Search results for: statistical approaches

Authors: Pratyusha Das

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A paradigm shift has been noticed in the interpretation of DNA evidence for determining paternity. Sometimes DNA evidence has been accepted while sometimes it was rejected by the Indian Courts. Courts have forwarded various justifications for acceptance and rejection of such evidence through legal and scientific means. Laws have also been changed to accommodate the necessities of society. Balances between both the legal and scientific approaches are required, to make the best possible use of DNA evidence for the well-being of the society. Specifications are to be framed as to when such evidence can be used in the future by pointing out the pros and cons. Judicial trend is to be formulated to find out the present situation. The study of cases of superior courts of India using an analytical and theoretical approach is driving the questions regarding the shared identity of the legal and scientific approaches. To assimilate the differences between the two approaches, the basic differences between them have to be formulated. Revelations are required to access the favorable decisions using the DNA evidence. Reasons are to be forwarded for the unfavorable decisions and the approach preferred in such cases. The outcome of the two methods has to be assessed in relation to the parties to the dispute, the society at large, the researcher and from the judicial point of view. The dependability of the two methods is to be studied in relation to the justice delivery system. A highlight of the chronological study of cases along with the changes in the laws with the aid of presumptions will address the questions of necessity of a method according to the facts and situations. Address is required in this respect whether the legal and scientific forces converge somewhere pushing the traditional identification of paternity towards a fundamental change.

Keywords: cases, evidence, legal, scientific

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Authors: Jianjia Wang, Tong Yu, Haoran Zhu, Kun Liu, Jinwei Hao

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The underlying pattern in the modern city is agglomeration. To some degree, the distribution of urban spatial density can be used to describe the status of this assemblage. There are three intrinsic characteristics to measure urban spatial density, namely, Floor Area Ratio (FAR), Building Coverage Ratio (BCR), and Average Storeys (AS). But the underlying mechanism that contributes to these quantities is still vague in the statistical urban study. In this paper, we explore the corresponding extrinsic factors related to spatial density. These factors can further provide the potential influence on the intrinsic quantities. Here, we take Shanghai Inner Ring Area and Manhattan in New York as examples to analyse the potential impacts on urban spatial density with six selected extrinsic elements. Ebery single factor presents the correlation to the spatial distribution, but the overall global impact of all is still implicit. To handle this issue, we attempt to develop the Boltzmann statistical model to explicitly explain the mechanism behind that. We derive a corresponding novel quantity, called capacity, to measure the global effects of all other extrinsic factors to the three intrinsic characteristics. The distribution of capacity presents a similar pattern to real measurements. This reveals the nonlinear influence on the multi-factor relations to the urban spatial density in agglomeration.

Keywords: urban spatial density, Boltzmann statistics, multi-factor correlation, spatial distribution

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Authors: Aishrith Rao

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Human activity recognition is an important aspect of video analytics, and many approaches have been recommended to enable action recognition. In this approach, the model is used to identify the action of the multiple people in the frame and classify them accordingly. A few approaches use RNNs and 3D CNNs, which are computationally expensive and cannot be trained with the small datasets which are currently available. Multi-person action recognition has been performed in order to understand the positions and action of people present in the video frame. The size of the video frame can be adjusted as a hyper-parameter depending on the hardware resources available. OpenPose has been used to calculate pose estimate using CNN to produce heap-maps, one of which provides skeleton features, which are basically joint features. The features are then extracted, and a classification algorithm can be applied to classify the action.

Keywords: human activity recognition, computer vision, pose estimates, convolutional neural networks

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Authors: Muhtasim Shafi Kader, Fizar Ahmed, Annesha Acharjee

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Machine learning and data mining are crucial in health care, as well as medical information and detection. Machine learning approaches are now being utilized to improve awareness of a variety of critical health issues, including diabetes detection, neuron cell tumor diagnosis, COVID 19 identification, and so on. Parkinson’s disease is basically a disease for our senior citizens in Bangladesh. Parkinson's Disease indications often seem progressive and get worst with time. People got affected trouble walking and communicating with the condition advances. Patients can also have psychological and social vagaries, nap problems, hopelessness, reminiscence loss, and weariness. Parkinson's disease can happen in both men and women. Though men are affected by the illness at a proportion that is around partial of them are women. In this research, we have to get out the accurate ML algorithm to find out the disease with a predictable dataset and the model of the following machine learning classifiers. Therefore, nine ML classifiers are secondhand to portion study to use machine learning approaches like as follows, Naive Bayes, Adaptive Boosting, Bagging Classifier, Decision Tree Classifier, Random Forest classifier, XBG Classifier, K Nearest Neighbor Classifier, Support Vector Machine Classifier, and Gradient Boosting Classifier are used.

Keywords: naive bayes, adaptive boosting, bagging classifier, decision tree classifier, random forest classifier, XBG classifier, k nearest neighbor classifier, support vector classifier, gradient boosting classifier

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Authors: Tibor Koltay

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Among varied new literacies, information literacy is not only the best-known one but displays numerous models and frameworks. Nonetheless, there is still a lack of its complex theoretical model that could be applied to information literacy education in public (K12) education, which often makes use of constructivist approaches. This paper aims to present the main features of such a model. To develop a complex model, the literature and practice of phenomenographic and sociocultural theories, as well as discourse analytical approaches to information literacy, have been reviewed. Besides these constructivist and expressive based educational approaches, the new model is intended to include the innovation of coupling them with a cognitive model that takes developing informational and operational knowledge into account. The convergences between different literacies (information literacy, media literacy, media and information literacy, and data literacy) were taken into account, as well. The model will also make use of a three-country survey that examined secondary school teachers’ attitudes to information literacy. The results of this survey show that only a part of the respondents feel properly prepared to teach information literacy courses, and think that they can teach information literacy skills by themselves, while they see a librarian as an expert in educating information literacy. The use of the resulting model is not restricted to enhancing theory. It is meant to raise the level of awareness about information literacy and related literacies, and the next phase of the model’s development will be a pilot study that verifies the usefulness of the methodology for practical information literacy education in selected Hungarian secondary schools.

Keywords: communication, data literacy, discourse analysis, information literacy education, media and information literacy media literacy, phenomenography, public education, sociocultural theory

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Authors: Angela Maria Fasnacht

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Detection of anomalies due to contaminants’ presence, also known as early detection systems in water treatment plants, has become a critical point that deserves an in-depth study for their improvement and adaptation to current requirements. The design of these systems requires a detailed analysis and processing of the data in real-time, so it is necessary to apply various statistical methods appropriate to the data generated, such as Spearman’s Correlation, Factor Analysis, Cross-Correlation, and k-fold Cross-validation. Statistical analysis and methods allow the evaluation of large data sets to model the behavior of variables; in this sense, statistical treatment or analysis could be considered a vital step to be able to develop advanced models focused on machine learning that allows optimized data management in real-time, applied to early detection systems in water treatment processes. These techniques facilitate the development of new technologies used in advanced sensors. In this work, these methods were applied to identify the possible correlations between the measured parameters and the presence of the glyphosate contaminant in the single-pass system. The interaction between the initial concentration of glyphosate and the location of the sensors on the reading of the reported parameters was studied.

Keywords: glyphosate, emergent contaminants, machine learning, probes, sensors, predictive

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Authors: Younghoon Kim, Seoung Bum Kim

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One-class classification plays an important role in detecting outlier and abnormality from normal observations. In the previous research, several attempts were made to extend the scope of application of the one-class classification techniques to statistical process control problems. For most previous approaches, such as support vector data description (SVDD) control chart, the design of the control limits is commonly based on the assumption that the proportion of abnormal observations is approximately equal to an expected Type I error rate in Phase I process. Because of the limitation of the one-class classification techniques based on convex optimization, we cannot make the proportion of abnormal observations exactly equal to expected Type I error rate: controlling Type I error rate requires to optimize constraints with integer decision variables, but convex optimization cannot satisfy the requirement. This limitation would be undesirable in theoretical and practical perspective to construct effective control charts. In this work, to address the limitation of previous approaches, we propose the one-class classification algorithm based on the mixed integer programming technique, which can solve problems formulated with continuous and integer decision variables. The proposed method minimizes the radius of a spherically shaped boundary subject to the number of normal data to be equal to a constant value specified by users. By modifying this constant value, users can exactly control the proportion of normal data described by the spherically shaped boundary. Thus, the proportion of abnormal observations can be made theoretically equal to an expected Type I error rate in Phase I process. Moreover, analogous to SVDD, the boundary can be made to describe complex structures by using some kernel functions. New multivariate control chart applying the effectiveness of the algorithm is proposed. This chart uses a monitoring statistic to characterize the degree of being an abnormal point as obtained through the proposed one-class classification. The control limit of the proposed chart is established by the radius of the boundary. The usefulness of the proposed method was demonstrated through experiments with simulated and real process data from a thin film transistor-liquid crystal display.

Keywords: control chart, mixed integer programming, one-class classification, support vector data description

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Authors: Vilem Paril, Dominika Tothova

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This paper discusses the value theory in cultural heritage and the value theory in environmental economics. Two economic views of the value theory are compared within the field of cultural heritage maintenance and within the field of the environment. The main aims are to find common features in these two differently structured theories under the layer of differently defined terms as well as really differing features of these two approaches, to clear the confusion which stems from different terminology as in fact these terms capture the same aspects of reality and to show possible inspiration these two perspectives can offer one another. Another aim is to present these two value systems in one value framework. First, important moments of the value theory from the economic perspective are presented, leading to the marginal revolution of (not only) the Austrian School. Then the theory of value within cultural heritage and environmental economics are explored. Finally, individual approaches are compared and their potential mutual inspiration searched for.

Keywords: cultural heritage, environmental economics, existence value, value theory

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Authors: Hasan Vural

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Turkey is a favorable country to produce a great variety of agricultural products because of her different geographic and climatic conditions which have been used to divide the country into four main and seven sub regions. This classification into seven regions traditionally has been used in order to data collection and publication especially related with agricultural production. Afterwards, nine agricultural regions were considered. Recently, the governmental body which is responsible of data collection and dissemination (Turkish Institute of Statistics-TIS) has used 12 classes which include 11 sub regions and Istanbul province. This study aims to evaluate these classification efforts based on the acreage of ten main crops in a ten years time period (1996-2005). The panel data grouped in 11 subregions has been evaluated by cluster and multivariate statistical methods. It was concluded that from the agricultural production point of view, it will be rather meaningful to consider three main and eight sub-agricultural regions throughout the country.

Keywords: agricultural region, factorial analysis, cluster analysis,

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Authors: Hassan Manouchehri

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The present study aimed to identify and evaluate the factors affecting violence against women. The statistical population included all couples referring to family counseling centers in Tehran due to domestic violence during the past year. A number of 305 people were selected as a statistical sample using simple random sampling and Cochran's formula in unlimited conditions. A researcher-made questionnaire including 110 items was used for data collection. The face validity and content validity of the questionnaire were confirmed by 30 experts and its reliability was obtained above 0.7 for all studied variables in a preliminary test with 30 subjects and it was acceptable. In order to analyze the data, descriptive statistical methods were used with SPSS software version 22 and inferential statistics were used for modeling structural equations in Smart PLS software version 2. Evaluating the theoretical framework and domestic and foreign studies indicated that, in general, four main factors, including cultural and social factors, economic factors, legal factors, as well as medical factors, underlie violence against women. In addition, structural equation modeling findings indicated that cultural and social factors, economic factors, legal factors, and medical factors affect violence against women.

Keywords: violence against women, cultural and social factors, economic factors, legal factors, medical factors

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Authors: M. Taibeche, L. Guerbous, M. Kechouane, R. Nedjar, T. Zergoug

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It is known that Y2O3 Material is the most important among the sesquioxides within the general class of refractory ceramics. Indeed, this compound has many applications such as sintering optical windows, components for rare-earth doped lasers as well as inorganic scintillators in the detection scintillation. In particular Eu2+ and Ce3+ are favored dopants in many the scintillators due to its allowed optical 5d-4f transition. In this work, we present new results concerning structural and electronic properties of Ce-doped Y2O3, investigated by density functional theory (DFT), using the Heyd–Scuseria–Ernzerhof (HSE) hybrid functional and DFT+U two approaches. When, we compared the results from the two methods we obtain a good agreement available experimental data. Furthermore, the effect of cerium on the material has also been studied and discussed in the same framework.

Keywords: DFT, vienne ab initio simulation packages, scintillators, Heyd–Scuseria–Ernzerhof (HSE) hybrid functional

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Authors: Ahmed Saeed Mohamed, Sadok Sassi, Mohammad Roshun Paurobally

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Gears are important machine components that are widely used to transmit power and change speed in many rotating machines. Any breakdown of these vital components may cause severe disturbance to production and incur heavy financial losses. One of the most common causes of gear failure is the tooth fatigue crack. Early detection of teeth cracks is still a challenging task for engineers and maintenance personnel. So far, to analyze the vibration behavior of gears, different approaches have been tried based on theoretical developments, numerical simulations, or experimental investigations. The objective of this study was to develop a numerical model that could be used to simulate the effect of teeth cracks on the resulting vibrations and hence to permit early fault detection for gear transmission systems. Unlike the majority of published papers, where only one single crack has been considered, this work is more realistic, since it incorporates the possibility of multiple simultaneous cracks with different lengths. As cracks significantly alter the gear mesh stiffness, we performed a finite element analysis using SolidWorks software to determine the stiffness variation with respect to the angular position for different combinations of crack lengths. A simplified six degrees of freedom non-linear lumped parameter model of a one-stage gear system is proposed to study the vibration of a pair of spur gears, with and without tooth cracks. The model takes several physical properties into account, including variable gear mesh stiffness and the effect of friction, but ignores the lubrication effect. The vibration simulation results of the gearbox were obtained via Matlab and Simulink. The results were found to be consistent with the results from previously published works. The effect of one crack with different levels was studied and very similar changes in the total mesh stiffness and the vibration response, both were observed and compared to what has been found in previous studies. The effect of the crack length on various statistical time domain parameters was considered and the results show that these parameters were not equally sensitive to the crack percentage. Multiple cracks are introduced at different locations and the vibration response and the statistical parameters were obtained.

Keywords: dynamic simulation, gear mesh stiffness, simultaneous tooth cracks, spur gear, vibration-based fault detection

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Authors: Thomas Benson

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This paper leverages theoretical insights into two different justice approaches – environmental justice and ecological justice – to examine the effectiveness of sustainable development within smart cities and related smart city technology initiatives. Through theoretical development, the author seeks to establish an Eco Framework for smart cities and urban sustainable development. In turn, this paper aims to proffer the notion that there are ecologically sustainable ways in which smart cities can get smarter, and that such strategies can be compatible with ecological justice and environmental justice. Ultimately, a single conceptual framework is put forward to integrate the above approaches and concepts with normative prescriptions, which can serve researchers in the continued examination of smart cities and policymakers in their sustainable development of smart cities.

Keywords: ecological justice, environmental justice, normative framework, smart cities, sustainable development

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Authors: Federico Pittino, Thomas Arnold

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The shredding of waste materials is a key step in the recycling process towards the circular economy. Industrial shredders for waste processing operate in very harsh operating conditions, leading to the need for frequent maintenance of critical components. Maintenance optimization is particularly important also to increase the machine’s efficiency, thereby reducing the operational costs. In this work, a monitoring system has been developed and deployed on an industrial shredder located at a waste recycling plant in Austria. The machine has been monitored for one year, and methods for predictive maintenance have been developed for two key components: the cutting knives and the drive belt. The large amount of collected data is leveraged by statistical machine learning techniques, thereby not requiring very detailed knowledge of the machine or its live operating conditions. The results show that, despite the wide range of operating conditions, a reliable estimate of the optimal time for maintenance can be derived. Moreover, the trade-off between the cost of maintenance and the increase in power consumption due to the wear state of the monitored components of the machine is investigated. This work proves the benefits of real-time monitoring system for the efficient operation of industrial shredders.

Keywords: predictive maintenance, circular economy, industrial shredder, cost optimization, statistical machine learning

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Authors: Priyadarsini Samal, Rajesh Singla

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Many mobile games come with the benefits of entertainment by introducing stress to the human brain. In recognizing this mental stress, the brain-computer interface (BCI) plays an important role. It has various neuroimaging approaches which help in analyzing the brain signals. Electroencephalogram (EEG) is the most commonly used method among them as it is non-invasive, portable, and economical. Here, this paper investigates the pattern in brain signals when introduced with mental stress. Two healthy volunteers played a game whose aim was to search hidden words from the grid, and the levels were chosen randomly. The EEG signals during gameplay were recorded to investigate the impacts of stress with the changing levels from easy to medium to hard. A total of 16 features of EEG were analyzed for this experiment which includes power band features with relative powers, event-related desynchronization, along statistical features. Support vector machine was used as the classifier, which resulted in an accuracy of 93.9% for three-level stress analysis; for two levels, the accuracy of 92% and 98% are achieved. In addition to that, another game that was similar in nature was played by the volunteers. A suitable regression model was designed for prediction where the feature sets of the first and second game were used for testing and training purposes, respectively, and an accuracy of 73% was found.

Keywords: brain computer interface, electroencephalogram, regression model, stress, word search

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Authors: Alexander Street, Nina Wollersberger, Paul Fernie, Leonardo Muller, Ming Hung HSU, Helen Odell-Miller, Jorg Fachner, Patrizia Di Campli San Vito, Stephen Brewster, Hari Shaji, Satvik Venkatesh, Paolo Itaborai, Nicolas Farina, Alexis Kirke, Sube Banerjee, Eduardo Reck Miranda

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Escalating neuropsychiatric symptoms (NPS) in people with dementia may lead to earlier care home admission. Music listening has been reported to stimulate cognitive function, potentially reducing agitation in this population. We present a scoping review, reporting on current developments and discussing the potential for music listening with related technology in managing agitation in dementia care. Of two searches for music listening studies, one focused on older people or people living with dementia where music listening interventions, including technology, were delivered in participants’ homes or in institutions to address neuropsychiatric symptoms, quality of life and independence. The second included any population focusing on the use of music technology for health and wellbeing. In search one 70/251 full texts were included. The majority reported either statistical significance (6, 8.5%), significance (17, 24.2%) or improvements (26, 37.1%). Agitation was specifically reported in 36 (51.4%). The second search included 51/99 full texts, reporting improvement (28, 54.9%), significance (11, 21.5%), statistical significance (1, 1.9%) and no difference compared to the control (6, 11.7%). The majority in the first focused on mood and agitation, and the second on mood and psychophysiological responses. Five studies used AI or machine learning systems to select music, all involving healthy controls and reporting benefits. Most studies in both reviews were not conducted in a home environment (review 1 = 12; 17.1%; review 2 = 11; 21.5%). Preferred music listening may help manage NPS in the care home settings. Based on these and other data extracted in the review, a reasonable progression would be to co-design and test music listening systems and protocols for NPS in all settings, including people’s homes. Machine learning and automated technology for music selection and arousal adjustment, driven by live biodata, have not been explored in dementia care. Such approaches may help deliver the right music at the appropriate time in the required dosage, reducing the use of medication and improving quality of life.

Keywords: music listening, dementia, agitation, scoping review, technology

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Authors: Lauren Provost

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The digital world remains increasingly vulnerable, making the development of effective cybersecurity approaches even more critical in supporting the success of the digital economy and national security. Although approaches to cybersecurity have shifted and improved in the last decade with new models, especially with cloud computing and mobility, a record number of high severity vulnerabilities were recorded in the National Institute of Standards and Technology (NIST), and its National Vulnerability Database (NVD) in 2020. This is due, in part, to the increasing complexity of cyber ecosystems. Security must be approached with a more comprehensive, multi-tool strategy that addresses the complexity of cyber ecosystems, including the human factor. Ethical hacking has emerged as such an approach: a more effective, multi-strategy, comprehensive approach to cyber security's most pressing needs, especially understanding the human factor. Research on ethical hacking, however, is limited in scope. The two main objectives of this work are to (1) provide highlights of case studies in ethical hacking, (2) provide a conceptual framework for research in ethical hacking that embraces and addresses both technical and nontechnical security measures. Recommendations include an improved conceptual framework for research centered on ethical hacking that addresses many factors and attributes of significant attacks that threaten computer security; a more robust, integrative multi-layered framework embracing the complexity of cybersecurity ecosystems.

Keywords: ethical hacking, literature review, penetration testing, social engineering

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Authors: Anjushi Verma, Tirthankar Gayen

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Although, reliability is an important attribute of quality, especially for mission critical systems, yet, there does not exist any versatile model even today for the reliability assessment of component-based software. The existing Black Box models are found to make various assumptions which may not always be realistic and may be quite contrary to the actual behaviour of software. They focus on observing the manner in which the system behaves without considering the structure of the system, the components composing the system, their interconnections, dependencies, usage frequencies, etc.As a result, the entropy (uncertainty) in assessment using these models is much high.Though, there are some models based on operation profile yet sometimes it becomes extremely difficult to obtain the exact operation profile concerned with a given operation. This paper discusses the drawbacks, deficiencies and limitations of Black Box approaches from the perspective of various authors and finally proposes a conceptual model for the reliability assessment of software.

Keywords: black box, faults, failure, software reliability

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Authors: Tunde Szecsi, Janka Szilagyi

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Students majoring in education are underrepresented in undergraduate scholarship. To enable and encourage teacher candidates to engage in scholarly activities, it is essential to infuse skills such as problem-solving, critical thinking, oral and written communication, collaboration and the utilization of information literacy, into courses in teacher preparation programs. In this empirical study, we examined two teacher education programs – one in New York State and one in Florida – in terms of the approaches of the course-based infusion of skills for undergraduate research, and the effectiveness of this infusion. First, course-related documents such as syllabi, assignment descriptions, and course activities were reviewed and analyzed. The goal of the document analysis was to identify and describe the targeted skills, and the pedagogical approaches and strategies for promoting research skills in teacher candidates. Next, a selection of teacher candidates’ scholarly products from the institution in Florida was used as a data set to examine teacher candidates’ skill development in the context of the identified assignments. This dataset was analyzed both quantitatively and qualitatively to describe the changes that occurred in teacher candidates’ critical thinking, communication, and information literacy skills, and to uncover patterns in the skill development at the two institutions. Descriptive statistics were calculated to explore the changes in these skills of teacher candidates over a period of three years. The findings based on data from the teacher education program in Florida indicated a steady gain in written communication and critical thinking and a modest increase in informational literacy. At the institution in New York, candidates’ submission and success rates on the edTPA, a New York State Teacher Certification exam, was used as a measure of scholarly skills. Overall, although different approaches were used for infusing the development of scholarly skills in the courses, the results suggest that a holistic and well-orchestrated infusion of the skills into most courses in the teacher education program might result in steadily developing scholarly skills. These results offered essential implications for teacher education programs in terms of further improvements in teacher candidates’ skills for engaging in undergraduate research and scholarship. In this presentation, our purpose is to showcase two approaches developed by two teacher education programs to demonstrate how diverse approaches toward the promotion of undergraduate scholarship activities are responsive to the context of the teacher preparation programs.

Keywords: critical thinking, pedagogical strategies, teacher education, undergraduate student research

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Authors: Mauricio Quintero Angel, Andrés A. Duque Nivia, Carlos H. Fajardo Toro

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This paper analyzes two approaches of sustainability, the weak and strong, considering a case of study of oil palm production for an industry of biodegradable detergent. In this case, a company demand the oil palm as the active element for washing and through its trademark aims to supply 10% of the Colombian market of washing powders. Under each approach the economic and ecological implications of the palm oil production and especially the implications for crop management are described. The crop production under the weak sustainability implies plantations, intensive use of agrochemicals and the inclusion of new areas of cultivation as the market grows. Under the strong sustainability the production system is limited by the productive vocation of the ecosystem, so that new approaches and creativity for making viable the nature conservancy and the business development are require.

Keywords: agriculture, environmental impacts, oil palm, strong sustainability, weak sustainability

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Authors: Morten Stigaard Laursen, Rasmus Nyholm Jørgensen, Henrik Skov Midtiby, Anders Krogh Mortensen, Sanmohan Baby

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This work contributes a statistical model and simulation framework yielding the best estimate possible for the potential herbicide reduction when using the MoDiCoVi algorithm all the while requiring a efficacy comparable to conventional spraying. In June 2013 a maize field located in Denmark were seeded. The field was divided into parcels which was assigned to one of two main groups: 1) Control, consisting of subgroups of no spray and full dose spraty; 2) MoDiCoVi algorithm subdivided into five different leaf cover thresholds for spray activation. In addition approximately 25% of the parcels were seeded with additional weeds perpendicular to the maize rows. In total 299 parcels were randomly assigned with the 28 different treatment combinations. In the statistical analysis, bootstrapping was used for balancing the number of replicates. The achieved potential herbicide savings was found to be 70% to 95% depending on the initial weed coverage. However additional field trials covering more seasons and locations are needed to verify the generalisation of these results. There is a potential for further herbicide savings as the time interval between the first and second spraying session was not long enough for the weeds to turn yellow, instead they only stagnated in growth.

Keywords: herbicide reduction, macrosprayer, weed crop discrimination, site-specific, sprayer boom

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Authors: Shah Muhammad Butt, Ahmed Masaud Ansari

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Present Financial crisis in Higher Educational Institutes (HEIs) facing lots of problems considerable budget cuts, make difficult to meet the ever growing IT-based research and learning needs, institutions are rapidly planning and promoting cloud-based approaches for their academic and research needs. A cost effective Hybrid Cloud framework for HEI’s will provide educational services for campus or intercampus communication. Hybrid Cloud Framework comprises Private and Public Cloud approaches. This paper will propose the framework based on the Open Source Cloud (OpenNebula for Virtualization, Eucalyptus for Infrastructure, and Aneka for programming development environment) combined with CSP’s services which are delivered to the end-user via the Internet from public clouds.

Keywords: educational services, hybrid campus cloud, open source, electrical and systems sciences

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Authors: Mojtaba Taheri, Saied Reza Ameli

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In this research, we have calculated the effect of membership in international climate change treaties for 20 developed countries based on the human development index (HDI) and compared this effect with the process of pollutant reduction in the Environmental Kuznets Curve (EKC) theory. For this purpose, the data related to The real GDP per capita with 2010 constant prices is selected from the World Development Indicators (WDI) database. Ecological Footprint (ECOFP) is the amount of biologically productive land needed to meet human needs and absorb carbon dioxide emissions. It is measured in global hectares (gha), and the data retrieved from the Global Ecological Footprint (2021) database will be used, and we will proceed by examining step by step and performing several series of targeted statistical regressions. We will examine the effects of different control variables, including Energy Consumption Structure (ECS) will be counted as the share of fossil fuel consumption in total energy consumption and will be extracted from The United States Energy Information Administration (EIA) (2021) database. Energy Production (EP) refers to the total production of primary energy by all energy-producing enterprises in one country at a specific time. It is a comprehensive indicator that shows the capacity of energy production in the country, and the data for its 2021 version, like the Energy Consumption Structure, is obtained from (EIA). Financial development (FND) is defined as the ratio of private credit to GDP, and to some extent based on the stock market value, also as a ratio to GDP, and is taken from the (WDI) 2021 version. Trade Openness (TRD) is the sum of exports and imports of goods and services measured as a share of GDP, and we use the (WDI) data (2021) version. Urbanization (URB) is defined as the share of the urban population in the total population, and for this data, we used the (WDI) data source (2021) version. The descriptive statistics of all the investigated variables are presented in the results section. Related to the theories of sustainable development, Environmental Kuznets Curve (EKC) is more significant in the period of study. In this research, we use more than fourteen targeted statistical regressions to purify the net effects of each of the approaches and examine the results.

Keywords: climate change, globalization, environmental economics, sustainable development, international climate treaty

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Authors: Baratali Monfarediraz

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Humans’ identity is not only under the influence of a certain society or social structure but also it is influenced by an international identity. This article is a research on role of education in the manifestation of universally accepted values such as, advancement of science, improvement in the quality of education, preservation of the natural environment, preservation, and spread of peace, exchange of knowledge and technology, equal educational opportunities, benefiting from a universal morality and etc. Therefore, the relation between universal beliefs and values and educational approaches and programs is the first thing to pay attention to. Studies indicate that the first step in achieving the above mentioned goals is offering learning strategies. Therefore the importance of educational approaches and programs as a tool for the transference of ideas, experiences and thoughts becomes quite clear. Proper education gives everyone the opportunity of acquiring knowledge while creating tendency toward social activities paves the way for achieving the universal values.

Keywords: globalization, universal values, education, universal goal, values, society

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Authors: Ssu-Han Chen, Der-Baau Perng

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Molding process in IC manufacturing secures chips against the harms done by hot, moisture or other external forces. While a chip was being molded, defects like cracks, dilapidation, or voids may be embedding on the molding surface. The molding surfaces the study poises to treat and the ones on the market, though, differ in the surface where texture similar to defects is everywhere. Manual inspection usually passes over low-contrast cracks or voids; hence an automatic optical inspection system for molding surface is necessary. The proposed system is consisted of a CCD, a coaxial light, a back light as well as a motion control unit. Based on the property of statistical textures of the molding surface, a series of digital image processing and classification procedure is carried out. After training of the parameter associated with above algorithm, result of the experiment suggests that the accuracy rate is up to 93.75%, contributing to the inspection quality of IC molding surface.

Keywords: molding surface, machine vision, statistical texture, discrete Fourier transformation

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Authors: Anass Rahouti, Guillaume Salze, Ruggiero Lovreglio, Sélim Datoussaïd

Abstract:

In healthcare facilities, training the staff for firefighting and evacuation in real buildings is very challenging due to the presence of a vulnerable population in such an environment. In a standard environment, traditional approaches, such as fire drills, are often used to train the occupants and provide them with information about fire safety procedures. However, those traditional approaches may be inappropriate for a vulnerable population and can be inefficient from an educational viewpoint as it is impossible to expose the occupants to scenarios similar to a real emergency. Immersive serious games could be used as an alternative to traditional approaches to overcome their limitations. Serious games are already being used in different safety domains such as fires, earthquakes and terror attacks for several building types (e.g., office buildings, train stations, tunnels, etc.). In this study, we developed an immersive serious game to improve the fire safety skills of staff in healthcare facilities. An accurate representation of the healthcare environment was built in Unity3D by including visual and audio stimuli inspired from those employed in commercial action games. The serious game is organised in three levels. In each of them, the trainee is presented with a specific fire emergency and s/he can perform protective actions (e.g., firefighting, helping non-ambulant occupants, etc.) or s/he can ignore the opportunity for action and continue the evacuation. In this paper, we describe all the steps required to develop such a prototype, as well as the key questions that need to be answered, to develop a serious game for firefighting and evacuation in healthcare facilities.

Keywords: fire safety, healthcare, serious game, training

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Authors: Bronwen Wade

Abstract:

Over the last half-century, child welfare systems have increasingly relied on quantitative risk assessment tools, such as actuarial or predictive risk tools. These tools are developed by performing statistical analysis of how attributes captured in administrative data are related to future child maltreatment. Some scholars argue that attributes in administrative data can serve as proxies for race and that quantitative risk assessment tools reify racial bias in decision-making. Others argue that these tools provide more “objective” and “scientific” guides for decision-making instead of subjective social worker judgment. This study performs a systematic review of the literature on the impact of quantitative risk assessment tools on racial disproportionality; it examines methodological biases in work on this topic, summarizes key findings, and provides suggestions for further work. A search of CINAHL, PsychInfo, Proquest Social Science Premium Collection, and the ProQuest Dissertations and Theses Collection was performed. Academic and grey literature were included. The review includes studies that use quasi-experimental methods and development, validation, or re-validation studies of quantitative risk assessment tools. PROBAST (Prediction model Risk of Bias Assessment Tool) and CHARMS (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) were used to assess the risk of bias and guide data extraction for risk development, validation, or re-validation studies. ROBINS-I (Risk of Bias in Non-Randomized Studies of Interventions) was used to assess for bias and guide data extraction for the quasi-experimental studies identified. Due to heterogeneity among papers, a meta-analysis was not feasible, and a narrative synthesis was conducted. 11 papers met the eligibility criteria, and each has an overall high risk of bias based on the PROBAST and ROBINS-I assessments. This is deeply concerning, as major policy decisions have been made based on a limited number of studies with a high risk of bias. The findings on racial disproportionality have been mixed and depend on the tool and approach used. Authors use various definitions for racial equity, fairness, or disproportionality. These concepts of statistical fairness are connected to theories about the reason for racial disproportionality in child welfare or social definitions of fairness that are usually not stated explicitly. Most findings from these studies are unreliable, given the high degree of bias. However, some of the less biased measures within studies suggest that quantitative risk assessment tools may worsen racial disproportionality, depending on how disproportionality is mathematically defined. Authors vary widely in their approach to defining and addressing racial disproportionality within studies, making it difficult to generalize findings or approaches across studies. This review demonstrates the power of authors to shape policy or discourse around racial justice based on their choice of statistical methods; it also demonstrates the need for improved rigor and transparency in studies of quantitative risk assessment tools. Finally, this review raises concerns about the impact that these tools have on child welfare systems and racial disproportionality.

Keywords: actuarial risk, child welfare, predictive risk, racial disproportionality

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Authors: Johannes Gantner, Michael Held, Matthias Fischer

Abstract:

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

Procedia PDF Downloads 270

Authors: Fatima Z. Cherhabil, Mammar Sedrati, Sonia-Sabrina Bendib‎

Abstract:

In order to support the continued growth, critical latency of ‎IoT ‎applications, and ‎various obstacles of traditional data centers, ‎mobile edge ‎computing (MEC) has ‎emerged as a promising solution that extends cloud data-processing and decision-making to edge devices. ‎By adopting a MEC structure, IoT applications could be executed ‎locally, on ‎an edge server, different fog nodes, or distant cloud ‎data centers. However, we are ‎often ‎faced with wanting to optimize conflicting criteria such as ‎minimizing energy ‎consumption of limited local capabilities (in terms of CPU, RAM, storage, bandwidth) of mobile edge ‎devices and trying to ‎keep ‎high performance (reducing ‎response time, increasing throughput and service availability) ‎at the same ‎time‎. Achieving one goal may affect the other, making task offloading (TO), ‎resource allocation (RA), and service placement (SP) complex ‎processes. ‎It is a nontrivial multi-objective optimization ‎problem ‎to study the trade-off between conflicting criteria. ‎The paper provides a survey on different TO, SP, and RA recent multi-‎objective optimization (MOO) approaches used in edge computing environments, particularly artificial intelligent (AI) ones, to satisfy various objectives, constraints, and dynamic conditions related to IoT applications‎.

Keywords: mobile edge computing, multi-objective optimization, artificial ‎intelligence ‎approaches, task offloading, resource allocation, ‎ service placement

Procedia PDF Downloads 88

Authors: Emanuel Koseos

Abstract:

Data-Driven Decision Making (DDDM) refers to making decisions that are based on historical data in order to inform practice, develop strategies and implement policies that benefit organizational settings. In educational technology, DDDM facilitates the implementation of differential educational learning approaches such as Educational Data Mining (EDM) and Competency-Based Education (CBE), which commonly target university classrooms. There is a current need for DDDM models applied to middle and secondary schools from a concern for assessing the needs, progress and performance of students and educators with respect to regional standards, policies and evolution of curriculums. To address these concerns, we propose a DDDM reference model developed using educational key process initiatives as inputs to a machine learning framework implemented with statistical software (SAS, R) to provide a best-practices, complex-free and automated approach for educators at their regional level. We assessed the efficiency of the model over a six-year period using data from 45 schools and grades K-12 in the Langley, BC, Canada regional school district. We concluded that the model has wider appeal, such as business learning systems.

Keywords: competency-based learning, data-driven decision making, machine learning, secondary schools

Procedia PDF Downloads 152