Abstract: t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embedding. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic, and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n2) to O(n2/k), and the memory requirement from n2 to 2(n/k)2 which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics.
Abstract: Sightseeing glass bridges located in steep valley area are being built on a large scale owing to the development of tourism. Consequently, their aerostatic stability is seriously affected by the wind field characteristics created by strong wind and special terrain, such as wind speed and wind attack angle. For instance, a cable-stayed pedestrian bridge without backstays comprised of a 60-m cantilever girder and the glass bridge deck is located in an abrupt valley, acting as a viewing platform. The bridge’s nonlinear aerostatic stability was analyzed by the segmental model test and numerical simulation in this paper. Based on aerostatic coefficients of the main girder measured in wind tunnel tests, nonlinear influences caused by the structure and aerostatic load, inhomogeneous distribution of torsion angle along the bridge axis, and the influence of initial attack angle were analyzed by using the incremental double iteration method. The results show that the aerostatic response varying with speed shows an obvious nonlinearity, and the aerostatic instability mode is of the characteristic of space deformation of bending-twisting coupling mode. The vertical and torsional deformation of the main girder is larger than its lateral deformation, with the wind speed approaching the critical wind speed. The flow of negative attack angle will reduce the bridges’ critical stability wind speed, but the influence of the negative attack angle on the aerostatic stability is more significant than that of the positive attack angle. The critical wind speeds of torsional divergence and lateral buckling are both larger than 200 m/s; namely, the bridge will not occur aerostatic instability under the action of various wind attack angles.
Abstract: High pressure carbon dioxide (CO2) absorption from a specific off-gas in a conventional column has been evaluated for the environmental concerns by the Aspen HYSYS simulator using a wide range of single absorbents and piperazine (PZ) blended solutions to estimate the outlet CO2 concentration, CO2 loading, reboiler power supply and regeneration heat duty to choose the most efficient solution in terms of CO2 removal and required heat duty. The property package, which is compatible with all applied solutions for the simulation in this study, estimates the properties based on electrolyte non-random two-liquid (E-NRTL) model for electrolyte thermodynamics and Peng-Robinson equation of state for vapor phase and liquid hydrocarbon phase properties. The results of the simulation indicate that PZ in addition to the mixture of PZ and monoethanolamine (MEA) demand the highest regeneration heat duty compared with other studied single and blended amine solutions respectively. The blended amine solutions with the lowest PZ concentrations (5wt% and 10wt%) were considered and compared to reduce the cost of process, among which the blended solution of 10wt%PZ+35wt%MDEA (methyldiethanolamine) was found as the most appropriate solution in terms of CO2 content in the outlet gas, rich-CO2 loading and regeneration heat duty.
Abstract: One area of special importance for the surface-level study of heat exchangers is tubes with internal micro-fins (< 0.5 mm tall). Micro-finned surfaces are a kind of extended solid surface in which energy is exchanged with water that acts as the source or sink of energy. Significant performance gains are possible for either shell, tube, or double pipe heat exchangers if the best surfaces are identified. The parametric studies of micro-finned tubes that have appeared in the literature left some key parameters unexplored. Specifically, they ignored three-dimensional (3D) micro-fin configurations, conduction heat transfer in the fins, and conduction in the solid surface below the micro-fins. Thus, this study aimed at implementing a parametric study of 3D micro-finned tubes that considered micro-fine height and discontinuity features. A 3D conductive and convective heat-transfer simulation through coupled solid and periodic fluid domains is applied in a commercial package, ANSYS Fluent 19.1. The simulation is steady-state with turbulent water flow cooling the inner wall of a tube with micro-fins. The simulation utilizes a constant and uniform temperature on the tube outer wall. Performance is mapped for 18 different simulation cases, including a smooth tube using a realizable k-ε turbulence model at a Reynolds number of 48,928. Results compared the performance of 3D tubes with results for the similar two-dimensional (2D) one. Results showed that the micro-fine height has a greater impact on performance factors than discontinuity features in 3D micro-fin tubes. A transformed 3D micro-fin tube can enhance heat transfer, and pressure drops up to 21% and 56% compared to a 2D one, respectfully.
Abstract: Currently, in the field of object posture estimation, there is research on estimating the position and angle of an object by storing a 3D model of the object to be estimated in advance in a computer and matching it with the model. However, in this research, we have succeeded in creating a module that is much simpler, smaller in scale, and faster in operation. Our 6D pose estimation model consists of two different networks – a classification network and a regression network. From a single RGB image, the trained model estimates the class of the object in the image, the coordinates of the object, and its rotation angle in 3D space. In addition, we compared the estimation accuracy of each camera position, i.e., the angle from which the object was captured. The highest accuracy was recorded when the camera position was 75°, the accuracy of the classification was about 87.3%, and that of regression was about 98.9%.
Abstract: This study is investigating the impact of ideological structures in terms of conservative and liberal on shaping immigration acceptance attitudes under the contribution of socio-economic status. According to motivated reasoning theory, political ideology is identified as a recurrent impact on the formation of attitude, while conservatives tend to express more hostility toward immigrants in comparison to liberals which are proposed to be more tolerant towards immigrants. Our finding suggests that political ideology will structure individual attitudes when citizens socio-economic vulnerability and level of education are low enough to consider immigrants as a threat. Therefore, economic vulnerability is proposed to weaken the ideological predispositions’ resistance. There has been some threats and factors such as level of education and economic condition proposed by group competition theory and labor market competition theory as fundamental factors which can strengthen or weaken the effects of political ideology on individuals’ attitudes towards immigration; those mechanisms for liberals and conservatives will be operated differently.
Abstract: In Nigeria, enormous efforts/resources had, over the years, been expended on promoting financial inclusion (FI); however, it is seemingly discouraging that many of its self-declared targets on FI remained unachieved, especially amongst the Rural Dwellers and Actors in the Informal Sectors (RDAIS). Expectedly, many reasons had been earmarked for these failures: low literacy level, huge informal/rural sectors etc. This study posits that in spite of these truly-debilitating factors, these FI policy failures could have been avoided or mitigated if the principles of active and better-managed citizens’ participation had been strictly followed in the (re)design/implementation of its FI policies. In other words, in a bid to mitigate the prevalent financial exclusion (FE) in Nigeria, this study hypothesizes the significant positive impact of involving the RDAIS in policy-wide decision making in the FI domain, backed by a preliminary empirical validation. Also, the study introduces the RDAIS-focused Participatory Financial Inclusion Policy (PFIP) as a major FI policy regeneration/improvement tool. The three categories of respondents that served as research subjects are FI experts in Nigeria (n = 72), RDAIS from the very rural/remote village of Unguwar Dogo in Northern Nigeria (n = 43) and RDAIS from another rural village of Sekere (n = 56) in the Southern region of Nigeria. Using survey design (5-point Likert scale questionnaires), random/stratified sampling, and descriptive/inferential statistics, the study often recorded independent consensus (amongst these three categories of respondents) that RDAIS’s active participation in iterative FI policy initiation, (re)design, implementation, (re)evaluation could indeed give improved FI outcomes. However, few questionnaire items also recorded divergent opinions and various statistically (in)significant differences on the mean scores of these three categories. The PFIP (or any customized version of it) should then be carefully integrated into the NFIS of Nigeria (and possibly in the NFIS of other developing countries) to truly/fully provide FI policy integration for these excluded RDAIS and arrest the prevalence of FE.
Abstract: The most important process of the water treatment plant process is coagulation, which uses alum and poly aluminum chloride (PACL). Therefore, determining the dosage of alum and PACL is the most important factor to be prescribed. This research applies an artificial neural network (ANN), which uses the Levenberg–Marquardt algorithm to create a mathematical model (Soft Jar Test) for chemical dose prediction, as used for coagulation, such as alum and PACL, with input data consisting of turbidity, pH, alkalinity, conductivity, and, oxygen consumption (OC) of the Bangkhen Water Treatment Plant (BKWTP), under the authority of the Metropolitan Waterworks Authority of Thailand. The data were collected from 1 January 2019 to 31 December 2019 in order to cover the changing seasons of Thailand. The input data of ANN are divided into three groups: training set, test set, and validation set. The coefficient of determination and the mean absolute errors of the alum model are 0.73, 3.18 and the PACL model are 0.59, 3.21, respectively.
Abstract: This article presents a methodology that improves reliability and cyclability of 2nd-life Li-ion battery system repurposed as energy storage system (ESS). Most of the 2nd-life retired battery systems in market have module/pack-level state of health (SOH) indicator, which is utilized for guiding appropriate depth of discharge (DOD) in the application of ESS. Due to the lack of cell-level SOH indication, the different degrading behaviors among various cells cannot be identified upon reaching retired status; in the end, considering end of life (EOL) loss and pack-level DOD, the repurposed ESS has to be oversized by > 1.5 times to complement the application requirement of reliability and cyclability. This proposed battery grading algorithm, using non-invasive methodology, is able to detect outlier cells based on historical voltage data and calculate cell-level historical maximum temperature data using semi-analytic methodology. In this way, the individual battery cell in the 2nd-life battery system can be graded in terms of SOH on basis of the historical voltage fluctuation and estimated historical maximum temperature variation. These grades will have corresponding DOD grades in the application of the repurposed ESS to enhance the system reliability and cyclability. In all, this introduced battery grading algorithm is non-invasive, compatible with all kinds of retired Li-ion battery systems which lack of cell-level SOH indication, as well as potentially being embedded into battery management software for preventive maintenance and real-time cyclability optimization.
Abstract: Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., entropy, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one-class classification (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, principal component analysis (PCA), kernel principal component analysis (KPCA), and autoassociative neural network (ANN) are presented and their performance are compared. It is also shown that, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 95%.
Abstract: The Rolling Element Bearing (REB) vibration diagnosis is worth of special interest by the variety of REB and the wide necessity of those elements in industrial applications. The presence of a localized fault in a REB gives rise to a vibrational response, characterized by the modulation of a carrier signal. Frequency content of carrier signal (Spectral Frequency –f) is mainly related to resonance frequencies of the REB. This carrier signal is modulated by another signal, governed by the periodicity of the fault impact (Cyclic Frequency –α). In this sense, REB fault vibration response gives rise to a second-order cyclostationary signal. Second order cyclostationary signals could be represented in a bi-spectral map, where Spectral Coherence –SCoh are plotted against f and α. The Improved Envelope Spectrum –IES, is a useful approach to execute REB fault diagnosis. IES could be applied by the integration of SCoh over a predefined bandwidth on the f axis. Approaches to select f-bandwidth have been recently exposed by the definition of a metric which intends to evaluate the magnitude of the IES at the fault characteristics frequencies. This metric is represented in a 1/3-binary tree as a function of the frequency bandwidth and centre. Based on this binary tree the optimal frequency band is selected. However, some advantages have been seen if the metric is changed, which in fact tends to dictate different optimal f-bandwidth and so improve the IES representation. This paper evaluates the behaviour of the IES from a different metric optimization. This metric is based on the sample correlation coefficient, detecting high peaks in the selected frequencies while penalizing high peaks in the neighbours of the selected frequencies. Prior results indicate an improvement on the signal-noise ratio (SNR) on around 86% of samples analysed, which belong to IMS database.
Abstract: With the advent of complex software and increased connectivity, security of life-critical medical devices is becoming an increasing concern, particularly with their direct impact to human safety. Security is essential, but it is impossible to develop completely secure and impenetrable systems at design time. Therefore, it is important to assess the potential impact on security and safety of exploiting a vulnerability in such critical medical systems. The common vulnerability scoring system (CVSS) calculates the severity of exploitable vulnerabilities. However, for medical devices, it does not consider the unique challenges of impacts to human health and privacy. Thus, the scoring of a medical device on which a human life depends (e.g., pacemakers, insulin pumps) can score very low, while a system on which a human life does not depend (e.g., hospital archiving systems) might score very high. In this paper, we present a Medical Vulnerability Scoring System (MVSS) that extends CVSS to address the health and privacy concerns of medical devices. We propose incorporating two new parameters, namely health impact and sensitivity impact. Sensitivity refers to the type of information that can be stolen from the device, and health represents the impact to the safety of the patient if the vulnerability is exploited (e.g., potential harm, life threatening). We evaluate 15 different known vulnerabilities in medical devices and compare MVSS against two state-of-the-art medical device-oriented vulnerability scoring system and the foundational CVSS.
Abstract: We analyze a first-class on the convergence of real number sequences, named hereafter sequences, to foster exploration and discovery of concepts through graphical representations before engaging students in proving. The main goal was to differentiate between sequences and continuous functions-of-a-real-variable and better understand concepts at an initial stage. We applied the analytic frame of Mathematical Working Spaces, which we expect to contribute to extending to sequences since, as far as we know, it has only developed for other objects, and which is relevant to analyze how mathematical work is built systematically by connecting the epistemological and cognitive perspectives, and involving the semiotic, instrumental, and discursive dimensions.
Abstract: Small cell deployment in 5G networks is a promising technology to enhance the capacity and coverage. However, unplanned deployment may cause high interference levels and high number of unnecessary handovers, which in turn result in an increase in the signalling overhead. To guarantee service continuity, minimize unnecessary handovers and reduce signalling overhead in heterogeneous networks, it is essential to properly model the handover decision problem. In this paper, we model the handover decision problem using Multiple Attribute Decision Making (MADM) method, specifically Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and propose a hybrid TOPSIS method to control the handover in heterogeneous network. The proposed method adopts a hybrid weighting policy, which is a combination of entropy and standard deviation. A hybrid weighting control parameter is introduced to balance the impact of the standard deviation and entropy weighting on the network selection process and the overall performance. Our proposed method show better performance, in terms of the number of frequent handovers and the mean user throughput, compared to the existing methods.
Abstract: The aim of this paper is to propose pedagogical design for learning management systems (LMS) that offers greater inclusion for students based on a number of theoretical perspectives and delineated through an example. Considering the impact of COVID-19, including on student mental health, the research suggesting the importance of student sense of belonging on retention, success, and student well-being, the author describes intentional LMS design incorporating theoretically based practices informed by critical theory, feminist theory, indigenous theory and practices, and new materiality. This article considers important aspects of these theories and practices which attend to inclusion, identities, and socially just learning environments. Additionally, increasing student sense of belonging and mental health through LMS design influenced by adult learning theory and the community of inquiry model are described. The process of thinking through LMS pedagogical design with inclusion intentionally in mind affords the opportunity to allow LMS to go beyond course use as a repository of documents, to an intentional community of practice that facilitates belonging and connection, something much needed in our times. In virtual learning environments it has been harder to discern how students are doing, especially in feeling connected to their courses, their faculty, and their student peers. Increasingly at the forefront of public universities is addressing the needs of students with multiple and intersecting identities and the multiplicity of needs and accommodations. Education in 2020, and moving forward, calls for embedding critical theories and inclusive ideals and pedagogies to the ways instructors design and teach in online platforms. Through utilization of critical theoretical frameworks and instructional practices, students may experience the LMS as a welcoming place with intentional plans for welcoming diversity in identities.
Abstract: Edgeworth Approximation, Bootstrap and Monte Carlo Simulations have a considerable impact on the achieving certain results related to different problems taken into study. In our paper, we have treated a financial case related to the effect that have the components of a Cash-Flow of one of the most successful businesses in the world, as the financial activity, operational activity and investing activity to the cash and cash equivalents at the end of the three-months period. To have a better view of this case we have created a Vector Autoregression model, and after that we have generated the impulse responses in the terms of Asymptotic Analysis (Edgeworth Approximation), Monte Carlo Simulations and Residual Bootstrap based on the standard errors of every series created. The generated results consisted of the common tendencies for the three methods applied, that consequently verified the advantage of the three methods in the optimization of the model that contains many variants.
Abstract: The evolution of quality management by companies was strongly enabled by, among others, ISO 9001 certification, which is considered a crucial requirement for several customers. Likewise, performance measurement provides useful insights for companies to identify the reflection of their decision-making process on their improvement. One of the most used performance measurement models is the balanced scorecard (BSC), which uses four perspectives to address a firm’s performance: financial, internal process, customer satisfaction, and learning and growth. Since ISO 9001 certified firms are likely to measure their performance through BSC approach, it is important to verify whether the certificate influences the firm performance or not. Therefore, this paper aims to verify the impact of ISO 9001:2015 on Brazilian firms’ performance based on the BSC perspective. Hence, nine certified companies located in the Southeast region of Brazil were studied through a multiple case study approach. Within this study, it was possible to identify the positive impact of ISO 9001 on firms’ overall performance, and four Critical Success Factors (CSFs) were identified as relevant on the linkage among ISO 9001 and firms’ performance: employee involvement, top management, process management, and customer focus. Due to the COVID-19 pandemic, the number of interviews was limited to the quality manager specialist, and the sample was limited since several companies were closed during the period of the study. This study presents an in-depth analysis of how the relationship between ISO 9001 certification and firms’ performance in a developing country is.
Abstract: This paper highlights skills that a leader needs to acquire to lead a team successfully. With an appropriate vision and strategy, a team can be inspired, influenced and easily led. The importance of setting codes of conduct and establishing mutual agreements between the team members can help in minimizing issues and improving overall productivity. Leadership skills include the power of questioning (PoQ), effective communication, identification of team member responsibilities, and assessment of self and the team. This paper will highlight the impact of good leadership on work progress and overall team performance. The paper explains how leaders make correct decisions by avoiding hasty actions that could generate new errors, mistakes, and issues. The importance of positive expectations for the team is addressed in this paper that could result in efficient control of the work with better outcomes.
Abstract: Data in IT systems in enterprises have been growing at phenomenal pace. This has provided opportunities to run analytics to gather intelligence on key business parameters that enable them to provide better products and services to customers. While there are several Artificial Intelligence/Machine Learning (AI/ML) and Business Intelligence (BI) tools and technologies available in marketplace to run analytics, there is a need for an integrated view when developing intelligent solutions in enterprises. This paper progressively elaborates a reference model for enterprise solutions, builds an integrated view of data, information and intelligence components and presents a reference architecture for intelligent enterprise solutions. Finally, it applies the reference architecture to an insurance organization. The reference architecture is the outcome of experience and insights gathered from developing intelligent solutions for several organizations.
Abstract: Although scientific methods have been the subject of a large number of papers, the term ‘scientific methods in educational management’ is still not well defined. In this paper, it is adopted the metasystems perspective to define the mentioned term and distinguish them from methods used in time of the scientific management and knowledge management paradigms. In our opinion, scientific methods in educational management rely on global phenomena, events, and processes and their influence on the educational organization. Currently, scientific methods in educational management are integrated with the phenomenon of globalization, cognitivisation, and openness, etc. of educational systems and with global events like the COVID-19 pandemic. Concrete scientific methods are nested in a hierarchy of more and more abstract models of educational management, which form the context of the global impact on education, in general, and learning outcomes, in particular. However, scientific methods can be assigned to a specific mission, strategy, or tactics of educational management of the concrete organization, either by the global management, local development of school organization, or/and development of the life-long successful learner. By accepting this assignment, the scientific method becomes a personal goal of each individual with the educational organization or the option to develop the educational organization at the global standards. In our opinion, in educational management, the scientific methods need to confine the scope to the deep analysis of concrete tasks of the educational system (i.e., teaching, learning, assessment, development), which result in concrete strategies of organizational development. More important are seeking the ways for dynamic equilibrium between the strategy and tactic of the planetary tasks in the field of global education, which result in a need for ecological methods of learning and communication. In sum, distinction between local and global scientific methods is dependent on the subjective conception of the task assignment, measurement, and appraisal. Finally, we conclude that scientific methods are not holistic scientific methods, but the strategy and tactics implemented in the global context by an effective educational/academic manager.