearing capacity, pavement

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Authors: Jaime Leavitt, Katy Haralampides

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Numerical modelling has been used to investigate the long-term impact of a large dam on downstream wetland areas, specifically in terms of changing sediment dynamics in the system. The Mactaquac Generating Station (MQGS) is a 672MW run-of-the-river hydroelectric facility, commissioned in 1968 on the mainstem of the Wolastoq|Saint John River in New Brunswick, Canada. New Brunswick Power owns and operates the dam and has been working closely with the Canadian Rivers Institute at UNB Fredericton on a multi-year, multi-disciplinary project investigating the impact the dam has on its surrounding environment. With focus on the downstream river, this research discusses the initialization, set-up, calibration, and preliminary results of a 2-D hydrodynamic model using the Delft3d Flexible Mesh Suite (successor of the Delft3d 4 Suite). The flexible mesh allows the model grid to be structured in the main channel and unstructured in the floodplains and other downstream regions with complex geometry. The combination of grid types improves computational time and output. As the movement of water governs the movement of sediment, the calibrated and validated hydrodynamic model was applied to sediment transport simulations, particularly of the fine suspended sediments. Several provincially significant Protected Natural Areas and federally significant National Wildlife Areas are located 60km downstream of the MQGS. These broad, low-lying floodplains and wetlands are known as the Grand Lake Meadows Complex (GLM Complex). There is added pressure to investigate the impacts of river regulation on these protected regions that rely heavily on natural river processes like sediment transport and flooding. It is hypothesized that the fine suspended sediment would naturally travel to the floodplains for nutrient deposition and replenishment, particularly during the freshet and large storms. The purpose of this research is to investigate the impacts of river regulation on downstream environments and use the model as a tool for informed decision making to protect and maintain biologically productive wetlands and floodplains.

Keywords: hydrodynamic modelling, national wildlife area, protected natural area, sediment transport.

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Authors: Anat Keller

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Given that macro-prudential supervision is a relatively new policy area and its empirical and analytical research are still in their infancy, its theoretical foundations are also lagging behind. This paper contributes to the developing discussion on effective legal and institutional macroprudential supervision frameworks. In the first part of the paper, it is argued that effectiveness as a key benchmark poses some challenges in the context of macroprudential supervision such as the difficulty in proving causality between supervisory actions and the achievement of the supervisor’s mission. The paper suggests that effectiveness in the macroprudential context should, therefore, be assessed at the supervisory decision-making process (to be differentiated from the supervisory outcomes). The second part of the essay examines whether insights from behavioural economics can point to biases in the macroprudential decision-making process. These biases include, inter alia, preference bias, groupthink bias and inaction bias. It is argued that these biases are exacerbated in the multilateral setting of the macroprudential supervision framework in the EU. The paper then examines how legal and institutional frameworks should be designed to acknowledge and perhaps contain these identified biases. The paper suggests that the effectiveness of macroprudential policy will largely depend on the existence of clear and robust transparency and accountability arrangements. Accountability arrangements can be used as a vehicle for identifying and addressing potential biases in the macro-prudential framework, in particular, inaction bias. Inclusiveness of the public in the supervisory process in the form of transparency and awareness of the logic behind policy decisions may assist in minimising their potential unpopularity thus promoting their effectiveness. Furthermore, a governance structure which facilitates coordination of the macroprudential supervisor with other policymakers and incorporates outside perspectives and opinions could ‘break-down’ groupthink bias as well as inaction bias.

Keywords: behavioural economics and biases, effectiveness of macroprudential supervision, legal and institutional macroprudential frameworks, macroprudential decision-making process

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Authors: Benjamin Aina Peter, Amos Wale Ogunsola

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In this paper, a mathematical model of malaria transmission was presented with the effect of seasonal shift, due to global fluctuation in temperature, on the increase of conveyor of the infectious disease, which probably alters the region transmission potential of malaria. A deterministic compartmental model was proposed and analyzed qualitatively. Both qualitative and quantitative approaches of the model were considered. The next-generation matrix is employed to determine the basic reproduction number of the model. Equilibrium points of the model were determined and analyzed. The numerical simulation is carried out using Excel Micro Software to validate and support the qualitative results. From the analysis of the result, the optimal temperature for the transmission of malaria is between and . The result also shows that an increase in temperature due to seasonal shift gives rise to the development of parasites which consequently leads to an increase in the widespread of malaria transmission in Kampala. It is also seen from the results that an increase in temperature leads to an increase in the number of infectious human hosts and mosquitoes.

Keywords: seasonal variation, indoor residual spray, efficacy of spray, temperature-dependent model

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Authors: Diogo José Morgado Rebelo, Francisco António Carneiro Pacheco de Andrade, Paulo Jorge Freitas de Oliveira Novais

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Not everything in AI-power consumer credit scoring turns out to be a wonder. When using AI in Creditworthiness Assessment (CWA), opacity and unfairness ‘sins’ must be considered to the task be deemed Responsible. AI software is not always 100% accurate, which can lead to misclassification. Discrimination of some groups can be exponentiated. A hetero personalized identity can be imposed on the individual(s) affected. Also, autonomous CWA sometimes lacks transparency when using black box models. However, for this intended purpose, human analysts ‘on-the-loop’ might not be the best remedy consumers are looking for in credit. This study seeks to explore the legality of implementing a Multi-Agent System (MAS) framework in consumer CWA to ensure compliance with the regulation outlined in Article 14(4) of the Proposal for an Artificial Intelligence Act (AIA), dated 21 April 2021 (as per the last corrigendum by the European Parliament on 19 April 2024), Especially with the adoption of Art. 18(8)(9) of the EU Directive 2023/2225, of 18 October, which will go into effect on 20 November 2026, there should be more emphasis on the need for hybrid oversight in AI-driven scoring to ensure fairness and transparency. In fact, the range of EU regulations on AI-based consumer credit will soon impact the AI lending industry locally and globally, as shown by the broad territorial scope of AIA’s Art. 2. Consequently, engineering the law of consumer’s CWA is imperative. Generally, the proposed MAS framework consists of several layers arranged in a specific sequence, as follows: firstly, the Data Layer gathers legitimate predictor sets from traditional sources; then, the Decision Support System Layer, whose Neural Network model is trained using k-fold Cross Validation, provides recommendations based on the feeder data; the eXplainability (XAI) multi-structure comprises Three-Step-Agents; and, lastly, the Oversight Layer has a 'Bottom Stop' for analysts to intervene in a timely manner. From the analysis, one can assure a vital component of this software is the XAY layer. It appears as a transparent curtain covering the AI’s decision-making process, enabling comprehension, reflection, and further feasible oversight. Local Interpretable Model-agnostic Explanations (LIME) might act as a pillar by offering counterfactual insights. SHapley Additive exPlanation (SHAP), another agent in the XAI layer, could address potential discrimination issues, identifying the contribution of each feature to the prediction. Alternatively, for thin or no file consumers, the Suggestion Agent can promote financial inclusion. It uses lawful alternative sources such as the share of wallet, among others, to search for more advantageous solutions to incomplete evaluation appraisals based on genetic programming. Overall, this research aspires to bring the concept of Machine-Centered Anthropocentrism to the table of EU policymaking. It acknowledges that, when put into service, credit analysts no longer exert full control over the data-driven entities programmers have given ‘birth’ to. With similar explanatory agents under supervision, AI itself can become self-accountable, prioritizing human concerns and values. AI decisions should not be vilified inherently. The issue lies in how they are integrated into decision-making and whether they align with non-discrimination principles and transparency rules.

Keywords: creditworthiness assessment, hybrid oversight, machine-centered anthropocentrism, EU policymaking

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Authors: Amin Jamili

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Nowadays, the majority of international trade in goods is carried by sea, and especially by ships deployed in the industrial and tramp segments. This paper addresses routing the tramp ships and determining the schedules including the arrival times to the ports, berthing times at the ports, and the departure times in an operational planning level. In the operational planning level, the weather can be almost exactly forecasted, however in some routes some uncertainties may remain. In this paper, the voyaging times between some of the ports are considered to be uncertain. To that end, a two-stage stochastic mathematical model is proposed. Moreover, a case study is tested with the presented model. The computational results show that this mathematical model is promising and can represent acceptable solutions.

Keywords: routing, scheduling, tram ships, two stage stochastic model, uncertainty

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Authors: Kolade Olawande Owoeye, George R. S. Weir

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The widespread of terrorism and extremism activities on the internet has become a major threat to the government and national securities due to their potential dangers which have necessitated the need for intelligence gathering via web and real-time monitoring of potential websites for extremist activities. However, the manual classification for such contents is practically difficult or time-consuming. In response to this challenge, an automated classification system called composite technique was developed. This is a computational framework that explores the combination of both semantics and syntactic features of textual contents of a web. We implemented the framework on a set of extremist webpages dataset that has been subjected to the manual classification process. Therein, we developed a classification model on the data using J48 decision algorithm, this is to generate a measure of how well each page can be classified into their appropriate classes. The classification result obtained from our method when compared with other states of arts, indicated a 96% success rate in classifying overall webpages when matched against the manual classification.

Keywords: extremist, web pages, classification, semantics, posit

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Authors: A. Zanj, F. He

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In the current paper, a domain independent conduction model compatible for multi-physical system dynamic investigations is suggested. By means of a port-based approach, a classical nonlinear conduction model containing physical states is first represented. A compatible discrete configuration of the thermal domain in line with the elastic domain is then generated through the enhancement of the configuration of the conventional thermal element. The presented simulation results of a sample structure indicate that the suggested conductive model can cover a wide range of dynamic behavior of the thermal domain.

Keywords: multi-physical domain, conduction model, port based modeling, dynamic interaction, physical modeling

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Authors: Anne Theresa Eidhoff, Sarah E. Stief, Markus Voeth, Sarah Gundlach

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With digitalization increasingly changing the rules of competition, firms face the need to adapt and assimilate digital technologies in order to remain competitive. Firms can choose from various possibilities to integrate digital technologies including the option to embed digital technologies aiming to innovate products or to develop digital products. However, the question of which specific factors influence a firm’s decision to pursue digital product innovation remains unanswered in research. By adopting the Technology-Organization-Environment (TOE)-framework we have designed a qualitative exploratory study including eleven German practitioners to investigate relevant contingency factors. Our results indicate that the most critical factors for a company’s decision to pursue digital product innovation can be found in the technological and environmental dimensions, namely customers, competitive pressure, technological change, as well as digitalization fit. 

Keywords: digital innovation, digitalization, product innovation, TOE-framework

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Authors: Aicha Aggoune, Abdelkrim Bouramoul, Mohamed Khiereddine Kholladi

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Recent research shows that user profile as important element can improve heterogeneous information retrieval with its content. In this context, we present our indexing model for heterogeneous multimedia documents. This model is based on the combination of user profile to the indexing process. The general idea of our proposal is to operate the common concepts between the representation of a document and the definition of a user through his profile. These two elements will be added as additional indexing entities to enrich the heterogeneous corpus documents indexes. We have developed IRONTO domain ontology allowing annotation of documents. We will present also the developed tool validating the proposed model.

Keywords: indexing model, user profile, multimedia document, heterogeneous of sources, ontology

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Authors: Elaheh Talebi, Fariba Yavari, Lucy Philip, Lesley Town

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The mining industry generates vast amounts of data, necessitating robust data management systems and advanced analytics tools to achieve better decision-making processes in the development of mining production and maintaining safety. This paper highlights the advantages of Power BI, a powerful intelligence tool, over traditional Excel-based approaches for effectively managing and harnessing mining data. Power BI enables professionals to connect and integrate multiple data sources, ensuring real-time access to up-to-date information. Its interactive visualizations and dashboards offer an intuitive interface for exploring and analyzing geotechnical data. Advanced analytics is a collection of data analysis techniques to improve decision-making. Leveraging some of the most complex techniques in data science, advanced analytics is used to do everything from detecting data errors and ensuring data accuracy to directing the development of future project phases. However, while Power BI is a robust tool, specific visualizations required by geotechnical engineers may have limitations. This paper studies the capability to use Python or R programming within the Power BI dashboard to enable advanced analytics, additional functionalities, and customized visualizations. This dashboard provides comprehensive tools for analyzing and visualizing key geotechnical data metrics, including spatial representation on maps, field and lab test results, and subsurface rock and soil characteristics. Advanced visualizations like borehole logs and Stereonet were implemented using Python programming within the Power BI dashboard, enhancing the understanding and communication of geotechnical information. Moreover, the dashboard's flexibility allows for the incorporation of additional data and visualizations based on the project scope and available data, such as pit design, rock fall analyses, rock mass characterization, and drone data. This further enhances the dashboard's usefulness in future projects, including operation, development, closure, and rehabilitation phases. Additionally, this helps in minimizing the necessity of utilizing multiple software programs in projects. This geotechnical dashboard in Power BI serves as a user-friendly solution for analyzing, visualizing, and communicating both new and historical geotechnical data, aiding in informed decision-making and efficient project management throughout various project stages. Its ability to generate dynamic reports and share them with clients in a collaborative manner further enhances decision-making processes and facilitates effective communication within geotechnical projects in the mining industry.

Keywords: geotechnical data analysis, power BI, visualization, decision-making, mining industry

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Authors: Ayoade Adeyemi, Leo Nherera, Paul Trueman, Antje Emmermann

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Background and objectives: Radiofrequency (RF) generated plasma chondroplasty is considered a promising treatment alternative to mechanical debridement (MD) with a shaver. The aim of the study was to perform a cost-effectiveness analysis comparing costs and outcomes following COBLATION chondroplasty versus mechanical debridement in patients with knee pain associated with a medial meniscus tear and idiopathic ICRS grade III focal lesion of the medial femoral condyle from a payer perspective. Methods: A decision-analytic model was developed comparing economic and clinical outcomes between the two treatment options in German patients following knee chondroplasty. Revision rates based on the frequency of repeat arthroscopy, osteotomy and conversion to total knee replacement, reimbursement costs and outcomes data over a 4-year time horizon were extracted from published literature. One-way sensitivity analyses were conducted to assess uncertainties around model parameters. Threshold analysis determined the revision rate at which model results change. All costs were reported in 2016 euros, future costs were discounted at a 3% annual rate. Results: Over a 4 year period, COBLATION chondroplasty resulted in an overall net saving cost of €461 due to a lower revision rate of 14% compared to 48% with MD. Threshold analysis showed that both options were associated with comparable costs if COBLATION revision rate was assumed to increase up to 23%. The initial procedure costs for COBLATION were higher compared to MD and outcome scores were significantly improved at 1 and 4 years post-operation versus MD. Conclusion: The analysis shows that COBLATION chondroplasty is a cost-effective option compared to mechanical debridement in the treatment of patients with a medial meniscus tear and idiopathic ICRS grade III defect of the medial femoral condyle.

Keywords: COBLATION, cost-effectiveness, knee chondroplasty, mechanical debridement

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Authors: Ying Shen, Weihua Zhu

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Medical images are important to help identifying different diseases, for example, Magnetic resonance imaging (MRI) can be used to investigate the brain, spinal cord, bones, joints, breasts, blood vessels, and heart. Image segmentation, in medical image analysis, is usually the first step to find out some characteristics with similar color, intensity or texture so that the diagnosis could be further carried out based on these features. This paper introduces an improved C-means model to segment the MRI images. The model is based on information entropy to evaluate the segmentation results by achieving global optimization. Several contributions are significant. Firstly, Genetic Algorithm (GA) is used for achieving global optimization in this model where fuzzy C-means clustering algorithm (FCMA) is not capable of doing that. Secondly, the information entropy after segmentation is used for measuring the effectiveness of MRI image processing. Experimental results show the outperformance of the proposed model by comparing with traditional approaches.

Keywords: magnetic resonance image (MRI), c-means model, image segmentation, information entropy

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Authors: Kawsar Ahmed, Hong Wang

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The study's goal was to estimate the causal mediation impact of tobacco tax before and after price hikes among low-income male smokers, with a particular emphasis on the effect estimating pathways framework for continuous and dichotomous variables. From July to December 2021, a cross-sectional investigation of observational data (n=739) was collected from Bangladeshi low-wage smokers. The Quasi-Bayesian technique, binomial probit model, and sensitivity analysis using a simulation of the computational tools R mediation package had been used to estimate the effect. After a price rise for tobacco products, the average number of cigarettes or bidis sticks taken decreased from 6.7 to 4.56. Tobacco product rising prices have a direct effect on low-income people's decisions to quit or lessen their daily smoking habits of Average Causal Mediation Effect (ACME) [effect=2.31, 95 % confidence interval (C.I.) = (4.71-0.00), p<0.01], Average Direct Effect (ADE) [effect=8.6, 95 percent (C.I.) = (6.8-0.11), p<0.001], and overall significant effects (p<0.001). Tobacco smoking choice is described by the mediated proportion of income effect, which is 26.1% less of following price rise. The curve of ACME and ADE is based on observational figures of the coefficients of determination that asses the model of hypothesis as the substantial consequence after price rises in the sensitivity analysis. To reduce smoking product behaviors, price increases through taxation have a positive causal mediation with income that affects the decision to limit tobacco use and promote low-income men's healthcare policy.

Keywords: causal mediation analysis, directed acyclic graphs, tobacco price policy, sensitivity analysis, pathway estimation

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Authors: V. Makis, L. Jafari

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In this paper, we develop an optimal Bayesian chart to control the expected number of defects per inspection unit in production processes with long production runs. We formulate this control problem in the optimal stopping framework. The objective is to determine the optimal stopping rule minimizing the long-run expected average cost per unit time considering partial information obtained from the process sampling at regular epochs. We prove the optimality of the control limit policy, i.e., the process is stopped and the search for assignable causes is initiated when the posterior probability that the process is out of control exceeds a control limit. An algorithm in the semi-Markov decision process framework is developed to calculate the optimal control limit and the corresponding average cost. Numerical examples are presented to illustrate the developed optimal control chart and to compare it with the traditional u-chart.

Keywords: Bayesian u-chart, economic design, optimal stopping, semi-Markov decision process, statistical process control

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Authors: A. Lashkari, H. Khalafi, H. Khazeminejad, S. Khakshourniya

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In this paper, a numerical model is presented. The model is used to analyze a steady state thermo-hydraulic and reactivity insertion transient in TRR reference cores respectively. The model predictions are compared with the experiments and PARET code results. The model uses the piecewise constant and lumped parameter methods for the coupled point kinetics and thermal-hydraulics modules respectively. The advantages of the piecewise constant method are simplicity, efficiency and accuracy. A main criterion on the applicability range of this model is that the exit coolant temperature remains below the saturation temperature, i.e. no bulk boiling occurs in the core. The calculation values of power and coolant temperature, in steady state and positive reactivity insertion scenario, are in good agreement with the experiment values. However, the model is a useful tool for the transient analysis of most research reactor encountered in practice. The main objective of this work is using simple calculation methods and benchmarking them with experimental data. This model can be used for training proposes.

Keywords: thermal-hydraulic, research reactor, reactivity insertion, numerical modeling

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Authors: Shannon Hashemi, Alireza Daneshfar

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Accounting ethical awareness has been widely promoted in recent years both in academia and in practice. However, the effectiveness of ethical awareness on accountants' judgment and choice of action is still debatable. This study investigates whether Machiavellianism and gender, as significant personality factors, influence the effect of ethical awareness on accountants' decision-making. Using an experiment, the results of ANOVA tests show that although introducing ethical awareness positively influences the accountants' judgment and choice of action, such an effect is significantly moderated by the accountants' Machiavellianism score and gender. Specifically, the test results show that the effect of introducing ethical awareness was higher on males with low Machiavellian score. The results also show that when the Machiavellian scores were high, the effect of ethical awareness was lower for both males and females. Applications of the results are discussed for accounting professionals as well as accounting ethics educators and researchers.

Keywords: ethical awareness, accounting decision making, Machiavellianism, ANOVA, ethics, accounting education

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Authors: Ajai Singh

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Simulation of hydrological processes at the watershed outlet through modelling approach is essential for proper planning and implementation of appropriate soil conservation measures in Damodar Barakar catchment, Hazaribagh, India where soil erosion is a dominant problem. This study quantifies the parametric uncertainty involved in simulation of stream flow using Soil and Water Assessment Tool (SWAT), a watershed scale model and Radial Basis Neural Network (RBNN), an artificial neural network model. Both the models were calibrated and validated based on measured stream flow and quantification of the uncertainty in SWAT model output was assessed using ‘‘Sequential Uncertainty Fitting Algorithm’’ (SUFI-2). Though both the model predicted satisfactorily, but RBNN model performed better than SWAT with R2 and NSE values of 0.92 and 0.92 during training, and 0.71 and 0.70 during validation period, respectively. Comparison of the results of the two models also indicates a wider prediction interval for the results of the SWAT model. The values of P-factor related to each model shows that the percentage of observed stream flow values bracketed by the 95PPU in the RBNN model as 91% is higher than the P-factor in SWAT as 87%. In other words the RBNN model estimates the stream flow values more accurately and with less uncertainty. It could be stated that RBNN model based on simple input could be used for estimation of monthly stream flow, missing data, and testing the accuracy and performance of other models.

Keywords: SWAT, RBNN, SUFI 2, bootstrap technique, stream flow, simulation

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Authors: Mujde Turkkan, Nurkan Yagiz

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The vibrations, caused by the irregularities of the road surface, are to be suppressed via suspension systems. In this paper, sliding mode control for a half bus model with air suspension system is presented. The bus is modelled as five degrees of freedom (DoF) system. The mathematical model of the half bus is developed using Lagrange Equations. For time domain analysis, the bus model is assumed to travel at certain speed over the bump road. The numerical results of the analysis indicate that the sliding mode controllers can be effectively used to suppress the vibrations and to improve the ride comfort of the busses.

Keywords: active suspension system, air suspension, bus model, sliding mode control

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Authors: Pooja Rani, G. S. Mahapatra, S. K. Pandey

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In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability.

Keywords: software reliability, flexible logistic growth curve model, software cumulative failure prediction, neural network, particle swarm optimization

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Authors: Hakan Erol, Altan Elibol, Ömer Eryılmaz, Mehmet Şimşek

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Organizations aim to manage information in a most possible effective way for sustainment and development. In doing so, they apply various procedures and methods. The very same situation is valid for each service of Armed Forces. During long-lasting endeavors such as shaping and maintaining security environment, supporting and securing peace, knowledge management is a crucial asset. Optimum Balance Model aims to promote the system from a decisive point to a higher decisive point. In this context, this paper analyses the application of optimum balance model to knowledge management in Armed Forces and tries to find answer to the question how Optimum Balance Model is integrated in knowledge management.

Keywords: optimum balance model, knowledge management, security environment, supporting peace

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Authors: Matthew Yeager, Christopher Willy, John Bischoff

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The conceptualization and design phases of a system lifecycle consume a significant amount of the lifecycle budget in the form of direct tasking and capital, as well as the implicit costs associated with unforeseeable design errors that are only realized during downstream phases. Ad hoc or iterative approaches to generating system requirements oftentimes fail to consider the full array of feasible systems or product designs for a variety of reasons, including, but not limited to: initial conceptualization that oftentimes incorporates a priori or legacy features; the inability to capture, communicate and accommodate stakeholder preferences; inadequate technical designs and/or feasibility studies; and locally-, but not globally-, optimized subsystems and components. These design pitfalls can beget unanticipated developmental or system alterations with added costs, risks and support activities, heightening the risk for suboptimal system performance, premature obsolescence or forgone development. Supported by rapid advances in learning algorithms and hardware technology, sensors and sensor systems have become commonplace in both commercial and industrial products. The evolving array of hardware components (i.e. sensors, CPUs, modular / auxiliary access, etc…) as well as recognition, data fusion and communication protocols have all become increasingly complex and critical for design engineers during both concpetualization and implementation. This work seeks to develop and utilize a non-deterministic approach for sensor system design within the multi-attribute tradespace exploration (MATE) paradigm, a technique that incorporates decision theory into model-based techniques in order to explore complex design environments and discover better system designs. Developed to address the inherent design constraints in complex aerospace systems, MATE techniques enable project engineers to examine all viable system designs, assess attribute utility and system performance, and better align with stakeholder requirements. Whereas such previous work has been focused on aerospace systems and conducted in a deterministic fashion, this study addresses a wider array of system design elements by incorporating both traditional tradespace elements (e.g. hardware components) as well as popular multi-sensor data fusion models and techniques. Furthermore, statistical performance features to this model-based MATE approach will enable non-deterministic techniques for various commercial systems that range in application, complexity and system behavior, demonstrating a significant utility within the realm of formal systems decision-making.

Keywords: multi-attribute tradespace exploration, data fusion, sensors, systems engineering, system design

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Authors: Bahadir Aydin

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Today it is difficult to reach accurate knowledge because of mass data. This huge data makes the environment more and more caotic. Data is a main piller of intelligence. There is a close tie between knowledge and intelligence. Information gathered from different sources can be modified, interpreted and classified by using knowledge development process. This process is applied in order to attain intelligence. Within this process the effect of knowledge is crucial. Knowledge is classified as explicit and tacit knowledge. Tacit knowledge can be seen as "only the tip of the iceberg”. This tacit knowledge accounts for much more than we guess in all intelligence cycle. If the concept of intelligence scrutinized, it can be seen that it contains risks, threats as well as success. The main purpose for all organization is to be succesful by eliminating risks and threats. Therefore, there is a need to connect or fuse existing information and the processes which can be used to develop it. By the help of process the decision-maker can be presented with a clear holistic understanding, as early as possible in the decision making process. Planning, execution and assessments are the key functions that connects to information to knowledge. Altering from the current traditional reactive approach to a proactive knowledge development approach would reduce extensive duplication of work in the organization. By new approach to this process, knowledge can be used more effectively.

Keywords: knowledge, intelligence cycle, tacit knowledge, KIDW

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Authors: Nitish Kumar Vaja, Oumar Barry, Brian DeJong

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Aeolian vibrations are the major cause for the failure of conductor cables. Using a Stockbridge damper reduces these vibrations and increases the life span of the conductor cable. Designing an efficient Stockbridge damper that suits the conductor cable requires a robust mathematical model with minimum assumptions. However it is not easy to analytically model the complex geometry of the messenger. Therefore an equivalent stiffness must be determined so that it can be used in the analytical model. This paper examines the bending stiffness of the cable and discusses the effect of this stiffness on the natural frequencies. The obtained equivalent stiffness compensates for the assumption of modeling the messenger as a rod. The results from the free vibration analysis of the analytical model with the equivalent stiffness is validated using the full scale finite element model of the Stockbridge damper.

Keywords: equivalent stiffness, finite element model, free vibration response, Stockbridge damper

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Authors: Dana Pugach, Michal Tamir

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Most criminal cases end with a plea agreement, an issue whose many aspects have been discussed extensively in legal literature. One important feature, however, has gained little notice, and that is crime victims’ place in plea agreements following the federal Crime Victims Rights Act of 2004. This law has provided victims some meaningful and potentially revolutionary rights, including the right to be heard in the proceeding and a right to appeal against a decision made while ignoring the victim’s rights. While victims’ rights literature has always emphasized the importance of such right, references to this provision in the general literature about plea agreements are sparse, if existing at all. Furthermore, there are a few cases only mentioning this right. This article purports to bridge between these two bodies of legal thinking – the vast literature concerning plea agreements and victims’ rights research– by using behavioral economics. The article will, firstly, trace the possible structural reasons for the failure of this right to be materialized. Relevant incentives of all actors involved will be identified as well as their inherent consequential processes that lead to the victims’ rights malfunction. Secondly, the article will use nudge theory in order to suggest solutions that will enhance incentives for the repeat players in the system (prosecution, judges, defense attorneys) and lead to the strengthening of weaker group’s interests – the crime victims. Behavioral psychology literature recognizes that the framework in which an individual confronts a decision can significantly influence his decision. Richard Thaler and Cass Sunstein developed the idea of ‘choice architecture’ - ‘the context in which people make decisions’ - which can be manipulated to make particular decisions more likely. Choice architectures can be changed by adjusting ‘nudges,’ influential factors that help shape human behavior, without negating their free choice. The nudges require decision makers to make choices instead of providing a familiar default option. In accordance with this theory, we suggest a rule, whereby a judge should inquire the victim’s view prior to accepting the plea. This suggestion leaves the judge’s discretion intact; while at the same time nudges her not to go directly to the default decision, i.e. automatically accepting the plea. Creating nudges that force actors to make choices is particularly significant when an actor intends to deviate from routine behaviors but experiences significant time constraints, as in the case of judges and plea bargains. The article finally recognizes some far reaching possible results of the suggestion. These include meaningful changes to the earlier stages of criminal process even before reaching court, in line with the current criticism of the plea agreements machinery.

Keywords: plea agreements, victims' rights, nudge theory, criminal justice

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Authors: Naveed Iqbal Chaudhry, Mian Saqib Mehmood, Asif Mehmood

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This study investigates the motivators of foreign direct investment (FDI) which will provide a panacea tool and ground breaking results related to it in case of Pakistan. The study considers exchange rate, market size and human capital as the motivators for attracting FDI. In this regard, time series data on annual basis has been collected for the period 1985–2010 and an Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests are utilized to determine the stationarity of the variables. A bound testing approach to co-integration was applied because the variables included in the model are at I(1) – first level stationary. The empirical findings of this study confirm the long run relationship among the variables. However, market size and human capital have strong positive and significant impact, in short and long-run, for attracting FDI but exchange rate shows negative impact in this regard. The significant negative coefficient of the ECM indicates that it converges towards equilibrium. CUSUM and CUSUMSQ tests plots are with in the lines of critical value, which indicates the stability of the estimated parameters. However, this model can be used by Pakistan in policy and decision making. For achieving higher economic growth and economies of scale, the country should concentrate on the ingredients of this study so that it could attract more FDI as compared to the other countries.

Keywords: ARDL, CUSUM and CUSUMSQ tests, ECM, exchange rate, FDI, human capital, market size, Pakistan

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Authors: Roman Graf, Sergiu Gordea, Heather M. Ryan

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This paper presents an approach for easy creation and classification of institutional risk profiles supporting endangerment analysis of file formats. The main contribution of this work is the employment of data mining techniques to support set up of the most important risk factors. Subsequently, risk profiles employ risk factors classifier and associated configurations to support digital preservation experts with a semi-automatic estimation of endangerment group for file format risk profiles. Our goal is to make use of an expert knowledge base, accuired through a digital preservation survey in order to detect preservation risks for a particular institution. Another contribution is support for visualisation of risk factors for a requried dimension for analysis. Using the naive Bayes method, the decision support system recommends to an expert the matching risk profile group for the previously selected institutional risk profile. The proposed methods improve the visibility of risk factor values and the quality of a digital preservation process. The presented approach is designed to facilitate decision making for the preservation of digital content in libraries and archives using domain expert knowledge and values of file format risk profiles. To facilitate decision-making, the aggregated information about the risk factors is presented as a multidimensional vector. The goal is to visualise particular dimensions of this vector for analysis by an expert and to define its profile group. The sample risk profile calculation and the visualisation of some risk factor dimensions is presented in the evaluation section.

Keywords: linked open data, information integration, digital libraries, data mining

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Authors: Guilherme B. B. Vieira, Rafael M. da Silva, Eliana T. P. Senna, Luiz A. S. Senna, Francisco J. Kliemann Neto

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Due to the importance of ports to trade and economic development of the regions in which they are inserted, in recent decades the number of studies devoted to this subject has increased. Part of these studies consider the ports as business agglomerations and focuses on port governance. This is an important approach since the port performance is the result of activities performed by actors belonging to the port-logistics chain, which need to be properly coordinated. This coordination takes place through a port governance model. Given this context, this study aims to analyze the governance model of the port of Santos from the perspective of port customers. To do this, a closed-ended questionnaire based on a conceptual model that considers the key dimensions associated with port governance was applied to the international freight forwarders that operate in the port. The results show the applicability of the considered model and highlight improvement opportunities to be implemented at the port of Santos.

Keywords: port governance, model, Port of Santos, customers’ perception

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Authors: Ragea Alqahtani

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The purpose of the study was to measure the effectiveness of the Goldstein’s social skill streaming model based on the special and general pre-service teachers’ knowledge about controlling their emotions in conflict situations. A review of previous pieces of literature guided the design and measurement of the effectiveness of the approach to the control of emotions. The teachers were assessed using the coping strategy, adult anger, and Goldstein’s skill streaming inventories. Lastly, the paper provides various recommendations on the sensitization of the Goldstein’s Social Skill streaming model to both the special and pre-service teachers to promote their knowledge about controlling emotions in conflicts.

Keywords: emotional control, Goldstein social skill streaming model, modeling technique, self- as-a-model, self-efficacy, self-regulation

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Authors: L. Korpinen, T. Kava, I. Salmi

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This study aimed to describe the operating model of obstructive sleep apnea. Due to the large number of patients, the role of nurses in the diagnosis and treatment of sleep apnea was important. Pulmonary physicians met only a minority of the patients. The sleep apnea study in 2018 included about 800 patients, of which about 28% were normal and 180 patients were classified as severe (apnea-hypopnea index [AHI] over 30). The operating model has proven to be workable and appropriate. The patients understand well that they may not be referred to a pulmonary doctor. However, specialized medical follow-up on professional drivers continues every year.

Keywords: sleep, apnea patient, operating model, hospital

Procedia PDF Downloads 132