Search results for: computer assisted learning language

Authors: Oleg Shovkovyy

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This research conducted in response to one of the most pressing questions on the agenda of many public administration offices around the world: “What could be done for better integration and assimilation of immigrants into hosting communities?” In author’s view, the answer could be suggested by immigrants themselves. They, often ‘bogged down in the past,’ snared by own idols and demons, perceive things differently, which, in turn, may result in their inability to integrate smoothly into hosting communities. Brief literature review suggests that perceptions of immigrants are completely neglected or something unsought in the current research on migrants, which, often, based on opinion polls by members of hosting communities themselves or superficial research data by various research organizations. Even those specimens that include voices of immigrants, unlikely to shed any additional light onto the problem simply because certain things are not made to speak out loud, especially to those in whose hands immigrants’ fate is (authorities). In this regard, this qualitative study, conducted by an insider to a few Russian-speaking communities, represents a unique opportunity for all stakeholders to look at the question of integration through the eyes of immigrants, from a different perspective and thus, makes research findings especially valuable for better understanding of the problem. Case study research employed ethnographic methods of gathering data where, approximately 200 Russian-speaking immigrants of first and second generations were closely observed by the Russian-speaking researcher in their usual setting, for eight months, and at different venues. The number of informal interviews with 27 key informants, with whom the researcher managed to establish a good rapport and who were keen enough to share their experiences voluntarily, were conducted. The field notes were taken at 14 locations (study sites) within the Brisbane region of Queensland, Australia. Moreover, all this time, researcher lived in dwelling of one of the immigrants and was an active participant in the social life (worship, picnics, dinners, weekend schools, concerts, cultural events, social gathering, etc.) of observed communities, whose members, to a large extent, belong to various religious lines of the Russian and Protestant Church. It was found that the majority of immigrants had experienced some discrimination in matters of hiring, employment, recognition of educational qualifications from home countries, and simply felt a sort of dislike from society in various everyday situations. Many noted complete absences or very limited state assistance in terms of employment, training, education, and housing. For instance, the Australian Government Department of Human Services not only does not stimulate job search but, on the contrary, encourages to refuse short-term works and employment. On the other hand, offered free courses on adaptation, and the English language proved to be ineffective and unpopular amongst immigrants. Many interviewees have reported overstated requirements for English proficiency and local work experience, whereas it was not critical for the given task or job. Based on the result of long-term monitoring, the researcher also had the courage to assert the negative and decelerating roles of immigrants’ communities, particularly religious communities, on processes of integration and assimilation. The findings suggest that governments should either change current immigration policies in the direction of their toughening or to take more proactive and responsible role in dealing with immigrant-related issues; for instance, increasing assistance and support to all immigrants and probably, paying more attention to and taking stake in managing and organizing lives of immigrants’ communities rather, simply leaving it all to chance.

Keywords: Australia, immigration, integration, perceptions

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Authors: Anna Öqvist, Malin Malmström

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Attaining a higher level of education has become an increasingly important prerequisite for people’s economic and social independence and mobility. Young people who do not pursue higher education are not as attractive as potential employees in the modern work environment. Although completing a higher education degree is not a guarantee for getting a job, it substantially increases the chances for employment and, consequently, the chances for a better life. Despite this, it’s a fact that in several regions in Sweden, fewer students are choosing to engage in higher education. Similar trends have been emphasized in, for instance, the US where high dropout patterns among young people have been noted. This is a threat to future employment and industry development in these regions because the future employment base for society is dependent upon students’ willingness to invest in higher education. Much of prior studies have focused on the role of parents’ involvement in their children’s’ school work and the positive influence parents involvement have on their children’s school performance. Parental influence on education in general has been a topic of interest among those concerned with optimal developmental and educational outcomes for children and youth in pre-, secondary- and high school. Across a range of studies, there has emerged a strong conclusion that parental influence on child and youths education generally benefits children's and youths learning and school success. Arguably then, we could expect that parents influence on whether or not to pursue a higher education would be of importance to understand young people’s choice to engage in higher education. Accordingly, understanding what drives students’ intentions to pursue higher education is an essential component of motivating students to aspire to make the most of their potential in their future work life. Drawing on the theory of planned behavior, this study examines the role of parents influence on students’ attitudes about whether higher education can be beneficial to their future work life. We used a qualitative approach by collecting interview data from 18 high school students in Sweden to capture students’ cognitive and motivational mechanisms (attitudes) to influence intentions to engage in higher education. We found that parents may positively or negatively influence students’ attitudes and subsequently a student's intention to pursue higher education. Accordingly, our results show that parents’ own attitudes and expectations on their children are keys for influencing students’ attitudes and intentions for higher education. Further, our finding illuminates the mechanisms that drive students in one direction or the other. As such, our findings show that the same categories of arguments are used for driving students’ attitudes and intentions in two opposite directions, namely; financial arguments and work life benefits arguments. Our results contribute to existing literature by showing that parents do affect young people’s intentions to engage in higher studies. The findings contribute to the theory of planned behavior and have implications for the literature on higher education and educational psychology and also provide guidance on how to inform students about facts of higher studies in school.

Keywords: higher studies, intentions, parents influence, theory of planned behavior

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Authors: Andrey Davydov, Konstantin Sveshnikov, Yulia Voronina

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There is now a lot of interest to the non-perturbative QED-effects, caused by diving of discrete levels into the negative continuum in the supercritical static or adiabatically slowly varying Coulomb fields, that are created by the localized extended sources with Z > Z_cr. Such effects have attracted a considerable amount of theoretical and experimental activity, since in 3+1 QED for Z > Z_cr,1 ≈ 170 a non-perturbative reconstruction of the vacuum state is predicted, which should be accompanied by a number of nontrivial effects, including the vacuum positron emission. Similar in essence effects should be expected also in both 2+1 D (planar graphene-based hetero-structures) and 1+1 D (one-dimensional ‘hydrogen ion’). This report is devoted to the study of such essentially non-perturbative vacuum effects for the supercritical Dirac-Coulomb systems in 1+1D and 2+1D, with the main attention drawn to the vacuum polarization energy. Although the most of works considers the vacuum charge density as the main polarization observable, vacuum energy turns out to be not less informative and in many respects complementary to the vacuum density. Moreover, the main non-perturbative effects, which appear in vacuum polarization for supercritical fields due to the levels diving into the lower continuum, show up in the behavior of vacuum energy even more clear, demonstrating explicitly their possible role in the supercritical region. Both in 1+1D and 2+1D, we explore firstly the renormalized vacuum density in the supercritical region using the Wichmann-Kroll method. Thereafter, taking into account the results for the vacuum density, we formulate the renormalization procedure for the vacuum energy. To evaluate the latter explicitly, an original technique, based on a special combination of analytical methods, computer algebra tools and numerical calculations, is applied. It is shown that, for a wide range of the external source parameters (the charge Z and size R), in the supercritical region the renormalized vacuum energy could significantly deviate from the perturbative quadratic growth up to pronouncedly decreasing behavior with jumps by (-2 x mc^2), which occur each time, when the next discrete level dives into the negative continuum. In the considered range of variation of Z and R, the vacuum energy behaves like ~ -Z^2/R in 1+1D and ~ -Z^3/R in 2+1D, exceeding deeply negative values. Such behavior confirms the assumption of the neutral vacuum transmutation into the charged one, and thereby of the spontaneous positron emission, accompanying the emergence of the next vacuum shell due to the total charge conservation. To the end, we also note that the methods, developed for the vacuum energy evaluation in 2+1 D, with minimal complements could be carried over to the three-dimensional case, where the vacuum energy is expected to be ~ -Z^4/R and so could be competitive with the classical electrostatic energy of the Coulomb source.

Keywords: non-perturbative QED-effects, one- and two-dimensional Dirac-Coulomb systems, supercritical fields, vacuum polarization

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Authors: Nahid Parween Anwar, Sadia Muzaffar Bhutta, Takbir Ali

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Mentoring is a structured activity which is designed to facilitate engagement between mentor and mentee to enhance mentee’s professional capability as an effective teacher. Both mentor and mentee are important elements of the ‘mentoring equation’ and play important roles in nourishing this dynamic, collaborative and reciprocal relationship. Cluster-Based Mentoring Programme (CBMP) provides an indigenous example of a project which focused on development of primary school teachers in selected clusters with a particular focus on their classroom practice. A study was designed to examine the efficacy of CBMP as part of Strengthening Teacher Education in Pakistan (STEP) project. This paper presents results of one of the components of this study. As part of the larger study, a cross-sectional survey was employed to explore nature and patterns of mentoring process from mentees’ perspectives in the selected districts of Sindh and Balochistan. This paper focuses on the results of the study related to the question: What are mentees’ perceptions of their mentors’ support for enhancing their classroom practice during mentoring process? Data were collected from mentees (n=1148) using a 5-point scale -‘Mentoring for Effective Primary Teaching’ (MEPT). MEPT focuses on seven factors of mentoring: personal attributes, pedagogical knowledge, modelling, feedback, system requirement, development and use of material, and gender equality. Data were analysed using SPSS 20. Mentees perceptions of mentoring practice of their mentors were summarized using mean and standard deviation. Results showed that mean scale scores on mentees’ perceptions of their mentors’ practices fell between 3.58 (system requirement) and 4.55 (personal attributes). Mentees’ perceives personal attribute of the mentor as the most significant factor (M=4.55) towards streamlining mentoring process by building good relationship between mentor and mentees. Furthermore, mentees have shared positive views about their mentors efforts towards promoting gender impartiality (M=4.54) during workshop and follow up visit. Contrary to this, mentees felt that more could have been done by their mentors in sharing knowledge about system requirement (e.g. school policies, national curriculum). Furthermore, some of the aspects in high scoring factors were highlighted by the mentees as areas for further improvement (e.g. assistance in timetabling, written feedback, encouragement to develop learning corners). Mentees’ perceptions of their mentors’ practices may assist in determining mentoring needs. The results may prove useful for the professional development programme for the mentors and mentees for specific mentoring programme in order to enhance practices in primary classrooms in Pakistan. Results would contribute into the body of much-needed knowledge from developing context.

Keywords: cluster-based mentoring programme, mentoring for effective primary teaching (MEPT), professional development, survey

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Authors: Matthew Yeager, Christopher Willy, John Bischoff

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

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

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Authors: Moen Torill, Rismark Marit, Astrid M. Solvberg

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In Norway, every municipality shall provide an educational psychological service, EPS, to support kindergartens and schools in their work with children and youths with special needs. The EPS focus its work on individuals, aiming to identify special needs and to give advice to teachers and parents when they ask for it. In addition, the service also give priority to prevention and system intervention in kindergartens and schools. To master these big tasks university courses are established to support EPS counsellors' continuous learning. There is, however, a need for more in-depth and systematic knowledge on how they experience the courses they attend. In this study, EPS counsellors’ reflection logs during a particular course are investigated. The research question is: what are the content and priorities of the reflections that are communicated in the logs produced by the educational psychological counsellors during a post-graduate course? The investigated course is a credit course organized over a one-year period in two one-semester modules. The altogether 55 students enrolled in the course work as EPS counsellors in various municipalities across Norway. At the end of each day throughout the course period, the participants wrote reflection logs about what they had experienced during the day. The data material consists of 165 pages of typed text. The collaborating researchers studied the data material to ascertain, differentiate and understand the meaning of the content in each log. The analysis also involved the search for similarity in content and development of analytical categories that described the focus and primary concerns in each of the written logs. This involved constant 'critical and sustained discussions' for mutual construction of meaning between the co-researchers in the developing categories. The process is inspired by Grounded Theory. This means that the concepts developed during the analysis derived from the data material and not chosen prior to the investigation. The analysis revealed that the concept 'Useful' frequently appeared in the participants’ reflections and, as such, 'Useful' serves as a core category. The core category is described through three major categories: (1) knowledge sharing (concerning direct and indirect work with students with special needs) with colleagues is useful, (2) reflections on models and theoretical concepts (concerning students with special needs) are useful, (3) reflection on the role as EPS counsellor is useful. In all the categories, the notion of useful occurs in the participants’ emphasis on and acknowledgement of the immediate and direct link between the university course content and their daily work practice. Even if each category has an importance and value of its own, it is crucial that they are understood in connection with one another and as interwoven. It is the connectedness that gives the core category an overarching explanatory power. The knowledge from this study may be a relevant contribution when it comes to designing new courses that support continuing professional development for EPS counsellors, whether for post-graduate university courses or local courses at the EPS offices or whether in Norway or other countries in the world.

Keywords: constructing and circulating knowledge, educational-psychological counsellor, higher education, professional development

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Authors: Xiaochen Mu

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Data rights confirmation, as a key legal issue in the development of the digital economy, is undergoing a transition from a traditional rights paradigm to a more complex private-economic paradigm. In this process, data rights confirmation has evolved from a simple claim of rights to a complex structure encompassing multiple dimensions of personality rights and property rights. Current data rights confirmation practices are primarily reflected in two models: holistic rights confirmation and process rights confirmation. The holistic rights confirmation model continues the traditional "one object, one right" theory, while the process rights confirmation model, through contractual relationships in the data processing process, recognizes rights that are more adaptable to the needs of data circulation and value release. In the design of the data property rights system, there is a hierarchical characteristic aimed at decoupling from raw data to data applications through horizontal stratification and vertical staging. This design not only respects the ownership rights of data originators but also, based on the usufructuary rights of enterprises, constructs a corresponding rights system for different stages of data processing activities. The subjects of data property rights include both data originators, such as users, and data producers, such as enterprises, who enjoy different rights at different stages of data processing. The intellectual property rights system, with the mission of incentivizing innovation and promoting the advancement of science, culture, and the arts, provides a complete set of mechanisms for protecting innovative results. However, unlike traditional private property rights, the granting of intellectual property rights is not an end in itself; the purpose of the intellectual property system is to balance the exclusive rights of the rights holders with the prosperity and long-term development of society's public learning and the entire field of science, culture, and the arts. Therefore, the intellectual property granting mechanism provides both protection and limitations for the rights holder. This perfectly aligns with the dual attributes of data. In terms of achieving the protection of data property rights, the granting of intellectual property rights is an important institutional choice that can enhance the effectiveness of the data property exchange mechanism. Although this is not the only path, the granting of data property rights within the framework of the intellectual property rights system helps to establish fundamental legal relationships and rights confirmation mechanisms and is more compatible with the classification and grading system of data. The modernity of the intellectual property rights system allows it to adapt to the needs of big data technology development through special clauses or industry guidelines, thus promoting the comprehensive advancement of data intellectual property rights legislation. This paper analyzes data protection under the virtual property layer theory and two-fold virtual property rights system. Based on the “bundle of right” theory, this paper establishes specific three-level data rights. This paper analyzes the cases: Google v. Vidal-Hall, Halliday v Creation Consumer Finance, Douglas v Hello Limited, Campbell v MGN and Imerman v Tchenquiz. This paper concluded that recognizing property rights over personal data and protecting data under the framework of intellectual property will be beneficial to establish the tort of misuse of personal information.

Keywords: data protection, property rights, intellectual property, Big data

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Authors: Yiea Funk Te, Daniel Mueller, Irena Pletikosa Cvijikj

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Small and medium enterprises (SMEs) play an important role in the economy of many countries. When the overall world economy is considered, SMEs represent 95% of all businesses in the world, accounting for 66% of the total employment. Existing studies show that the current business environment is characterized as highly turbulent and strongly influenced by modern information and communication technologies, thus forcing SMEs to experience more severe challenges in maintaining their existence and expanding their business. To support SMEs at improving their competitiveness, researchers recently turned their focus on applying data mining techniques to build risk and growth prediction models. However, data used to assess risk and growth indicators is primarily obtained via questionnaires, which is very laborious and time-consuming, or is provided by financial institutes, thus highly sensitive to privacy issues. Recently, web mining (WM) has emerged as a new approach towards obtaining valuable insights in the business world. WM enables automatic and large scale collection and analysis of potentially valuable data from various online platforms, including companies’ websites. While WM methods have been frequently studied to anticipate growth of sales volume for e-commerce platforms, their application for assessment of SME risk and growth indicators is still scarce. Considering that a vast proportion of SMEs own a website, WM bears a great potential in revealing valuable information hidden in SME websites, which can further be used to understand SME risk and growth indicators, as well as to enhance current SME risk and growth prediction models. This study aims at developing an automated system to collect business-relevant data from the Web and predict future growth trends of SMEs by means of WM and data mining techniques. The envisioned system should serve as an 'early recognition system' for future growth opportunities. In an initial step, we examine how structured and semi-structured Web data in governmental or SME websites can be used to explain the success of SMEs. WM methods are applied to extract Web data in a form of additional input features for the growth prediction model. The data on SMEs provided by a large Swiss insurance company is used as ground truth data (i.e. growth-labeled data) to train the growth prediction model. Different machine learning classification algorithms such as the Support Vector Machine, Random Forest and Artificial Neural Network are applied and compared, with the goal to optimize the prediction performance. The results are compared to those from previous studies, in order to assess the contribution of growth indicators retrieved from the Web for increasing the predictive power of the model.

Keywords: data mining, SME growth, success factors, web mining

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Authors: Feng-Sheng Wang, Chao-Ting Cheng

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Developing a drug from conception to launch is costly and time-consuming. Computer-aided methods can reduce research costs and accelerate the development process during the early drug discovery and development stages. This study developed a fuzzy multi-objective hierarchical optimization framework for identifying potential anticancer targets in a metabolic model. First, RNA-seq expression data of colorectal cancer samples and their healthy counterparts were used to reconstruct tissue-specific genome-scale metabolic models. The aim of the optimization framework was to identify anticancer targets that lead to cancer cell death and evaluate metabolic flux perturbations in normal cells that have been caused by cancer treatment. Four objectives were established in the optimization framework to evaluate the mortality of cancer cells for treatment and to minimize side effects causing toxicity-induced tumorigenesis on normal cells and smaller metabolic perturbations. Through fuzzy set theory, a multiobjective optimization problem was converted into a trilevel maximizing decision-making (MDM) problem. The applied nested hybrid differential evolution was applied to solve the trilevel MDM problem using two nutrient media to identify anticancer targets in the genome-scale metabolic model of colorectal cancer, respectively. Using Dulbecco’s Modified Eagle Medium (DMEM), the computational results reveal that the identified anticancer targets were mostly involved in cholesterol biosynthesis, pyrimidine and purine metabolisms, glycerophospholipid biosynthetic pathway and sphingolipid pathway. However, using Ham’s medium, the genes involved in cholesterol biosynthesis were unidentifiable. A comparison of the uptake reactions for the DMEM and Ham’s medium revealed that no cholesterol uptake reaction was included in DMEM. Two additional media, i.e., a cholesterol uptake reaction was included in DMEM and excluded in HAM, were respectively used to investigate the relationship of tumor cell growth with nutrient components and anticancer target genes. The genes involved in the cholesterol biosynthesis were also revealed to be determinable if a cholesterol uptake reaction was not induced when the cells were in the culture medium. However, the genes involved in cholesterol biosynthesis became unidentifiable if such a reaction was induced.

Keywords: Cancer metabolism, genome-scale metabolic model, constraint-based model, multilevel optimization, fuzzy optimization, hybrid differential evolution

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Authors: Baha Eddine Aissou, Aichouche Belhadj Aissa

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Light detection and ranging (LiDAR) is an active remote sensing technology used for several applications. Airborne LiDAR is becoming an important technology for the acquisition of a highly accurate dense point cloud. A classification of airborne laser scanning (ALS) point cloud is a very important task that still remains a real challenge for many scientists. Support vector machine (SVM) is one of the most used statistical learning algorithms based on kernels. SVM is a non-parametric method, and it is recommended to be used in cases where the data distribution cannot be well modeled by a standard parametric probability density function. Using a kernel, it performs a robust non-linear classification of samples. Often, the data are rarely linearly separable. SVMs are able to map the data into a higher-dimensional space to become linearly separable, which allows performing all the computations in the original space. This is one of the main reasons that SVMs are well suited for high-dimensional classification problems. Only a few training samples, called support vectors, are required. SVM has also shown its potential to cope with uncertainty in data caused by noise and fluctuation, and it is computationally efficient as compared to several other methods. Such properties are particularly suited for remote sensing classification problems and explain their recent adoption. In this poster, the SVM classification of ALS LiDAR data is proposed. Firstly, connected component analysis is applied for clustering the point cloud. Secondly, the resulting clusters are incorporated in the SVM classifier. Radial basic function (RFB) kernel is used due to the few numbers of parameters (C and γ) that needs to be chosen, which decreases the computation time. In order to optimize the classification rates, the parameters selection is explored. It consists to find the parameters (C and γ) leading to the best overall accuracy using grid search and 5-fold cross-validation. The exploited LiDAR point cloud is provided by the German Society for Photogrammetry, Remote Sensing, and Geoinformation. The ALS data used is characterized by a low density (4-6 points/m²) and is covering an urban area located in residential parts of the city Vaihingen in southern Germany. The class ground and three other classes belonging to roof superstructures are considered, i.e., a total of 4 classes. The training and test sets are selected randomly several times. The obtained results demonstrated that a parameters selection can orient the selection in a restricted interval of (C and γ) that can be further explored but does not systematically lead to the optimal rates. The SVM classifier with hyper-parameters is compared with the most used classifiers in literature for LiDAR data, random forest, AdaBoost, and decision tree. The comparison showed the superiority of the SVM classifier using parameters selection for LiDAR data compared to other classifiers.

Keywords: classification, airborne LiDAR, parameters selection, support vector machine

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Authors: Sergio J. Calleja, Adria Roca, José D. Santotoribio

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Pancreatic endocrine diseases include pathologies like insulin resistance (IR), prediabetes, and type 2 diabetes mellitus (DM2). Some of them are highly prevalent in the U.S.—40% of U.S. adults have IR, 38% of U.S. adults have prediabetes, and 12% of U.S. adults have DM2—, as reported by the National Center for Biotechnology Information (NCBI). Building upon this imperative, the objective of the present study was to develop a non-invasive test for the assessment of the patient’s pancreatic endocrine function and to evaluate its accuracy in detecting various pancreatic endocrine diseases, such as IR, prediabetes, and DM2. This approach to a routine blood and urine test is based around serum and urine biomarkers. It is made by the combination of several independent public algorithms, such as the Adult Treatment Panel III (ATP-III), triglycerides and glucose (TyG) index, homeostasis model assessment-insulin resistance (HOMA-IR), HOMA-2, and the quantitative insulin-sensitivity check index (QUICKI). Additionally, it incorporates essential measurements such as the creatinine clearance, estimated glomerular filtration rate (eGFR), urine albumin-to-creatinine ratio (ACR), and urinalysis, which are helpful to achieve a full image of the patient’s pancreatic endocrine disease. To evaluate the estimated accuracy of this test, an iterative process was performed by a machine learning (ML) algorithm, with a training set of 9,391 patients. The sensitivity achieved was 97.98% and the specificity was 99.13%. Consequently, the area under the receiver operating characteristic (AUROC) curve, the positive predictive value (PPV), and the negative predictive value (NPV) were 92.48%, 99.12%, and 98.00%, respectively. The algorithm was validated with a randomized controlled trial (RCT) with a target sample size (n) of 314 patients. However, 50 patients were initially excluded from the study, because they had ongoing clinically diagnosed pathologies, symptoms or signs, so the n dropped to 264 patients. Then, 110 patients were excluded because they didn’t show up at the clinical facility for any of the follow-up visits—this is a critical point to improve for the upcoming RCT, since the cost of each patient is very high and for this RCT almost a third of the patients already tested were lost—, so the new n consisted of 154 patients. After that, 2 patients were excluded, because some of their laboratory parameters and/or clinical information were wrong or incorrect. Thus, a final n of 152 patients was achieved. In this validation set, the results obtained were: 100.00% sensitivity, 100.00% specificity, 100.00% AUROC, 100.00% PPV, and 100.00% NPV. These results suggest that this approach to a routine blood and urine test holds promise in providing timely and accurate diagnoses of pancreatic endocrine diseases, particularly among individuals aged 40 and above. Given the current epidemiological state of these type of diseases, these findings underscore the significance of early detection. Furthermore, they advocate for further exploration, prompting the intention to conduct a clinical trial involving 26,000 participants (from March 2025 to December 2026).

Keywords: algorithm, diabetes, laboratory medicine, non-invasive

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Authors: Dmitry A. Koptelov, Leonid M. Mestetskiy

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Voronoi Diagram (VD) of finite set of disjoint simple polygons, called sites, is a partition of plane into loci (for each site at the locus) – regions, consisting of points that are closer to a given site than to all other. Set of polygons is a universal model for many applications in engineering, geoinformatics, design, computer vision, and graphics. VD of polygons construction usually done with a reduction to task of constructing VD of segments, for which there are effective O(n log n) algorithms for n segments. Preprocessing – constructing segments from polygons’ sides, and postprocessing – polygon’s loci construction by merging the loci of the sides of each polygon are also included in reduction. This approach doesn’t take into account two specific properties of the resulting segment sites. Firstly, all this segments are connected in pairs in the vertices of the polygons. Secondly, on the one side of each segment lies the interior of the polygon. The polygon is obviously included in its locus. Using this properties in the algorithm for VD construction is a resource to reduce computations. The article proposes an algorithm for the direct construction of VD of polygonal sites. Algorithm is based on sweepline paradigm, allowing to effectively take into account these properties. The solution is performed based on reduction. Preprocessing is the constructing of set of sites from vertices and edges of polygons. Each site has an orientation such that the interior of the polygon lies to the left of it. Proposed algorithm constructs VD for set of oriented sites with sweepline paradigm. Postprocessing is a selecting of edges of this VD formed by the centers of empty circles touching different polygons. Improving the efficiency of the proposed sweepline algorithm in comparison with the general Fortune algorithm is achieved due to the following fundamental solutions: 1. Algorithm constructs only such VD edges, which are on the outside of polygons. Concept of oriented sites allowed to avoid construction of VD edges located inside the polygons. 2. The list of events in sweepline algorithm has a special property: the majority of events are connected with “medium” polygon vertices, where one incident polygon side lies behind the sweepline and the other in front of it. The proposed algorithm processes such events in constant time and not in logarithmic time, as in the general Fortune algorithm. The proposed algorithm is fully implemented and tested on a large number of examples. The high reliability and efficiency of the algorithm is also confirmed by computational experiments with complex sets of several thousand polygons. It should be noted that, despite the considerable time that has passed since the publication of Fortune's algorithm in 1986, a full-scale implementation of this algorithm for an arbitrary set of segment sites has not been made. The proposed algorithm fills this gap for an important special case - a set of sites formed by polygons.

Keywords: voronoi diagram, sweepline, polygon sites, fortunes' algorithm, segment sites

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Authors: Elham Soltani Dehnavi

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In today’s compact cities, bringing daylighting and fresh air to buildings is a significant challenge, but it also presents opportunities to reduce energy consumption in buildings by reducing the need for artificial lighting and mechanical systems. Simple adjustments to building form can contribute to their efficiency. This paper examines how the relationship between the width and depth of the rooms in office buildings affects visual and thermal comfort, and consequently energy savings. Based on these evaluations, we can determine the best location for sedentary areas in a room. We can also propose improvements to occupant experience and minimize the difference between the predicted and measured performance in buildings by changing other design parameters, such as natural ventilation strategies, glazing properties, and shading. This study investigates the condition of spatial daylighting and thermal comfort for a range of room configurations using computer simulations, then it suggests the best depth for optimizing both daylighting and thermal comfort, and consequently energy performance in each room type. The Window-to-Wall Ratio (WWR) is 40% with 0.8m window sill and 0.4m window head. Also, there are some fixed parameters chosen according to building codes and standards, and the simulations are done in Seattle, USA. The simulation results are presented as evaluation grids using the thresholds for different metrics such as Daylight Autonomy (DA), spatial Daylight Autonomy (sDA), Annual Sunlight Exposure (ASE), and Daylight Glare Probability (DGP) for visual comfort, and Predicted Mean Vote (PMV), Predicted Percentage of Dissatisfied (PPD), occupied Thermal Comfort Percentage (occTCP), over-heated percent, under-heated percent, and Standard Effective Temperature (SET) for thermal comfort that are extracted from Grasshopper scripts. The simulation tools are Grasshopper plugins such as Ladybug, Honeybee, and EnergyPlus. According to the results, some metrics do not change much along the room depth and some of them change significantly. So, we can overlap these grids in order to determine the comfort zone. The overlapped grids contain 8 metrics, and the pixels that meet all 8 mentioned metrics’ thresholds define the comfort zone. With these overlapped maps, we can determine the comfort zones inside rooms and locate sedentary areas there. Other parts can be used for other tasks that are not used permanently or need lower or higher amounts of daylight and thermal comfort is less critical to user experience. The results can be reflected in a table to be used as a guideline by designers in the early stages of the design process.

Keywords: occupant experience, office buildings, space depth, thermal comfort, visual comfort

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Authors: Jalil ur Rehman, Ramesh C. Tailor, Muhammad Isa Khan, Jahnzeeb Ashraf, Muhammad Afzal, Geofferry S. Ibbott

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Purpose: The purpose of this analysis was to investigate dose distribution of different techniques (3D-CRT, IMRT and VMAT) of head and neck cancer using 3-dimensional dosimeter called PRESAGETM Dosimeter. Materials and Methods: Computer tomography (CT) scans of radiological physics center (RPC) head and neck anthropomorphic phantom with both RPC standard insert and PRESAGETM insert were acquired separated with Philipp’s CT scanner and both CT scans were exported via DICOM to the Pinnacle version 9.4 treatment planning system (TPS). Each plan was delivered twice to the RPC phantom first containing the RPC standard insert having TLD and film dosimeters and then again containing the Presage insert having 3-D dosimeter (PRESAGETM) by using a Varian True Beam linear accelerator. After irradiation, the standard insert including point dose measurements (TLD) and planar Gafchromic® EBT film measurement were read using RPC standard procedure. The 3D dose distribution from PRESAGETM was read out with the Duke Midsized optical scanner dedicated to RPC (DMOS-RPC). Dose volume histogram (DVH), mean and maximal doses for organs at risk were calculated and compared among each head and neck technique. The prescription dose was same for all head and neck radiotherapy techniques which was 6.60 Gy/friction. Beam profile comparison and gamma analysis were used to quantify agreements among film measurement, PRESAGETM measurement and calculated dose distribution. Quality assurances of all plans were performed by using ArcCHECK method. Results: VMAT delivered the lowest mean and maximum doses to organ at risk (spinal cord, parotid) than IMRT and 3DCRT. Such dose distribution was verified by absolute dose distribution using thermoluminescent dosimeter (TLD) system. The central axial, sagittal and coronal planes were evaluated using 2D gamma map criteria(± 5%/3 mm) and results were 99.82% (axial), 99.78% (sagital), 98.38% (coronal) for VMAT plan and found the agreement between PRESAGE and pinnacle was better than IMRT and 3D-CRT plan excludes a 7 mm rim at the edge of the dosimeter. Profile showed good agreement for all plans between film, PRESAGE and pinnacle and 3D gamma was performed for PTV and OARs, VMAT and 3DCRT endow with better agreement than IMRT. Conclusion: VMAT delivered lowered mean and maximal doses to organs at risk and better PTV coverage during head and neck radiotherapy. TLD, EBT film and PRESAGETM dosimeters suggest that VMAT was better for the treatment of head and neck cancer than IMRT and 3D-CRT.

Keywords: RPC, 3DCRT, IMRT, VMAT, EBT2 film, TLD, PRESAGETM

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Authors: Avi Shrivastava

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In 2021, two heart-wrenching scenes, shown live on television screens across countries, painted a grim picture of refugees. One of them was of people clinging onto an airplane's wings in their desperate attempt to flee war-torn Afghanistan. They ultimately fell to their death. The other scene was the U.S. government authorities separating children from their parents or guardians to deter migrants/refugees from coming to the U.S. These events show the desperation refugees feel when they are trying to leave their homes in disaster zones. However, data paints a grave picture of the current refugee situation. It also indicates that a bleak future lies ahead for the refugees across the globe. Data and information are the two threads that intertwine to weave the shimmery fabric of modern society. Data and information are often used interchangeably, but they differ considerably. For example, information analysis reveals rationale, and logic, while data analysis, on the other hand, reveals a pattern. Moreover, patterns revealed by data can enable us to create the necessary tools to combat huge problems on our hands. Data analysis paints a clear picture so that the decision-making process becomes simple. Geopolitical and economic data can be used to predict future refugee hotspots. Accurately predicting the next refugee hotspots will allow governments and relief agencies to prepare better for future refugee crises. The refugee crisis does not have binary answers. Given the emotionally wrenching nature of the ground realities, experts often shy away from realistically stating things as they are. This hesitancy can cost lives. When decisions are based solely on data, emotions can be removed from the decision-making process. Data also presents irrefutable evidence and tells whether there is a solution or not. Moreover, it also responds to a nonbinary crisis with a binary answer. Because of all that, it becomes easier to tackle a problem. Data science and A.I. can predict future refugee crises. With the recent explosion of data due to the rise of social media platforms, data and insight into data has solved many social and political problems. Data science can also help solve many issues refugees face while staying in refugee camps or adopted countries. This paper looks into various ways data science can help solve refugee problems. A.I.-based chatbots can help refugees seek legal help to find asylum in the country they want to settle in. These chatbots can help them find a marketplace where they can find help from the people willing to help. Data science and technology can also help solve refugees' many problems, including food, shelter, employment, security, and assimilation. The refugee problem seems to be one of the most challenging for social and political reasons. Data science and machine learning can help prevent the refugee crisis and solve or alleviate some of the problems that refugees face in their journey to a better life. With the explosion of data in the last decade, data science has made it possible to solve many geopolitical and social issues.

Keywords: refugee crisis, artificial intelligence, data science, refugee camps, Afghanistan, Ukraine

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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: Rodrigo De A. Ferrazza, Henry D. M. Garcia, Viviana H. V. Aristizabal, Camilla De S. Nogueira, Cecilia J. Verissimo, Jose Roberto Sartori, Roberto Sartori, Joao Carlos P. Ferreira

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Environmental factors adversely influence sustainability in livestock production system. Dairy herds are the most affected by heat stress among livestock industries. This clearly implies in development of new strategies for mitigating heat, which should be based on physiological and metabolic adaptations of the animal. In this study, we incorporated the effect of climate variables and heat exposure time on the thermoregulatory responses in order to clarify the adaptive mechanisms for bovine heat dissipation under intense thermal stress induced experimentally in climate chamber. Non-lactating Holstein cows were contemporaneously and randomly assigned to thermoneutral (TN; n=12) or heat stress (HS; n=12) treatments during 16 days. Vaginal temperature (VT) was measured every 15 min with a microprocessor-controlled data logger (HOBO®, Onset Computer Corporation, Bourne, MA, USA) attached to a modified vaginal controlled internal drug release insert (Sincrogest®, Ourofino, Brazil). Rectal temperature (RT), respiratory rate (RR) and heart rate (HR) were measured twice a day (0700 and 1500h) and dry matter intake (DMI) was estimated daily. The ambient temperature and air relative humidity were 25.9±0.2°C and 73.0±0.8%, respectively for TN, and 36.3± 0.3°C and 60.9±0.9%, respectively for HS. Respiratory rate of HS cows increased immediately after exposure to heat and was higher (76.02±1.70bpm; P<0.001) than TN (39.70±0.71bpm), followed by rising of RT (39.87°C±0.07 for HS versus 38.56±0.03°C for TN; P<0.001) and VT (39.82±0.10°C for HS versus 38.26±0.03°C for TN; P<0.001). A diurnal pattern was detected, with higher (P<0.01) afternoon temperatures than morning and this effect was aggravated for HS cows. There was decrease (P<0.05) of HR for HS cows (62.13±0.99bpm) compared to TN (66.23±0.79bpm), but the magnitude of the differences was not the same over time. From the third day, there was a decrease of DMI for HS in attempt to maintain homeothermy, while TN cows increased DMI (8.27kg±0.33kg d-1 for HS versus 14.03±0.29kg d-1 for TN; P<0.001). By regression analysis, RT and RR better reflected the response of cows to changes in the Temperature Humidity Index and the effect of climate variables from the previous day to influence the physiological parameters and DMI was more important than the current day, with ambient temperature the most important factor. Comparison between acute (0 to 3 days) and chronic (13 to 16 days) exposure to heat stress showed decreasing of the slope of the regression equations for RR and DMI, suggesting an adaptive adjustment, however with no change for RT. In conclusion, intense heat stress exerted strong influence on the thermoregulatory mechanisms, but the acclimation process was only partial.

Keywords: acclimation, bovine, climate chamber, hyperthermia, thermoregulation

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Authors: Kalliopi Kanaki, Michael Kalogiannakis

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Nowadays, in the digital era, the early acquisition of basic programming skills and knowledge is encouraged, as it facilitates students’ exposure to computational thinking and empowers their creativity, problem-solving skills, and cognitive development. More and more researchers and educators investigate the introduction of computational thinking in K-12 since it is expected to be a fundamental skill for everyone by the middle of the 21st century, just like reading, writing and arithmetic are at the moment. In this paper, a doctoral research in the process is presented, which investigates the infusion of computational thinking into science curriculum in early childhood education. The whole attempt aims to develop young children’s computational thinking by introducing them to the fundamental concepts of object-oriented programming in an enjoyable, yet educational framework. The backbone of the research is the digital environment PhysGramming (an abbreviation of Physical Science Programming), which provides children the opportunity to create their own digital games, turning them from passive consumers to active creators of technology. PhysGramming deploys an innovative hybrid schema of visual and text-based programming techniques, with emphasis on object-orientation. Through PhysGramming, young students are familiarized with basic object-oriented programming concepts, such as classes, objects, and attributes, while, at the same time, get a view of object-oriented programming syntax. Nevertheless, the most noteworthy feature of PhysGramming is that children create their own digital games within the context of physical science courses, in a way that provides familiarization with the basic principles of object-oriented programming and computational thinking, even though no specific reference is made to these principles. Attuned to the ethical guidelines of educational research, interventions were conducted in two classes of second grade. The interventions were designed with respect to the thematic units of the curriculum of physical science courses, as a part of the learning activities of the class. PhysGramming was integrated into the classroom, after short introductory sessions. During the interventions, 6-7 years old children worked in pairs on computers and created their own digital games (group games, matching games, and puzzles). The authors participated in these interventions as observers in order to achieve a realistic evaluation of the proposed educational framework concerning its applicability in the classroom and its educational and pedagogical perspectives. To better examine if the objectives of the research are met, the investigation was focused on six criteria; the educational value of PhysGramming, its engaging and enjoyable characteristics, its child-friendliness, its appropriateness for the purpose that is proposed, its ability to monitor the user’s progress and its individualizing features. In this paper, the functionality of PhysGramming and the philosophy of its integration in the classroom are both described in detail. Information about the implemented interventions and the results obtained is also provided. Finally, several limitations of the research conducted that deserve attention are denoted.

Keywords: computational thinking, early childhood education, object-oriented programming, physical science courses

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Authors: Christo Swart, Lidia Pottas, David Maree

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Background: It is a sine qua non that the South African educational system finds itself in a profound crisis and that traditional school leadership styles are outdated and hinder quality education. New thinking is mandatory to improve the status quo and school leadership has an immense role to play to improve the current situation. It is believed that the servant leadership paradigm, when practiced by school leadership, may have a significant influence on the school environment in totality. This study investigates the private school segment in search of constructive answers to assist with the educational crises in South Africa. It is assumed that where school leadership can augment a supportive and empowering environment for teachers to constructively engage in their teaching and learning activities - then many challenges facing by school system may be subjugated in a productive manner. Aim: The aim of this study is fourfold. To outline the constructs of servant leadership which are perceived by teachers of private schools as priorities to enhance a successful school environment. To describe the constructs of organizational climate which are observed by teachers of private schools as priorities to enhance a successful school environment. To investigate whether the participants perceived a link between the constructs of servant leadership and organizational climate. To consider the process to be followed to introduce the constructs of SL and OC the school system in general as perceived by participants. Method: This study utilized a qualitative approach to explore the mediation between school leadership and the organizational climate in private schools in the search for amicable answers. The participants were purposefully selected for the study. Focus group interviews were held with participants from primary and secondary schools and a focus group discussion was conducted with principals of both primary and secondary schools. The interview data were transcribed and analyzed and identical patterns of coded data were grouped together under emerging themes. Findings: It was found that the practice of servant leadership by school leadership indeed mediates a constructive and positive school climate. It was found that the constructs of empowerment, accountability, humility and courage – interlinking with one other - are prominent of servant leadership concepts that are perceived by teachers of private schools as priorities for school leadership to enhance a successful school environment. It was confirmed that the groupings of training and development, communication, trust and work environment are perceived by teachers of private schools as prominent features of organizational climate as practiced by school leadership to augment a successful school environment. It can be concluded that the participants perceived several links between the constructs of servant leadership and organizational climate that encourage a constructive school environment and that there is a definite positive consideration and motivation that the two concepts be introduced to the school system in general. It is recommended that school leadership mentors and guides teachers to take ownership of the constructs of servant leadership as well as organizational climate and that public schools be researched and consider to implement the two paradigms. The study suggests that aspirant teachers be exposed to leadership as well as organizational paradigms during their studies at university.

Keywords: empowering environment for teachers and learners, new thinking required, organizational climate, school leadership, servant leadership

Procedia PDF Downloads 220

Authors: Elham Koohi, Silvio Jose Gumiere, Hossein Bonakdari, Saeid Homayouni

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Water used in agricultural crops can be managed by irrigation scheduling based on soil moisture levels and plant water stress thresholds. Automated irrigation scheduling limits crop physiological damage and yield reduction. Knowledge of crop water stress monitoring approaches can be effective in optimizing the use of agricultural water. Understanding the physiological mechanisms of crop responding and adapting to water deficit ensures sustainable agricultural management and food supply. This aim could be achieved by analyzing and diagnosing crop characteristics and their interlinkage with the surrounding environment. Assessments of plant functional types (e.g., leaf area and structure, tree height, rate of evapotranspiration, rate of photosynthesis), controlling changes, and irrigated areas mapping. Calculating thresholds of soil water content parameters, crop water use efficiency, and Nitrogen status make irrigation scheduling decisions more accurate by preventing water limitations between irrigations. Combining Remote Sensing (RS), the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning Algorithms (MLAs) can improve measurement accuracies and automate irrigation scheduling. This paper is a review structured by surveying about 100 recent research studies to analyze varied approaches in terms of providing high spatial and temporal resolution mapping, sensor-based Variable Rate Application (VRA) mapping, the relation between spectral and thermal reflectance and different features of crop and soil. The other objective is to assess RS indices formed by choosing specific reflectance bands and identifying the correct spectral band to optimize classification techniques and analyze Proximal Optical Sensors (POSs) to control changes. The innovation of this paper can be defined as categorizing evaluation methodologies of precision irrigation (applying the right practice, at the right place, at the right time, with the right quantity) controlled by soil moisture levels and sensitiveness of crops to water stress, into pre-processing, processing (retrieval algorithms), and post-processing parts. Then, the main idea of this research is to analyze the error reasons and/or values in employing different approaches in three proposed parts reported by recent studies. Additionally, as an overview conclusion tried to decompose different approaches to optimizing indices, calibration methods for the sensors, thresholding and prediction models prone to errors, and improvements in classification accuracy for mapping changes.

Keywords: agricultural crops, crop water stress detection, irrigation scheduling, precision agriculture, remote sensing

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Authors: Eunjung Lee

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This research investigates the intricate connection between cultural differences and online consumer behaviors by integrating Hofstede's Cultural Dimensions theory with analysis methodologies such as text mining, data mining, and topic analysis. Our aim is to provide a comprehensive understanding of how national cultural differences influence individuals' behaviors when engaging with online reviews. To ensure the relevance of our investigation, we systematically analyze and interpret the cultural nuances influencing online consumer behaviors, especially in the context of online reviews. By anchoring our research in Hofstede's Cultural Dimensions theory, we seek to offer valuable insights for marketers to tailor their strategies based on the cultural preferences of diverse global consumer bases. In our methodology, we employ advanced text mining techniques to extract insights from a diverse range of online reviews gathered globally for a specific product or service like Netflix. This approach allows us to reveal hidden cultural cues in the language used by consumers from various backgrounds. Complementing text mining, data mining techniques are applied to extract meaningful patterns from online review datasets collected from different countries, aiming to unveil underlying structures and gain a deeper understanding of the impact of cultural differences on online consumer behaviors. The study also integrates topic analysis to identify recurring subjects, sentiments, and opinions within online reviews. Marketers can leverage these insights to inform the development of culturally sensitive strategies, enhance target audience segmentation, and refine messaging approaches aligned with cultural preferences. Anchored in Hofstede's Cultural Dimensions theory, our research employs sophisticated methodologies to delve into the intricate relationship between cultural differences and online consumer behaviors. Applied to specific cultural dimensions, such as individualism vs. collectivism, masculinity vs. femininity, uncertainty avoidance, and long-term vs. short-term orientation, the study uncovers nuanced insights. For example, in exploring individualism vs. collectivism, we examine how reviewers from individualistic cultures prioritize personal experiences while those from collectivistic cultures emphasize communal opinions. Similarly, within masculinity vs. femininity, we investigate whether distinct topics align with cultural notions, such as robust features in masculine cultures and user-friendliness in feminine cultures. Examining information-seeking behaviors under uncertainty avoidance reveals how cultures differ in seeking detailed information or providing succinct reviews based on their comfort with ambiguity. Additionally, in assessing long-term vs. short-term orientation, the research explores how cultural focus on enduring benefits or immediate gratification influences reviews. These concrete examples contribute to the theoretical enhancement of Hofstede's Cultural Dimensions theory, providing a detailed understanding of cultural impacts on online consumer behaviors. As online reviews become increasingly crucial in decision-making, this research not only contributes to the academic understanding of cultural influences but also proposes practical recommendations for enhancing online review systems. Marketers can leverage these findings to design targeted and culturally relevant strategies, ultimately enhancing their global marketing effectiveness and optimizing online review systems for maximum impact.

Keywords: comparative analysis, cultural dimensions, marketing intelligence, national culture, online consumer behavior, text mining

Procedia PDF Downloads 47

Authors: Dian Zhang, Brendan Heery, Maria O’Neill, Ciprian Briciu-Burghina, Noel E. O’Connor, Fiona Regan

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Recent developments in sensors, wireless data communication and the cloud computing have brought the sensor web to a whole new generation. The introduction of the concept of ‘Internet of Thing (IoT)’ has brought the sensor research into a new level, which involves the developing of long lasting, low cost, environment friendly and smart sensors; new wireless data communication technologies; big data analytics algorithms and cloud based solutions that are tailored to large scale smart sensor network. The next generation of smart sensor network consists of several layers: physical layer, where all the smart sensors resident and data pre-processes occur, either on the sensor itself or field gateway; data transmission layer, where data and instructions exchanges happen; the data process layer, where meaningful information is extracted and organized from the pre-process data stream. There are many definitions of smart sensor, however, to summarize all these definitions, a smart sensor must be Intelligent and Adaptable. In future large scale sensor network, collected data are far too large for traditional applications to send, store or process. The sensor unit must be intelligent that pre-processes collected data locally on board (this process may occur on field gateway depends on the sensor network structure). In this case study, three smart sensing methods, corresponding to simple thresholding, statistical model and machine learning based MoPBAS method, are introduced and their strength and weakness are discussed as an introduction to the smart sensing concept. Data fusion, the integration of data and knowledge from multiple sources, are key components of the next generation smart sensor network. For example, in the water level monitoring system, weather forecast can be extracted from external sources and if a heavy rainfall is expected, the server can send instructions to the sensor notes to, for instance, increase the sampling rate or switch on the sleeping mode vice versa. In this paper, we describe the deployment of 11 affordable water level sensors in the Dublin catchment. The objective of this paper is to use the deployed river level sensor network at the Dodder catchment in Dublin, Ireland as a case study to give a vision of the next generation of a smart sensor network for flood monitoring to assist agencies in making decisions about deploying resources in the case of a severe flood event. Some of the deployed sensors are located alongside traditional water level sensors for validation purposes. Using the 11 deployed river level sensors in a network as a case study, a vision of the next generation of smart sensor network is proposed. Each key component of the smart sensor network is discussed, which hopefully inspires the researchers who are working in the sensor research domain.

Keywords: smart sensing, internet of things, water level sensor, flooding

Procedia PDF Downloads 381

Authors: Muhammad Kashif Fida

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In the bank, teller’s job (cash officer) is highly important and critical as at one end it requires soft and brisk customer services and on the other side, handling cash with integrity. It is always challenging for recruiters to hire competent and trustworthy tellers. According to author’s knowledge, there is no comprehensive test available that may provide assistance in recruitment in Pakistan. So there is a dire need of a psychometric battery that could provide support in recruitment of potential candidates for the teller’ position. So, the aim of the present study was to construct ABL-Teller Aptitude Test (ABL-TApT). Three major phases have been designed by following American Psychological Association’s guidelines. The first phase was qualitative, indicators of the test have been explored by content analysis of the a) teller’s job descriptions (n=3), b) interview with senior tellers (n=6) and c) interview with HR personals (n=4). Content analysis of above yielded three border constructs; i). Personality, ii). Integrity/honesty, iii). Professional Work Aptitude. Identified indicators operationalized and statements (k=170) were generated using verbatim. It was then forwarded to the five experts for review of content validity. They finalized 156 items. In the second phase; ABL-TApT (k=156) administered on 323 participants through a computer application. The overall reliability of the test shows significant alpha coefficient (α=.81). Reliability of subscales have also significant alpha coefficients. Confirmatory Factor Analysis (CFA) performed to estimate the construct validity, confirms four main factors comprising of eight personality traits (Confidence, Organized, Compliance, Goal-oriented, Persistent, Forecasting, Patience, Caution), one Integrity/honesty factor, four factors of professional work aptitude (basic numerical ability and perceptual accuracy of letters, numbers and signature) and two factors for customer services (customer services, emotional maturity). Values of GFI, AGFI, NNFI, CFI, RFI and RMSEA are in recommended range depicting significant model fit. In third phase concurrent validity evidences have been pursued. Personality and integrity part of this scale has significant correlations with ‘conscientiousness’ factor of NEO-PI-R, reflecting strong concurrent validity. Customer services and emotional maturity have significant correlations with ‘Bar-On EQI’ showing another evidence of strong concurrent validity. It is concluded that ABL-TAPT is significantly reliable and valid battery of tests, will assist in objective recruitment of tellers and help recruiters in finding a more suitable human resource.

Keywords: concurrent validity, construct validity, content validity, reliability, teller aptitude test, objective recruitment

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Authors: Angelis P. Barlampas

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Learning Objectives: The purpose of this study is to inform briefly the reader about the applications of AI in chest radiology. Background: Currently, there are 190 FDA-approved radiology AI applications, with 42 (22%) pertaining specifically to thoracic radiology. Imaging findings OR Procedure details Aids of AI in chest radiology1: Detects and segments pulmonary nodules. Subtracts bone to provide an unobstructed view of the underlying lung parenchyma and provides further information on nodule characteristics, such as nodule location, nodule two-dimensional size or three dimensional (3D) volume, change in nodule size over time, attenuation data (i.e., mean, minimum, and/or maximum Hounsfield units [HU]), morphological assessments, or combinations of the above. Reclassifies indeterminate pulmonary nodules into low or high risk with higher accuracy than conventional risk models. Detects pleural effusion . Differentiates tension pneumothorax from nontension pneumothorax. Detects cardiomegaly, calcification, consolidation, mediastinal widening, atelectasis, fibrosis and pneumoperitoneum. Localises automatically vertebrae segments, labels ribs and detects rib fractures. Measures the distance from the tube tip to the carina and localizes both endotracheal tubes and central vascular lines. Detects consolidation and progression of parenchymal diseases such as pulmonary fibrosis or chronic obstructive pulmonary disease (COPD).Can evaluate lobar volumes. Identifies and labels pulmonary bronchi and vasculature and quantifies air-trapping. Offers emphysema evaluation. Provides functional respiratory imaging, whereby high-resolution CT images are post-processed to quantify airflow by lung region and may be used to quantify key biomarkers such as airway resistance, air-trapping, ventilation mapping, lung and lobar volume, and blood vessel and airway volume. Assesses the lung parenchyma by way of density evaluation. Provides percentages of tissues within defined attenuation (HU) ranges besides furnishing automated lung segmentation and lung volume information. Improves image quality for noisy images with built-in denoising function. Detects emphysema, a common condition seen in patients with history of smoking and hyperdense or opacified regions, thereby aiding in the diagnosis of certain pathologies, such as COVID-19 pneumonia. It aids in cardiac segmentation and calcium detection, aorta segmentation and diameter measurements, and vertebral body segmentation and density measurements. Conclusion: The future is yet to come, but AI already is a helpful tool for the daily practice in radiology. It is assumed, that the continuing progression of the computerized systems and the improvements in software algorithms , will redder AI into the second hand of the radiologist.

Keywords: artificial intelligence, chest imaging, nodule detection, automated diagnoses

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Authors: Elham Bagheri, Yalda Mohsenzadeh

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Image memorability refers to the phenomenon where certain images are more likely to be remembered by humans than others. It is a quantifiable and intrinsic attribute of an image. Understanding how visual perception and memory interact is important in both cognitive science and artificial intelligence. It reveals the complex processes that support human cognition and helps to improve machine learning algorithms by mimicking the brain's efficient data processing and storage mechanisms. To explore the computational underpinnings of image memorability, this study examines the relationship between an image's reconstruction error, distinctiveness in latent space, and its memorability score. A trained autoencoder is used to replicate human-like memorability assessment inspired by the visual memory game employed in memorability estimations. This study leverages a VGG-based autoencoder that is pre-trained on the vast ImageNet dataset, enabling it to recognize patterns and features that are common to a wide and diverse range of images. An empirical analysis is conducted using the MemCat dataset, which includes 10,000 images from five broad categories: animals, sports, food, landscapes, and vehicles, along with their corresponding memorability scores. The memorability score assigned to each image represents the probability of that image being remembered by participants after a single exposure. The autoencoder is finetuned for one epoch with a batch size of one, attempting to create a scenario similar to human memorability experiments where memorability is quantified by the likelihood of an image being remembered after being seen only once. The reconstruction error, which is quantified as the difference between the original and reconstructed images, serves as a measure of how well the autoencoder has learned to represent the data. The reconstruction error of each image, the error reduction, and its distinctiveness in latent space are calculated and correlated with the memorability score. Distinctiveness is measured as the Euclidean distance between each image's latent representation and its nearest neighbor within the autoencoder's latent space. Different structural and perceptual loss functions are considered to quantify the reconstruction error. The results indicate that there is a strong correlation between the reconstruction error and the distinctiveness of images and their memorability scores. This suggests that images with more unique distinct features that challenge the autoencoder's compressive capacities are inherently more memorable. There is also a negative correlation between the reduction in reconstruction error compared to the autoencoder pre-trained on ImageNet, which suggests that highly memorable images are harder to reconstruct, probably due to having features that are more difficult to learn by the autoencoder. These insights suggest a new pathway for evaluating image memorability, which could potentially impact industries reliant on visual content and mark a step forward in merging the fields of artificial intelligence and cognitive science. The current research opens avenues for utilizing neural representations as instruments for understanding and predicting visual memory.

Keywords: autoencoder, computational vision, image memorability, image reconstruction, memory retention, reconstruction error, visual perception

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Authors: Shilpa Sahoo, Michel Louge, Anthony Reeves, Olivier Desjardins, Susan Daniel, Sadik Omowunmi

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The microgravity on the International Space Station (ISS) was exploited to study the imbibition of water into a network of hydrophilic cylindrical capillaries on time and length scales long enough to observe details hitherto inaccessible under Earth gravity. When a drop touches a porous medium, it spreads as if laid on a composite surface. The surface first behaves as a hydrophobic material, as liquid must penetrate pores filled with air. When contact is established, some of the liquid is drawn into pores by a capillarity that is resisted by viscous forces growing with length of the imbibed region. This process always begins with an inertial regime that is complicated by possible contact pinning. To study imbibition on Earth, time and distance must be shrunk to mitigate gravity-induced distortion. These small scales make it impossible to observe the inertial and pinning processes in detail. Instead, in the International Space Station (ISS), astronaut Luca Parmitano slowly extruded water spheres until they touched any of nine capillary plates. The 12mm diameter droplets were large enough for high-speed GX1050C video cameras on top and side to visualize details near individual capillaries, and long enough to observe dynamics of the entire imbibition process. To investigate the role of contact pinning, a text matrix was produced which consisted nine kinds of porous capillary plates made of gold-coated brass treated with Self-Assembled Monolayers (SAM) that fixed advancing and receding contact angles to known values. In the ISS, long-term microgravity allowed unambiguous observations of the role of contact line pinning during the inertial phase of imbibition. The high-speed videos of spreading and imbibition on the porous plates were analyzed using computer vision software to calculate the radius of the droplet contact patch with the plate and height of the droplet vs time. These observations are compared with numerical simulations and with data that we obtained at the ESA ZARM free-fall tower in Bremen with a unique mechanism producing relatively large water spheres and similarity in the results were observed. The data obtained from the ISS can be used as a benchmark for further numerical simulations in the field.

Keywords: droplet imbibition, hydrophilic surface, inertial phase, porous medium

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Authors: Nayer Mofidtabatabaei

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Advancements in artificial intelligence (AI) have transformed various fields, including psychology and behavioral sciences. This paper explores the diverse ways in which AI is applied to enhance research, diagnosis, therapy, and understanding of human behavior and mental health. We discuss the potential benefits and challenges associated with AI in these fields, emphasizing the ethical considerations and the need for collaboration between AI researchers and psychological and behavioral science experts. Artificial Intelligence (AI) has gained prominence in recent years, revolutionizing multiple industries, including healthcare, finance, and entertainment. One area where AI holds significant promise is the field of psychology and behavioral sciences. AI applications in this domain range from improving the accuracy of diagnosis and treatment to understanding complex human behavior patterns. This paper aims to provide an overview of the various AI applications in psychological and behavioral sciences, highlighting their potential impact, challenges, and ethical considerations. Mental Health Diagnosis AI-driven tools, such as natural language processing and sentiment analysis, can analyze large datasets of text and speech to detect signs of mental health issues. For example, chatbots and virtual therapists can provide initial assessments and support to individuals suffering from anxiety or depression. Autism Spectrum Disorder (ASD) Diagnosis AI algorithms can assist in early ASD diagnosis by analyzing video and audio recordings of children's behavior. These tools help identify subtle behavioral markers, enabling earlier intervention and treatment. Personalized Therapy AI-based therapy platforms use personalized algorithms to adapt therapeutic interventions based on an individual's progress and needs. These platforms can provide continuous support and resources for patients, making therapy more accessible and effective. Virtual Reality Therapy Virtual reality (VR) combined with AI can create immersive therapeutic environments for treating phobias, PTSD, and social anxiety. AI algorithms can adapt VR scenarios in real-time to suit the patient's progress and comfort level. Data Analysis AI aids researchers in processing vast amounts of data, including survey responses, brain imaging, and genetic information. Privacy Concerns Collecting and analyzing personal data for AI applications in psychology and behavioral sciences raise significant privacy concerns. Researchers must ensure the ethical use and protection of sensitive information. Bias and Fairness AI algorithms can inherit biases present in training data, potentially leading to biased assessments or recommendations. Efforts to mitigate bias and ensure fairness in AI applications are crucial. Transparency and Accountability AI-driven decisions in psychology and behavioral sciences should be transparent and subject to accountability. Patients and practitioners should understand how AI algorithms operate and make decisions. AI applications in psychological and behavioral sciences have the potential to transform the field by enhancing diagnosis, therapy, and research. However, these advancements come with ethical challenges that require careful consideration. Collaboration between AI researchers and psychological and behavioral science experts is essential to harness AI's full potential while upholding ethical standards and privacy protections. The future of AI in psychology and behavioral sciences holds great promise, but it must be navigated with caution and responsibility.

Keywords: artificial intelligence, psychological sciences, behavioral sciences, diagnosis and therapy, ethical considerations

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Authors: Derek Wardle, Tarik Al-Shemmeri, Neil Packer

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This paper presents a small tube/wire type electrostatic precipitator (ESP). In the ESPs present form, particle charging and collecting voltages and airflow rates were individually varied throughout 200 ambient temperature test runs ranging from 10 to 30 kV in increments on 5 kV and 0.5 m/s to 1.5 m/s, respectively. It was repeatedly observed that, at input air velocities of between 0.5 and 0.9 m/s and voltage settings of 20 kV to 30 kV, the collection efficiency remained above 95%. The outcomes of preliminary tests at combustion flue temperatures are, at present, inconclusive although indications are that there is little or no drop in comparable performance during ideal test conditions. A limited set of similar tests was carried out during which the collecting electrode was grounded, having been disconnected from the static generator. The collecting efficiency fell significantly, and for that reason, this approach was not pursued further. The collecting efficiencies during ambient temperature tests were determined by mass balance between incoming and outgoing dry PM. The efficiencies of combustion temperature runs are determined by analysing the difference in opacity of the flue gas at inlet and outlet compared to a reference light source. In addition, an array of Leit tabs (carbon coated, electrically conductive adhesive discs) was placed at inlet and outlet for a number of four-day continuous ambient temperature runs. Analysis of the discs’ contamination was carried out using scanning electron microscopy and ImageJ computer software that confirmed collection efficiencies of over 99% which gave unequivocal support to all the previous tests. The average efficiency for these runs was 99.409%. Emissions collected from a woody biomass combustion unit, classified to a diameter of 100 µm, were used in all ambient temperature trials test runs apart from two which collected airborne dust from within the laboratory. Sawdust and wood pellets were chosen for laboratory and field combustion trials. Video recordings were made of three ambient temperature test runs in which the smoke from a wood smoke generator was drawn through the precipitator. Although these runs were visual indicators only, with no objective other than to display, they provided a strong argument for the device’s claimed efficiency, as no emissions were visible at exit when energised.  The theoretical performance of ESPs, when applied to the geometry and configuration of the tested model, was compared to the actual performance and was shown to be in good agreement with it.

Keywords: electrostatic precipitators, air quality, particulates emissions, electron microscopy, image j

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Authors: Kevin Baxter, Ron Grove, James Pitney, John Harrison, Ozlem Gumus

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The purpose of this research is to determine the mediating effect of effectiveness of the training provided by Northwestern University’s School of Police Staff and Command (SPSC), on the ability of law enforcement mid-level managers to learn transformational and authentic leadership traits. This study will also evaluate the leadership styles, of course, graduates compared to non-attendees using a static group comparison design. The Louisiana State Police pay approximately $40,000 in salary, tuition, housing, and meals for each state police lieutenant attending the 10-week program of the SPSC. This school lists the development of transformational leaders as an increasing element. Additionally, the SPSC curriculum addresses all four components of authentic leadership - self-awareness, transparency, ethical/moral, and balanced processing. Upon return to law enforcement in roles of mid-level management, there are questions as to whether or not students revert to an “autocratic” leadership style. Insufficient evidence exists to support claims for the effectiveness of management training or leadership development. Though it is widely recognized that transformational styles are beneficial to law enforcement, there is little evidence that suggests police leadership styles are changing. Police organizations continue to hold to a more transactional style (i.e., most senior police leaders remain autocrats). Additionally, research in the application of transformational, transactional, and laissez-faire leadership related to police organizations is minimal. The population of the study is law enforcement mid-level managers from various states within the United States who completed leadership training presented by the SPSC. The sample will be composed of 66 active law enforcement mid-level managers (lieutenants and captains) who have graduated from SPSC and 65 active law enforcement mid-level managers (lieutenants and captains) who have not attended SPSC. Participants will answer demographics questions, Multifactor Leadership Questionnaire, Authentic Leadership Questionnaire, and the Kirkpatrick Hybrid Evaluation Survey. Analysis from descriptive statistics, group comparison, one-way MANCOVA, and the Kirkpatrick Evaluation Model survey will be used to determine training effectiveness in the four levels of reaction, learning, behavior, and results. Independent variables are SPSC graduates (two groups: upper and lower) and no-SPSC attendees, and dependent variables are transformational and authentic leadership scores. SPSC graduates are expected to have higher MLQ scores for transformational leadership traits and higher ALQ scores for authentic leadership traits than SPSC non-attendees. We also expect the graduates to rate the efficacy of SPSC leadership training as high. This study will validate (or invalidate) the benefits, costs, and resources required for leadership development from a nationally recognized police leadership program, and it will also help fill the gap in the literature that exists between law enforcement professional development and transformational and authentic leadership styles.

Keywords: training effectiveness, transformational leadership, authentic leadership, law enforcement mid-level manager

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Authors: Elham Koohikerade, Silvio Jose Gumiere

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In this research, irrigation is highlighted as crucial for improving both the yield and quality of ‎potatoes due to their high sensitivity to soil moisture changes. The study presents a hybrid Long ‎Short-Term Memory (LSTM) model aimed at optimizing irrigation scheduling in potato fields in ‎Quebec City, Canada. This model integrates model-based and satellite-derived datasets to simulate ‎soil moisture content, addressing the limitations of field data. Developed under the guidance of the ‎Food and Agriculture Organization (FAO), the simulation approach compensates for the lack of direct ‎soil sensor data, enhancing the LSTM model's predictions. The model was calibrated using indices ‎like Surface Soil Moisture (SSM), Normalized Vegetation Difference Index (NDVI), Enhanced ‎Vegetation Index (EVI), and Normalized Multi-band Drought Index (NMDI) to effectively forecast ‎soil moisture reductions. Understanding soil moisture and plant development is crucial for assessing ‎drought conditions and determining irrigation needs. This study validated the spectral characteristics ‎of vegetation and soil using ECMWF Reanalysis v5 (ERA5) and Moderate Resolution Imaging ‎Spectrometer (MODIS) data from 2019 to 2023, collected from agricultural areas in Dolbeau and ‎Peribonka, Quebec. Parameters such as surface volumetric soil moisture (0-7 cm), NDVI, EVI, and ‎NMDI were extracted from these images. A regional four-year dataset of soil and vegetation moisture ‎was developed using a machine learning approach combining model-based and satellite-based ‎datasets. The LSTM model predicts soil moisture dynamics hourly across different locations and ‎times, with its accuracy verified through cross-validation and comparison with existing soil moisture ‎datasets. The model effectively captures temporal dynamics, making it valuable for applications ‎requiring soil moisture monitoring over time, such as anomaly detection and memory analysis. By ‎identifying typical peak soil moisture values and observing distribution shapes, irrigation can be ‎scheduled to maintain soil moisture within Volumetric Soil Moisture (VSM) values of 0.25 to 0.30 ‎m²/m², avoiding under and over-watering. The strong correlations between parcels suggest that a ‎uniform irrigation strategy might be effective across multiple parcels, with adjustments based on ‎specific parcel characteristics and historical data trends. The application of the LSTM model to ‎predict soil moisture and vegetation indices yielded mixed results. While the model effectively ‎captures the central tendency and temporal dynamics of soil moisture, it struggles with accurately ‎predicting EVI, NDVI, and NMDI.‎

Keywords: irrigation scheduling, LSTM neural network, remotely sensed indices, soil and vegetation ‎monitoring

Procedia PDF Downloads 41