Search results for: on-line analytical processing

Authors: Hong Lu, Xin Chen, Zhengcheng Fan

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Prior research has claimed a close association between handwriting and mathematics attainment with the help of spatial cognition. However, the exact mechanism behind this relationship remains un-investigated. Focusing on visual-motor integration (VMI), one critical spatial skill, this meta-analysis aims to estimate the size of the handwriting- visual-motor integration relationship and examine the moderating effect of handwriting dimensions on the link. With a random effect model, a medium relation (r=.26, 95%CI [.22, .30]) between handwriting and VMI was summarized in 38 studies with 55 unique samples and 141 effect sizes. Findings suggested handwriting dimensions significantly moderated the handwriting- VMI relationship, with handwriting legibility showing a substantial correlation with VMI, but neither handwriting speed nor pressure. Identifying the essential relationship between handwriting legibility and VMI, this study adds to the literature about the key cognitive processing needs underlying handwriting, and spatial cognition thus highlights the cognitive mechanism regarding handwriting, spatial cognition, and mathematics performances.

Keywords: handwriting, visual-motor integration, legibility, meta-analysis

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Authors: Merav Ben Natan, Moran Makhoul Khuri, Haviel Hammer, Maya Yarkoni

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Background: Studies indicate that nursing students often rank hospice nursing among their least preferred career paths. Understanding factors influencing their intent to work in hospice care is essential for improving interest in this field. Aim: This study aimed to explore the relationship between nursing students' intention to pursue a career in hospice care and various factors, including their attitudes towards caring for dying patients, death anxiety, personal or professional experience with dying patients, and the type of nursing program they are enrolled in. Methods: In this cross-sectional study, 200 nursing students completed an online survey using the Frommelt Attitude Toward Care of the Dying Scale and the Turkish Death Anxiety Scale. The survey assessed students' intentions to work in hospice care and related variables. Results: Only 11% of participants expressed an interest in working in hospice care. Students in the accelerated program for non-nursing Bachelor of Arts graduates showed a higher intention to work in hospice care compared to those in the generic program (β = 0.27, P < .001). Conversely, completion of clinical experience in a medical ward was associated with a lower intention to work in hospice care (β = −0.21, P < .01). Conclusions: The findings suggest that nursing students in accelerated programs for non-nursing graduates are more likely to intend to work in hospice care. Enhanced experience and support are recommended to sustain their interest. Clinical experience in medical wards does not effectively substitute for hospice-specific clinical experience.

Keywords: hospice nursing, nursing students, death anxiety, career intentions

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Authors: Annamária Käferné Rácz, Bence Jáger, Balázs Kövesdi, László Dunai

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Due to the numerous advantages of steel corrugated web girders, its application field is growing for bridges as well as for buildings. The global stability behavior of such girders is significantly larger than those of conventional I-girders with flat web, thus the application of the structural steel material can be significantly reduced. Design codes and specifications do not provide clear and complete rules or recommendations for the determination of the lateral torsional buckling (LTB) resistance of corrugated web girders. Therefore, the authors made a thorough investigation regarding the LTB resistance of the corrugated web girders. Finite element (FE) simulations have been performed to develop new design formulas for the determination of the LTB resistance of trapezoidally corrugated web girders. FE model is developed considering geometrical and material nonlinear analysis using equivalent geometric imperfections (GMNI analysis). The equivalent geometric imperfections involve the initial geometric imperfections and residual stresses coming from rolling, welding and flame cutting. Imperfection sensitivity analysis was performed to determine the necessary magnitudes regarding only the first eigenmodes shape imperfections. By the help of the validated FE model, an extended parametric study is carried out to investigate the LTB resistance for different trapezoidal corrugation profiles. First, the critical moment of a specific girder was calculated by FE model. The critical moments from the FE calculations are compared to the previous analytical calculation proposals. Then, nonlinear analysis was carried out to determine the ultimate resistance. Due to the numerical investigations, new proposals are developed for the determination of the LTB resistance of trapezoidally corrugated web girders through a modification factor on the design method related to the conventional flat web girders.

Keywords: corrugated web, lateral torsional buckling, critical moment, FE modeling

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Authors: Bernard Moeketsi Hlalele

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Drought is a complicated natural phenomenon that creates significant economic, social, and environmental problems. An analysis of paleoclimatic data indicates that severe and extended droughts are inevitable part of natural climatic circle. This study characterised drought in Lejweleputswa using both Standardised Precipitation Index (SPI) and neural networks (NN) to quantify and predict respectively. Monthly 37-year long time series precipitation data were obtained from online NASA database. Prior to the final analysis, this dataset was checked for outliers using SPSS. Outliers were removed and replaced by Expectation Maximum algorithm from SPSS. This was followed by both homogeneity and stationarity tests to ensure non-spurious results. A non-parametric Mann Kendall's test was used to detect monotonic trends present in the dataset. Two temporal scales SPI-3 and SPI-12 corresponding to agricultural and hydrological drought events showed statistically decreasing trends with p-value = 0.0006 and 4.9 x 10⁻⁷, respectively. The study area has been plagued with severe drought events on SPI-3, while on SPI-12, it showed approximately a 20-year circle. The concluded the analyses with a seasonal analysis that showed no significant trend patterns, and as such NN was used to predict possible SPI-3 for the last season of 2018/2019 and four seasons for 2020. The predicted drought intensities ranged from mild to extreme drought events to come. It is therefore recommended that farmers, agri-business owners, and other relevant stakeholders' resort to drought resistant crops as means of adaption.

Keywords: drought, risk, neural networks, agri-businesses, project, Lejweleputswa

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Authors: Pingping Lin, Xudong Luo, Yifan Fan

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Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis.

Keywords: document analysis, deep learning, multimodal sentiment analysis, natural language processing

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Authors: Youssef A. Yakoub, Ramy M. Shaaban

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Covid-19 has changed and affected the whole world dramatically in a new way that the entire world, even scientists, have not imagined before. The educational institutions around the world have been fighting since Covid-19 hit the world last December to keep the educational process unchanged for all students. E-learning was a must for almost all US universities during the pandemic. It was specifically more challenging to use eLearning instead of regular classes among students who take practical education. The aim of this study is to examine the perception of STEM students towards using eLearning instead of traditional methods during their practical study. Focus groups of STEM students studying at a western Pennsylavian, mid-size university were interviewed. Semi-structured interviews were designed to get an insight on students’ perception towards the alternative educational methods they used in the past seven months. Using convenient sampling, four students were chosen from different STEM fields: science of physics, technology, electrical engineering, and mathematics. The interview was primarily about the extent to which these students were satisfied, and their educational needs were met through distance education during the pandemic. The interviewed students were generally able to do a satisfactory performance during their virtual classes, but they were not satisfied enough with the learning methods. The main challenges they faced included the inability to have real practical experience, insufficient materials posted by the faculty, and some technical problems associated with their study. However, they reported they were satisfied with the simulation programs they had. They reported these simulations provided them with a good alternative to their traditional practical education. In conclusion, this study highlighted the challenges students face during the pandemic. It also highlighted the various learning tools students see as good alternatives to their traditional education.

Keywords: eLearning, STEM education, COVID-19 crisis, online practical training

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Authors: Hyun-Woo Cho

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Earlier detection of incipient abnormal operations in terms of plant-wide process management is quite necessary in order to improve product quality and process safety. And generating warning signals or alarms for operating personnel plays an important role in process automation and intelligent plant health monitoring. Various methodologies have been developed and utilized in this area such as expert systems, mathematical model-based approaches, multivariate statistical approaches, and so on. This work presents a nonlinear empirical monitoring methodology based on the real-time analysis of massive process data. Unfortunately, the big data includes measurement noises and unwanted variations unrelated to true process behavior. Thus the elimination of such unnecessary patterns of the data is executed in data processing step to enhance detection speed and accuracy. The performance of the methodology was demonstrated using simulated process data. The case study showed that the detection speed and performance was improved significantly irrespective of the size and the location of abnormal events.

Keywords: detection, monitoring, process data, noise

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Authors: Vaishnavi Shivde, Shrishti Sinha, Trapti Mishra

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Traffic light detection from a moving vehicle is an important technology both for driver safety assistance functions as well as for autonomous driving in the city. This paper proposed a deep-learning-based traffic light recognition method that consists of a pixel-wise image segmentation technique and a fully convolutional network i.e., UNET architecture. This paper has used a method for detecting the position and recognizing the state of the traffic lights in video sequences is presented and evaluated using Traffic Light Dataset which contains masked traffic light image data. The first stage is the detection, which is accomplished through image processing (image segmentation) techniques such as image cropping, color transformation, segmentation of possible traffic lights. The second stage is the recognition, which means identifying the color of the traffic light or knowing the state of traffic light which is achieved by using a Convolutional Neural Network (UNET architecture).

Keywords: traffic light detection, image segmentation, machine learning, classification, convolutional neural networks

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Authors: Prafulla Kumar Choubey, Krishna Kishor Jha, S. B. Vaisakh Punnekkattu Chirayil

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In Graphics-display subsystem, frame buffers are shared between producer i.e. content rendering and consumer i.e. display. If a common buffer is operated by both producer and consumer simultaneously, their processing rates mismatch can cause tearing effect in displayed content. Therefore, Android OS employs triple buffered system, taking in to account an additional composition stage. Three stages-rendering, composition and display refresh, operate synchronously on three different buffers, which is achieved by using vsync pulses. This synchronization, however, brings in to the pipeline an additional latency of up to 26ms. The present study details about the existing synchronization mechanism of android graphics-display pipeline and discusses a new adaptive architecture which reduces the wait time to 5ms-16ms in all the use-cases. The proposed method uses two adaptive software vsyncs (PLL) for achieving the same result.

Keywords: Android graphics system, vertical synchronization, atrace, adaptive system

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Authors: Regita P. Permata, Ulfa S. Nuraini

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Traveling Salesman Problem (TSP) is the most studied problem in combinatorial optimization. In simple language, TSP can be described as a problem of finding a minimum distance tour to a city, starting and ending in the same city, and exactly visiting another city. In product distribution, companies often get problems in determining the minimum distance that affects the time allocation. In this research, we aim to apply TSP heuristic methods to simulate nodes as city coordinates in product distribution. The heuristics used are sub tour reversal, nearest neighbor, farthest insertion, cheapest insertion, nearest insertion, and arbitrary insertion. We have done simulation nodes using Euclidean distances to compare the number of cities and processing time, thus we get optimum heuristic method. The results show that the optimum heuristic methods are farthest insertion and nearest insertion. These two methods can be recommended to solve product distribution problems in certain companies.

Keywords: Euclidean, heuristics, simulation, TSP

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Authors: M. V. Manoj Kumar, Likewin Thomas, Annappa

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Process mining provides methods and techniques for analyzing event logs recorded in modern information systems that support real-world operations. While analyzing an event-log, state-of-the-art techniques available in process mining believe that the operational process as a static entity (stationary). This is not often the case due to the possibility of occurrence of a phenomenon called concept drift. During the period of execution, the process can experience concept drift and can evolve with respect to any of its associated perspectives exhibiting various patterns-of-change with a different pace. Work presented in this paper discusses the main aspects to consider while addressing concept drift phenomenon and proposes a method for detecting and localizing the sudden concept drifts in control-flow perspective of the process by using features extracted by processing the traces in the process log. Our experimental results are promising in the direction of efficiently detecting and localizing concept drift in the context of process mining research discipline.

Keywords: abrupt drift, concept drift, sudden drift, control-flow perspective, detection and localization, process mining

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Authors: Chorom Baek, Sungwon Choi

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The purpose of this study is to analyze the supervision system, and the training and continuing education of mental health professionals in USA, UK, Australia (New Zealand), Japan, and so on, and to deduce the implications of Korean mental health service system. In order to accomplish the purpose of this study, following methodologies were adopted: review on the related literatures, statistical data, the related manuals, online materials, and previous studies concerning issues in those countries for the past five years. The training program in Korea was compared with the others’ through this literature analysis. The induced matters were divided with some parts such as training program, continuing education, educational procedure, and curriculum. Based on the analysis, discussion and implications, the conclusion and further suggestion of this study are as follows: First, Korean Clinical Psychology of Association (KCPA) should become more powerful health main training agency for quality control. Second, actual authority of health main training agency should be a grant to training centers. Third, quality control of mental health professionals should be through standardization and systemization of promotion and qualification management. Fourth, education and training about work of supervisors and unification of criteria for supervision should be held. Fifth, the training program for mental health license should be offered by graduate schools. Sixth, legitimated system to protect the right of mental health trainees is needed. Seventh, regularly continuing education after licensed should be compulsory to keep the certification. Eighth, the training program in training centers should meet KCPA requirement. If not, KCPA can cancel the certification of the centers.

Keywords: clinical psychology, Korea, mental health system, training program

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Authors: Rossella Arcucci, Luisa D'Amore, Simone Celestino, Giuseppe Scotti, Giuliano Laccetti

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This work is the first dowel in a rather wide research activity in collaboration with Euro Mediterranean Center for Climate Changes, aimed at introducing scalable approaches in Ocean Circulation Models. We discuss designing and implementation of a parallel algorithm for solving the Variational Data Assimilation (DA) problem on Graphics Processing Units (GPUs). The algorithm is based on the fully scalable 3DVar DA model, previously proposed by the authors, which uses a Domain Decomposition approach (we refer to this model as the DD-DA model). We proceed with an incremental porting process consisting of 3 distinct stages: requirements and source code analysis, incremental development of CUDA kernels, testing and optimization. Experiments confirm the theoretic performance analysis based on the so-called scale up factor demonstrating that the DD-DA model can be suitably mapped on GPU architectures.

Keywords: data assimilation, GPU architectures, ocean models, parallel algorithm

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Authors: Diafat Abdelouahab, Ekhalfi A Hammoudia, Meribai Abdelmalek A, Bahloul Ahmedb

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The present study aimed to characterize 30 honey samples from the Bordj Bou Arreridj region (Algeria) regarding their floral origins, physicochemical parameters, mineral composition and microbial safety. Mean values obtained for physicochemical parameters were: pH 4.11, 17.17% moisture, 0.0061% ash, 370.57μS cm−1 electrical conductivity, 21.98 meq/kg free acidity, and 9.703 mg/kg HMF. The mineral content was determined by atomic absorption spectrometry. The mean values obtained were (mg/kg): Fe, 7.5714; Mg, 37.68; Na, 186,63; Zn, 3,86; Pb, 0,4869 × 10-3 ; Cd, 267 × 10-3. Aerobic mesophiles, fecal coliforms and sulphite-reducing clostridia were the microbial contaminants of interest studied. Microbiologically, the honey quality was considered good and all samples showed to be negative in respect to safety parameters. The results obtained for physicochemical characteristics of Bordj Bou Arreridj honey indicate a good quality level, adequate processing, good maturity and freshness.

Keywords: pollen analysis, physicochemical analysis, mineral content, microbial contaminants

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Authors: K. O. Oriola, A. O. Raji, O. E. Akintola, O. T. Ismail

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Thermal properties of roots of three improved cassava varieties (TME419, TMS 30572, and TMS 0326) were determined on samples harvested at 12, 15 and 18 Months After Planting (MAP) conditioned to moisture contents of 50, 55, 60, 65, 70% (wb). Thermal conductivity at 12, 15 and 18 MAP ranged 0.4770 W/m.K to 0.6052W/m.K; 0.4804 W/m.K to 0.5530 W/m.K and 0.3764 to 0.6102 W/m.K respectively, thermal diffusivity from 1.588 to 2.426 x 10-7m2/s; 1.290 to 2.010 x 10-7m2/s and 0.1692 to 4.464 x 10-7m2/s and specific heat capacity from 2.3626 to 3.8991 kJ/kg.K; 1.8110 to 3.9703 kJ/kgK and 1.7311 to 3.8830 kJ/kg.K respectively within the range of moisture content studied across the varieties. None of the samples over the ages studied showed similar or definite trend in variation with others across the moisture content. However, second order polynomial models fitted all the data. Age on the other hand had a significant effect on the three thermal properties studied for TME 419 but not on thermal conductivity of TMS30572 and specific heat capacity of TMS 0326. Information obtained will provide better insight into thermal processing of cassava roots into stable products.

Keywords: thermal conductivity, thermal diffusivity, specific heat capacity, moisture content, tuber age

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Authors: Samrah Saeed

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Introduction: This study considers a measure of positive body image and the associations between body appreciation, beauty ideals internalization, dietary habits, and physical activity in young adults. Positive body image is assessed by Body Appreciation Scale 2. It is used to assess a person's acceptance of the body, the degree of positivity, and respect for the body.Regular physical activity and healthy eating arebasically important for the body, and they play an important role in creating a positive image of the body. Objectives: To identify the associations between body appreciation and beauty ideals internalization. To compare body appreciation and body ideals internalization among students of different physical activity. To explore the associations between dietary habits (unhealthy, healthy), body appreciation and body ideals internalization. Research methods and organization: Study participants were young adult students, aged 18-35, both male and female.The research questionnaire consisted of four areas: body appreciation, beauty ideals internalization, dietary habits, and physical activity.The questionnaire was created in Google Forms online survey platform.The questionnaire was filled out anonymously Result and Discussion: Physical dissatisfaction, diet, eating disorders and exercise disorders are found in young adults all over the world.Thorough nutrition helps people understand who they are by reassuring them that they are okay without judging or accepting themselves. Social media can positively influence body image in many ways.A healthy body image is important because it affect self-esteem, self-acceptance, and your attitude towards food and exercise.

Keywords: pysical activity, dietary habits, body image, beauty ideals internalization, body appreciation

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Authors: Nomin-Erdene Erdenetsogt, Tseedulam Khuut, Batsaikhan Tserenpil, Bayarsaikhan Enkhee

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As the framework of 60 years of development of Astronomical and Geophysical science in modern Mongolia, various geophysical methods (electrical tomography, ground-penetrating radar, and high-resolution reflection seismic profiles) were used to image an active fault in-depth range between few decimeters to few tens meters. An active fault was fractured by an earthquake magnitude 7.6 during 1967. After geophysical investigations, trench excavations were done at the sites to expose the fault surfaces. The complex geophysical survey in the Mogod fault, Bulgan region of central Mongolia shows an interpretable reflection arrivals range of < 5 m to 50 m with the potential for increased resolution. Reflection profiles were used to help interpret the significance of neotectonic surface deformation at earthquake active fault. The interpreted profiles show a range of shallow fault structures and provide subsurface evidence with support of paleoseismologic trenching photos, electrical surveys.

Keywords: Mogod fault, geophysics, seismic processing, seismic reflection survey

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Authors: Zyra Cheska Hidalgo, Joehiza Mae Renon, Kzarina Buen, Girlie Mitrado

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ABSTRACT: The global coronavirus disease (COVID-19) has dramatically impacted the lives of many, including education and our economy. Thus, it presents a massive challenge for medical education as instructors are mandated to deliver their lectures virtually to ensure the continuity of the medical education process and ensure students' safety. The purpose of this research paper is to determine the lived experiences of paramedical students who are engaged in virtual hands-on learning and to determine the different coping strategies they used to deal with virtual hands-on learning. The researchers used the survey method of descriptive research design to determine the lived experiences and coping strategies of twenty (20) paramedical students from Lorma Colleges (particularly the College of Medicine Department). The data were collected through online questionnaires, particularly with the use of google forms. This study shows technical issues, difficulty in adapting styles, distractions and time management issues, mental and physical health issues, and lack of interest and motivation are the most common problems and challenges experienced by paramedical students. On the other hand, the coping strategies used by paramedical students to deal with those challenges include time management, engagement in leisure activities, acceptance of responsibilities, studying, and adapting. With the data gathered, the researchers concluded that virtual hands-on learning effectively increases the knowledge of paramedical students. However, teaching and learning barriers must have to be considered to implement virtual hands-on learning successfully.

Keywords: virtual hands-on learning, E-learning, paramedical students, medical education

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Authors: Raghad A. Altowairgi

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With the wide spread of social media platforms, young people tend to use them extensively as the first means of communication due to their ease and modernity. But these platforms often create a fertile ground for bullies to practice their aggressive behavior against their victims. Platform usage cannot be reduced, but intelligent mechanisms can be implemented to reduce the abuse. This is where machine learning comes in. Understanding and classifying text can be helpful in order to minimize the act of cyberbullying. Artificial intelligence techniques have expanded to formulate an applied tool to address the phenomenon of cyberbullying. In this research, machine learning models are built to classify text into two classes; cyberbullying and non-cyberbullying. After preprocessing the data in 4 stages; removing characters that do not provide meaningful information to the models, tokenization, removing stop words, and lowering text. BoW and TF-IDF are used as the main features for the five classifiers, which are; logistic regression, Naïve Bayes, Random Forest, XGboost, and Catboost classifiers. Each of them scores 92%, 90%, 92%, 91%, 86% respectively.

Keywords: cyberbullying, machine learning, Bag-of-Words, term frequency-inverse document frequency, natural language processing, Catboost

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Authors: Mark R. Skopec, Hamdi M. Issa, Henock B. Taddese, Kate Ippolito, Matthew J. Harris

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In peer review, there is a skew toward research from high-income countries, otherwise known as geographic bias. Research from well-known and prestigious institutions is often favored in the peer review process and is more frequently cited in biomedical research. English clinicians have been found to rate research from low-income countries worse compared to the same research presented as if from high-income countries. This entrenched bias, which is rooted in the perceived superiority of academic institutions in high-income countries is damaging in many regards. Crucially, it reinforces colonial roots by strengthening the dominance of knowledge bases in high-income contexts and perpetuates the perceived inferiority of research from low-income settings. We report on the interventions that Imperial College London is conducting to “decolonize” the higher education curriculum – a root and branch review of reading material in the Masters of Public Health course; identification of unconscious bias against low-income country research in faculty and staff; in-depth interviews with faculty members on their experiences and practices with respect to inclusion of low-income country research in their own teaching and learning practice; and exploring issues surrounding entrenched biases and structural impediments for enabling desirable changes. We intend to use these findings to develop frameworks and approaches, including workshops and online resources, to effect sustainable changes to diversify the curriculum at Imperial College London.

Keywords: curriculum design, diversity, geographic bias, higher education, implicit associations, inclusivity

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Authors: Reskino Resky

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This research purposes to create a model to detecting financial statement fraud. This research examines the variable of fraud triangle and auditor industry specialization with financial statement fraud. This research used sample of company which is listed in Indonesian Stock Exchange that have sanctions and cases by Financial Services Authority in 2011-2013. The number of company that were became in this research were 30 fraud company and 30 non-fraud company. The method of determining the sample is by using purposive sampling method with judgement sampling, while the data processing methods used by researcher are mann-whitney u and discriminants analysis. This research have two from five variable that can be process with discriminant analysis. The result shows the financial targets can be detect financial statement fraud, while financial stability can’t be detect financial statement fraud.

Keywords: fraud triangle analysis, financial targets, financial stability, auditor industry specialization, financial statement fraud

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Authors: Hemant Kumar, R. N. K. Sharma, A. P. Krishna

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The study has been carried out on the Saranda in Jharkhand and a part of Odisha state. Geospatial data of Hyperion, a remote sensing satellite, have been used. This study has used a wide variety of patterns related to image processing to enhance and extract the mining class of Fe and Mn ores.Landsat-8, OLI sensor data have also been used to correctly explore related minerals. In this way, various processes have been applied to increase the mineralogy class and comparative evaluation with related frequency done. The Hyperion dataset for hyperspectral remote sensing has been specifically verified as an effective tool for mineral or rock information extraction within the band range of shortwave infrared used. The abundant spatial and spectral information contained in hyperspectral images enables the differentiation of different objects of any object into targeted applications for exploration such as exploration detection, mining.

Keywords: Hyperion, hyperspectral, sensor, Landsat-8

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Authors: Reza Vakili, Hassan Rostami rahvard

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To assess the effect of different levels of soybean oil (SO: 1, 2%) and different types of pellet binders (sodium bentonite (SB), calcium lignosulfonate (Ca-Ls), and plant compounds (PC) on the pellet quality, and broilers’ performance, 480 one-day-old male broiler chickens (Ross 308) were used. Treatments included 1) 1% SO+1% SB (1-SB), 2) 1% SO+0.5 % Ca-Ls (1- Ca-Ls), 3) 1% SO+0.5% PC (1-PC), 4) 1% SO+ no pellet binder (1-None), 5) 2% SO+1% SB (2-SB), 6) 2% SO+0.5% Ca-Ls (2- Ca-Ls), 7) 2% SO+0.5% PC (2-PC), 8) 2% SO+ no pellet binder (2-None). The results showed that in the starter diet, the 1-PC group had the highest pellet durability index (PDI) (P<0.05). The PDI of the grower diet containing SB and PC was higher than others (P<0.05). The highest pellet hardness was observed in groups 1-SB, 1-PC, 2-SB, and 2-PC for the starter diet (P<0.05). For the finisher diet, the hardness of pellets containing SB and PC was higher (P<0.05). During the starter phase, the best feed conversion ratio (FCR) was obtained in 1-SB (P<0.05). The lowest and highest daily feed intake was observed in groups 2-PC and 1-SB, respectively, during the finisher phase. During the finisher and whole phases, the most daily body weight gain was observed in the SB group (P<0.05).

Keywords: bentonite, feed processing, pellet durability, soybean oil

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Authors: Samira Eskandari

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The image of Tehran has been impacted in recent years due to poor urban management and fragmented governance. There is no cohesive urban beautification framework in Tehran to enforce builders take aesthetic factors seriously when design and construct new buildings. The existing guidelines merely provide people with recommendations, not regulations. Obviously, Tehran needs a more comprehensive and strict urban beautification framework to restore its image. The damaged image has impacted the city’s social, economic and environmental growth. This research aims to find and examine a solution by which the employment of urban beautification regulation would be guaranteed, and city image would be organized. The methodology is based on a qualitative approach associated with analytical methods, in-depth surveys and interviews with Tehran citizens, authorities and experts, and use of academic resources as well as simulation. As a result, one practical solution is to incorporate aesthetic guidelines into a survival-related framework like a seismic guideline. Tehran is a seismic site, and all the buildings in Tehran have to be retrofitted against earthquake during construction. Hence, by integrating seismic regulations and aesthetic disciplines, urban beautification will be somehow guaranteed. Besides, the seismic image can turn into Tehran’s brand and enhances city identity. This research is trying to increase the social, environmental, and economic interconnectedness between urban planning and seismic planning by the usage of landscape architecture methods. As a case study, the potential outcomes are simulated in Tajrish, a suburb located in the north of Tehran. The result is that, by the redefinition of the morphology of seismic retrofitting systems, used in the significant city image elements, and re-function them in accordance with the Iranian culture and traditions, the city image would become more harmonized and legible.

Keywords: earthquake, retrofitting systems, Tehran image, urban beautification

Procedia PDF Downloads 126

Authors: Mohd Dahlan Abdul Malek, Muhamad Idris, Adi Fahrudin, Ida Shafinaz Mohamed Kamil, Husmiati Yusuf, Edeymend Reny Japil, Wan Anor Wan Sulaiman, Lailawati Madlan, Alfred Chan, Nurfarhana Adillah Aftar, Mahirah Masdin

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This research intended to develop a valid and reliable instrument of the Satisfaction with Life Scale (SWLS) to measure satisfaction with life (SWL) constructs among public and private university students in Sabah, Malaysia, through the exploratory factor analysis (EFA) procedure. The pilot study obtained a sample of 108 students from public and private education institutions in Sabah, Malaysia, through an online survey using a self-administered questionnaire. The researchers performed the EFA procedure on SWL construct using IBM SPSS 25. The Bartletts' Test of Sphericity is highly significant (Sig. = .000). Furthermore, the sampling adequacy by Kaiser-Meyer-Olkin (KMO = 0.839) is excellent. Using the extraction method of Principal Component Analysis (PCA) with Varimax Rotation, a component of the SWL construct is extracted with an eigenvalue of 3.101. The variance explained for this component is 62.030%. The construct of SWL has Cronbach's alpha value of .817. The development scale and validation confirmed that the instrument is consistent and stable with both private and public college and university student samples. It adds a remarkable contribution to the measurement of SWLS, mainly in the context of higher education institution students. The EFA outcomes formed a configuration that extracts a component of SWL, which can be measured by the original five items established in this research. This research reveals that the SWL construct is applicable to this study.

Keywords: satisfaction, university students, measurement, scale development

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Authors: Pavan K. Rallabandi, Kailash C. Patidar

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In this paper, we present an optimization technique or a learning algorithm using the hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov models (HMMs). In order to improve the sequence or pattern recognition/ classification performance by applying a hybrid/neural symbolic approach, a gradient descent learning algorithm is developed using the Real Time Recurrent Learning of Recurrent Neural Network for processing the knowledge represented in trained Hidden Markov Models. The developed hybrid algorithm is implemented on automata theory as a sample test beds and the performance of the designed algorithm is demonstrated and evaluated on learning the deterministic finite state automata.

Keywords: hybrid systems, hidden markov models, recurrent neural networks, deterministic finite state automata

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Authors: Mary Josephine

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Waste of certain process can be the input source of other sectors in order to reduce environmental pollution. Today there are more and more solid wastes are generated, but only very small amount of those are recycled. So, the threatening of environmental pressure to public health is very serious. The methods considered for the treatment of solid waste are biogas tanks or processing to make animal feed and fertilizer, however, they did not perform well. An alternative approach is growing mushrooms on waste residues. This is regarded as an environmental friendly solution with potential economic benefit. The substrate producers do their best to produce quality substrate at low cost. Apart from other methods, this can be achieved by employing biologically degradable wastes used as the resource material component of the substrate. Mushroom growing is a significant tool for the restoration, replenishment and remediation of Earth’s overburdened ecosphere. One of the rational methods of waste utilization involves locally available wastes. The present study aims to find out the yield of mushroom grown on locally available waste for free and to conserve our environment by recycling wastes.

Keywords: biodegradable, environment, mushroom, remediation

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Authors: Ja-Keoung Koo, Kensuke Nakamura, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Byung-Woo Hong

Abstract:

The machine learning techniques based on a convolutional neural network (CNN) have been actively developed and successfully applied to a variety of image analysis tasks including reconstruction, noise reduction, resolution enhancement, segmentation, motion estimation, object recognition. The classical visual information processing that ranges from low level tasks to high level ones has been widely developed in the deep learning framework. It is generally considered as a challenging problem to derive visual interpretation from high dimensional imagery data. A CNN is a class of feed-forward artificial neural network that usually consists of deep layers the connections of which are established by a series of non-linear operations. The CNN architecture is known to be shift invariant due to its shared weights and translation invariance characteristics. However, it is often computationally intractable to optimize the network in particular with a large number of convolution layers due to a large number of unknowns to be optimized with respect to the training set that is generally required to be large enough to effectively generalize the model under consideration. It is also necessary to limit the size of convolution kernels due to the computational expense despite of the recent development of effective parallel processing machinery, which leads to the use of the constantly small size of the convolution kernels throughout the deep CNN architecture. However, it is often desired to consider different scales in the analysis of visual features at different layers in the network. Thus, we propose a CNN model where different sizes of the convolution kernels are applied at each layer based on the random projection. We apply random filters with varying sizes and associate the filter responses with scalar weights that correspond to the standard deviation of the random filters. We are allowed to use large number of random filters with the cost of one scalar unknown for each filter. The computational cost in the back-propagation procedure does not increase with the larger size of the filters even though the additional computational cost is required in the computation of convolution in the feed-forward procedure. The use of random kernels with varying sizes allows to effectively analyze image features at multiple scales leading to a better generalization. The robustness and effectiveness of the proposed CNN based on random kernels are demonstrated by numerical experiments where the quantitative comparison of the well-known CNN architectures and our models that simply replace the convolution kernels with the random filters is performed. The experimental results indicate that our model achieves better performance with less number of unknown weights. The proposed algorithm has a high potential in the application of a variety of visual tasks based on the CNN framework. Acknowledgement—This work was supported by the MISP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by IITP, and NRF-2014R1A2A1A11051941, NRF2017R1A2B4006023.

Keywords: deep learning, convolutional neural network, random kernel, random projection, dimensionality reduction, object recognition

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Authors: Broccolo Cinzia, Grigaliunaite Ruta, Saint-Nom Cloé, Savasta Guido

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The present article analyses the root causes of under-reporting and under-recording of hate crimes against Muslim women in Italy. The main findings emerged from the survey conducted between May and September 2022 within the framework of the TRUST project (co-funded by the CERV programme (CERV-2021-EQUAL) of the European Union) with relevant practitioners and members of the Muslim community, including first-generation and second-generation Muslim women residing in Italy. The findings reveal that multiple factors contribute to the low reporting rate as well as to the flaws in recording episodes of intolerance and hatred against the above-mentioned group. Lack of trust in the judiciary or the police may represent one of the main causes of under-reporting; however, the phenomenon is not limited to such aspects, and additional factors and sources of discrimination paving the way to under-recording have been identified during the survey. The significant “tendency” to not report a case of intolerance as the difficulties in identifying the discriminatory nature of the crime are two faces of the same coin and are particularly intertwined; despite this, at first, both issues need to be assessed and analysed separately in order to take their own specificities into duly consideration. By contrast, the potential solution to low recording and reporting trends should be found collectively, namely by involving all the relevant parties and bodies facing the above-mentioned issues. In this regard, a participatory and multi-agency approach may curb the root causes leading Muslim women not to report and, besides this, support law enforcement officials as well as public authorities in providing a more effective service to the victims of hatred, whether offline or online.

Keywords: hate crime, under-reporting, under-recording, Islamophobia, Muslim women

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Authors: Karin Schoenpflug, Christine M. Klapeer

Abstract:

In a large study for the Vienna City Council’s Antidiscrimination unit (WASt) an interdisciplinary team (in the fields of economics, sociology and political science) working with urban economics, critical citizenship studies, the sociology of work & inequality and urban political/human geography conducted an online survey asking LGBTIs (lesbians, gays, bisexuals, transgender and intersex people) in Vienna detailed questions on their quality-of-life, happiness and well-being. 3.161 persons responded and provided us with a rich data set concerning: 1) Labor market structures, discrimination, working conditions and employment practices (economic citizenship); 2) access to health care, welfare, education and safety in public spaces (social citizenship); 3) political participation as well as access to legal institutions (political citizenship). All those fields are important dimensions in regards to “full” citizenship and the well-being of the LGBTI population, but are also constitutive for the inclusion of sexual and gender minorities into the city population(s) of Vienna. Our data also allows us to map the sexual geography of Vienna as LGBTI communities are more likely to live in certain districts; some places are considered safe(r) and “friendlier”. In this way our work helps to fill a research gap connecting (urban) spaces and sexuality, and it produces new data and insights on the quality-of-life of this subpopulation. Our findings allow for urban (policy) planning and limiting violence and discrimination and improving the collective wellbeing and social cohesion.

Keywords: urban sexual geographies, LGBTI, socio-economic status, Vienna, sitizenship status

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