Search results for: decision problems

Authors: Karina Zaccari, Ernesto Cordeiro Marujo

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This paper presents a Machine Learning (ML) approach to support Meningitis diagnosis in patients at a children’s hospital in Sao Paulo, Brazil. The aim is to use ML techniques to reduce the use of invasive procedures, such as cerebrospinal fluid (CSF) collection, as much as possible. In this study, we focus on predicting the probability of Meningitis given the results of a blood and urine laboratory tests, together with the analysis of pain or other complaints from the patient. We tested a number of different ML algorithms, including: Adaptative Boosting (AdaBoost), Decision Tree, Gradient Boosting, K-Nearest Neighbors (KNN), Logistic Regression, Random Forest and Support Vector Machines (SVM). Decision Tree algorithm performed best, with 94.56% and 96.18% accuracy for training and testing data, respectively. These results represent a significant aid to doctors in diagnosing Meningitis as early as possible and in preventing expensive and painful procedures on some children.

Keywords: machine learning, medical diagnosis, meningitis detection, pediatric research

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Authors: Dina Siniora, Jasia Baig

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During the past few decades, the healthcare system has undergone profound changes in their healthcare decision-making competencies and moral aptitudes due to the vast advancement in technology, clinical skills, and scientific knowledge. Healthcare decision-making deals with morally contentious dilemmas ranging from illness, life and death judgments that require sensitivity and awareness towards the patient’s preferences while taking into consideration medicine’s abilities and boundaries. As the ever-evolving field of medicine continues to become more scientifically and morally multifarious; physicians and the hospital administrators increasingly rely on ethics committees to resolve problems that arise in everyday patient care. The role and latitude of responsibilities of ethics committees which includes being dispute intermediaries, moral analysts, policy educators, counselors, advocates, and reviewers; suggest the importance and effectiveness of a fully integrated committee. Despite achievements on Integrated Ethics and progress in standards and competencies, there is an imminent necessity for further improvement in quality within ethics consultation services in areas of credentialing, professionalism and standards of quality, as well as the quality of healthcare throughout the system. These concerns can be resolved first by collecting data about particular quality gaps and comprehend the level to which ethics committees are consistent with newly published ASBH quality standards. Policymakers should pursue improvement strategies that target both academic bioethics community and major stakeholders at hospitals, who directly influence ethics committees. This broader approach oriented towards education and intervention outcome in conjunction with preventive ethics to address disparities in quality on a systematic level. Adopting tools for improving competencies and processes within ethics consultation by implementing a credentialing process, upholding normative significance for the ASBH core competencies, advocating for professional Code of Ethics, and further clarifying the internal structures will improve productivity, patient satisfaction, and institutional integrity. This cannot be systemically achieved without a written certification exam for HCEC practitioners, credentialing and privileging HCEC practitioners at the hospital level, and accrediting HCEC services at the institutional level.

Keywords: ethics consultation, hospital, medical ethics, quality

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Authors: R. Shamsi, F. Sharifi

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In this paper, we obtain the projection of inefficient units in data envelopment analysis (DEA) in the case of stochastic inputs and outputs using the multi-objective programming (MOP) structure. In some problems, the inputs might be stochastic while the outputs are deterministic, and vice versa. In such cases, we propose a multi-objective DEA-R model because in some cases (e.g., when unnecessary and irrational weights by the BCC model reduce the efficiency score), an efficient decision-making unit (DMU) is introduced as inefficient by the BCC model, whereas the DMU is considered efficient by the DEA-R model. In some other cases, only the ratio of stochastic data may be available (e.g., the ratio of stochastic inputs to stochastic outputs). Thus, we provide a multi-objective DEA model without explicit outputs and prove that the input-oriented MOP DEA-R model in the invariable return to scale case can be replaced by the MOP-DEA model without explicit outputs in the variable return to scale and vice versa. Using the interactive methods for solving the proposed model yields a projection corresponding to the viewpoint of the DM and the analyst, which is nearer to reality and more practical. Finally, an application is provided.

Keywords: DEA-R, multi-objective programming, stochastic data, data envelopment analysis

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Authors: Inci Aksu Kargin

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The civil war which began in the Daraa province of Syria in March 2011, has caused thousands of Syrians to die and millions more to seek refuge in other countries such as Turkey, Lebanon, Jordan, Iraq, and Egypt. In order to understand the Syrian refugees’ living conditions and the problems they have experienced in Turkey in-depth, and to analyze how the arrival of the Syrian refugees in Turkey has affected the local people who live in Turkish-Syrian border, this study employed interviews, which were conducted with three different groups. First, 60 Syrian refugees, who have settled in Hatay and Gaziantep, were interviewed. Then, the Turkish government institutions, and NGOs, which are responsible for assisting the refugees, were interviewed. These interviews revealed that many Syrian refugees have encountered with several issues such as access to labor and housing markets as well as free healthcare and public education services. Second, 60 Turkish citizens living in Hatay and Gaziantep provinces were interviewed. These interviews shed light on the many issues (e.g., increase of unemployment, increase in the rental and sale prices of the houses, decrease in the quality of healthcare services, increase in traffic problems, problems with regard to the usage of parks and gardens) that Turkish citizens began experiencing after mass asylum claim of the Syrian refugees to Turkey. In addition to these, the existing social problems in Turkey such as child labor, begging, child brides, and illegal marriages (religious marriages) worsen.

Keywords: migration, refugees, Syrian civil war, Turkey

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Authors: Stefan Kurbatfinski, Aliyah Dosani, Andrew F. Hayes, Deborah Dewey, Nicole Letourneau

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Background: Maternal adverse childhood experiences (ACEs) have been associated with internalizing and externalizing behavioral problems in preschool children. However, little is known about the influence of maternal adult attachment patterns on this association. Further, potential moderation by maternal mental health, maternal social support, or child sex is poorly understood. Therefore, this study examined associations between 1) maternal ACEs and preschool children’s behavioural problems, with mediation through maternal attachment patterns and moderation by maternal mental health, maternal social support, and child sex; and 2) maternal attachment patterns and children’s behavioural problems, with mediation through maternal mental health and social support and moderation by child sex. Methods: This secondary analysis used data (n=625) from a high socioeconomic, longitudinally prospective cohort (Alberta Pregnancy Outcomes and Nutrition). Child behaviour (BASC-2) and maternal adult attachment (ECR-Q) were measured at five years of child age, maternal ACEs (ACEs Questionnaire) at around 12 months, and maternal mental health (CESD and SCL-90-R) and social support (SSQ) across various prenatal and postnatal time points. All moderation and mediation analyses occurred through RStudio using PROCESS, interpreting significant interactions through Johnson-Neyman plots. Findings: Maternal ACEs interacted with maternal anxiety symptoms to predict both behavioural problems (pexternalizing=0.007; pinternalizing=0.0159). An indirect pathway via dismissive attachment was moderated by maternal social support ([0.0058, 0.0596]). Attachment patterns predicted all behavioural problems (p<0.05) and interacted with maternal anxiety symptoms to predict internalizing behaviours among male children ([0.0321, 0.1307]; [0.0321, 0.1291]). Interpretation: Maternal attachment patterns may predict children’s behavioural problems more than ACEs. Social support interventions may not always be beneficial for highly dismissively attached mothers. Implications for policy and child health include mandatory sex and gender education for teachers; assessing attachment patterns prior to recommending social support as an intervention; and anxiety-focused interventions for mothers in higher socioeconomic populations.

Keywords: maternal adverse childhood experiences, internalizing behaviours, externalizing behaviours, mediators and moderators, attachment patterns, child health

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Authors: Modupe Beatrice Adeyinka

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French, as a foreign language in Nigeria, is pronounced to be the second official language and a compulsory subject in the primary school level; hence, colleges of education across the nation are saddled with the responsibility of training teachers for the subject. However, it has been observed that this policy has not been fully implemented, for French teachers in training, do face many challenges, of which translation is chief. In a bid to investigate the major cause of the perceived translation problem, this study examined French translation problems of pre-service teachers in selected colleges of education in the southwest, Nigeria. This study adopted a descriptive survey research design. The simple random sampling technique was used to select four colleges of education in the southwest, where 100 French students were randomly selected by selecting 25 from each school. The pre-service teachers’ French translation problems’ questionnaire (PTFTPQ) was used as an instrument while four research questions were answered and three null hypotheses were tested. Among others, the findings revealed that students do have problems with false friends, though mainly with its interpretation when attempting French-English translation and vice versa; majority of the students make use of French dictionary as a way out and found the material very useful for their understanding of false friends. Teachers were, therefore, urged to attend in-service training where they would be exposed to new and emerging strategies, approaches and methodologies of French language teaching that will make students overcome the challenge of translation in learning French.

Keywords: false friends, French language, pre-service teachers, source language, target language, translation

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Authors: Lucy W. Mburu, Richard Karanja, Simon N. Mwendia

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Over recent years, learners have experienced a constant need to access on demand knowledge that is fully aligned with the paradigm of cloud computing. As motivated by the global sustainable development goal to ensure inclusive and equitable learning opportunities, this research has developed a framework hinged on the knowledge-as-a-service architecture that utilizes knowledge from ambient learning systems. Through statistical analysis and decision tree modeling, the study discovers influential variables for ambient learning among university students. The main aim is to generate a platform for disseminating and exploiting the available knowledge to aid the learning process and, thus, to improve educational support on the ambient learning system. The research further explores how collaborative effort can be used to form a knowledge network that allows access to heterogeneous sources of knowledge, which benefits knowledge consumers, such as the developers of ambient learning systems.

Keywords: actionable knowledge, ambient learning, cloud computing, decision trees, knowledge as a service

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Authors: Marcin Michalak

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Biclustering is the way of two-dimensional data analysis. For several years it became possible to express such issue in terms of Boolean reasoning, for processing continuous, discrete and binary data. The mathematical backgrounds of such approach — proved ability of induction of exact and inclusion–maximal biclusters fulfilling assumed criteria — are strong advantages of the method. Unfortunately, the core of the method has quite high computational complexity. In the paper the basics of Boolean reasoning approach for biclustering are presented. In such context the problems of computation parallelization are risen.

Keywords: Boolean reasoning, biclustering, parallelization, prime implicant

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Authors: Bello Abdullahi, Kabir Bala, Yahaya M. Ibrahim, Ahmed D. Ibrahim

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Public sector tendering has traditionally been conducted using manual paper-based processes which are known to be inefficient, less transparent, and more prone to manipulations and errors. However, the advent of the Internet and its associated technologies has led to the development of numerous e-Tendering systems that addressed many of the problems associated with the manual paper-based tendering system. Currently, in Nigeria, the public tendering processes are largely conducted based on manual paper-based system that is bedevilled by a number of problems such as inordinate delays, inefficiencies, manipulation of the tender evaluation process, corruption, lack of transparency and competition, among other problems. These problems can be addressed through the adoption of existing web-based e-Tendering systems which are known to address most of these problems. However, these existing e-Tendering systems that have been developed are not based on the Nigerian legal procurement processes and as such their suitability for local application is very limited. This paper is part of a larger study that attempt to address this problem through the development of an e-Tendering system that is based on the requirements of the Nigerian public procuring entities. In this paper, the identified tendering processes commonly used by Nigerian public procuring entities in the selection of construction sources are presented. A multi-methods research approach was used to identify those tendering processes. Specifically, 19 existing business use cases used by Nigerian public procuring entities were identified and 61 system use cases were prescribed based on the identified business use cases. The use cases were used as the basis for the development of domain and software conceptual models. The models were successfully used to guide the development of an e-Tendering system called NPS-eTender. Ripple and Unified Process were adopted as the software development methodologies.

Keywords: e-tendering, e-procurement, requirement model, conceptual model, public sector tendering, public procurement

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Authors: Y. Mehdipour, S. Ebrahimi, A. Jahanpour, F. Seyedzaei, B. Sabayan, A. Karimi, H. Amirifard

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Ischemic stroke is one of the common reasons for disability and mortality. The fourth leading cause of death in the world and the third in some other sources. Only 1/3 of the patients with ischemic stroke fully recover, 1/3 of them end in permanent disability and 1/3 face death. Thus, the use of predictive models to predict stroke has a vital role in reducing the complications and costs related to this disease. Thus, the aim of this study was to specify the effective factors and predict ischemic stroke with the help of DM methods. The present study was a descriptive-analytic study. The population was 213 cases from among patients referring to Ali ibn Abi Talib (AS) Hospital in Zahedan. Data collection tool was a checklist with the validity and reliability confirmed. This study used DM algorithms of decision tree for modeling. Data analysis was performed using SPSS-19 and SPSS Modeler 14.2. The results of the comparison of algorithms showed that CHAID algorithm with 95.7% accuracy has the best performance. Moreover, based on the model created, factors such as anemia, diabetes mellitus, hyperlipidemia, transient ischemic attacks, coronary artery disease, and atherosclerosis are the most effective factors in stroke. Decision tree algorithms, especially CHAID algorithm, have acceptable precision and predictive ability to determine the factors affecting ischemic stroke. Thus, by creating predictive models through this algorithm, will play a significant role in decreasing the mortality and disability caused by ischemic stroke.

Keywords: data mining, ischemic stroke, decision tree, Bayesian network

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Authors: Bashar Albaalbaki, Roger E. Khayat

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This work is a comparative study on the effect of Delaunay triangulation algorithms on discretization error for conduction-convection conservation problems. A structured triangulation and many unstructured Delaunay triangulations using three popular algorithms for node placement strategies are used. The numerical method employed is the vertex-centered finite volume method. It is found that when the computational domain can be meshed using a structured triangulation, the discretization error is lower for structured triangulations compared to unstructured ones for only low Peclet number values, i.e. when conduction is dominant. However, as the Peclet number is increased and convection becomes more significant, the unstructured triangulations reduce the discretization error. Also, no statistical correlation between triangulation angle extremums and the discretization error is found using 200 samples of randomly generated Delaunay and non-Delaunay triangulations. Thus, the angle extremums cannot be an indicator of the discretization error on their own and need to be combined with other triangulation quality measures, which is the subject of further studies.

Keywords: conduction-convection problems, Delaunay triangulation, discretization error, finite volume method

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Authors: Abdullah Alsehaimi

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Over decades, there have been many studies of delay in construction, and this type of study continues to be popular in construction management research. A synthesis and critical evaluation of delay studies in developing countries reveals that poor project management is cited as one of the main causes of delay. However, despite such consensus, most of the previous studies fall short in providing clear recommendations demonstrating how project management practice could be improved. Moreover, the majority of recommendations are general and not devoted to solving the difficulties associated with particular delay causes. This paper aims to demonstrate that the root cause of this state of affairs is that typical research into delay tends to be descriptive and explanatory, making it inadequate for solving persistent managerial problems in construction. It is contended that many problems in construction could be mitigated via alternative research approaches, i.e. action and constructive research. Such prescriptive research methods can assist in the development and implementation of innovative tools tackling managerial problems of construction, including that of delay. In so doing, those methods will better connect research and practice, and thus strengthen the relevance of academic construction management.

Keywords: construction delay, action research, constructive research, industrial engineering

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Authors: Muhammad Saqlain, Nazar Abbas Saqib, Muazzam A. Khan

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Heart Failure (HF) has become the major health problem in our society. The prevalence of HF has increased as the patient’s ages and it is the major cause of the high mortality rate in adults. A successful identification and progression of HF can be helpful to reduce the individual and social burden from this syndrome. In this study, we use a real data set of cardiac patients to propose a classification model for the identification and progression of HF. The data set has divided into three age groups, namely young, adult, and old and then each age group have further classified into four classes according to patient’s current physical condition. Contemporary Data Mining classification algorithms have been applied to each individual class of every age group to identify the HF. Decision Tree (DT) gives the highest accuracy of 90% and outperform all other algorithms. Our model accurately diagnoses different stages of HF for each age group and it can be very useful for the early prediction of HF.

Keywords: decision tree, heart failure, data mining, classification model

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Authors: Harshit Garg, Shakshi Gupta

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One of the major problems in today’s world is the growing pollution. The cause for all environmental problems is the increasing pollution rate. Looking upon the statistics, one can find out that most of the pollution is caused by the vehicular pollution which is more than 70 % of the total pollution, effecting the environment as well as human health proportionally. One is aware of the fact that vehicles run on roads so why not having the roads which could adsorb that pollution, not only once but a number of times. Every problem has a solution which can be solved by the state of art of technology, that is one can use the innovative ideas and thoughts to make technology as a solution to the problem of vehicular pollution on roads. Solving the problem up to a certain limit/ percentage can be formulated into a new term called ECO ROADS.

Keywords: environment, pollution, roads, sustainibility

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Authors: Rasha Waheeb, Bjorn Andersen, Rafa Shakir

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Purpose The purpose of this study is to avoid delays that occur in emergency reconstruction projects especially in post disaster circumstances whether if they were natural or manmade due to their particular national and humanitarian importance. We presented a theoretical and practical concepts for projects management in the field of construction industry that deal with a range of global and local trails. This study aimed to identify the factors of effective delay in construction projects in Iraq that affect the time and the specific quality cost, and find the best solutions to address delays and solve the problem by setting parameters to restore balance in this study. 30 projects were selected in different areas of construction were selected as a sample for this study. Design/methodology/approach This study discusses the reconstruction strategies and delay in time and cost caused by different delay factors in some selected projects in Iraq (Baghdad as a case study).A case study approach was adopted, with thirty construction projects selected from the Baghdad region, of different types and sizes. Project participants from the case projects provided data about the projects through a data collection instrument distributed through a survey. Mixed approach and methods were applied in this study. Mathematical data analysis was used to construct models to predict delay in time and cost of projects before they started. The artificial neural networks analysis was selected as a mathematical approach. These models were mainly to help decision makers in construction project to find solutions to these delays before they cause any inefficiency in the project being implemented and to strike the obstacles thoroughly to develop this industry in Iraq. This approach was practiced using the data collected through survey and questionnaire data collection as information form. Findings The most important delay factors identified leading to schedule overruns were contractor failure, redesigning of designs/plans and change orders, security issues, selection of low-price bids, weather factors, and owner failures. Some of these are quite in line with findings from similar studies in other countries/regions, but some are unique to the Iraqi project sample, such as security issues and low-price bid selection. Originality/value we selected ANN’s analysis first because ANN’s was rarely used in project management , and never been used in Iraq to finding solutions for problems in construction industry. Also, this methodology can be used in complicated problems when there is no interpretation or solution for a problem. In some cases statistical analysis was conducted and in some cases the problem is not following a linear equation or there was a weak correlation, thus we suggested using the ANN’s because it is used for nonlinear problems to find the relationship between input and output data and that was really supportive.

Keywords: construction projects, delay factors, emergency reconstruction, innovation ANN, post disasters, project management

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Authors: Omar Boutkhoum, Mohamed Hanine, Abdessadek Bendarag

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Sustainable economic growth is nowadays driving firms to extend toward the adoption of many green supply chain management (GSCM) solutions. However, the evaluation and selection of these solutions is a matter of concern that needs very serious decisions, involving complexity owing to the presence of various associated factors. To resolve this problem, a comparative analysis approach based on multi-criteria decision-making methods is proposed for adequate evaluation of sustainable supply chain management solutions. In the present paper, we propose an integrated decision-making model based on FAHP (Fuzzy Analytic Hierarchy Process), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and PROMETHEE (Preference Ranking Organisation METHod for Enrichment Evaluations) to contribute to a better understanding and development of new sustainable strategies for industrial organizations. Due to the varied importance of the selected criteria, FAHP is used to identify the evaluation criteria and assign the importance weights for each criterion, while TOPSIS and PROMETHEE methods employ these weighted criteria as inputs to evaluate and rank the alternatives. The main objective is to provide a comparative analysis based on TOPSIS and PROMETHEE processes to help make sound and reasoned decisions related to the selection problem of GSCM solution.

Keywords: GSCM solutions, multi-criteria analysis, decision support system, TOPSIS, FAHP, PROMETHEE

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Authors: Suteera Pradubwong, Pattama Surit, Sumalee Pongpagatip, Tharinee Pethchara, Bowornsilp Chowchuen

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Background: Cleft lip and palate (CLP) is a congenital anomaly of the lip and palate that is caused by several factors. It was found in approximately one per 500 to 550 live births depending on nationality and socioeconomic status. The Tawanchai Center and out-patients surgical room of Srinagarind Hospital are responsible for providing care to patients with CLP (starting from birth to adolescent) and their caregivers. From the observations and interviews with nurses working in these units, they reported that both patients and their caregivers confronted many problems which affected their physical and mental health. Based on the Soukup’s model (2000), the researchers used evidence triggers from clinical practice (practice triggers) and related literature (knowledge triggers) to investigate the problems. Objective: The purpose of this study was to investigate the problems of care for patients with CLP in the Tawanchai Center and out-patient surgical room of Srinagarind Hospital. Material and Method: The descriptive method was used in this study. For practice triggers, the researchers obtained the data from medical records of ten patients with CLP and from interviewing two patients with CLP, eight caregivers, two nurses, and two assistant workers. Instruments for the interview consisted of a demographic data form and a semi-structured questionnaire. For knowledge triggers, the researchers used a literature search. The data from both practice and knowledge triggers were collected between February and May 2016. The quantitative data were analyzed through frequency and percentage distributions, and the qualitative data were analyzed through a content analysis. Results: The problems of care gained from practice and knowledge triggers were consistent and were identified as holistic issues, including 1) insufficient feeding, 2) risks of respiratory tract infections and physical disorders, 3) psychological problems, such as anxiety, stress, and distress, 4) socioeconomic problems, such as stigmatization, isolation, and loss of income, 5)spiritual problems, such as low self-esteem and low quality of life, 6) school absence and learning limitation, 7) lack of knowledge about CLP and its treatments, 8) misunderstanding towards roles among the multidisciplinary team, 9) no available services, and 10) shortage of healthcare professionals, especially speech-language pathologists (SLPs). Conclusion: From evidence-triggers, the problems of care affect the patients and their caregivers holistically. Integrated long-term care by the multidisciplinary team is needed for children with CLP starting from birth to adolescent. Nurses should provide effective care to these patients and their caregivers by using a holistic approach and working collaboratively with other healthcare providers in the multidisciplinary team.

Keywords: evidence-triggers, cleft lip, cleft palate, problems of care

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Authors: Amina Salem Attia

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This east project consists in studying The child's mental health is the medium through which he expresses his thoughts, so pay attention to it because it is an essential building block in the process of building the child's future personality, where it gives him a balance between feelings and mental thoughts, and since the family is the child's first guardian, it greatly affects his personality and psychological development. As the disturbed environment contributes to behavioral deviations and mental disorders, unlike the stable environment, which plays a major role in developing the child's abilities and forming his psychologically sound attitudes, this should not be forgotten about the role of the school, it is also the second social institution after the family and has a major impact on the child's mental health as it contributes It is important in forming the child's personality and developing his skills and achieving his healthy psychological development, by providing him with psychological care and helping him to solve his problems by using models that are valid for the behavior that is taught to him or that the teachers present in their daily behavior with him.

Keywords: psychological, behavioral problems, children, school education

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Authors: Johannes Gantner, Michael Held, Matthias Fischer

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The refurbishment of the building stock in terms of energy supply and efficiency is one of the major challenges of the German turnaround in energy policy. As the building sector accounts for 40% of Germany’s total energy demand, additional insulation is key for energy efficient refurbished buildings. Nevertheless the energetic benefits often the environmental and economic performances of insulation materials are questioned. The methods Life Cycle Assessment (LCA) as well as Life Cycle Costing (LCC) can form the standardized basis for answering this doubts and more and more become important for material producers due efforts such as Product Environmental Footprint (PEF) or Environmental Product Declarations (EPD). Due to increasing use of LCA and LCC information for decision support the robustness and resilience of the results become crucial especially for support of decision and policy makers. LCA and LCC results are based on respective models which depend on technical parameters like efficiencies, material and energy demand, product output, etc.. Nevertheless, the influence of parameter uncertainties on lifecycle results are usually not considered or just studied superficially. Anyhow the effect of parameter uncertainties cannot be neglected. Based on the example of an exterior wall the overall lifecycle results are varying by a magnitude of more than three. As a result simple best case worst case analyses used in practice are not sufficient. These analyses allow for a first rude view on the results but are not taking effects into account such as error propagation. Thereby LCA practitioners cannot provide further guidance for decision makers. Probabilistic analyses enable LCA practitioners to gain deeper understanding of the LCA and LCC results and provide a better decision support. Within this study, the environmental and economic impacts of an exterior wall system over its whole lifecycle are illustrated, and the effect of different uncertainty analysis on the interpretation in terms of resilience and robustness are shown. Hereby the approaches of error propagation and Monte Carlo Simulations are applied and combined with statistical methods in order to allow for a deeper understanding and interpretation. All in all this study emphasis the need for a deeper and more detailed probabilistic evaluation based on statistical methods. Just by this, misleading interpretations can be avoided, and the results can be used for resilient and robust decisions.

Keywords: uncertainty, life cycle assessment, life cycle costing, Monte Carlo simulation

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Authors: Mohamed Sharof Mostafa, Atefeh Ahmadi

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Most of traditional single modality psychotherapy and counselling approaches to clients’ problems are based on the application of one therapy in all sessions. Modern developments in these sciences focus on eclectic and integrative interventions to consider all dimensions of an issue and all characteristics of the clients. This paper presents and overview eclectic therapy and its pros and cons. In addition, multiple intelligence theory and its application in eclectic therapy approaches are mentioned.

Keywords: eclectic therapy, client, multiple intelligence theory, dimensions

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Authors: Alireza Nikbakht Shahbazi, Emadeddin Shirali

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Since optimization issues of water resources are complicated due to the variety of decision making criteria and objective functions, it is sometimes impossible to resolve them through regular optimization methods or, it is time or money consuming. Therefore, the use of modern tools and methods is inevitable in resolving such problems. An accurate and essential utilization policy has to be determined in order to use natural resources such as water reservoirs optimally. Water reservoir programming studies aim to determine the final cultivated land area based on predefined agricultural models and water requirements. Dam utilization rule curve is also provided in such studies. The basic information applied in water reservoir programming studies generally include meteorological, hydrological, agricultural and water reservoir related data, and the geometric characteristics of the reservoir. The system of Dez dam water resources was simulated applying the basic information in order to determine the capability of its reservoir to provide the objectives of the performed plan. As a meta-exploratory method, genetic algorithm was applied in order to provide utilization rule curves (intersecting the reservoir volume). MATLAB software was used in order to resolve the foresaid model. Rule curves were firstly obtained through genetic algorithm. Then the significance of using rule curves and the decrease in decision making variables in the system was determined through system simulation and comparing the results with optimization results (Standard Operating Procedure). One of the most essential issues in optimization of a complicated water resource system is the increasing number of variables. Therefore a lot of time is required to find an optimum answer and in some cases, no desirable result is obtained. In this research, intersecting the reservoir volume has been applied as a modern model in order to reduce the number of variables. Water reservoir programming studies has been performed based on basic information, general hypotheses and standards and applying monthly simulation technique for a statistical period of 30 years. Results indicated that application of rule curve prevents the extreme shortages and decrease the monthly shortages.

Keywords: optimization, rule curve, genetic algorithm method, Dez dam reservoir

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Authors: Kamariah Abu Bakar, Jennifer Way

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This study investigated how young children (six years old) constructed and used representations in mathematics classroom; particularly in problem solving. The purpose of this study is to explore the ways children used representations in solving addition problems and to determine whether their representations can play a supportive role in understanding the problem situation and solving them correctly. Data collection includes observations, children’s artifact, photographs and conversation with children during task completion. The results revealed that children were able to construct and use various representations in solving problems. However, they have certain preferences in generating representations to support their problem solving.

Keywords: young children, representations, addition, problem solving

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Authors: Mariam Shah

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The way the human mind interprets and comprehends the world it occupies has long been a topic of discussion amongst philosophers and phenomenologists. This paper will focus predominantly on the ideologies espoused by the phenomenologist Alfred Schutz and will investigate how we attribute meaning to an event through the process of typification, and the production and usage of ‘types' and ‘stereotypes.' This paper will then discuss how subjective ideologies innate within us result in unique and subjective decision outcomes, based on a phenomenologically influenced theoretical framework which will illustrate how we form ‘types’ in order to ‘typecast’ and form judgements of everything and everyone we experience. The framework used will be founded in theory espoused by Alfred Schutz, and will review the different types of knowledge we rely on innately to inform our judgements, the relevance we attribute to the information which we acquire, and how we consciously and unconsciously apply this framework to everyday situations. An assessment will then be made of the potential impact that these subjective meaning structures can present when dispensing justice in criminal courts. This paper will investigate how these subjective meaning structures can influence our consciousness on both a conscious and unconscious level, and how this could potentially result in bias judicial outcomes due to negative ‘types’ or ‘stereotypes.' This paper will ultimately illustrate that we unconsciously and unreflexively use pre-formed types and stereotypes to inform our judgements and give meaning to what we have just experienced.

Keywords: Alfred Schutz, criminal courts, decision making, judicial decision making, phenomenology, Schutzian stereotypes, types, typification

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Authors: Ewa M. Laskowska, Jorn Vatn

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Industry 4.0 focuses on digital optimization of industrial processes. The idea is to use extracted data in order to build a decision support model enabling use of those data for real time decision making. In terms of predictive maintenance, the desired decision support tool would be a model enabling prognostics of system's health based on the current condition of considered equipment. Within area of system prognostics and health management, a commonly used health indicator is Remaining Useful Lifetime (RUL) of a system. Because the RUL is a random variable, it has to be estimated based on available health indicators. Health indicators can be of different types and come from different sources. They can be process variables, equipment performance variables, data related to number of experienced failures, etc. The aim of this study is the analysis of performance variables of emergency shutdown valves (ESV) used in oil and gas industry. ESV is inspected periodically, and at each inspection torque and time of valve operation are registered. The data will be analyzed by means of machine learning or statistical analysis. The purpose is to investigate whether the available data could be used as a health indicator for a prognostic purpose. The second objective is to examine what is the most efficient way to incorporate the data into predictive model. The idea is to check whether the data can be applied in form of explanatory variables in Markov process or whether other stochastic processes would be a more convenient to build an RUL model based on the information coming from registered data.

Keywords: emergency shutdown valves, health indicator, prognostics, remaining useful lifetime, RUL

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Authors: Mohammad Y. Badiee, Saeed Golestani, Mir Saman Pishvaee

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In recent years consumers and governments have been pushing companies to design their activities in such a way as to reduce negative environmental impacts by producing renewable product or threat free disposal policy more and more. It is therefore important to focus more accurate to the optimization of various aspect of total supply chain. Modeling a supply chain can be a challenging process due to the fact that there are a large number of factors that need to be considered in the model. The use of multi-objective optimization can lead to overcome those problems since more information is used when designing the model. Uncertainty is inevitable in real world. Considering uncertainty on parameters in addition to use multi-objectives are ways to give more flexibility to the decision making process since the process can take into account much more constraints and requirements. In this paper we demonstrate a stochastic scenario based robust model to cope with uncertainty in a closed-loop multi-objective supply chain. By applying the proposed model in a real world case, the power of proposed model in handling data uncertainty is shown.

Keywords: supply chain management, closed-loop supply chain, multi-objective optimization, goal programming, uncertainty, robust optimization

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Authors: Lele Dominic Dummene

Abstract:

Economic development and environmental development have been a long-lasting debate between capitalist and environmentalist. It is also seen as a debate between modernisation, globalisation at one end, and environmental justice at the other end. Our society today is moving rapidly towards development and increased industrial revolutions, and globalisation. Indigenous communities in Ogoniland are also experiencing such development due to multinationals’ exploration of crude oil in the communities. The oil exploration activities have caused environmental, socio-economic, health, and political problems in indigenous communities in Ogoniland. These issues require depth understanding from all sectors (public, government, and corporate sectors) to address them. Hence, this paper presents the types of environmental education programs used in indigenous communities in Ogoniland to address environmental issues and other problems caused by oil exploration in Ogoniland, Nigeria. These environmental education programs contributes to environmental policy creation, development of environmental curriculum, and pragmatic actions towards mitigating environmental degradation and related environmental socio-economic and political issues in indigenous communities.

Keywords: environmental education, indigenous communities, environmental problems, ogoniland

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Authors: Norazlan Alias, Mohd. Hasimi Yaacob

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This study examines the impact of governance structure via corporate restructuring decision on selected firm characteristics and performance. Results of selected ratios that represent corporate decision, governance structure and performance in pre and post restructuring are analyzed for some conclusions. This study uses annual data of companies that are consistently listed on the Main Board of Bursa Malaysia and announced completed corporate restructuring. The results show that only debt ratio is significantly different before and after asset restructuring. This study concludes that firms do not view corporate restructuring namely asset restructuring as an opportunity to simultaneous enhance governance structure that could also contribute enhance firm performance and board of directors’ structure subsequent to asset restructuring only has significantly influence on changing capital structure but not on firm performance.

Keywords: board of directors, capital structure, corporate restructuring, performance

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Authors: Rebecca Hafner, Daniel Read, David Elmes

Abstract:

The persuasive potential of normative and feedback (financial vs. environmental) information in ‘nudging’ people towards making environmentally sound decisions was explored in a hypothetical choice experiment. The research was specifically focused on determining how subtle variations in the decision frame could be used to increase the selection of energy efficient vs. standard technologies, using the context of home heating choice. Participants were given a choice of a standard heating system (a gas boiler) and a relatively more-energy efficient option (a heat pump). The experiment had a 2 (normative vs. no normative information) by 3 feedback type (financial, environmental, none) design. The last group constituted the control. Half of the participants were given normative information about what the majority of others in their neighbourhood had opted to do when faced with the same choice set, prior to making their decision. The other half received no such information. Varying feedback frames were incorporated by providing participants with information on either financial or environmental savings that could be achieved by choosing the heat pump. No such information was provided in the control group. A significant interaction was found between normative information and feedback frame type. Specifically, the impact of feedback frames was found to be reduced when normative information was provided; illustrating the overriding influence of normative information on option preference. Participants were significantly more likely to select the heat pump if they were vs. were not given normative information. Yet when no normative information was provided, the persuasive influence of the financial frame was increased – highlighting this as an effective means of encouraging uptake of new technologies in this instance. Conversely, the environmental frame was not found to differ significantly from the control. Marginal carryover effects were also found for stated future real-life decision-making behaviour, with participants who were versus were not given normative information being marginally more likely to state they would consider installing a heat pump when they next need to replace their heating system in real life. We conclude that normative and financial feedback framing techniques are highly effective in increasing uptake of new, energy efficient heating technologies involving significant upfront financial outlay. The implications for researchers looking to promote ‘green’ choice in the context of new technology adoption are discussed.

Keywords: energy-efficient technology adoption, environmental decision making, financial vs. environmental feedback framing techniques, social norms

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Authors: Reham Banwair, Lana Alshehri, Sara Hadrawi

Abstract:

The purpose of this project is to identify user satisfaction with the AI functions on Snapchat, in order to generate improvement proposals that allow its development within the app. To achieve this, a qualitative analysis was carried out through interviews to people who usually use the application, revealing their satisfaction or dissatisfaction with the usefulness of the AI. In addition, the background of the company and its introduction in these algorithms were analyzed. Furthermore, the characteristics of the three main functions of AI were explained: identify songs, solve mathematical problems, and recognize plants. As a result, it was obtained that 50% still do not know the characteristics of AI, 50% still believe song recognition is not always correct, 41.7% believe that math problems are usually accurate and 91.7% believes the plant detection tool is working properly.

Keywords: artificial intelligence, scanning, Snapchat, machine learning

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Authors: Sangeeta Palekar, Neeraj Rangwani, Akash Poddar, Jayu Kalambe

Abstract:

The bio-medical analysis is an indispensable procedure for identifying health-related diseases like diabetes. Monitoring the glucose level in our body regularly helps us identify hyperglycemia and hypoglycemia, which can cause severe medical problems like nerve damage or kidney diseases. This paper presents a method for predicting the glucose concentration in blood samples using image processing and machine learning algorithms. The glucose solution is prepared by the glucose oxidase (GOD) and peroxidase (POD) method. An experimental database is generated based on the colorimetric technique. The image of the glucose solution is captured by the raspberry pi camera and analyzed using image processing by extracting the RGB, HSV, LUX color space values. Regression algorithms like multiple linear regression, decision tree, RandomForest, and XGBoost were used to predict the unknown glucose concentration. The multiple linear regression algorithm predicts the results with 97% accuracy. The image processing and machine learning-based approach reduce the hardware complexities of existing platforms.

Keywords: artificial intelligence glucose detection, glucose oxidase, peroxidase, image processing, machine learning

Procedia PDF Downloads 182