Search results for: selection of support tenants

Authors: Senay Yitmen

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This research investigated individual’s support and helping intentions towards Syrian refugees in Turkey. This is examined in relation to perceived threat and negative emotions, and also to the perceptions of whether one’s intimate social network (family and friends) considers Syrians a threat (descriptive network norm) and whether this network morally supports Syrian refugees (injunctive norms). A questionnaire study was conducted among Turkish participants (n= 565) and the results showed that perception of threat was associated with negative emotions which, in turn, were related to less support of Syrian refugees. Additionally, descriptive norms moderated the relationship between perceived threat and negative emotions towards Syrian refugees. Furthermore, injunctive norms moderated the relationship between negative emotions and support to Syrian refugees. Specifically, the findings indicate that perceived threat is associated with less support of Syrian refugees through negative emotions when descriptive norms are weak and injunctive norms are strong. Injunctive norms appear to trigger a dilemma over the decision to conform or not to conform: when one has negative emotions as a result of perceived threat, it becomes more difficult to conform to the moral obligation of injunctive norms which is associated with less support of Syrian refugees. Hence, these findings demonstrate that both descriptive and injunctive norms are important and play different roles in individual’s support of Syrian refugees.

Keywords: descriptive norms, emotions, injunctive norms, the perception of threat

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Authors: Heramb Somthankar, Anujoy Chakraborty

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This paper presents a classification study on yield prediction in semiconductor production using machine learning approaches. A complicated semiconductor production process is generally monitored continuously by signals acquired from sensors and measurement sites. A monitoring system contains a variety of signals, all of which contain useful information, irrelevant information, and noise. In the case of each signal being considered a feature, "Feature Selection" is used to find the most relevant signals. The open-source UCI SECOM Dataset provides 1567 such samples, out of which 104 fail in quality assurance. Feature extraction and selection are performed on the dataset, and useful signals were considered for further study. Afterward, common machine learning algorithms were employed to predict whether the signal yields pass or fail. The most relevant algorithm is selected for prediction based on the accuracy and loss of the ML model.

Keywords: deep learning, feature extraction, feature selection, machine learning classification algorithms, semiconductor production monitoring, signal processing, time-series analysis

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Authors: Zeyad Abdelmageid, Xianbin Wang

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Choosing the operational channel for a WLAN access point (AP) in WLAN networks has been a static channel assignment process initiated by the user during the deployment process of the AP, which fails to cope with the dynamic conditions of the assigned channel at the station side afterward. However, the dramatically growing number of Wi-Fi APs and stations operating in the unlicensed band has led to dynamic, distributed, and often severe interference. This highlights the urgent need for the AP to dynamically select the best overall channel of operation for the basic service set (BSS) by considering the distributed and changing channel conditions at all stations. Consequently, dynamic channel selection algorithms which consider feedback from the station side have been developed. Despite the significant performance improvement, existing channel selection algorithms suffer from very high feedback overhead. Feedback latency from the STAs, due to the high overhead, can cause the eventually selected channel to no longer be optimal for operation due to the dynamic sharing nature of the unlicensed band. This has inspired us to develop our own dynamic channel selection algorithm with reduced overhead through the proposed low-overhead, cluster-based station reporting mechanism. The main idea behind the cluster-based station reporting is the observation that STAs which are very close to each other tend to have very similar channel conditions. Instead of requesting each STA to report on every candidate channel while causing high overhead, the AP divides STAs into clusters then assigns each STA in each cluster one channel to report feedback on. With the proper design of the cluster based reporting, the AP does not lose any information about the channel conditions at the station side while reducing feedback overhead. The simulation results show equal performance and, at times, better performance with a fraction of the overhead. We believe that this algorithm has great potential in designing future dynamic channel selection algorithms with low overhead.

Keywords: channel assignment, Wi-Fi networks, clustering, DBSCAN, overhead

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Authors: Calvin Chiu, Samar Saade Needham

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The factors associated with the development of major depressive disorder (MDD) have been an ongoing area of concern within the field of psychopathology. Among parents, the rise in stress factors for individuals receiving less social support contributes to an increase in MDD cases. Understanding the causal aspects of MDD through the interworking of stress development within social support disparities provides critical insights into preventive measures for depressive symptoms. The present study seeks to assess the impact of social support on stress formation within MDD. Such that single parents lacking social support prompt an increase in stress formation, which proliferates the progression of MDD. Participants in this study were 450 ethnic minority mothers and fathers experiencing health inequities during pregnancy and early childhood. Perceived stress, social support, and depression are assessed by multi-item questionnaires that produce score ranges for general findings. Results indicated that lower social support scores resulted in higher depression scores, and higher perceived stress scores produced higher depression scores. Furthermore, single parents reported higher depression scores. These findings overlap with studies on paternal depression and suggest that MDD is a product of stress accumulation due to declining social support systems. Future studies may specify effective social support systems for decreasing stress accumulation in MDD formation in preventive strategies.

Keywords: major depressive disorder, stress formation, cognitive-behavioral outcomes, deficit-based behaviors

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Authors: Chiranjit Das, Sanjay Jharkharia

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This study is focused on transport mode selection under lead time variability and emissions constraint. In order to reduce the carbon emissions generation due to transportation, organization has often faced a dilemmatic choice of transport mode selection since logistic cost and emissions reduction are complementary with each other. Another important aspect of transportation decision is lead-time variability which is least considered in transport mode selection problem. Thus, in this study, we provide a comprehensive mathematical based analytical model to decide transport mode selection under emissions constraint. We also extend our work through analysing the effect of lead time variability in the transport mode selection by a sensitivity analysis. In order to account lead time variability into the model, two identically normally distributed random variables are incorporated in this study including unit lead time variability and lead time demand variability. Therefore, in this study, we are addressing following questions: How the decisions of transport mode selection will be affected by lead time variability? How lead time variability will impact on total supply chain cost under carbon emissions? To accomplish these objectives, a total transportation cost function is developed including unit purchasing cost, unit transportation cost, emissions cost, holding cost during lead time, and penalty cost for stock out due to lead time variability. A set of modes is available to transport each node, in this paper, we consider only four transport modes such as air, road, rail, and water. Transportation cost, distance, emissions level for each transport mode is considered as deterministic and static in this paper. Each mode is having different emissions level depending on the distance and product characteristics. Emissions cost is indirectly affected by the lead time variability if there is any switching of transport mode from lower emissions prone transport mode to higher emissions prone transport mode in order to reduce penalty cost. We provide a numerical analysis in order to study the effectiveness of the mathematical model. We found that chances of stock out during lead time will be higher due to the higher variability of lead time and lad time demand. Numerical results show that penalty cost of air transport mode is negative that means chances of stock out zero, but, having higher holding and emissions cost. Therefore, air transport mode is only selected when there is any emergency order to reduce penalty cost, otherwise, rail and road transport is the most preferred mode of transportation. Thus, this paper is contributing to the literature by a novel approach to decide transport mode under emissions cost and lead time variability. This model can be extended by studying the effect of lead time variability under some other strategic transportation issues such as modal split option, full truck load strategy, and demand consolidation strategy etc.

Keywords: carbon emissions, inventory theoretic model, lead time variability, transport mode selection

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Authors: Omolara Lade, David Oloke

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The approach to water management worldwide is currently in transition, with a shift from centralised infrastructures to greater consideration of decentralised technologies, such as rainwater harvesting (RWH). However, in Nigeria, implementation of sustainable water management, such as RWH systems, is inefficient and social, environmental and technical barriers, concerns and knowledge gaps exist, which currently restrict its widespread utilisation. This inefficiency contributes to water scarcity, water-borne diseases, and loss of lives and property due to flooding. Meanwhile, several RWH technologies have been developed to improve SWM through both demand and storm-water management. Such technologies involve the use of reinforced concrete cement (RCC) storage tanks, surface water reservoirs and ground-water recharge pits as storage systems. A framework was developed to assess the significance and extent of water management problems, match the problems with existing RWH-based solutions and develop a robust ready-to-use decision support tool that can quantify the costs and benefits of implementing several RWH-based storage systems. The methodology adopted was the mixed method approach, involving a detailed literature review, followed by a questionnaire survey of household respondents, Nigerian Architects and Civil Engineers and focus group discussion with stakeholders. 18 selection attributes have been defined and three alternatives have been identified in this research. The questionnaires were analysed using SPSS, excel and selected statistical methods to derive weightings of the attributes for the tool. Following this, three case studies were modelled using RainCycle software. From the results, the MDA model chose RCC tank as the most appropriate storage system for RWH.

Keywords: rainwater harvesting, modelling, hydraulic assessment, whole life cost, decision support system

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Authors: Bushra Zafar, Usman Qamar

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Large data sample size and dimensions render the effectiveness of conventional data mining methodologies. A data mining technique are important tools for collection of knowledgeable information from variety of databases and provides supervised learning in the form of classification to design models to describe vital data classes while structure of the classifier is based on class attribute. Classification efficiency and accuracy are often influenced to great extent by noisy and undesirable features in real application data sets. The inherent natures of data set greatly masks its quality analysis and leave us with quite few practical approaches to use. To our knowledge first time, we present a new approach for investigation of structure and quality of datasets by providing a targeted analysis of localization of noisy and irrelevant features of data sets. Machine learning is based primarily on feature selection as pre-processing step which offers us to select few features from number of features as a subset by reducing the space according to certain evaluation criterion. The primary objective of this study is to trim down the scope of the given data sample by searching a small set of important features which may results into good classification performance. For this purpose, a heuristic for wrapper-based feature selection using genetic algorithm and for discriminative feature selection an external classifier are used. Selection of feature based on its number of occurrence in the chosen chromosomes. Sample dataset has been used to demonstrate proposed idea effectively. A proposed method has improved average accuracy of different datasets is about 95%. Experimental results illustrate that proposed algorithm increases the accuracy of prediction of different diseases.

Keywords: data mining, generic algorithm, KNN algorithms, wrapper based feature selection

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Authors: Amina El-Warari, Agata Vitale, Laura Caulfield, Jennifer Kinloch

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Background: Unemployment levels among refugees in the UK are much higher than voluntary migrants and UK-born citizens. The lack of employment and/or of suitable employment has detrimental consequences on refugees’ ability to integrate and become active citizens in the host country. Research indicates that, when individuals are forced to migrate, one of the most significant aspects to building their identity is their previous profession; this particularly applies to qualified refugees. Despite this, there is little support available to them. The current study is set in this context and aims to explore highly qualified refugees’ employment-related experiences in the UK as well as their suggestions on how to develop specific interventions that can support them in finding suitable employment. Methods: A qualitative study design was employed. Qualitative methods are in fact well suited to research with refugees, as they allow them to give their direct opinion, rather than this being filtered by stakeholders. Listening to ‘the refugee’s voice’ means developing ‘a refugee centered perspective’ where the diverse narratives told by participants are organized to tell their direct collective story. A total of 12 refugees, attending a non-profit refugee organization in the south-west of England, took part in the study. The selection criteria were being over 18, having a level of English that allows them to sustain a conversation, and having a University degree and/or professional qualification. All participants were interviewed individually; the data were transcribed and analyzed thematically. Findings: Participants had very little support in finding suitable employment; this often only consisted of a few sessions in their local job centers and English tutorials. They indicated that being unemployed/underemployed negatively affected their sense of identity, their acculturative stress, and their in-group/ out-group relations. They suggested that specific employment interventions for qualified refugees should be delivered to them individually in order to address their specific needs. Furthermore, most participants suggested that these interventions should support them in volunteering in organizations that match their skills/ qualifications. They also indicated that the employment interventions should support them in having their qualifications recognized in the UK as well as building links with universities/ centers where they can receive adequate training on how to understand and adapt to the employments needs in the UK. Conclusions: These findings will provide the basis for the second stage of the research where specific employment interventions will be designed and tested with highly qualified refugees. In addition, these findings shed light refugee integration policy.

Keywords: employment interventions, identity, integration, qualified refugees

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Authors: Vesile Evrim, Aliyu Awwal

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By the evolvement in technology, the way of expressing opinions switched the direction to the digital world. The domain of politics as one of the hottest topics of opinion mining research merged together with the behavior analysis for affiliation determination in text which constitutes the subject of this paper. This study aims to classify the text in news/blogs either as Republican or Democrat with the minimum number of features. As an initial set, 68 features which 64 are constituted by Linguistic Inquiry and Word Count (LIWC) features are tested against 14 benchmark classification algorithms. In the later experiments, the dimensions of the feature vector reduced based on the 7 feature selection algorithms. The results show that Decision Tree, Rule Induction and M5 Rule classifiers when used with SVM and IGR feature selection algorithms performed the best up to 82.5% accuracy on a given dataset. Further tests on a single feature and the linguistic based feature sets showed the similar results. The feature “function” as an aggregate feature of the linguistic category, is obtained as the most differentiating feature among the 68 features with 81% accuracy by itself in classifying articles either as Republican or Democrat.

Keywords: feature selection, LIWC, machine learning, politics

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Authors: Imad Zeyad Ramadan

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In this paper a genetic algorithm was developed to construct the optimal portfolio based on the Treynor method. The GA maximizes the Treynor ratio under budget constraint to select the best allocation of the budget for the companies in the portfolio. The results show that the GA was able to construct a conservative portfolio which includes companies from the three sectors. This indicates that the GA reduced the risk on the investor as it choose some companies with positive risks (goes with the market) and some with negative risks (goes against the market).

Keywords: oOptimization, genetic algorithm, portfolio selection, Treynor method

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Authors: Usman Qamar, Younus Javed

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Frequent pattern mining is the process of finding a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set. It was proposed in the context of frequent itemsets and association rule mining. Frequent pattern mining is used to find inherent regularities in data. What products were often purchased together? Its applications include basket data analysis, cross-marketing, catalog design, sale campaign analysis, Web log (click stream) analysis, and DNA sequence analysis. However, one of the bottlenecks of frequent itemset mining is that as the data increase the amount of time and resources required to mining the data increases at an exponential rate. In this investigation a new algorithm is proposed which can be uses as a pre-processor for frequent itemset mining. FASTER (FeAture SelecTion using Entropy and Rough sets) is a hybrid pre-processor algorithm which utilizes entropy and rough-sets to carry out record reduction and feature (attribute) selection respectively. FASTER for frequent itemset mining can produce a speed up of 3.1 times when compared to original algorithm while maintaining an accuracy of 71%.

Keywords: rough-sets, classification, feature selection, entropy, outliers, frequent itemset mining

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Authors: S. Grinnell

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A PhD Research Project exploring the Social Health and Well-Being Impacts associated with Hydraulic Fracturing, with an aim to produce a Best Practice Support Guidance for those anticipating dealing with planning applications or submitting Environmental Impact Assessments (EIAs). Amid a possible global energy crisis, founded upon a number of factors, including unstable political situations, increasing world population growth, people living longer, it is perhaps inevitable that Hydraulic Fracturing (commonly referred to as ‘fracking’) will become a major player within the global long-term energy and sustainability agenda. As there is currently no best practice guidance for governing bodies the Best Practice Support Document will be targeted at a number of audiences including, consultants undertaking EIAs, Planning Officers, those commissioning EIAs Industry and interested public stakeholders. It will offer a robust, evidence-based criteria and recommendations which provide a clear narrative and consistent and shared approach to the language used along with containing an understanding of the issues identified. It is proposed that the Best Practice Support Document will also support the mitigation of health impacts identified. The Best Practice Support Document will support the newly amended Environmental Impact Assessment Directive (2011/92/EU), to be transposed into UK law by 2017. A significant amendment introduced focuses on, ‘higher level of protection to the environment and health.’ Methodology: A qualitative research methods approach is being taken with this research. It will have a number of key stages. A literature review has been undertaken and been critically reviewed and analysed. This was followed by a descriptive content analysis of a selection of international and national policies, programmes and strategies along with published Environmental Impact Assessments and associated planning guidance. In terms of data collection, a number of stakeholders were interviewed as well as a number of focus groups of local community groups potentially affected by fracking. These were determined from across the UK. A theme analysis of all the data collected and the literature review will be undertaken, using NVivo. Best Practice Supporting Document will be developed based on the outcomes of the analysis and be tested and piloted in the professional fields, before a live launch. Concluding statement: Whilst fracking is not a new concept, the technology is now driving a new force behind the use of this engineering to supply fuels. A number of countries have pledged moratoria on fracking until further investigation from the impacts on health have been explored, whilst other countries including Poland and the UK are pushing to support the use of fracking. If this should be the case, it will be important that the public’s concerns, perceptions, fears and objections regarding the wider social health and well-being impacts are considered along with the more traditional biomedical health impacts.

Keywords: fracking, hydraulic fracturing, socio-economic health, well-being

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Authors: Zohreh Hajiha

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Culture affects the ability of the organization in expressing and achieving the goals. Organizational culture influences the selection of instruments applied in the management of organizations. All the instruments applied in organizations to control, promote and create innovations are influenced by organizational culture. This research studies organizational culture based on the cultural model of Muijen and its relationship with applying management accounting innovations in Iranian listed firms. Management accounting innovations of this study include activity-based costing, activity-based management, balanced scorecard, target costing, standard costing, quality costing, Kaizen costing and dimensions of organizational culture include support orientation, innovation orientation, rules orientation and goal orientation. 105 questionnaires were sent to financial executives of production companies and 73 questionnaires were returned. The findings show that there is a significant difference between organizational culture of firms that have applied management accounting innovations and those which have used these innovations less. Also, dimensions of support orientation and culture goal orientation are the highest in groups that apply management accounting innovations. The findings suggest that proper organization culture could promote the use od management accounting tools in Iranian firms.

Keywords: organizational culture, innovation, management accounting, muijen model

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Authors: Kristiana Haryanti, Theresia Dwi Hastuti, Agustine Eva Maria Soekesi

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Entrepreneurship is an important topic today that is widely discussed in the business world. The COVID-19 pandemic has devastated all businesses in the world, especially businesses at the micro-level. This study tries to prove the relationship between job satisfaction of micro-level business owners and family social support for their resilience. The respondents of this study amounted to 58 entrepreneurs. The results of this study indicate that there is a relationship between job satisfaction and social support with entrepreneurial resilience in continuing the family business.

Keywords: family business, family social support, job satisfaction, resilience

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Authors: Seyhan Karaçavuş, Bülent Yılmaz, Ömer Kayaaltı, Semra İçer, Arzu Taşdemir, Oğuzhan Ayyıldız, Kübra Eset, Eser Kaya

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In this study, our goal was to perform tumor staging and subtype determination automatically using different texture analysis approaches for a very common cancer type, i.e., non-small cell lung carcinoma (NSCLC). Especially, we introduced a texture analysis approach, called Law’s texture filter, to be used in this context for the first time. The 18F-FDG PET images of 42 patients with NSCLC were evaluated. The number of patients for each tumor stage, i.e., I-II, III or IV, was 14. The patients had ~45% adenocarcinoma (ADC) and ~55% squamous cell carcinoma (SqCCs). MATLAB technical computing language was employed in the extraction of 51 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and Laws’ texture filters. The feature selection method employed was the sequential forward selection (SFS). Selected textural features were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). In the automatic classification of tumor stage, the accuracy was approximately 59.5% with k-NN classifier (k=3) and 69% with SVM (with one versus one paradigm), using 5 features. In the automatic classification of tumor subtype, the accuracy was around 92.7% with SVM one vs. one. Texture analysis of FDG-PET images might be used, in addition to metabolic parameters as an objective tool to assess tumor histopathological characteristics and in automatic classification of tumor stage and subtype.

Keywords: cancer stage, cancer cell type, non-small cell lung carcinoma, PET, texture analysis

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Authors: Therese Scali

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Couple separations and divorces seem to be commonplace events. However, their frequency does not reduce their impact. Indeed, the adverse effects of parental divorce on children have been well documented. Thus, supporting the children from divorced families is a key concern. Several preventive interventions have been developed for children of divorced parents, such as Children’s Support Group. The present paper aims at presenting the program that has been created in Liege (Belgium). The setting and the tools will be presented. This Children’s Support Group is based on psychoeducational and systemic principles, art-therapy, and aims at acquiring coping skills and seeking social support. Also, the effectiveness of the program will be discussed. Results show that after parental divorce, a group intervention for children can be efficacious in promoting children’s well-being and parent-child communication. This paper contributes to enrich the understanding of children’s needs and to highlight the existence and efficacy of a program that helps them overcome the difficulties of divorce.

Keywords: art-therapy, children’s support group, divorce, efficacy, separation

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Authors: Joann Douziech

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Individuals with intellectual and developmental disabilities (IDD) possess characteristics that present both challenges and gifts. Individuals with IDD require and are worthy of intentional, strategic, and specialized support throughout their lifespan to ensure optimum quality-of-life outcomes. The current global advocacy movement advancing the rights of individuals with IDD emphasizes a high degree of choice over life decisions. For some individuals, this degree of choice results in a variety of negative health and well-being outcomes. Improving the quality of life outcomes requires the combination of a commitment to the rights of the individual with a responsibility to provide support and choice commensurate with individual capacity. A belief that individuals with IDD are capable of learning and they are worthy of being taught provides the foundation for a holistic model of support throughout their lifespan. This model is based on three pillars of engineering the environment, promoting skill development and maintenance, and staff support. In an ever-changing world, supporting quality of life requires attention to moments, phases, and changes in stages throughout the lifespan. Balancing these complexities with strategic, responsive, and dynamic interventions enhances the quality of life of individuals with ID throughout their lifespan.

Keywords: achieving optimum quality of life, comprehensive support, lifespan approach, philosophy and pedagogy

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Authors: Mohamed Alimoussa, Alice Porebski, Nicolas Vandenbroucke, Rachid Oulad Haj Thami, Sana El Fkihi

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Color and texture are highly discriminant visual cues that provide an essential information in many types of images. Color texture representation and classification is therefore one of the most challenging problems in computer vision and image processing applications. Color textures can be represented in different color spaces by using multiple image descriptors which generate a high dimensional set of texture features. In order to reduce the dimensionality of the feature set, feature selection techniques can be used. The goal of feature selection is to find a relevant subset from an original feature space that can improve the accuracy and efficiency of a classification algorithm. Traditionally, feature selection is focused on removing irrelevant features, neglecting the possible redundancy between relevant ones. This is why some feature selection approaches prefer to use feature clustering analysis to aid and guide the search. These techniques can be divided into two categories. i) Feature clustering-based ranking algorithm uses feature clustering as an analysis that comes before feature ranking. Indeed, after dividing the feature set into groups, these approaches perform a feature ranking in order to select the most discriminant feature of each group. ii) Feature clustering-based subset search algorithms can use feature clustering following one of three strategies; as an initial step that comes before the search, binded and combined with the search or as the search alternative and replacement. In this paper, we propose a new feature clustering-based sequential selection approach for the purpose of color texture representation and classification. Our approach is a three step algorithm. First, irrelevant features are removed from the feature set thanks to a class-correlation measure. Then, introducing a new automatic feature clustering algorithm, the feature set is divided into several feature clusters. Finally, a sequential search algorithm, based on a filter model and a separability measure, builds a relevant and non redundant feature subset: at each step, a feature is selected and features of the same cluster are removed and thus not considered thereafter. This allows to significantly speed up the selection process since large number of redundant features are eliminated at each step. The proposed algorithm uses the clustering algorithm binded and combined with the search. Experiments using a combination of two well known texture descriptors, namely Haralick features extracted from Reduced Size Chromatic Co-occurence Matrices (RSCCMs) and features extracted from Local Binary patterns (LBP) image histograms, on five color texture data sets, Outex, NewBarktex, Parquet, Stex and USPtex demonstrate the efficiency of our method compared to seven of the state of the art methods in terms of accuracy and computation time.

Keywords: feature selection, color texture classification, feature clustering, color LBP, chromatic cooccurrence matrix

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Authors: Carlos R. Duharte Rodríguez, Ibrain Ceballo Acosta, Carmen B. Busoch Morlán, Angel Regueiro Gómez, Annet Martinez Hernández

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This work shows the designing and characterization of a prototype of laboratory incubator as support of research in Microbiology, in particular during studies of bacterial growth in biological samples, with the help of optic methods (Turbidimetry) and electrometric measurements of bioimpedance. It shows the results of simulation and experimentation of the design proposed for the canals of measurement of the variables: temperature and humidity, with a high linearity from the adequate selection of sensors and analogue components of every channel, controlled with help of a microcontroller AT89C51 (ATMEL) with adequate benefits for this type of application.

Keywords: microbiology, bacterial growth, incubation station, microorganisms

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Authors: Fulya Zaralı, Harun Resit Yazgan

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Logistics centers represent areas that all national and international logistics and activities related to logistics can be implemented by the various businesses. Logistics centers have a key importance in joining the transport stream and the transport system operations. Therefore, it is important where these centers are positioned to be effective and efficient and to show the expected performance of the centers. In this study, the location selection problem to position the logistics center is discussed. Alternative centers are evaluated according certain criteria. The most appropriate center is identified using the axiomatic design method.

Keywords: axiomatic design, logistic center, facility location, information systems

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Authors: Todd Zhou, Mikhail Yurochkin

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Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

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Authors: Kang-Mo Jung

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The use of regularization for statistical methods has become popular. The least absolute shrinkage and selection operator (LASSO) framework has become the standard tool for sparse regression. However, it is well known that the LASSO is sensitive to outliers or leverage points. We consider a new robust estimation which is composed of the weighted loss function of the pairwise difference of residuals and the adaptive penalty function regulating the tuning parameter for each variable. Rank regression is resistant to regression outliers, but not to leverage points. By adopting a weighted loss function, the proposed method is robust to leverage points of the predictor variable. Furthermore, the adaptive penalty function gives us good statistical properties in variable selection such as oracle property and consistency. We develop an efficient algorithm to compute the proposed estimator using basic functions in program R. We used an optimal tuning parameter based on the Bayesian information criterion (BIC). Numerical simulation shows that the proposed estimator is effective for analyzing real data set and contaminated data.

Keywords: adaptive penalty function, robust penalized regression, variable selection, weighted rank regression

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Authors: Umut Erksan Senalp

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The aim of this study is to examine the link between firm level productivity heterogeneity and firm’s decision to export. Thus, we test the self selection hypothesis which suggests only more productive firms self select themselves to export markets. We analyze UK manufacturing sector by using firm-level data for the period 2003-2011. Although our preliminary results suggest that exporters outperform non-exporters when we pool all manufacturing industries, when we examine each industry individually, we find that self-selection hypothesis does not hold for each industries.

Keywords: total factor productivity, firm heterogeneity, international trade, decision to export

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Authors: Tomas Kos

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An increasing body of research has explored patterns of interaction and peer support among young learners. Although some studies suggest that young learners can collaborate and support each other, other studies indicate that young learners may lack the ability to work together and support one another when interacting on classroom tasks. Moreover, despite the claims that peer collaboration is conducive to learning, studies have not paid enough attention to the “how” to enhance peer collaboration on classroom tasks. To fill this gap, this “how-to” article proposes that teaching young learners how to work together is a powerful pedagogical tool that can greatly improve collaborative behavior and a sense of mutuality among young learners. This article will pay particular attention to primary schools and the context of English as a foreign language. It will first review literature related to patterns of interaction and peer support conducted in the cognitive and sociocultural framework. It will then address what it actually means to collaborate. At the heart of the article, it will discuss some practical pedagogical ideas for language teachers, which entail teaching collaborative principles and strategies that will help their students to support each other and engage in communication with each other.

Keywords: young learners, peer collaboration, peer interaction, peer support, patterns of interaction

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Authors: K. Marciniak, M. Owoc

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Managers as decision-makers present in different sectors should be supported in efficient and more and more sophisticated way. There are huge number of software tools developed for such users starting from simple registering data from business area – typical for operational level of management – up to intelligent techniques with delivering knowledge - for tactical and strategic levels of management. There is a big challenge for software developers to create intelligent management dashboards allowing to support different decisions. In more advanced solutions there is even an option for selection of intelligent techniques useful for managers in particular decision-making phase in order to deliver valid knowledge-base. Such a tool (called Intelligent Dashboard for SME Managers–InKOM) is prepared in the Business Intelligent framework of Teta products. The aim of the paper is to present solutions assumed for InKOM concerning on management of stored knowledge bases offering for business managers. The paper is managed as follows. After short introduction concerning research context the discussed supporting managers via information systems the InKOM platform is presented. In the crucial part of paper a process of knowledge transformation and validation is demonstrated. We will focus on potential and real ways of knowledge-bases acquiring, storing and validation. It allows for formulation conclusions interesting from knowledge engineering point of view.

Keywords: business intelligence, decision support systems, knowledge management, knowledge transformation, knowledge validation, managerial systems

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Authors: Hamidrza Joodaki

Abstract:

The most complex issue in urban land use planning is site selection that needs to assess the verity of elements and factors. Multi Criteria Decision Making (MCDM) methods are the best approach to deal with complex problems. In this paper, combination of the analytical hierarchy process (AHP) model and FUZZY logic was used as MCDM methods to select the best site for gas station in the 4th gas zone of Tehran. The first and the most important step in FUZZY-AHP model is selection of criteria and sub-criteria. Population, accessibility, proximity and natural disasters were considered as the main criteria in this study. After choosing the criteria, they were weighted based on AHP by EXPERT CHOICE software, and FUZZY logic was used to enhance accuracy and to approach the reality. After these steps, criteria layers were produced and weighted based on FUZZY-AHP model in GIS. Finally, through ARC GIS software, the layers were integrated and the 4th gas zone in TEHRAN was selected as the best site to locate gas station.

Keywords: multiple criteria decision making (MCDM), analytic hierarchy process (AHP), FUZZY logic, geographic information system (GIS)

Procedia PDF Downloads 328

Authors: Wieke Ellen Bouwes

Abstract:

This study aims to unravel which factors enhance accessibility of video consultations (VCs) for patients with low digital literacy. Thirteen in-depth interviews with patients, hospital employees, eHealth experts, and digital support organizations were held. Patients with low digital literacy received in-home support during real-time video consultations and are observed during the set-up of these consultations. Key findings highlight the importance of patient acceptance, emphasizing video consultations benefits and avoiding standardized courses. The lack of a uniform video consultation system across healthcare providers poses a barrier. Familiarity with support organizations – to support patients in usage of digital tools - among healthcare practitioners enhances accessibility. Moreover, considerations regarding the Dutch General Data Protection Regulation (GDPR) law influence support patients receive. Also, provider readiness to use video consultations influences patient access. Further, alignment between learning styles and support methods seems to determine abilities to learn how to use video consultations. Future research could delve into tailored learning styles and technological solutions for remote access to further explore effectiveness of learning methods.

Keywords: video consultations, digital literacy skills, effectiveness of support, intra- and inter-organizational relationships, patient acceptance of video consultations

Procedia PDF Downloads 43

Authors: Lukasz Szydlowski, Jiri Ehlich, Igor Goryanin

Abstract:

We demonstrate a single chamber, 96-well-plated based Microbial Fuel Cell (MFC) with printed, electronic components. This invention is aimed at robust selection of electrogenic microbial community under specific conditions, e.g., electrode potential, pH, nutrient concentration, salt concentration that can be altered within the 96 well plate array. This invention enables robust selection of electrogenic microbial community under the homogeneous reactor, with multiple conditions that can be altered to allow comparative analysis. It can be used as a standalone technique or in conjunction with other selective processes, e.g., flow cytometry, microfluidic-based dielectrophoretic trapping. Mobile conductive elements, like carbon paper, carbon sponge, activated charcoal granules, metal mesh, can be inserted inside to increase the anode surface area in order to collect electrogenic microorganisms and to transfer them into new reactors or for other analytical works. An array of 96-well plate allows this device to be operated by automated pipetting stations.

Keywords: bioengineering, electrochemistry, electromicrobiology, microbial fuel cell

Procedia PDF Downloads 115

Authors: M. Javahar, H. B. Dong

Abstract:

Single crystal components manufactured using Ni-base Superalloys are routinely used in the hot sections of aero engines and industrial gas turbines due to their outstanding high temperature strength, toughness and resistance to degradation in corrosive and oxidative environments. To control the quality of the single crystal turbine blades, particular attention has been paid to grain selection, which is used to obtain the single crystal morphology from a plethora of columnar grains. For this purpose, different designs of grain selectors are employed and the most common type is the spiral grain selector. A typical spiral grain selector includes a starter block and a spiral (helix) located above. It has been found that the grains with orientation well aligned to the thermal gradient survive in the starter block by competitive grain growth while the selection of the single crystal grain occurs in the spiral part. In the present study, 2D spiral selectors with different geometries were designed and produced using a state-of-the-art Bridgeman Directional Solidification casting furnace to investigate the competitive growth during grain selection in 2d grain selectors. The principal advantage of using a 2-D selector is to facilitate the wax injection process in investment casting by enabling significant degree of automation. The automation within the process can be derived by producing 2D grain selector wax patterns parts using a split die (metal mold model) coupled with wax injection stage. This will not only produce the part with high accuracy but also at an acceptable production rate.

Keywords: grain selector, single crystal, directional solidification, CMSX-4 superalloys, investment casting

Procedia PDF Downloads 554

Authors: Uma U. Uma, Uzoechi Laz

Abstract:

Protection of equipment insulation against lightning over voltages and selection of lightning arrester that will discharge at lower voltage level than the voltage required to breakdown the electrical equipment insulation is examined. The objectives of this paper are to design a computer based model using standard equations for the selection of appropriate lightning arrester with the lowest rated surge arrester that will provide adequate protection of equipment insulation and equally have a satisfactory service life when connected to a specified line voltage in power system network. The effectiveness and non-effectiveness of the earthing system of substation determine arrester properties. MATLAB program with GUI (graphic user interphase) its subprogram is used in the development of the model for the determination of required parameters like voltage rating, impulse spark over voltage, power frequency spark over voltage, discharge current, current rating and protection level of lightning arrester of a specified voltage level of a particular line.

Keywords: lightning arrester, GUIs, MatLab program, computer based model

Procedia PDF Downloads 390