Search results for: one side class algorithm

Authors: Mercedes Rodriguez Sanchez, Laura Luceño Casals

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With the rise of social media, creative people and brands everywhere are constantly generating content. The students with Bachelor's Degrees in Fashion Design use platforms such as Instagram or TikTok to look for inspiration and entertainment, as well as a way to develop their own ideas and share them with a wide audience. Information and Communications Technologies (ICT) have become a central aspect of higher education, virtually affecting every aspect of the student experience. Following the current trend, during the first semester of the second year, a collaborative project across two subjects –Design Management and History of Fashion Design– was implemented. After an introductory class focused on the relationship between fashion and cinema, as well as a brief history of 20th-century fashion, the students freely chose a work team and an iconic look from a movie costume. They researched the selected movie and its sociocultural context, analyzed the costume and the work of the designer, and studied the style, fashion magazines and most popular films of the time. Students then redesigned and recreated the costume, for which they were compelled to recycle the materials they had available at home as an unavoidable requirement of the activity. Once completed the garment, students delivered in-class, team-based presentations supported by the final design, a project summary poster and a making-of video, which served as a documentation tool of the costume design process. The methodologies used include Challenge-Based Learning (CBL), debates, Internet research, application of Information and Communications Technologies, and viewing clips of classic films, among others. After finishing the projects, students were asked to complete two electronic surveys to measure the acquisition of transversal and specific competencies of each subject. Results reveal that this activity helped the students' knowledge acquisition, a deeper understanding of both subjects and their skills development. The classroom dynamic changed. The multidisciplinary approach encouraged students to collaborate with their peers, while educators were better able to keep students' interest and promote an engaging learning process. As a result, the activity discussed in this paper confirmed the research hypothesis: it is positive to propose innovative teaching projects that combine academic research with playful learning environments.

Keywords: cinema, cooperative learning, fashion design, higher education, upcycling

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Authors: David Blaine, George Raschbaum

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The evaluation of DHCP has synthesized SCSI disks, and current trends suggest that the exploration of e-business that would allow for further study into robots will soon emerge. Given the current status of embedded algorithms, hackers worldwide obviously desire the exploration of replication, which embodies the confusing principles of programming languages. In our research we concentrate our efforts on arguing that erasure coding can be made "fuzzy", encrypted, and game-theoretic.

Keywords: SCHI disks, robot, algorithm, hacking, programming language

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Authors: Priyankar P. Deka, Sutanu Samanta

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This paper describes the development of new class of epoxy based hybrid composites reinforced with jute and filled with rice husk flour. Rice husk flour is added in 0%, 1%, 3%, 5% by weight. Epoxy resin and triethylene tetramine (T.E.T.A) is used as matrix and hardener respectively. It investigates the mechanical properties of the composites and a comparison is done for monolithic jute composite and the filled ones. The specimens are prepared according to the ASTM standards and experimentation is carried out using INSTRON 8801. The result shows that with the increase of filler percentage the tensile properties increases but compressive and flexural properties decreases.

Keywords: jute, mechanical characterization, natural fiber, rice husk

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Authors: Harini Chakkera

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Background: Inpatient hyperglycemia is an established independent risk factor among several patient cohorts with hospital readmission. This has not been studied after kidney transplantation. Nearly one-third of patients who have undergone a kidney transplant reportedly experience 30-day readmission. Methods: Data on first-time solitary kidney transplantations were retrieved between September 2015 to December 2018. Information was linked to the electronic health record to determine a diagnosis of diabetes mellitus and extract glucometeric and insulin therapy data. Univariate logistic regression analysis and the XGBoost algorithm were used to predict 30-day readmission. We report the average performance of the models on the testing set on five bootstrapped partitions of the data to ensure statistical significance. Results: The cohort included 1036 patients who received kidney transplantation, and 224 (22%) experienced 30-day readmission. The machine learning algorithm was able to predict 30-day readmission with an average AUC of 77.3% (95% CI 75.30-79.3%). We observed statistically significant differences in the presence of pretransplant diabetes, inpatient-hyperglycemia, inpatient-hypoglycemia, and minimum and maximum glucose values among those with higher 30-day readmission rates. The XGBoost model identified the index admission length of stay, presence of hyper- and hypoglycemia and recipient and donor BMI values as the most predictive risk factors of 30-day readmission. Additionally, significant variations in the therapeutic management of blood glucose by providers were observed. Conclusions: Suboptimal glucose metrics during hospitalization after kidney transplantation is associated with an increased risk for 30-day hospital readmission. Optimizing the hospital blood glucose management, a modifiable factor, after kidney transplantation may reduce the risk of 30-day readmission.

Keywords: kidney, transplant, diabetes, insulin

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Authors: Hossein Rezaie

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Academic institutions, esp. universities, have been known as cradles of learning and teaching great thinkers while creating the type of knowledge that is supposed to be bereft of utilitarian motives. Nonetheless, it seems that such intellectual centers have entered into a competition with each other for attracting the attention of potential clients. The traditional values of (higher) education such as nurturing criticality and fostering intellectuality in students have been replaced with strategic planning, quality assurance, performance assessment, and academic audits. Not being immune from the whims and wishes of marketization, the system of higher education in the UK has been recalibrated by policy makers to address the demand and supply of student education, academic research and other university activities on the basis of monetary factors. As an immediate example in this vein, the Russell Group in the UK, which is comprised of 24 leading UK research universities, has explicitly expressed it policy on its official website as follows: ‘Russell Group universities are global businesses competing for staff, students and funding with the best in the world’. Furthermore, certain attempts have been made to corporatize the system of HE which have been manifested in remodeling of university governing bodies on corporate lines and developing measurement scales for indicating the performance of teaching practitioners. Nevertheless, it seems that such structural changes in policies toward the system of HE have bearing on the practices of practitioners and educators as well as the identity of students who are the customers of educational services. The effects of marketization have been examined mainly in terms of students’ perceptions and motivation, institutional policies and university management. However, the teaching practitioner side seems to be an under-studied area with regard to any changes in its expectations, satisfaction and perception of professional identity in the aftermath of introducing market-wise values into HE of the UK. As a result, this research aims to investigate the possible outcomes of market-driven values on the practitioner side of HE in the UK and finally seeks to address the following research questions: 1-How is the change in the mission of HE in the UK reflected in institutional documents? 1-A- How is the change of mission represented in job adverts? 1-B- How is the change of mission represented in university prospectuses? 2-How are teaching practitioners represented regarding their roles and obligations in the prospectuses and job ads published by UK HE institutions? In order to address these questions, the researcher will analyze 30 prospectuses and job ads published by Russel Group universities by taking Critical Discourse Analysis as his point of departure and the analytical methods of genre analysis and Systemic Functional Linguistics to probe into the generic features and representation of participants, in this case teaching practitioners, in the selected corpus.

Keywords: higher education, job advertisements, marketization of higher education, prospectuses

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Authors: Temesgen Geremew

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Functionalized nanoparticles have emerged as a revolutionary platform for drug delivery, offering significant advantages over traditional methods. By strategically modifying their surface properties, these nanoparticles can be designed to target specific tissues and cells, significantly reducing off-target effects and enhancing therapeutic efficacy. This targeted approach allows for lower drug doses, minimizing systemic exposure and potential side effects. Additionally, functionalization enables controlled release of the encapsulated drug, improving drug stability and reducing the frequency of administration, leading to improved patient compliance. This work explores the immense potential of functionalized nanoparticles in revolutionizing drug delivery, addressing limitations associated with conventional therapies and paving the way for personalized medicine with precise and targeted treatment strategies.

Keywords: nanoparticles, drug, nanomaterials, applications

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Authors: Jingling Li, Yi Zhang, Wei Liang, Tao Cui, Jun Li

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Compared with terrestrial network, the traffic of spatial information network has both self-similarity and short correlation characteristics. By studying its traffic prediction method, the resource utilization of spatial information network can be improved, and the method can provide an important basis for traffic planning of a spatial information network. In this paper, considering the accuracy and complexity of the algorithm, the spatial information network traffic is decomposed into approximate component with long correlation and detail component with short correlation, and a time series hybrid prediction model based on wavelet decomposition is proposed to predict the spatial network traffic. Firstly, the original traffic data are decomposed to approximate components and detail components by using wavelet decomposition algorithm. According to the autocorrelation and partial correlation smearing and truncation characteristics of each component, the corresponding model (AR/MA/ARMA) of each detail component can be directly established, while the type of approximate component modeling can be established by ARIMA model after smoothing. Finally, the prediction results of the multiple models are fitted to obtain the prediction results of the original data. The method not only considers the self-similarity of a spatial information network, but also takes into account the short correlation caused by network burst information, which is verified by using the measured data of a certain back bone network released by the MAWI working group in 2018. Compared with the typical time series model, the predicted data of hybrid model is closer to the real traffic data and has a smaller relative root means square error, which is more suitable for a spatial information network.

Keywords: spatial information network, traffic prediction, wavelet decomposition, time series model

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Authors: A. M. Byrne, E. Bonfiglio, C. Rigby, N. Edelstyn

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Introduction: Neuromarketing employs numerous methodologies when investigating products and advertisement effectiveness. Electroencephalography (EEG), a non-invasive measure of electrical activity from the brain, is commonly used in neuromarketing. EEG data can be considered using time-frequency (TF) analysis, where changes in the frequency of brainwaves are calculated to infer participant’s mental states, or event-related potential (ERP) analysis, where changes in amplitude are observed in direct response to a stimulus. This presentation discusses the findings of a systematic review of EEG measures in neuromarketing. A systematic review summarises evidence on a research question, using explicit measures to identify, select, and critically appraise relevant research papers. Thissystematic review identifies which EEG measures are the most robust predictor of customer preference and purchase intention. Methods: Search terms identified174 papers that used EEG in combination with marketing-related stimuli. Publications were excluded if they were written in a language other than English or were not published as journal articles (e.g., book chapters). The review investigated which TF effect (e.g., theta-band power) and ERP component (e.g., N400) most consistently reflected preference and purchase intention. Machine-learning prediction was also investigated, along with the use of EEG combined with physiological measures such as eye-tracking. Results: Frontal alpha asymmetry was the most reliable TF signal, where an increase in activity over the left side of the frontal lobe indexed a positive response to marketing stimuli, while an increase in activity over the right side indexed a negative response. The late positive potential, a positive amplitude increase around 600 ms after stimulus presentation, was the most reliable ERP component, reflecting the conscious emotional evaluation of marketing stimuli. However, each measure showed mixed results when related to preference and purchase behaviour. Predictive accuracy was greatly improved through machine-learning algorithms such as deep neural networks, especially when combined with eye-tracking or facial expression analyses. Discussion: This systematic review provides a novel catalogue of the most effective use of each EEG measure commonly used in neuromarketing. Exciting findings to emerge are the identification of the frontal alpha asymmetry and late positive potential as markers of preferential responses to marketing stimuli. Predictive accuracy using machine-learning algorithms achieved predictive accuracies as high as 97%, and future research should therefore focus on machine-learning prediction when using EEG measures in neuromarketing.

Keywords: EEG, ERP, neuromarketing, machine-learning, systematic review, time-frequency

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Authors: Ziying Wu, Danfeng Yan

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Multi-Access Edge Computing (MEC) is one of the key technologies of the future 5G network. By deploying edge computing centers at the edge of wireless access network, the computation tasks can be offloaded to edge servers rather than the remote cloud server to meet the requirements of 5G low-latency and high-reliability application scenarios. Meanwhile, with the development of IOV (Internet of Vehicles) technology, various delay-sensitive and compute-intensive in-vehicle applications continue to appear. Compared with traditional internet business, these computation tasks have higher processing priority and lower delay requirements. In this paper, we design a 5G-based Vehicle-Aware Multi-Access Edge Computing Network (VAMECN) and propose a joint optimization problem of minimizing total system cost. In view of the problem, a deep reinforcement learning-based joint computation offloading and task migration optimization (JCOTM) algorithm is proposed, considering the influences of multiple factors such as concurrent multiple computation tasks, system computing resources distribution, and network communication bandwidth. And, the mixed integer nonlinear programming problem is described as a Markov Decision Process. Experiments show that our proposed algorithm can effectively reduce task processing delay and equipment energy consumption, optimize computing offloading and resource allocation schemes, and improve system resource utilization, compared with other computing offloading policies.

Keywords: multi-access edge computing, computation offloading, 5th generation, vehicle-aware, deep reinforcement learning, deep q-network

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Authors: Boukli Hacene Faiza, Soufi Wassila, Ghalem Said

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The oral antidiabetics drugs such as alpha glucosidase inhibitors present undesirable effects like acarbose. Flavonoids are class of molecules widely distributed in plants, for this reason we are interested in our work to study the inhibition in silico of alpha glucosidase by natural ligands ( flavonoids analogues) using molecular modeling methods using MOE (Molecular Operating Environment) software to predict their interaction with this enzyme with score energy, ADME /T tests and druglikeness properties experiments. Two flavonoids Beicalein and Apigenin have high binding affinity with alpha glucosidase with lower IC50 supposed potent inhibitors.

Keywords: alpha glucosidase, flavonoides analogues, drug research, molecular modeling

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Authors: Muhammad Haris Aziz, Shoaib Sarfraz, Chanchal Saha

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The basic functions of a university are research and education. Research develops theories and education provides the link between the theory and the practical. Being an applied science, the link between theory and practice needs to be strong in engineering disciplines. But there remains a gap between industry and academy due lack of understanding and awareness from both sides. This gap is been shorten with an industrial engineering graduate class composed of a mix of students from industrial background and from theoretical background. Results are four industrial case studies which are the outcome of group projects in a course on operations research.

Keywords: industrial-academia linkage, operations research, industrial engineering, engineering education.

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Authors: Sender Dovchin, Alastair Pennycook

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This paper looks at the relationship between online space, urban space and digital literacies. The everyday digital literacy practices of Facebook users (with a particular focus on young urban Mongolians) can be understood as ‘metrolingual’ because of the varied ways in which linguistic and cultural resources, spatial repertoires, and online activities are bound together to make meaning. Whereas the initial development of the term metrolingualism was dependent on a notion of physical urban space, we here argue that the digital practices of these Facebook users perform a range of social and cultural identities (sexual, ethnic, and class-based identities) that are both parts of but also adjacent to the metrolingual fabric.

Keywords: metrolingualism, digital literacy, Mongolia, Facebook

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Authors: A. Shakoor, M. Arshad

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The utilization of groundwater resources in irrigation has significantly increased during the last two decades due to constrained canal water supplies. More than 70% of the farmers in the Punjab, Pakistan, depend directly or indirectly on groundwater to meet their crop water demands and hence, an unchecked paradigm shift has resulted in aquifer depletion and deterioration. Therefore, a comprehensive research was carried at central Punjab-Pakistan, regarding spatiotemporal variation in groundwater level and quality. Processing MODFLOW for window (PMWIN) and MT3D (solute transport model) models were used for existing and future prediction of groundwater level and quality till 2030. The comprehensive data set of aquifer lithology, canal network, groundwater level, groundwater salinity, evapotranspiration, groundwater abstraction, recharge etc. were used in PMWIN model development. The model was thus, successfully calibrated and validated with respect to groundwater level for the periods of 2003 to 2007 and 2008 to 2012, respectively. The coefficient of determination (R2) and model efficiency (MEF) for calibration and validation period were calculated as 0.89 and 0.98, respectively, which argued a high level of correlation between the calculated and measured data. For solute transport model (MT3D), the values of advection and dispersion parameters were used. The model used for future scenario up to 2030, by assuming that there would be no uncertain change in climate and groundwater abstraction rate would increase gradually. The model predicted results revealed that the groundwater would decline from 0.0131 to 1.68m/year during 2013 to 2030 and the maximum decline would be on the lower side of the study area, where infrastructure of canal system is very less. This lowering of groundwater level might cause an increase in the tubewell installation and pumping cost. Similarly, the predicted total dissolved solids (TDS) of the groundwater would increase from 6.88 to 69.88mg/L/year during 2013 to 2030 and the maximum increase would be on lower side. It was found that in 2030, the good quality would reduce by 21.4%, while marginal and hazardous quality water increased by 19.28 and 2%, respectively. It was found from the simulated results that the salinity of the study area had increased due to the intrusion of salts. The deterioration of groundwater quality would cause soil salinity and ultimately the reduction in crop productivity. It was concluded from the predicted results of groundwater model that the groundwater deteriorated with the depth of water table i.e. TDS increased with declining groundwater level. It is recommended that agronomic and engineering practices i.e. land leveling, rainwater harvesting, skimming well, ASR (Aquifer Storage and Recovery Wells) etc. should be integrated to meliorate management of groundwater for higher crop production in salt affected soils.

Keywords: groundwater quality, groundwater management, PMWIN, MT3D model

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Authors: Rajendra Kumar Kanojia

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Background; Massive bone gaps are common following road side accidents and injury to the tibia, specially open grade three fractures. It has been seen that the diaphyseal fractures in the tibia are prone to non-union, there are certain reasons known very well, like less soft tissues around the lower third tibia, less vascularity, less options of fixation of the fractures after trauma and prolonged surgical time, operation theatre time and special surgical means. Aim of study; To know the suitability of the ilizarov ring fixators in staged treatment of the fracture of the both bones leg, including tibia, we wish to see the role of ilizarov in management of open grade three fractures which have been operated and debrided, for getting the length use of ilizaorv ring in a tertiary canter is the aim of the study.

Keywords: open fracture, staged management, ilizarov, bone grafting, lengthening

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Authors: A. Magno-França, A. M. Magalhães-Neto, F. Bachini, E. Cataldi, A. Bassini, L. C. Cameron

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Sprint canoeing (SC) is part of the Olympic Games since 1936. Athletes compete in solo or double races of 200m and 1000m (40 sec and 240 sec, respectively). Due to its high intensity and duration, SC is extremely useful to study the blood kinetics of some metabolites in high energetic demand. Sportomics is a field of study combining “-omics” sciences with classical biochemical analyses in order to understand sports induced systemic changes. Here, we compare Sportomics findings during SC training sessions to describe metabolic responses of five top-level canoeists. Five Olympic world-class male athletes were evaluated during two days of training.

Keywords: biochemistry of exercise, metabolomics, injury markers, sportomics

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Authors: Reza Moloudi, Steve Oh, Charles Chun Yang, Majid Ebrahimi Warkiani, May Win Naing

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Inertial microfluidics has emerged recently as a promising tool for high-throughput manipulation of particles and cells for a wide range of flow cytometric tasks including cell separation/filtration, cell counting, and mechanical phenotyping. Inertial focusing is profoundly reliant on the cross-sectional shape of the channel and its impacts not only on the shear field but also the wall-effect lift force near the wall region. Despite comprehensive experiments and numerical analysis of the lift forces for rectangular and non-rectangular microchannels (half-circular and triangular cross-section), which all possess planes of symmetry, less effort has been made on the 'flow field structure' of trapezoidal straight microchannels and its effects on inertial focusing. On the other hand, a rectilinear channel with trapezoidal cross-sections breaks down all planes of symmetry. In this study, particle focusing dynamics inside trapezoid straight microchannels was first studied systematically for a broad range of channel Re number (20 < Re < 800). The altered axial velocity profile and consequently new shear force arrangement led to a cross-laterally movement of equilibration toward the longer side wall when the rectangular straight channel was changed to a trapezoid; however, the main lateral focusing started to move backward toward the middle and the shorter side wall, depending on particle clogging ratio (K=a/Hmin, a is particle size), channel aspect ratio (AR=W/Hmin, W is channel width, and Hmin is smaller channel height), and slope of slanted wall, as the channel Reynolds number further increased (Re > 50). Increasing the channel aspect ratio (AR) from 2 to 4 and the slope of slanted wall up to Tan(α)≈0.4 (Tan(α)=(Hlonger-sidewall-Hshorter-sidewall)/W) enhanced the off-center lateral focusing position from the middle of channel cross-section, up to ~20 percent of the channel width. It was found that the focusing point was spoiled near the slanted wall due to the dissymmetry; it mainly focused near the bottom wall or fluctuated between the channel center and the bottom wall, depending on the slanted wall and Re (Re < 100, channel aspect ratio 4:1). Eventually, as a proof of principle, a trapezoidal straight microchannel along with a bifurcation was designed and utilized for continuous filtration of a broader range of particle clogging ratio (0.3 < K < 1) exiting through the longer wall outlet with ~99% efficiency (Re < 100) in comparison to the rectangular straight microchannels (W > H, 0.3 ≤ K < 0.5).

Keywords: cell/particle sorting, filtration, inertial microfluidics, straight microchannel, trapezoid

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Authors: Jiangbo Li, Wenqian Huang

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Early detection of fungal infection in citrus fruit is one of the major problems in the postharvest commercialization process. The automatic and nondestructive detection of infected fruits is still a challenge for the citrus industry. At present, the visual inspection of rotten citrus fruits is commonly performed by workers through the ultraviolet induction fluorescence technology or manual sorting in citrus packinghouses to remove fruit subject with fungal infection. However, the former entails a number of problems because exposing people to this kind of lighting is potentially hazardous to human health, and the latter is very inefficient. Orange is used as a research object. This study would focus on this problem and proposed an effective method based on Vis-NIR hyperspectral imaging in the wavelength range of 400-1000 nm with a spectroscopic resolution of 2.8 nm. In this work, three normalization approaches are applied prior to analysis to reduce the effect of sample curvature on spectral profiles, and it is found that mean normalization was the most effective pretreatment for decreasing spectral variability due to curvature. Then, principal component analysis (PCA) was applied to a dataset composing of average spectra from decayed and normal tissue to reduce the dimensionality of data and observe the ability of Vis-NIR hyper-spectra to discriminate data from two classes. In this case, it was observed that normal and decayed spectra were separable along the resultant first principal component (PC1) axis. Subsequently, five wavelengths (band) centered at 577, 702, 751, 808, and 923 nm were selected as the characteristic wavelengths by analyzing the loadings of PC1. A multispectral combination image was generated based on five selected characteristic wavelength images. Based on the obtained multispectral combination image, the intensity slicing pseudocolor image processing method is used to generate a 2-D visual classification image that would enhance the contrast between normal and decayed tissue. Finally, an image segmentation algorithm for detection of decayed fruit was developed based on the pseudocolor image coupled with a simple thresholding method. For the investigated 238 independent set samples including infected fruits infected by Penicillium digitatum and normal fruits, the total success rate is 100% and 97.5%, respectively, and, the proposed algorithm also used to identify the orange infected by penicillium italicum with a 100% identification accuracy, indicating that the proposed multispectral algorithm here is an effective method and it is potential to be applied in citrus industry.

Keywords: citrus fruit, early rotten, fungal infection, hyperspectral imaging

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Authors: Brindha Senthil Kumar, Payel Chakraborty, Senthil Kumar Nachimuthu, Arindam Maitra, Prem Nath

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Gastric cancer is predominantly caused by demographic and diet factors as compared to other cancer types. The aim of the study is to predict Early Gastric Cancer (ECG) from diet and lifestyle factors using supervised machine learning algorithms. For this study, 160 healthy individual and 80 cases were selected who had been followed for 3 years (2016-2019), at Civil Hospital, Aizawl, Mizoram. A dataset containing 11 features that are core risk factors for the gastric cancer were extracted. Supervised machine algorithms: Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Multilayer perceptron, and Random Forest were used to analyze the dataset using Python Jupyter Notebook Version 3. The obtained classified results had been evaluated using metrics parameters: minimum_false_positives, brier_score, accuracy, precision, recall, F1_score, and Receiver Operating Characteristics (ROC) curve. Data analysis results showed Naive Bayes - 88, 0.11; Random Forest - 83, 0.16; SVM - 77, 0.22; Logistic Regression - 75, 0.25 and Multilayer perceptron - 72, 0.27 with respect to accuracy and brier_score in percent. Naive Bayes algorithm out performs with very low false positive rates as well as brier_score and good accuracy. Naive Bayes algorithm classification results in predicting ECG showed very satisfactory results using only diet cum lifestyle factors which will be very helpful for the physicians to educate the patients and public, thereby mortality of gastric cancer can be reduced/avoided with this knowledge mining work.

Keywords: Early Gastric cancer, Machine Learning, Diet, Lifestyle Characteristics

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Authors: Satyanarayana Engu, Ahmed Mohd, V. Murugan

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We study the large time asymptotics of solutions to the Cauchy problem for a forced Burgers equation (FBE) with the initial data, which is continuous and summable on R. For which, we first derive explicit solutions of FBE assuming a different class of initial data in terms of Hermite polynomials. Later, by violating this assumption we prove the existence of a solution to the considered Cauchy problem. Finally, we give an asymptotic approximate solution and establish that the error will be of order O(t^(-1/2)) with respect to L^p -norm, where 1≤p≤∞, for large time.

Keywords: Burgers equation, Cole-Hopf transformation, Hermite polynomials, large time asymptotics

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Authors: Takuro Kida, Yuichi Kida

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In this paper, it is shown a base of the new trend of algorithm mathematically that treats a historical reason of continuous discrimination in the world as well as its solution by introducing new concepts of parallel world that includes an invisible set of errors as its companion. With respect to a matrix operator-filter bank that the matrix operator-analysis-filter bank H and the matrix operator-sampling-filter bank S are given, firstly, we introduce the detail algorithm to derive the optimum matrix operator-synthesis-filter bank Z that minimizes all the worst-case measures of the matrix operator-error-signals E(ω) = F(ω) − Y(ω) between the matrix operator-input-signals F(ω) and the matrix operator-output-signals Y(ω) of the matrix operator-filter bank at the same time. Further, feedback is introduced to the above approximation theory, and it is indicated that introducing conversations with feedback do not superior automatically to the accumulation of existing knowledge of signal prediction. Secondly, the concept of category in the field of mathematics is applied to the above optimum signal approximation and is indicated that the category-based approximation theory is applied to the set-theoretic consideration of the recognition of humans. Based on this discussion, it is shown naturally why the narrow perception that tends to create isolation shows an apparent advantage in the short term and, often, why such narrow thinking becomes intimate with discriminatory action in a human group. Throughout these considerations, it is presented that, in order to abolish easy and intimate discriminatory behavior, it is important to create a parallel world of conception where we share the set of invisible error signals, including the words and the consciousness of both worlds.

Keywords: matrix filterbank, optimum signal approximation, category theory, simultaneous minimization

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Authors: Javad Jamali Khouei, Mohammadreza Khoshravan

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Considering that application of functionally graded material is increasing in most industries, it seems necessary to present a methodology for designing optimal profile of structures such as plate under mechanical loading which is highly consumed in industries. Therefore, volume fraction variation profile of functionally graded circular plate which has been considered two-directional is optimized so that stress of structure is minimized. For this purpose, equilibrium equations of two-directional functionally graded circular plate are solved by applying semi analytical-numerical method under mechanical loading and support conditions. By solving equilibrium equations, deflections and stresses are obtained in terms of control variables of volume fraction variation profile. As a result, the problem formula can be defined as an optimization problem by aiming at minimization of critical von-mises stress under constraints of deflections, stress and a physical constraint relating to structure of material. Then, the related problem can be solved with help of one of the metaheuristic algorithms such as genetic algorithm. Results of optimization for the applied model under constraints and loadings and boundary conditions show that functionally graded plate should be graded only in radial direction and there is no need for volume fraction variation of the constituent particles in thickness direction. For validating results, optimal values of the obtained design variables are graphically evaluated.

Keywords: two-directional functionally graded material, single objective optimization, semi analytical-numerical solution, genetic algorithm, graphical solution with contour

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Authors: Hung-Shuo Wu, Huan-Chieh Chiu, Xiang-Yao Zheng, Yu-Cheng Yang, Chien-Hao Wang, Jen-Cheng Wang, Chwan-Lu Tseng, Joe-Air Jiang

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Dynamic Thermal Rating (DTR) provides crucial information by estimating the ampacity of transmission lines to improve power dispatching efficiency. To perform the DTR, it is necessary to install on-line thermal sensors to monitor conductor temperature and weather variables. A simple and intuitive strategy is to allocate a thermal sensor to every span of transmission lines, but the cost of sensors might be too high to bear. To deal with the cost issue, a thermal sensor placement problem must be solved. This research proposes and implements a hybrid algorithm which combines proper orthogonal decomposition (POD) with particle swarm optimization (PSO) methods. The proposed hybrid algorithm solves a multi-objective optimization problem that concludes the minimum number of sensors and the minimum error on conductor temperature, and the optimal sensor placement is determined simultaneously. The data of 345 kV transmission lines and the hourly weather data from the Taiwan Power Company and Central Weather Bureau (CWB), respectively, are used by the proposed method. The simulated results indicate that the number of sensors could be reduced using the optimal placement method proposed by the study and an acceptable error on conductor temperature could be achieved. This study provides power companies with a reliable reference for efficiently monitoring and managing their power grids.

Keywords: dynamic thermal rating, proper orthogonal decomposition, particle swarm optimization, sensor placement, smart grid

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Authors: Rhommel Grutas, Ishmael Narag, Harley Lacbawan

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Construction of high-rise buildings is a means to address the increasing population in Metro Manila, Philippines. The existence of the Valley Fault System within the metropolis and other nearby active faults poses threats to a densely populated city. The distant, shallow and large magnitude earthquakes have the potential to generate slow and long-period vibrations that would affect medium-to-high rise buildings. Heavy damage and building collapse are consequences of prolonged shaking of the structure. If the ground and the building have almost the same period, there would be a resonance effect which would cause the prolonged shaking of the building. Microzoning the long-period ground response would aid in the seismic design of medium to high-rise structures. The shear-wave velocity structure of the subsurface is an important parameter in order to evaluate ground response. Borehole drilling is one of the conventional methods of determining shear-wave velocity structure however, it is an expensive approach. As an alternative geophysical exploration, microtremor array measurements can be used to infer the structure of the subsurface. Microtremor array measurement system was used to survey fifty sites around Metro Manila including some municipalities of Rizal and Cavite. Measurements were carried out during the day under good weather conditions. The team was composed of six persons for the deployment and simultaneous recording of the microtremor array sensors. The instruments were laid down on the ground away from sewage systems and leveled using the adjustment legs and bubble level. A total of four sensors were deployed for each site, three at the vertices of an equilateral triangle with one sensor at the centre. The circular arrays were set up with a maximum side length of approximately four kilometers and the shortest side length for the smallest array is approximately at 700 meters. Each recording lasted twenty to sixty minutes. From the recorded data, f-k analysis was applied to obtain phase velocity curves. Inversion technique is applied to construct the shear-wave velocity structure. This project provided a microzonation map of the metropolis and a profile showing the long-period response of the deep sedimentary basin underlying Metro Manila which would be suitable for local administrators in their land use planning and earthquake resistant design of medium to high-rise buildings.

Keywords: earthquake, ground motion, microtremor, seismic microzonation

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Authors: Shaina Nagpal, Amit Gupta

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In this work, all optical Stimulated Brillouin Scattering (SBS) generated single sideband with suppressed carrier is presented to provide better efficiency. The generation of single sideband and enhanced carrier power signal using the SBS technique is further used to strengthen the low shifted sideband and to suppress the upshifted sideband. These generated single sideband signals are able to work at high frequency ranges. Also, generated single sideband is validated over 90 km transmission using single mode fiber with acceptable bit error rate. The results for an equivalent are then compared so that the acceptable technique is chosen and also the required quality for the optimum performance of the system is reported.

Keywords: stimulated Brillouin scattering, radio over fiber, upper side band, quality factor

Procedia PDF Downloads 238

Authors: Lami Mohammed, Hussain J. Al Faifi, Abdel Aziz Al Bassam, Osama M. K. Kassem

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The Neoproterozoic basement rocks of the Ar Rayn terrane have been identified as parts of the Eastern Arabian Shield. It focuses on the petrological and finite strain properties to display the tectonic setting of the Al Amar suture for high deformed volcanic and granitoids rocks. The volcanic rocks are classified into two major series: the eastern side cycle, which includes dacite, rhyodacite, rhyolite, and ignimbrites, and the western side cycle, which includes andesite and pyroclastics. Granitoids rocks also contain monzodiorite, tonalite, granodiorite, and alkali-feldspar granite. To evaluate the proportions of shear contributions in penetratively deformed rocks. Asymmetrical porphyroclast and sigmoidal structural markers along the suture's strike, namely the Al Amar, are expected to reveal strain factors. The Rf/phi and Fry techniques are used to characterize quartz and feldspar porphyroclast, biotite, and hornblende grains in Abt schist, high deformed volcanic rock, and granitoids. The findings exposed that these rocks had experienced shape flattening, finite strain accumulation, and overall volume loss. The magnitude of the strain appears to increase across the nappe contacts with neighboring lithologies. Subhorizontal foliation likely developed in tandem with thrusting and nappe stacking, almost parallel to tectonic contacts. The ductile strain accumulation that occurred during thrusting along the Al Amar suture mostly includes a considerable pure shear component. Progressive thrusting by overlaid transpression and oblique convergence is shown by stacked nappes and diagonal stretching lineations along the thrust axes. The subhorizontal lineation might be the result of the suture's most recent activity. The current study's findings contradict the widely accepted model that links orogen-scale structures in the Arabian Shield to oblique convergence with dominant simple shear deformation. A significant pure shear component/crustal thickening increment should have played a significant role in the evolution of the suture and thus in the Shield's overall deformation history. This foliation was primarily generated by thrusting nappes together, showing that nappe stacking was linked to substantial vertical shortening induced by the active Al Amar suture on a massive scale.

Keywords: petrology, finite strain analysis, al amar region, ar-rayn terrane, Arabian shield

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Authors: Vivek Upadhayay, Siddharth Deshmukh

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In recent years utilization of renewable energy sources has increased majorly because of the increase in global warming concerns. Organization these days are generally operated by Micro grid or smart grid on a small level. Power optimization and optimal load tripping is possible in a smart grid based industry. In any plant or industry loads can be divided into different categories based on their importance to the plant and power requirement pattern in the working days. Coming up with an idea to divide loads in different such categories and providing different power management algorithm to each category of load can reduce the power cost and can come handy in balancing stability and reliability of power. An objective function is defined which is subjected to a variable that we are supposed to minimize. Constraint equations are formed taking difference between the power usages pattern of present day and same day of previous week. By considering the objectives of minimal load tripping and optimal power distribution the proposed problem formulation is a multi-object optimization problem. Through normalization of each objective function, the multi-objective optimization is transformed to single-objective optimization. As a result we are getting the optimized values of power required to each load for present day by use of the past values of the required power for the same day of last week. It is quite a demand response scheduling of power. These minimized values then will be distributed to each load through an algorithm used to optimize the power distribution at a greater depth. In case of power storage exceeding the power requirement, profit can be made by selling exceeding power to the main grid.

Keywords: power flow optimization, power trading enhancement, smart grid, multi-object optimization

Procedia PDF Downloads 529

Authors: Norbert Tomaszewski

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Modern politicians tend to resign from the traditional media as Web 2.0 tools allow them to interact with a much bigger audience while spending less money on their campaign. Current studies on this subject prove that in order to win the elections, the candidate needs to show his personal side during the campaign to appeal to the voter as an average citizen. Because of that, the internet user may engage in the politician's campaign by spreading his message along with his followers. The aim of the study is to determine how did the Democratic candidates use the Web 2.0 tools during the 2018 midterm elections campaign and whether they managed to succeed. Taking into consideration the fact that the United States as a country, has always set important milestones for the political marketing as a field of science, the result of the research can set some examples on how to manage the modern internet campaign in less developed countries.

Keywords: political campaign, midterm elections, social media, Web 2.0

Procedia PDF Downloads 151

Authors: Valentin Père, Mathieu Milhé, Fabien Baillon, Jean-Louis Dirion

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Microgrids offer potential solutions to meet the need for local grid stability and increase isolated networks autonomy with the integration of intermittent renewable energy production and storage facilities. In such a context, sizing production and storage for a given network is a complex task, highly depending on input data such as power load profile and renewable resource availability. This work aims at developing an operating cost computation methodology for different microgrid designs based on the use of deep reinforcement learning (RL) algorithms to tackle the optimal operation problem in stochastic environments. RL is a data-based sequential decision control method based on Markov decision processes that enable the consideration of random variables for control at a chosen time scale. Agents trained via RL constitute a promising class of Energy Management Systems (EMS) for the operation of microgrids with energy storage. Microgrid sizing (or design) is generally performed by minimizing investment costs and operational costs arising from the EMS behavior. The latter might include economic aspects (power purchase, facilities aging), social aspects (load curtailment), and ecological aspects (carbon emissions). Sizing variables are related to major constraints on the optimal operation of the network by the EMS. In this work, an islanded mode microgrid is considered. Renewable generation is done with photovoltaic panels; an electrochemical battery ensures short-term electricity storage. The controllable unit is a hydrogen tank that is used as a long-term storage unit. The proposed approach focus on the transfer of agent learning for the near-optimal operating cost approximation with deep RL for each microgrid size. Like most data-based algorithms, the training step in RL leads to important computer time. The objective of this work is thus to study the potential of Batch-Constrained Q-learning (BCQ) for the optimal sizing of microgrids and especially to reduce the computation time of operating cost estimation in several microgrid configurations. BCQ is an off-line RL algorithm that is known to be data efficient and can learn better policies than on-line RL algorithms on the same buffer. The general idea is to use the learned policy of agents trained in similar environments to constitute a buffer. The latter is used to train BCQ, and thus the agent learning can be performed without update during interaction sampling. A comparison between online RL and the presented method is performed based on the score by environment and on the computation time.

Keywords: batch-constrained reinforcement learning, control, design, optimal

Procedia PDF Downloads 129

Authors: Yuanyuan Song

Abstract:

Background: As we all know, culture plays a pivotal role in the realm of education, influencing study perceptions and outcomes. Nevertheless, there remains a need to delve into how culture specifically impacts the perception of courses. Therefore, the impact of culture on students' perceptions and academic performance is explored in this study. Drawing from cultural constructionism and conflict theories, it is posited that when students hailing from diverse cultures and backgrounds converge in the same classroom, their perceptions of course content may diverge significantly. This study seeks to unravel the tangible disparities and ascertain how cultural nuances shape students' perceptions of classroom content when encountering diverse cultural contexts within the same learning environment. Methodology: Given the diverse cultural backgrounds of students within the US, this study draws upon data collected from a course offered by a US college. In pursuit of answers to these inquiries, a qualitative approach was employed, involving semi-structured interviews conducted in a college-level science class in the US during 2023. The interviews encompassed approximately nine questions, spanning demographic particulars, cultural backgrounds, science learning experiences, academic outcomes, and more. Participants were exclusively drawn from science-related majors, with each student originating from a distinct cultural context. All participants were undergraduates, and most of them were from eighteen to twenty-five years old, totaling six students who attended the class and willingly participated in the interviews. The duration of each interview was approximately twenty minutes. Results: The findings gleaned from the interview data underscore the notable impact of varying cultural contexts on students' perceptions. This study argues that female science students, for instance, are influenced by gender dynamics due to the predominant male presence in science majors, creating an environment where female students feel reticent about expressing themselves in public. Students of East Asian origin exhibit a stronger belief in the efficacy of personal efforts when contrasted with their North American counterparts. Minority students indicated that they grapple with integration into the predominantly white mainstream society, influencing their eagerness to engage in classroom activities that are conducted by white professors. All of them emphasized the importance of learning science.

Keywords: multiculture education, educational sociology, educational equality, STEM education

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Authors: R. B. Ogunrinde, C. C. Jibunoh

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In this paper, a spectral decomposition method is developed for the direct integration of stiff and nonstiff homogeneous linear (ODE) systems with linear, constant, or zero right hand sides (RHSs). The method does not require iteration but obtains solutions at any random points of t, by direct evaluation, in the interval of integration. All the numerical solutions obtained for the class of systems coincide with the exact theoretical solutions. In particular, solutions of homogeneous linear systems, i.e. with zero RHS, conform to the exact analytical solutions of the systems in terms of t.

Keywords: spectral decomposition, linear RHS, homogeneous linear systems, eigenvalues of the Jacobian

Procedia PDF Downloads 331