Search results for: Mandarin Chinese processing

Authors: Saleh Alshehri

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Human skin detection is a vital step in many applications. Some of the applications are critical especially those related to security. This leverages the importance of a high-performance detection algorithm. To validate the accuracy of the algorithm, image databases are usually used. However, the suitability of these image databases is still questionable. It is suggested that the suitability can be measured mainly by the span the database covers of the color space. This research investigates the validity of three famous image databases.

Keywords: image databases, image processing, pattern recognition, neural networks

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Authors: Mohammad Karimi Moridani, Mohammad Abdi Zadeh, Zahra Shahiazar Mazraeh

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In recent years, the use of intelligent systems in biomedical engineering has increased dramatically, especially in the diagnosis of various diseases. Also, due to the relatively simple recording of the electrocardiogram signal (ECG), this signal is a good tool to show the function of the heart and diseases associated with it. The aim of this paper is to design an intelligent system for automatically detecting a normal electrocardiogram signal from abnormal one. Using this diagnostic system, it is possible to identify a person's heart condition in a very short time and with high accuracy. The data used in this article are from the Physionet database, available in 2016 for use by researchers to provide the best method for detecting normal signals from abnormalities. Data is of both genders and the data recording time varies between several seconds to several minutes. All data is also labeled normal or abnormal. Due to the low positional accuracy and ECG signal time limit and the similarity of the signal in some diseases with the normal signal, the heart rate variability (HRV) signal was used. Measuring and analyzing the heart rate variability with time to evaluate the activity of the heart and differentiating different types of heart failure from one another is of interest to the experts. In the preprocessing stage, after noise cancelation by the adaptive Kalman filter and extracting the R wave by the Pan and Tampkinz algorithm, R-R intervals were extracted and the HRV signal was generated. In the process of processing this paper, a new idea was presented that, in addition to using the statistical characteristics of the signal to create a return map and extraction of nonlinear characteristics of the HRV signal due to the nonlinear nature of the signal. Finally, the artificial neural networks widely used in the field of ECG signal processing as well as distinctive features were used to classify the normal signals from abnormal ones. To evaluate the efficiency of proposed classifiers in this paper, the area under curve ROC was used. The results of the simulation in the MATLAB environment showed that the AUC of the MLP and SVM neural network was 0.893 and 0.947, respectively. As well as, the results of the proposed algorithm in this paper indicated that the more use of nonlinear characteristics in normal signal classification of the patient showed better performance. Today, research is aimed at quantitatively analyzing the linear and non-linear or descriptive and random nature of the heart rate variability signal, because it has been shown that the amount of these properties can be used to indicate the health status of the individual's heart. The study of nonlinear behavior and dynamics of the heart's neural control system in the short and long-term provides new information on how the cardiovascular system functions, and has led to the development of research in this field. Given that the ECG signal contains important information and is one of the common tools used by physicians to diagnose heart disease, but due to the limited accuracy of time and the fact that some information about this signal is hidden from the viewpoint of physicians, the design of the intelligent system proposed in this paper can help physicians with greater speed and accuracy in the diagnosis of normal and patient individuals and can be used as a complementary system in the treatment centers.

Keywords: neart rate variability, signal processing, linear and non-linear features, classification methods, ROC Curve

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Authors: Jean Paul Hategekimana, Josiane Irakoze, Eugene Niyonzima, Annick Ndekezi

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Cassava (Manihot esculenta Crantz) is a root crop comprising an anti-nutritive factor known as cyanide. The compound can be removed by numerous processing methods such as boiling, fermentation, blanching, and sun drying to avoid the possibility of cyanide poisoning. Inappropriate processing mean can lead to disease and death. Cassava-based dishes are consumed in different ways, where cassava is cultivated according to their culture and preference. However, they have been shown to be unsafe based on high cyanide levels. The current study targeted to resolve the problem of high cyanide in cassava consumed in Rwanda. This study was conducted to determine the effect of slicing, blanching, and soaking time to reduce the fermentation duration of cassava for hydrogen cyanide (HCN) in mg/g removal. Cassava was sliced into three different portions (1cm, 2cm, and 5cm). The first portions were naturally fermented for seven days, where each portion was removed every 24 hours from soaking tanks and then oven dried at a temperature of 60°C and then milled to obtain naturally fermented cassava flours. Other portions of 1cm, 2cm, and 5cm were blanched for 2, 5, 10 min, respectively, and each similarly dried at 60°C and milled to produce blanched cassava flour. Other blanched portions were used to follow the previous fermentation steps. The last portions, which formed the control, were simply chopped. Cyanide content and starch content in mg/100g were investigated. According to the conducted analysis on different cassava treatments for detoxification, found that usual fermentation can be used, but for sliced portions aimed to size reduction for the easy hydrogen cyanide diffuse out and it takes four days to complete fermentation, which has reduced at 94.44% with significantly different (p<0.05)of total hydrogen cyanide contained in cassava to safe level of consumption, and what is recommended as more effective is to apply blanching combined with fermentation due to the fact that, it takes three days to complete hydrogen cyanide removal at 95.56% on significantly different (p<0.05) of reduction to the safe level of consumption.

Keywords: cassava, cyanide, blanching, drying, fermentation

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Authors: Anisul Islam

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The U.S. has a very strong trade relationship with China but with a large and persistent trade deficit. Some has argued that the undervalued Chinese Yuan is to be blamed for the persistent trade deficit. The empirical results are mixed at best. This paper empirically estimates the U.S. export function along with the U.S. import function with its trade with China with the purpose of testing for the existence of the Marshall-Lerner (ML) condition as well for the possible existence of the J-curve hypothesis. Annual export and import data will be utilized for as long as the time series data exists. The export and import functions will be estimated using advanced econometric techniques, along with appropriate diagnostic tests performed to examine the validity and reliability of the estimated results. The annual time-series data covers from 1975 to 2022 with a sample size of 48 years, the longest period ever utilized before in any previous study. The data is collected from several sources, such as the World Bank’s World Development Indicators, IMF Financial Statistics, IMF Direction of Trade Statistics, and several other sources. The paper is expected to shed important light on the ongoing debate regarding the persistent U.S. trade deficit with China and the policies that may be useful to reduce such deficits over time. As such, the paper will be of great interest for the academics, researchers, think tanks, global organizations, and policy makers in both China and the U.S.

Keywords: exports, imports, marshall-lerner condition, j-curve hypothesis, united states, china

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Authors: Netanel Stern

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Artificial intelligence (AI) algorithms have emerged as powerful tools in the field of psychiatry, offering new possibilities for enhancing diagnosis and treatment outcomes. This article explores the utilization of AI algorithms in psychiatry, highlighting their potential to revolutionize patient care. Various AI algorithms, including machine learning, natural language processing (NLP), reinforcement learning, clustering, and Bayesian networks, are discussed in detail. Moreover, ethical considerations and future directions for research and implementation are addressed.

Keywords: AI, software engineering, psychiatry, neuroimaging

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Authors: H. Saikouk, D. Laux, Emmanuel Le Clézio, B. Lacroix, K. Audic, R. Largenton, E. Federici, G. Despaux

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Pressurized Water Reactor (PWR) fuel rods are made of ceramic pellets (e.g. UO2 or (U,Pu) O2) assembled in a zirconium cladding tube. By design, an initial gap exists between these two elements. During irradiation, they both undergo transformations leading progressively to the closure of this gap. A local and non destructive examination of the pellet/cladding interface could constitute a useful help to identify the zones where the two materials are in contact, particularly at high burnups when a strong chemical bonding occurs under nominal operating conditions in PWR fuel rods. The evolution of the pellet/cladding bonding during irradiation is also an area of interest. In this context, the Institute of Electronic and Systems (IES- UMR CNRS 5214), in collaboration with the Alternative Energies and Atomic Energy Commission (CEA), is developing a high frequency acoustic microscope adapted to the control and imaging of the pellet/cladding interface with high resolution. Because the geometrical, chemical and mechanical nature of the contact interface is neither axially nor radially homogeneous, 2D images of this interface need to be acquired via this ultrasonic system with a highly performing processing signal and by means of controlled displacement of the sample rod along both its axis and its circumference. Modeling the multi-layer system (water, cladding, fuel etc.) is necessary in this present study and aims to take into account all the parameters that have an influence on the resolution of the acquired images. The first prototype of this microscope and the first results of the visualization of the inner face of the cladding will be presented in a poster in order to highlight the potentials of the system, whose final objective is to be introduced in the existing bench MEGAFOX dedicated to the non-destructive examination of irradiated fuel rods at LECA-STAR facility in CEA-Cadarache.

Keywords: high-frequency acoustic microscopy, multi-layer model, non-destructive testing, nuclear fuel rod, pellet/cladding interface, signal processing

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Authors: Doaa Hamdi, Ahmed Hashem

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The present study aims at integrating the ASTER data and Landsat 8 data to discriminate and map alteration and/or mineralization zones in addition to delineating different lithological units of Humr Akarim Granites area. The study area is located at 24º9' to 24º13' N and 34º1' to 34º2'45"E., covering a total exposed surface area of about 17 km². The area is characterized by rugged topography with low to moderate relief. Geologic fieldwork and petrographic investigations revealed that the basement complex of the study area is composed of metasediments, mafic dikes, older granitoids, and alkali-feldspar granites. Petrographic investigations revealed that the secondary minerals in the study area are mainly represented by chlorite, epidote, clay minerals and iron oxides. These minerals have specific spectral signatures in the region of visible near-infrared and short-wave infrared (0.4 to 2.5 µm). So that the ASTER imagery processing was concentrated on VNIR-SWIR spectrometric data in order to achieve the purposes of this study (geologic mapping of hydrothermal alteration zones and delineate possible radioactive potentialities). Mapping of hydrothermal alterations zones in addition to discriminating the lithological units in the study area are achieved through the utilization of some different image processing, including color band composites (CBC) and data transformation techniques such as band ratios (BR), band ratio codes (BRCs), principal component analysis(PCA), Crosta Technique and minimum noise fraction (MNF). The field verification and petrographic investigation confirm the results of ASTER imagery and Landsat 8 data, proposing a geological map (scale 1:50000).

Keywords: remote sensing, petrography, mineralization, alteration detection

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Authors: Branci Sarra, Branci Saadia

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Because of the multiplied data growth, many computer science tools have been developed to process and analyze these Big Data. High-performance computing architectures have been designed to meet the treatment needs of Big Data (view transaction processing standpoint, strategic, and tactical analytics). The purpose of this article is to provide a historical and global perspective on the recent trend of high-performance computing architectures especially what has a relation with Analytics and Data Mining.

Keywords: high performance computing, HPC, big data, data analysis

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Authors: Thierry Sammour Sawaya, Heikki Deschacht

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In commercial aircraft operations, almost half of the accidents happen during approach or landing phases. Automatic guidance and automatic landings have proven to bring significant safety value added for this challenging landing phase. This is why Airbus and ScioTeq have decided to work together to explore the capability of image-based landing solutions as additional landing aids to further expand the possibility to perform automatic approach and landing to runways where the current guiding systems are either not fitted or not optimum. Current systems for automated landing often depend on radio signals provided by airport ground infrastructure on the airport or satellite coverage. In addition, these radio signals may not always be available with the integrity and performance required for safe automatic landing. Being independent from these radio signals would widen the operations possibilities and increase the number of automated landings. Airbus and ScioTeq are joining their expertise in the field of Computer Vision in the European Program called Clean Sky 2 Large Passenger Aircraft, in which they are leading the IMBALS (IMage BAsed Landing Solutions) project. The ultimate goal of this project is to demonstrate, develop, validate and verify a certifiable automatic landing system guiding an airplane during the approach and landing phases based on an onboard camera system capturing images, enabling automatic landing independent from radio signals and without precision instrument for landing. In the frame of this project, ScioTeq is responsible for the development of the Image Processing Platform (IPP), while Airbus is responsible for defining the functional and system requirements as well as the testing and integration of the developed equipment in a Large Passenger Aircraft representative environment. The aim of this paper will be to describe the system as well as the associated methods and tools developed for validation and verification.

Keywords: aircraft landing system, aircraft safety, autoland, avionic system, computer vision, image processing

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Authors: Murat Erkoc, Kadir Ertogral

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Maintenance, repair and overhauling (MRO) of high cost equipment used in many industries such as transportation, military and construction are typically subject to regulations set by local governments or international agencies. Aircrafts are prime examples for this kind of equipment. Such equipment must be overhauled at certain intervals for continuing permission of use. As such, the overhaul must be completed by strict deadlines, which often times cannot be exceeded. Due to the fact that the overhaul is typically a long process, MRO companies carry so called rotable inventory for exchange of expensive modules in the overhaul process of the equipment so that the equipment continue its services with minimal interruption. The extracted module is overhauled and returned back to the inventory for future exchange, hence the name rotable inventory. However, since the rotable inventory and overhaul capacity are limited, it may be necessary to carry out some of the exchanges earlier than their deadlines in order to produce a feasible overhaul schedule. An early exchange results with a decrease in the equipment’s cycle time in between overhauls and as such, is not desired by the equipment operators. This study introduces an integer programming model for the optimal overhaul and exchange scheduling. We assume that there is certain number of rotables at hand at the beginning of the planning horizon for a single type module and there are multiple demands with known deadlines for the exchange of the modules. We consider an MRO system with identical parallel processing lines. The model minimizes total earliness by generating optimal overhaul start times for rotables on parallel processing lines and exchange timetables for orders. We develop a fast exact solution algorithm for the model. The algorithm employs full-delay scheduling approach with backward allocation and can easily be used for overhaul scheduling problems in various MRO settings with modular rotable items. The proposed procedure is demonstrated by a case study from the aerospace industry.

Keywords: rotable inventory, full-delay scheduling, maintenance, overhaul, total earliness

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Authors: Fathi Kallel, Abdulelah Alabd Uljabbar, Abdulrahman Aldukhail, Abdulaziz Alomran

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The brain is an important organ in our body since it is responsible about the majority actions such as vision, memory, etc. However, different diseases such as Alzheimer and tumors could affect the brain and conduct to a partial or full disorder. Regular diagnosis are necessary as a preventive measure and could help doctors to early detect a possible trouble and therefore taking the appropriate treatment, especially in the case of brain tumors. Different imaging modalities are proposed for diagnosis of brain tumor. The powerful and most used modality is the Magnetic Resonance Imaging (MRI). MRI images are analyzed by doctor in order to locate eventual tumor in the brain and describe the appropriate and needed treatment. Diverse image processing methods are also proposed for helping doctors in identifying and analyzing the tumor. In fact, a large Computer Aided Diagnostic (CAD) tools including developed image processing algorithms are proposed and exploited by doctors as a second opinion to analyze and identify the brain tumors. In this paper, we proposed a new advanced CAD for brain tumor identification, classification and feature extraction. Our proposed CAD includes three main parts. Firstly, we load the brain MRI. Secondly, a robust technique for brain tumor extraction is proposed. This technique is based on both Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA). DWT is characterized by its multiresolution analytic property, that’s why it was applied on MRI images with different decomposition levels for feature extraction. Nevertheless, this technique suffers from a main drawback since it necessitates a huge storage and is computationally expensive. To decrease the dimensions of the feature vector and the computing time, PCA technique is considered. In the last stage, according to different extracted features, the brain tumor is classified into either benign or malignant tumor using Support Vector Machine (SVM) algorithm. A CAD tool for brain tumor detection and classification, including all above-mentioned stages, is designed and developed using MATLAB guide user interface.

Keywords: MRI, brain tumor, CAD, feature extraction, DWT, PCA, classification, SVM

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Authors: Sanari Chalin Moriri, Kwabena Kingsley Ayisi, Alina Mofokeng

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Dryland smallholder farmers in South Africa are vulnerable to all kinds of risks, and it negatively affects crop productivity and profit. Pigeonpea is a leguminous and multipurpose crop that provides food, fodder, and wood for smallholder farmers. The majority of these farmers are still growing pigeonpea from traditional unimproved seeds, which comprise a mixture of genotypes. The objectives of the study were to identify the key risk factors that affect pigeonpea productivity and to develop management strategies on how to alleviate the risk factors in pigeonpea production. The study was conducted in two provinces (Limpopo and Mpumalanga) of South Africa in six municipalities during the 2020/2021 growing seasons. The non-probability sampling method using purposive and snowball sampling techniques were used to collect data from the farmers through a structured questionnaire. A total of 114 pigeonpea producers were interviewed individually using a questionnaire. Key stakeholders in each municipality were also identified, invited, and interviewed to verify the information given by farmers. Data collected were subjected to SPSS statistical software 25 version. The findings of the study were that majority of farmers affected by risk factors were women, subsistence, and old farmers resulted in low food production. Drought, unavailability of improved pigeonpea seeds for planting, access to information, and processing equipment were found to be the main risk factors contributing to low crop productivity in farmer’s fields. Above 80% of farmers lack knowledge on the improvement of the crop and also on the processing techniques to secure high prices during the crop off-season. Market availability, pricing, and incidence of pests and diseases were found to be minor risk factors which were triggered by the major risk factors. The minor risk factors can be corrected only if the major risk factors are first given the necessary attention. About 10% of the farmers found to use the crop as a mulch to reduce soil temperatures and to improve soil fertility. The study revealed that most of the farmers were unaware of its utilisation as fodder, much, medicinal, nitrogen fixation, and many more. The risk of frequent drought in dry areas of South Africa where farmers solely depend on rainfall poses a serious threat to crop productivity. The majority of these risk factors are caused by climate change due to unrealistic, low rainfall with extreme temperatures poses a threat to food security, water, and the environment. The use of drought-tolerant, multipurpose legume crops such as pigeonpea, access to new information, provision of processing equipment, and support from all stakeholders will help in addressing food security for smallholder farmers. Policies should be revisited to address the prevailing risk factors faced by farmers and involve them in addressing the risk factors. Awareness should be prioritized in promoting the crop to improve its production and commercialization in the dryland farming system of South Africa.

Keywords: management strategies, pigeonpea, risk factors, smallholder farmers

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Authors: Geetanjali Sharma

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There are always provincial variations which deserve more detailed treatment. Before the arrival of modern era, geography and cultural homogeneity were determining factors of human habitat and migration. Boundaries as if we see them, did not exist earlier. The connectivity of the world was also different as of now. The reinforcement of the old silk route will improve economic cooperation and connectivity between Asian, European and African countries, but obviously, it is designed to improve China’s geopolitical and geostrategic position in the world. The paper is based on the secondary sources of data. Analytical and historical approach has been used to clarify the ties between the old silk routes and new One-Belt-One-Road initiative China. The paper begins with an explanation of the historical background of the old Silk Route, its origin and development, trailed by an analysis of latest declarations by the Chinese leaders to revive it. It also discusses the impacts of this initiative on India’s economy and cultural exchange between associated regions. Lastly, the paper sums up the findings and suggestions for keeping a balance between the security and economic relationship between the countries. It concludes that the silk route is an effort in commencing a ‘grand strategy’ for global trade and cooperation with hidden objectives of China to increase the investment of China in other continents as well. The revival of silk route may prove to be a very helpful in reinforcing cooperation and raising it to a new level of economic establishments. However, China has yet to promote the much-needed political and strategic trust.

Keywords: OBOR (One-Belt-One-Road), geopolitics, economic relation, security concerns

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Authors: Yanyue Yuan

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In this paper, the author shares the findings of a pilot study that examines students’ creative expressions and their perceptions of creativity when engaged in project-based learning. The study is based on an elective course that the author co-designed and co-taught with a colleague to sixteen grade six and seven students over the spring semester in 2019. Using the Little Prince story as the main prompt, they facilitated students’ original creation of a storytelling concert that integrated script writing, music production, lyrics, songs, and visual design as a result of both individual and collaborative work. The author will share the specific challenges we met during the project, including learning cultures of the school, class management, teachers' and parents’ attitude, process-oriented versus product-oriented mindset, and facilities and logistical resources. The findings of this pilot study will inform the ongoing research initiative of exploring how we can foster creative learning in public schools in the Chinese context. While K-12 schools of China’s public education system are still dominated by exam-oriented and teacher-centered approaches, the author proposes that expression-based learning can be a starting point for promoting students’ creativity and can serve as experimental efforts to initiate incremental changes within the current education framework. The paper will also touch upon insights gained from collaborations between university and K-12 schools.

Keywords: creativity, expression-based learning, K-12, incremental changes

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Authors: Javier Comes Fayos, Isabel Rodríguez Moreno, Sara Bressanutti, Marisol Lila, Angel Romero Martínez, Luis Moya Albiol

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Empathy is a cognitive-affective capacity whose deterioration is associated with aggressive behaviour. Deficient affective processing is one of the predominant risk factors in men convicted of intimate partner violence (IPV perpetrators), since it makes their capacity to empathize very difficult. The objective of this study is to compare the response of electrodermal activity (EDA), as an indicator of emotionality, to an empathic induction task, between IPV perpetrators and men without a history of violence. The sample was composed of 51 men who attended the CONTEXTO program, with penalties for gender violence under two years, and 47 men with no history of violence. Empathic induction was achieved through the visualization of 4 negative emotional-eliciting videos taken from an emotional induction battery of videos validated for the Spanish population. The participants were asked to actively empathize with the video characters (previously pointed out). The psychophysiological recording of the EDA was accomplished by the "Vrije Universiteit Ambulatory Monitoring System (VU-AMS)." An analysis of repeated measurements was carried out with 10 intra-subject measurements (time) and "group" (IPV perpetrators and non-violent perpetrators) as the inter-subject factor. First, there were no significant differences between groups in the baseline AED levels. Yet, a significant interaction between the “time” and “group” was found with IPV perpetrators exhibiting lower EDA response than controls after the empathic induction task. These findings provide evidence of a subdued EDA response after an empathic induction task in IPV perpetrators with respect to men without a history of violence. Therefore, the lower psychophysiological activation would be indicative of difficulties in the emotional processing and response, functions that are necessary for the empathic function. Consequently, the importance of addressing possible empathic difficulties in IPV perpetrator psycho-educational programs is reinforced, putting special emphasis on the affective dimension that could hinder the empathic function.

Keywords: electrodermal activity, emotional induction, empathy, intimate partner violence

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Authors: Sixing Chen, Shuai Yang

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Online celebrity business, also known as Internet celebrity business (or Wanghong business in Chinese), is an emerging relational C2C business model, and an alternative to traditional C2C transactional business models. There are already millions of these consumers, and this number is growing. In this model, consumer purchase decisions are driven by recommendations and endorsements in videos posted online by celebrities. The purpose of this paper is to determine the relational constructs within consumer relationships in the Internet celebrity business model and to investigate relationships between the constructs and consumer purchase intention. A questionnaire-based study was conducted with consumers who had an awareness of, or prior purchase experience with online celebrities. The results of exploratory factor analysis (EFA) and multiple regression analysis revealed three valid relational constructs: product experience sharing, lifestyle association, and real-time interaction. This study indicated that these constructs had the direct effect on consumer preference and purchase intention. The findings of this study provide insight into a business model in which online shopping is driven by celebrities. They suggest that online celebrities should pay more attention to product experience sharing, life style association and real-time interaction for managing their product promotions. These are the most salient factors with respect to the relational constructs identified in this study.

Keywords: customer relationship, customer to customer, Internet celebrity, online celebrity, online marketing, purchase intention

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Authors: Huan Ting Liao

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In a typical manufacturing environment, computer numerical control (CNC) machining is essential for automating production through precise computer-controlled tool operations, significantly enhancing efficiency and ensuring consistent product quality. However, traditional CNC production lines often rely on manual loading and unloading, limiting operational efficiency and scalability. Although automated loading systems have been developed, they frequently lack sufficient intelligence and configuration efficiency, requiring extensive setup adjustments for different products and impacting overall productivity. This research addresses the job shop scheduling problem (JSSP) in CNC machining environments, aiming to minimize total completion time (makespan) and maximize CNC machine utilization. We propose a novel approach using reinforcement learning (RL), specifically the Q-learning algorithm, to optimize scheduling decisions. The study simulates the JSSP, incorporating robotic arm operations, machine processing times, and work order demand allocation to determine optimal processing sequences. The Q-learning algorithm enhances machine utilization by dynamically balancing workloads across CNC machines, adapting to varying job demands and machine states. This approach offers robust solutions for complex manufacturing environments by automating decision-making processes for job assignments. Additionally, we evaluate various layout configurations to identify the most efficient setup. By integrating RL-based scheduling optimization with layout analysis, this research aims to provide a comprehensive solution for improving manufacturing efficiency and productivity in CNC-based job shops. The proposed method's adaptability and automation potential promise significant advancements in tackling dynamic manufacturing challenges.

Keywords: job shop scheduling problem, reinforcement learning, operations sequence, layout optimization, q-learning

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Authors: Al-Tammar M. Shahah

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Background and Objective: A novel pneumonia caused by coronavirus disease 2019 (COVID-19), which is spreading domestically and internationally, has been identified by the Chinese city of Wuhan since the end of December 2019. Limited studies examined the psychological experience such as anxiety, depression, and stress during Corona pandemic. Moreover, to the best of author's knowledge, there is no study to date has examined the psychological resilience and mental health during Corona pandemic in Kuwait. Therefore, the present research investigates the role of psychological resilience in predicting psychological distress among Kuwaiti adults during Corona pandemic. Method: Kuwaiti citizens (N = 735) completed an online survey, which includes four scales the Hospital Anxiety and Depression Scale HADS (anxiety and depression), the Connor-Davidson Resilience Scale (CD-RISC-25), and the Perceived Stress Scale (PSS). A cross-sectional correlational design was used. Results: A high level of stress was observed, with 59% reported moderate to severe stress. In contrast, low levels of anxiety and depression were observed; with 70% reporting no anxiety symptoms and 74% report no depression symptoms. Psychological resilience was negatively correlated with anxiety, depression, and stress, consistent with previous studies. As expected, resilience was found to account for significant variance in anxiety and stress after controlling for quarantine variables and demographic variables. Conclusion: The findings suggest that increasing psychological resilience might help reduce psychological distress after confronting with stressful live events in Kuwaiti citizen.

Keywords: anxiety, corona, depression, psychological resilience, stress

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Authors: Tingting Zhou

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The partial privatization of state-owned enterprises (SOEs) is a dynamic process. The main features of this process lie in not only gradual and sequential privatizations, but also privatized shares transfer. For partially privatized SOEs, the introduction of private sector ownership is not the end of the story because the previously introduced private owners may choose to leave the SOEs by transferring the privatized shares after privatization, a process that is called “privatized shares transfer”. This paper investigates the determinants of privatized shares transfer from the perspective of large shareholders’ control rights. The results captures the fact that the higher control rights of large shareholders lead to more privatized shares transfer. After exploring the impacts of excessive control rights, the results provide evidence supporting the idea that firms with excessive numbers of directors, senior managers or supervisors who also have positions in the largest controlling shareholder’s entity are more likely to transfer privatized shares owned by private owners. In addition, the largest shareholders’ ownership also plays a role in privatized shares transfer. This evidence suggests that the large shareholders’ control rights should be limited to an appropriate range during the process of privatization, thereby giving private shareholders more opportunity to participate in the operation of firms, strengthen the state and enhance the competitiveness of state capital.

Keywords: control rights of large shareholders, partial privatization, privatized shares transfer, state-owned listed companies

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Authors: K. A. Weidenmann, M. Grigo, B. Brylka, P. Elsner, T. Böhlke

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In order to reduce fuel consumption, the weight of automobiles has to be reduced. Fiber reinforced polymers offer the potential to reach this aim because of their high stiffness to weight ratio. Additionally, the use of fiber reinforced polymers in automotive applications has to allow for an economic large-scale production. In this regard, long fiber reinforced thermoplastics made by direct processing offer both mechanical performance and processability in injection moulding and compression moulding. The work presented in this contribution deals with long glass fiber reinforced polypropylene directly processed in compression moulding (D-LFT). For the use in automotive applications both the temperature and the time dependency of the materials properties have to be investigated to fulfill performance requirements during crash or the demands of service temperatures ranging from -40 °C to 80 °C. To consider both the influence of temperature and time, quasistatic tensile tests have been carried out at different temperatures. These tests have been complemented by high speed tensile tests at different strain rates. As expected, the increase in strain rate results in an increase of the elastic modulus which correlates to an increase of the stiffness with decreasing service temperature. The results are in good accordance with results determined by dynamic mechanical analysis within the range of 0.1 to 100 Hz. The experimental results from different testing methods were grouped and interpreted by using different time temperature shift approaches. In this regard, Williams-Landel-Ferry and Arrhenius approach based on kinetics have been used. As the theoretical shift factor follows an arctan function, an empirical approach was also taken into consideration. It could be shown that this approach describes best the time and temperature superposition for glass fiber reinforced polypropylene manufactured by D-LFT processing.

Keywords: composite, dynamic mechanical analysis, long fibre reinforced thermoplastics, mechanical properties, time temperature superposition

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Authors: Deboraj Muchahary, Amlan Deep Borah Abir J. Mondal, Alak Majumder

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A discrete cosine transform (DCT) is described and a technique to compute it using fast Fourier transform (FFT) is developed. In this work, DCT of a finite length sequence is obtained by incorporating CORDIC methodology in radix-2 FFT algorithm. The proposed methodology is simple to comprehend and maintains a regular structure, thereby reducing computational complexity. DCTs are used extensively in the area of digital processing for the purpose of pattern recognition. So the efficient computation of DCT maintaining a transparent design flow is highly solicited.

Keywords: DCT, DFT, CORDIC, FFT

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Authors: Xuewen Chen, Aizhen Liang, Xiaoping Zhang

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To better understand the spatial variability of soil penetration resistance (SPR) and soil water content (SWC) induced by different tillage and crop rotation in a Mollisol of Northeast China, the soil was sampled from the tillage experiment which was established in Dehui County, Jilin Province, Northeast China, in 2001. Effect of no-tillage (NT), moldboard plow (MP) and ridge tillage (RT) under corn-soybean rotation (C-S) and continuous corn (C-C) system on SPR and SWC were compared with horizontal and vertical variations. The results showed that SPR and SWC spatially varied across the ridge. SPR in the rows was higher than inter-rows, especially in topsoil (2.5-15 cm) of NT and RT plots. SPR of MP changed in the trend with the curve-shaped ridge. In contrast to MP, NT, and RT resulted in average increment of 166.3% and 152.3% at a depth of 2.5-17.5 cm in the row positions, respectively. The mean SPR in topsoil in the rows means soil compaction is not the main factor limiting plant growth and crop yield. SPR in the row of RT soil was lower than NT at a depth of 2.5-12.5 cm. The SWC in NT and RT soil was highest in the inter-rows and least in the rows or shoulders, respectively. However, the lateral variation trend of MP was opposite to NT. From the profile view of SWC, MP was greater than NT and RT in 0-20 cm of the rows. SWC in RT soil was higher than NT in the row of 0-20 cm. Crop rotation did not have a marked impact on SPR and SWC. In addition to the tillage practices, the factor which affects SPR greatly was depth but not position. These two factors have significant effects on SWC. These results indicated that the adoption of RT was a more suitable conservation tillage practices than NT in the black soil of Northeast China.

Keywords: row, soil penetration resistance, spatial variability, tillage practice

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Authors: Mirian Graciella Dalla Porta, Humberto Jorge José, Danielle de Bem Luiz, Regina de F. P. M.Moreira

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Similar to most processing industries, fish processing produces large volumes of wastewater, which contains especially organic contaminants, salts and oils dispersed therein. Different processes have been used for the treatment of fishery wastewaters, but the most commonly used are chemical coagulation and flotation. These techniques are well known but sometimes the characteristics of the treated effluent do not comply with legal standards for discharge. Electrocoagulation (EC) is an electrochemical process that can be used to treat wastewaters in terms of both organic matter and nutrient removal. The process is based on the use of sacrificial electrodes such as aluminum, iron or zinc, that are oxidized to produce metal ions that can be used to coagulate and react with organic matter and nutrients in the wastewater. While EC processes are effective to treatment of several types of wastewaters, applications have been limited due to the high energy demands and high current densities. Generally, the for EC process can be performed without additional chemicals or pre-treatment, but the costs should be reduced for EC processes to become more applicable. In this work, we studied the treatment of a real wastewater from fishmeal industry by electrocoagulation process. Removal efficiencies for chemical oxygen demand (COD), total organic carbon (TOC) turbidity, phosphorous and nitrogen concentration were determined as a function of the operating conditions, such as pH, current density and operating time. The optimum operating conditions were determined to be operating time of 10 minutes, current density 100 A.m-2, and initial pH 4.0. COD, TOC, phosphorous concentration, and turbidity removal efficiencies at the optimum operating conditions were higher than 90% for aluminum electrode. Operating costs at the optimum conditions were calculated as US$ 0.37/m3 (US$ 0.038/kg COD) for Al electrode. These results demonstrate that the EC process is a promising technology to remove nutrients from fishery wastewaters, as the process has both a high efficiency of nutrient removal, and low energy requirements.

Keywords: electrocoagulation, fish, food industry, wastewater

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Authors: J. M. Hernández-Ontiveros, E. E. García-Guerrero, E. Inzunza-González, O. R. López-Bonilla

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This paper presents the implementation of an algorithm that identifies and accounts different fish species: Catfish, Sea bream, Sawfish, Tilapia, and Totoaba. The main contribution of the method is the fusion of the characteristics of invariance to the position, rotation and scale of the Hu moments, with the proper counting of fish. The identification and counting is performed, from an image under different noise conditions. From the experimental results obtained, it is inferred the potentiality of the proposed algorithm to be applied in different scenarios of aquaculture production.

Keywords: counting fish, digital image processing, invariant moments, pattern recognition

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Authors: Jing Fang, Yao Cui, Mingming Wang, Shengli She, Jianping Yuan

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Wearable robotics is a potential solution in aiding gait rehabilitation of lower limbs dyskinesia patients, such as knee osteoarthritis or stroke afflicted patients. Many wearable robots have been developed in the form of rigid exoskeletons, but their bulk devices, high cost and control complexity hinder their popularity in the field of gait rehabilitation. Thus, the development of a portable, compliant and low-cost wearable robot for gait rehabilitation is necessary. Inspired by Chinese traditional folding fans and balloon inflators, the authors present an inflatable, foldable and variable stiffness knee exosuit (IFVSKE) in this paper. The pneumatic actuator of IFVSKE was fabricated in the shape of folding fans by using thermoplastic polyurethane (TPU) fabric materials. The geometric and mechanical properties of IFVSKE were characterized with experimental methods. To assist the knee joint smartly, an intelligent control profile for IFVSKE was proposed based on the concept of full-cycle energy management of the biomechanical energy during human movement. The biomechanical energy of knee joints in a walking gait cycle of patients could be collected and released to assist the joint motion just by adjusting the inner pressure of IFVSKE. Finally, a healthy subject was involved to walk with and without the IFVSKE to evaluate the assisting effects.

Keywords: biomechanical energy management, knee exosuit, gait rehabilitation, wearable robotics

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Authors: K. R. Roopesh Bharatwaj, Avinash Maharana, Favour Tobi Aborisade, Roger Young

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Presently, there are few cases of complete automation of drones and its allied intelligence capabilities. In essence, the potential of the drone has not yet been fully utilized. This paper presents feasible methods to build an intelligent drone with smart capabilities such as self-driving, and obstacle avoidance. It does this through advanced Reinforcement Learning Techniques and performs object detection using latest advanced algorithms, which are capable of processing light weight models with fast training in real time instances. For the scope of this paper, after researching on the various algorithms and comparing them, we finally implemented the Deep-Q-Networks (DQN) algorithm in the AirSim Simulator. In future works, we plan to implement further advanced self-driving and object detection algorithms, we also plan to implement voice-based speech recognition for the entire drone operation which would provide an option of speech communication between users (People) and the drone in the time of unavoidable circumstances. Thus, making drones an interactive intelligent Robotic Voice Enabled Service Assistant. This proposed drone has a wide scope of usability and is applicable in scenarios such as Disaster management, Air Transport of essentials, Agriculture, Manufacturing, Monitoring people movements in public area, and Defense. Also discussed, is the entire drone communication based on the satellite broadband Internet technology for faster computation and seamless communication service for uninterrupted network during disasters and remote location operations. This paper will explain the feasible algorithms required to go about achieving this goal and is more of a reference paper for future researchers going down this path.

Keywords: convolution neural network, natural language processing, obstacle avoidance, satellite broadband technology, self-driving

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Authors: Cheng-Ying Li, Sheng-Yuan Chu

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This article deposited LZO-doped Mg piezoelectric thin films via RF sputtering and observed microstructure and electrical characteristics by varying the deposition temperature. The XRD analysis results indicate that LZO-doped Mg exhibits excellent (002) orientation, and there is no presence of ZnO(100), Influenced by the temperature's effect on the lattice constant, the (002) peak intensity increases with rising temperature. Finally, we conducted deformation intensity analysis on the films, revealing an over fourfold increase in deformation at a processing temperature of 500°C.

Keywords: RF sputtering, piezoelectricity, ZnO, Mg

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Authors: Ebipatei Victoria Tunyan, T. A. Cao, Cheol Young Ock

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Detecting subjectively biased statements is a vital task. This is because this kind of bias, when present in the text or other forms of information dissemination media such as news, social media, scientific texts, and encyclopedias, can weaken trust in the information and stir conflicts amongst consumers. Subjective bias detection is also critical for many Natural Language Processing (NLP) tasks like sentiment analysis, opinion identification, and bias neutralization. Having a system that can adequately detect subjectivity in text will boost research in the above-mentioned areas significantly. It can also come in handy for platforms like Wikipedia, where the use of neutral language is of importance. The goal of this work is to identify the subjectively biased language in text on a sentence level. With machine learning, we can solve complex AI problems, making it a good fit for the problem of subjective bias detection. A key step in this approach is to train a classifier based on BERT (Bidirectional Encoder Representations from Transformers) as upstream model. BERT by itself can be used as a classifier; however, in this study, we use BERT as data preprocessor as well as an embedding generator for a Bi-LSTM (Bidirectional Long Short-Term Memory) network incorporated with attention mechanism. This approach produces a deeper and better classifier. We evaluate the effectiveness of our model using the Wiki Neutrality Corpus (WNC), which was compiled from Wikipedia edits that removed various biased instances from sentences as a benchmark dataset, with which we also compare our model to existing approaches. Experimental analysis indicates an improved performance, as our model achieved state-of-the-art accuracy in detecting subjective bias. This study focuses on the English language, but the model can be fine-tuned to accommodate other languages.

Keywords: subjective bias detection, machine learning, BERT–BiLSTM–Attention, text classification, natural language processing

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Authors: Chularat Leewuttanakul, Khanitta Ruttarattanamongkol, Sasivimon Chittrakorn

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Rice during grain development stage is a rich source of many bioactive compounds. Dough stage rice contains high amounts of photochemical and can be used for rice milling industries. However, rice grain at dough stage had low milling quality due to high moisture content. Thermal processing can be applied to rice grain for improving milled rice yield. This experiment was conducted to study the chemical and physic properties of dough stage rice grain after roasting treatment. Rice were roasted with two different methods including traditional pan roasting at 140 °C for 60 minutes and using the electrical roasting machine at 140 °C for 30, 40, and 50 minutes. The chemical, physical properties, and bioactive compounds of brown rice and milled rice were evaluated. The result of this experiment showed that moisture content of brown and milled rice was less than 10 % and amylose contents were in the range of 26-28 %. Rice grains roasting for 30 min using electrical roasting machine had high head rice yield and length and breadth of grain after milling were close to traditional pan roasting (p > 0.05). The lightness (L*) of rice did not affect by roasting treatment (p > 0.05) and the a* indicated the yellowness of milled rice was lower than brown rice. The bioactive compounds of brown and milled rice significantly decreased with increasing of drying time. Brown rice roasted for 30 minutes had the highest of total phenolic content, antioxidant activity, α-tocopherol, and ɤ-oryzanol content. Volume expansion and elongation of cooked rice decreased as roasting time increased and quality of cooked rice roasted for 30 min was comparable to traditional pan roasting. Hardness of cooked rice as measured by texture analyzer increased with increasing roasting time. The results indicated that rice grains at dough stage, containing a high amount of bioactive compounds, have a great potential for rice milling industries and the electrical roasting machine can be used as an alternative to pan roasting which decreases processing time and labor costs.

Keywords: bioactive compounds, cooked rice, dough stage rice grain, grain development, roasting

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Authors: Ruanzhenghao Shi

Abstract:

Student movements have persisted in mainland China, especially in elite universities since the Tiananmen Prodemocracy Movement, contrary to the lack of studies on them. However, the participants' repertoire, mobilization and mode of interaction with authorities are vastly different from their predecessors in the 1980s as well as their western counterparts. In most of the cases, agents, cognizant of the high cost of action and their vulnerability to the authorities, consciously curtailed certain repertoire and themes of resistance. Thus these movements, without appreciable organized force, were self-interested, fragmentally mobilized, lowly integrated and limited within the campus. This study documents the 2014 protest against Yanching Academy program at Peking University, a top-tier Chinese university that played the leading role in the 1989 protest. The 2014 case is different from abovementioned trend of submissive resistance in the last twenty years, insofar as it is a value-oriented and emotion-driven collective action with the resurgence of some repertoire. The participants perceived the university's contemporary ineffectiveness and clumsiness in control and administration, higher Party authorities' indifference to less-political themes, and an increasing number of potential advocates, including students, intellectuals and social media. It shows that resisters' perception of their relative strength to their opponents - in this case, the university and its system for controlling students - under specific circumstances, not merely political opportunities or institutional changes, stimulates the participants and thus contributes to the mobilization and organization of a collective action, even under severe social control.

Keywords: collective action, China, university students, resistance

Procedia PDF Downloads 153