Search results for: virtual machine migration

Authors: Meiyun Meng

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Urban China is now undergoing social transformation based on its rapid economic growth, developing its individualism and feminism. This paper approaches emergent relationships between female individuals’ everyday lives and urban China through internal migration, home-making practices and life-course perspectives. Focusing on Shenzhen, it explores how ten highly educated female migrants pursue aspirations of accommodating ‘non-majority’ identities, such as lesbians, divorced, or childless women, in urban China. Based on life stories and home video tours, this paper finds how these women develop non-majority lifestyles to negotiate their aspirations. On the one hand, they ‘sail’ away from past/present situations where collectivist and hetero-patriarchal norms marginalised their non-majority identities. On the other hand, they ‘anchor’ in places where ‘new’ socio-cultural contexts allow female individuals to pursue alternative opportunities and preferential lifestyles. This paper provides fresh insights to interpret the social transformation in urban China, under the collectivist culture and hetero-patriarchal norms, through the lens of individual everyday home-making practices.

Keywords: home-making practices, internal migration, highly educated women, shenzhen, transforming urban China

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Authors: Sang-Wook Han, In-Hui Hwang, Zhenlan Jin, Chang-In Park

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We used temperature-dependent X-Ray absorption fine structure (XAFS) measurements to examine the local structural properties around vanadium atoms at the V K edge from VO₂ films. A direct comparison of simultaneously-measured resistance and XAFS from the VO₂ films showed that the thermally-driven structural phase transition (SPT) occurred prior to the metal-insulator transition (MIT) during heating, whereas these changed simultaneously during cooling. XAFS revealed a significant increase in the Debye-Waller factors of the V-O and V-V pairs in the {111} direction of the R-phase VO₂ due to the phonons of the V-V arrays along the direction in a metallic phase. A substantial amount of structural disorder existing on the V-V pairs along the c-axis in both M₁ and R phases indicates the structural instability of V-V arrays in the axis. The anomalous structural disorder observed on all atomic sites at the SPT prevents the migration of the V 3d¹ electrons, resulting in a Mott insulator in the M₂-phase VO₂. The anomalous structural disorder, particularly, at vanadium sites, effectively affects the migration of metallic electrons, resulting in the Mott insulating properties in M₂ phase and a non-congruence of the SPT, MIT, and local density of state. The thermally-induced phonons in the {111} direction assist the delocalization of the V 3d¹ electrons in the R phase VO₂ and the electrons likely migrate via the V-V array in the {111} direction as well as the V-V dimerization along the c-axis. This study clarifies that the tetragonal symmetry is essentially important for the metallic phase in VO₂.

Keywords: metal-insulator transition, XAFS, VO₂, structural-phase transition

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Authors: Claudia Donoso

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This study compares the restriction of mobility of migrants and asylum seekers during the COVID-19 pandemic in the United States and Ecuador. Based on the discourse analysis of anti-migrant rhetoric in press articles, migrant stories in the press, reports, and border control practices, the study examines the Ecuadorian government’s response to the migration flow of Venezuelans and the United States enforcement practices against Latin American asylum seekers. By exploring Giorgio Agamben’s concept of bare life, the article argues that this failure to protect mobility rights is due to the United States and Ecuador’s views of forced migrants as bare life and carriers of the virus, justifying xenophobia, resistance to humanitarian international law, and exceptionalism. By drawing on a feminist intersectional approach, the study adds to recent research on the securitization of forced migration and challenge the race/ethnicity, immigration status, class, and nationality-based discrimination of the measures undertaken during the pandemic. The article illustrates how the treatment of forced migrants as bare life was aggravated by their intersectional inequalities. It concludes by providing recommendations that could be enforced by the US and Ecuadorian governments to protect the right to freedom of mobility.

Keywords: bare life, intersectionality, mobility rights, COVID-19, Ecuador, United States

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Authors: Celeste Lawson

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Learning to embrace and value teamwork assessment at a university level is critical for students, as graduates enter a real-world working environment where teamwork is likely to occur virtually. Student disdain for teamwork exercises is an area often overlooked or disregarded by academics. This research explored the implementation of an online teamwork assessment approach at a regional Australian university with a significant cohort of Distance Education students. Students had disliked teamwork for three reasons: it was not relevant to their study, the grading was unfair amongst team members, and managing the task was challenging in a virtual environment. Teamwork assessment was modified so that the task was an authentic task that could occur in real-world practice; team selection was based on the task topic rather than randomly; grading was based on the individual’s contribution to the task, and students were provided virtual team management skills as part of a the assessment. In this way, management of the team became an output of the task itself. Data was gathered over three years from student satisfaction surveys, failure rates, attrition figures, and unsolicited student comments. In one unit where this approach was adopted (Advanced Public Relations), student satisfaction increased from 3.6 (out of 5) in 2012 to 4.6 in 2016, with positive comments made about the teamwork approach. The attrition rate for another unit (Public Relations and the Media) reduced from 20.7% in 2012 to 2.2% in 2015. In 2012, criticism of teamwork assessment made up 50% of negative student feedback in Public Relations and the Media. By 2015, following the successful implementation of the teamwork assessment approach, only 12.5% of negative comments on the student satisfaction survey were critical of teamwork, while 33% of positive comments related to a positive teamwork experience. In 2016, students explicitly nominated teamwork as the best part of this unit. The approach is transferable to other disciplines and was adopted by other academics within the institution with similar results.

Keywords: assessment, distance education, teamwork, virtual

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Authors: Najoua El Hajjaji El Idrissi, Necip Gokhan Kasapoglu

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Polarimetric Synthetic Aperture Radar-based imaging is a powerful technique used for earth observation and classification of surfaces. Forest evolution has been one of the vital areas of attention for the remote sensing experts. The information about forest areas can be achieved by remote sensing, whether by using active radars or optical instruments. However, due to several weather constraints, such as cloud cover, limited information can be recovered using optical data and for that reason, Polarimetric Synthetic Aperture Radar (PolSAR) is used as a powerful tool for forestry inventory. In this [14paper, we applied support vector machine (SVM) and Mahalanobis distance to the fully polarimetric AIRSAR P, L, C-bands data from the Nezer forest areas, the classification is based in the separation of different tree ages. The classification results were evaluated and the results show that the SVM performs better than the Mahalanobis distance and SVM achieves approximately 75% accuracy. This result proves that SVM classification can be used as a useful method to evaluate fully polarimetric SAR data with sufficient value of accuracy.

Keywords: classification, synthetic aperture radar, SAR polarimetry, support vector machine, mahalanobis distance

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Authors: Manal Suleiman, George Abu-Aqil, Uraib Sharaha, Klaris Riesenberg, Itshak Lapidot, Ahmad Salman, Mahmoud Huleihel

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Klebsiella pneumoniae is one of the most aggressive multidrug-resistant bacteria associated with human infections resulting in high mortality and morbidity. Thus, for an effective treatment, it is important to diagnose both the species of infecting bacteria and their susceptibility to antibiotics. Current used methods for diagnosing the bacterial susceptibility to antibiotics are time-consuming (about 24h following the first culture). Thus, there is a clear need for rapid methods to determine the bacterial susceptibility to antibiotics. Infrared spectroscopy is a well-known method that is known as sensitive and simple which is able to detect minor biomolecular changes in biological samples associated with developing abnormalities. The main goal of this study is to evaluate the potential of infrared spectroscopy in tandem with Random Forest and XGBoost machine learning algorithms to diagnose the susceptibility of Klebsiella pneumoniae to antibiotics within approximately 20 minutes following the first culture. In this study, 1190 Klebsiella pneumoniae isolates were obtained from different patients with urinary tract infections. The isolates were measured by the infrared spectrometer, and the spectra were analyzed by machine learning algorithms Random Forest and XGBoost to determine their susceptibility regarding nine specific antibiotics. Our results confirm that it was possible to classify the isolates into sensitive and resistant to specific antibiotics with a success rate range of 80%-85% for the different tested antibiotics. These results prove the promising potential of infrared spectroscopy as a powerful diagnostic method for determining the Klebsiella pneumoniae susceptibility to antibiotics.

Keywords: urinary tract infection (UTI), Klebsiella pneumoniae, bacterial susceptibility, infrared spectroscopy, machine learning

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Authors: Shreya Goyal

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The study aims to build user-friendly software to understand patient arrival patterns and compute the number of potential patients who will visit a particular health facility for a given period by using a machine learning algorithm. The underlying machine learning algorithm used in this study is the Support Vector Machine (SVM). Accurate prediction of patient arrival allows hospitals to operate more effectively, providing timely and efficient care while optimizing resources and improving patient experience. It allows for better allocation of staff, equipment, and other resources. If there's a projected surge in patients, additional staff or resources can be allocated to handle the influx, preventing bottlenecks or delays in care. Understanding patient arrival patterns can also help streamline processes to minimize waiting times for patients and ensure timely access to care for patients in need. Another big advantage of using this software is adhering to strict data protection regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States as the hospital will not have to share the data with any third party or upload it to the cloud because the software can read data locally from the machine. The data needs to be arranged in. a particular format and the software will be able to read the data and provide meaningful output. Using software that operates locally can facilitate compliance with these regulations by minimizing data exposure. Keeping patient data within the hospital's local systems reduces the risk of unauthorized access or breaches associated with transmitting data over networks or storing it in external servers. This can help maintain the confidentiality and integrity of sensitive patient information. Historical patient data is used in this study. The input variables used to train the model include patient age, time of day, day of the week, seasonal variations, and local events. The algorithm uses a Supervised learning method to optimize the objective function and find the global minima. The algorithm stores the values of the local minima after each iteration and at the end compares all the local minima to find the global minima. The strength of this study is the transfer function used to calculate the number of patients. The model has an output accuracy of >95%. The method proposed in this study could be used for better management planning of personnel and medical resources.

Keywords: machine learning, SVM, HIPAA, data

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Authors: Cheng-Cao Sun, Shu-Jun Li, Cuili Yang, Yongyong Xi, Liang Wang, Feng Zhang, De-Jia Li

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MicroRNAs (miRNAs) act as key regulators of multiple cancers. Hsa-miR-329 (miR-329) functions as a tumor suppressor in some malignancies. However, its role on lung cancer remains poorly understood. In this study, we investigated the role of miR-329 on the development of lung cancer. The results indicated that miR-329 was decreased in primary lung cancer tissues compared with matched adjacent normal lung tissues and very low levels were found in a non-small cell lung cancer (NSCLC) cell lines. Ectopic expression of miR-329 in lung cancer cell lines substantially repressed cell growth as evidenced by cell viability assay, colony formation assay and BrdU staining, through inhibiting cyclin D1, cyclin D2, and up-regulatiing p57(Kip2) and p21(WAF1/CIP1). In addition, miR-329 promoted NSCLC cell apoptosis, as indicated by up-regulation of key apoptosis gene cleaved caspase-3, and down-regulation of anti-apoptosis gene Bcl2. Moreover, miR-329 inhibited cellular migration and invasiveness through inhibiting matrix metalloproteinases (MMP)-7 and MMP-9. Further, oncogene MET was revealed to be a putative target of miR-329, which was inversely correlated with miR-329 expression. Furthermore, down-regulation of MET by siRNA performed similar effects to over-expression of miR-329. Collectively, our results demonstrated that miR-329 played a pivotal role in lung cancer through inhibiting cell proliferation, migration, invasion, and promoting apoptosis by targeting oncogenic MET.

Keywords: hsa-miRNA-329(miR-329), MET, non-small cell lung cancer (NSCLC), proliferation, apoptosis

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Authors: Aysegul Sarac

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Washer-dryers are the machines which can either wash the laundries or can dry them. In other words, we can define a washer-dryer as a washing machine and a dryer in one machine. Washing machines are characterized by the loading capacity, cabinet depth and spin speed. Dryers are characterized by the drying technology. On the other hand, energy efficiency, water consumption, and noise levels are main characteristics that influence customer decisions to buy washers. Water condensation technology is the most common drying technology existing in the washer-dryer market. Water condensation technology uses water to dry the laundry inside the machine. Thus, in this type of the drying technology water consumption is at high levels comparing other technologies. Water condensation technology sprays cold water in the drum to condense the humidity of hot weather in order to dry the laundry inside. Thus, water consumption influences the drying performance. The scope of this study is to optimize water consumption during drying process under a constraint of energy consumption and drying performance. We are using 6-Sigma methodology to find the optimum water consumption by comparing drying performances of different drying algorithms.

Keywords: optimization, 6-Sigma methodology, washer-dryers, water condensation technology

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Authors: Harun Aydilek, Asiye Aydilek, Ali Allahverdi

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Our objective is to minimize the total tardiness in an m-machine two-stage assembly flowshop scheduling problem. The objective is an important performance measure because of the fact that the fulfillment of due dates of customers has to be taken into account while making scheduling decisions. In the literature, the problem is considered with zero setup times which may not be realistic and appropriate for some scheduling environments. Considering separate setup times from processing times increases machine utilization by decreasing the idle time and reduces total tardiness. We propose two new algorithms and adapt four existing algorithms in the literature which are different versions of simulated annealing and genetic algorithms. Moreover, a dominance relation is developed based on the mathematical formulation of the problem. The developed dominance relation is incorporated in our proposed algorithms. Computational experiments are conducted to investigate the performance of the newly proposed algorithms. We find that one of the proposed algorithms performs significantly better than the others, i.e., the error of the best algorithm is less than those of the other algorithms by minimum 50%. The newly proposed algorithm is also efficient for the case of zero setup times and performs better than the best existing algorithm in the literature.

Keywords: algorithm, assembly flowshop, scheduling, simulation, total tardiness

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Authors: Jipeng Yan, Xingchen Yang, Xiaowei Zhou, Mengxing Tang, Honghai Liu

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Shear elastic modulus of skeletal muscles can be obtained by shear wave elastography (SWE) and has been linearly related to muscle force. However, SWE is currently implemented using array probes. Price and volumes of these probes and their driving equipment prevent SWE from being used in wearable human-machine interfaces (HMI). Moreover, beamforming processing for array probes reduces the real-time performance. To achieve SWE by wearable HMIs, a customized three-element probe is adopted in this work, with one element for acoustic radiation force generation and the others for shear wave tracking. In-phase quadrature demodulation and 2D autocorrelation are adopted to estimate velocities of tissues on the sound beams of the latter two elements. Shear wave speeds are calculated by phase shift between the tissue velocities. Three agar phantoms with different elasticities were made by changing the weights of agar. Values of the shear elastic modulus of the phantoms were measured as 8.98, 23.06 and 36.74 kPa at a depth of 7.5 mm respectively. This work verifies the feasibility of measuring shear elastic modulus by wearable devices.

Keywords: shear elastic modulus, skeletal muscle, ultrasound, wearable human-machine interface

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Authors: Ransford Kwaku Afeadie

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Purpose – The health challenges that characterises most of the migrants’ urban slums raise a lot of concern for their well-being. Health-seeking behaviour becomes an important step towards maintaining a healthy life. The importance of contextual issues is necessary to help meet specific community health needs and programmes. Therefore, this study aims to bridge the knowledge gap by investigating health-seeking behaviour disparity among rural-urban labour migrant slum dwellers before and after migration to the urban slums of Madina in the Greater Accra Region, Ghana. Design/methodology/approach – The author used explanatory sequential approach of research investigation. Questionnaire and interview guides were used to collect data from the respondents; however, in the absence of an existing reliable sampling frame, the various communities were selected by the use of cluster sampling proportional to size. At the second stage, a simple random sampling was used to select the various household heads. A total of 241 questionnaires were retrieved from the respondents representing a response rate of 100%. The author used the purposive sampling technique to conduct eight in-depth interviews and six key informants’ interviews. Findings – The author found various discrepancies in many of the activities that could fulfill substantial health-seeking behaviour in the slum as compared to migrant’s places of origin. The reason for coming to the slum amidst many settlements needs and low education background are the factors that accounted for this. This study, therefore, contradicts the proposition held by the health belief model. It is, therefore, important to note that contextual issues are key, in this case, rural-urban migrant slums present a different dynamic that must be taken into account when designing health programmes for such settings. Originality/value – Many, if not all the, studies on health-seeking behaviour have focused on urban slums without taking into account urban migrants’ slums. Such a failure to take into account the variations of the health needs of migrants’ urban slum settings can eventually lead to a mismatch of health programmes meant to address their challenges. Therefore, this study brings to the fore such variations that must be taken into account when designing health programmes. The study also indicates that even with the same people, there were disparities in terms of health-seeking behaviour in the slum and at places of origin.

Keywords: health-seeking behaviour, rural–urban migration, urban slums, health belief model

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Authors: Amira Ben Rabeh, Faouzi Benzarti, Hamid Amiri, Mouna Bouaziz

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Alzheimer is a disease that affects the brain. It causes degeneration of nerve cells (neurons) and in particular cells involved in memory and intellectual functions. Early diagnosis of Alzheimer Diseases (AD) raises ethical questions, since there is, at present, no cure to offer to patients and medicines from therapeutic trials appear to slow the progression of the disease as moderate, accompanying side effects sometimes severe. In this context, analysis of medical images became, for clinical applications, an essential tool because it provides effective assistance both at diagnosis therapeutic follow-up. Computer Assisted Diagnostic systems (CAD) is one of the possible solutions to efficiently manage these images. In our work; we proposed an application to detect Alzheimer’s diseases. For detecting the disease in early stage we used the three sections: frontal to extract the Hippocampus (H), Sagittal to analysis the Corpus Callosum (CC) and axial to work with the variation features of the Cortex(C). Our method of classification is based on Support Vector Machine (SVM). The proposed system yields a 90.66% accuracy in the early diagnosis of the AD.

Keywords: Alzheimer Diseases (AD), Computer Assisted Diagnostic(CAD), hippocampus, Corpus Callosum (CC), cortex, Support Vector Machine (SVM)

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Authors: Mamadou Dione, Eric Matzner-lober, Philippe Alexandre

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The Energy Transition Act defined by the French State has precise implications on Renewable Energies, in particular on its remuneration mechanism. Until then, a purchase obligation contract permitted the sale of wind-generated electricity at a fixed rate. Tomorrow, it will be necessary to sell this electricity on the Market (at variable rates) before obtaining additional compensation intended to reduce the risk. This sale on the market requires to announce in advance (about 48 hours before) the production that will be delivered on the network, so to be able to predict (in the short term) this production. The fundamental problem remains the variability of the Wind accentuated by the geographical situation. The objective of the project is to provide, every day, short-term forecasts (48-hour horizon) of wind production using weather data. The predictions of the GFS model and those of the ECMWF model are used as explanatory variables. The variable to be predicted is the production of a wind farm. We do two approaches: a direct approach that predicts wind generation directly from weather data, and an integrated approach that estimâtes wind from weather data and converts it into wind power by power curves. We used machine learning techniques to predict this production. The models tested are random forests, CART + Bagging, CART + Boosting, SVM (Support Vector Machine). The application is made on a wind farm of 22MW (11 wind turbines) of the Compagnie du Vent (that became Engie Green France). Our results are very conclusive compared to the literature.

Keywords: forecast aggregation, machine learning, spatio-temporal dynamics modeling, wind power forcast

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Authors: Dhanpat Singh Dhania

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Kurma-avatara and the Kachchhapraj is the name of the same person. Tortoise is called Kurma in Kerma valley (Nubia) and also called Kachchhap in India. Wherever a culture migrates, its faiths and beliefs remain intact. The tortoise culture of Kurma valley migrated to Dholavira, and its cultural symbolism remained the same as Kurma, the tortoise. Culture is known by burial traditions, pottery formations, language use, faiths, and beliefs. Following the cultural identification methodology, the Kurma culture buried their dead in circular burials found during excavation at Toshka, Nubia, and built their houses the type of tortoise shell. The Nubian tortoise of a specific species had a triangular on the shell found to be extinct was the cultural symbolism of the culture found on the excavated pottery. Kurma cultural head known as the Seth was known as Kurma-avatara. The Seth of Egypt came to know when the combined efforts of the Seth and the Osiris defeated the Egyptian 1st dynastic rule in about 2775 BCE. Osiris became the king of the 2nd dynastic Egypt. It annoyed Seth. He killed the Osiris and went to Rann of Kachchh and declared him as the Chachchhapraj, the king of Kachchh (now Gujarat, India). The Kurma (Kachchhap) culture migration at Dholavira (Gujarat) attested by the Dholavira signboard found during excavation and deciphered as the ‘Chakradhar’, the eighth incarnation of Kurma-avatara.

Keywords: Kurma, Egyptian, Kachchhap, Dholavira, Harappan

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Authors: Baljit Singh Khehra, Amar Partap Singh Pharwaha

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Clusters of Microcalcifications (MCCs) are most frequent symptoms of Ductal Carcinoma in Situ (DCIS) recognized by mammography. Least-Square Support Vector Machine (LS-SVM) is a variant of the standard SVM. In the paper, LS-SVM is proposed as a classifier for classifying MCCs as benign or malignant based on relevant extracted features from enhanced mammogram. To establish the credibility of LS-SVM classifier for classifying MCCs, a comparative evaluation of the relative performance of LS-SVM classifier for different kernel functions is made. For comparative evaluation, confusion matrix and ROC analysis are used. Experiments are performed on data extracted from mammogram images of DDSM database. A total of 380 suspicious areas are collected, which contain 235 malignant and 145 benign samples, from mammogram images of DDSM database. A set of 50 features is calculated for each suspicious area. After this, an optimal subset of 23 most suitable features is selected from 50 features by Particle Swarm Optimization (PSO). The results of proposed study are quite promising.

Keywords: clusters of microcalcifications, ductal carcinoma in situ, least-square support vector machine, particle swarm optimization

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Authors: Milad Abbasi

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Carbon fiber-reinforced polymer (CFRP) composite structures are increasingly being utilized in the automotive industry due to their lightweight and specific energy absorption capabilities. Although it is impossible to predict composite mechanical properties directly using theoretical methods, various research has been conducted so far in the literature for accurate simulation of CFRP structures' energy-absorbing behavior. In this research, axial compression experiments were carried out on hand lay-up unidirectional CFRP composite tubes. The fabrication method allowed the authors to extract the material properties of the CFRPs using ASTM D3039, D3410, and D3518 standards. A neural network machine learning algorithm was then utilized to build a robust prediction model to forecast the axial compressive properties of CFRP tubes while reducing high-cost experimental efforts. The predicted results have been compared with the experimental outcomes in terms of load-carrying capacity and energy absorption capability. The results showed high accuracy and precision in the prediction of the energy-absorption capacity of the CFRP tubes. This research also demonstrates the effectiveness and challenges of machine learning techniques in the robust simulation of composites' energy-absorption behavior. Interestingly, the proposed method considerably condensed numerical and experimental efforts in the simulation and calibration of CFRP composite tubes subjected to compressive loading.

Keywords: CFRP composite tubes, energy absorption, crushing behavior, machine learning, neural network

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Authors: B. Ladghem Chikouche

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The influence of slot wedges permeability on the electromagnetic performance of three-phase permanent magnet synchronous machine is investigated in this paper. It is shown that the back-EMF waveform, electromagnetic torque and electromagnetic torque ripple are all significantly affected by slot wedges permeability. The paper presents an accurate analytical subdomain model and confirmed by finite-element analyses.

Keywords: exact analytical calculation, finite-element method, magnetic field distribution, permanent magnet machines performance, stator slot wedges permeability

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Authors: Liang Wang, Beste F. Yuksel, David Guy Brizan

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A computational model of affect that can distinguish between spontaneous and posed smiles with no errors on a large, popular data set using deep learning techniques is presented in this paper. A Long Short-Term Memory (LSTM) classifier, a type of Recurrent Neural Network, is utilized and compared to human classification. Results showed that while human classification (mean of 0.7133) was above chance, the LSTM model was more accurate than human classification and other comparable state-of-the-art systems. Additionally, a high accuracy rate was maintained with small amounts of training videos (70 instances). The derivation of important features to further understand the success of our computational model were analyzed, and it was inferred that thousands of pairs of points within the eyes and mouth are important throughout all time segments in a smile. This suggests that distinguishing between a posed and spontaneous smile is a complex task, one which may account for the difficulty and lower accuracy of human classification compared to machine learning models.

Keywords: affective computing, affect detection, computer vision, deep learning, human-computer interaction, machine learning, posed smile detection, spontaneous smile detection

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Authors: Supria Sarkar, Vasit Sagan, Sourav Bhadra, Meghnath Pokharel, Felix B.Fritschi

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Soybean and their derivatives are very important agricultural commodities around the world because of their wide applicability in human food, animal feed, biofuel, and industries. However, the significance of soybean production depends on the quality of the soybean seeds rather than the yield alone. Seed composition is widely dependent on plant physiological properties, aerobic and anaerobic environmental conditions, nutrient content, and plant phenological characteristics, which can be captured by high temporal resolution remote sensing datasets. Planet scope (PS) satellite images have high potential in sequential information of crop growth due to their frequent revisit throughout the world. In this study, we estimate soybean seed composition while the plants are in the field by utilizing PlanetScope (PS) satellite images and different machine learning algorithms. Several experimental fields were established with varying genotypes and different seed compositions were measured from the samples as ground truth data. The PS images were processed to extract 462 hand-crafted vegetative and textural features. Four machine learning algorithms, i.e., partial least squares (PLSR), random forest (RFR), gradient boosting machine (GBM), support vector machine (SVM), and two recurrent neural network architectures, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) were used in this study to predict oil, protein, sucrose, ash, starch, and fiber of soybean seed samples. The GRU and LSTM architectures had two separate branches, one for vegetative features and the other for textures features, which were later concatenated together to predict seed composition. The results show that sucrose, ash, protein, and oil yielded comparable prediction results. Machine learning algorithms that best predicted the six seed composition traits differed. GRU worked well for oil (R-Squared: of 0.53) and protein (R-Squared: 0.36), whereas SVR and PLSR showed the best result for sucrose (R-Squared: 0.74) and ash (R-Squared: 0.60), respectively. Although, the RFR and GBM provided comparable performance, the models tended to extremely overfit. Among the features, vegetative features were found as the most important variables compared to texture features. It is suggested to utilize many vegetation indices for machine learning training and select the best ones by using feature selection methods. Overall, the study reveals the feasibility and efficiency of PS images and machine learning for plot-level seed composition estimation. However, special care should be given while designing the plot size in the experiments to avoid mixed pixel issues.

Keywords: agriculture, computer vision, data science, geospatial technology

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Authors: V. B. Bastos, M. P. Lima, P. R. G. Kurka

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This paper applies to the creation of virtual 3D environments for the study and development of mobile robot image based navigation algorithms and techniques, which need to operate robustly and efficiently. The test of these algorithms can be performed in a physical way, from conducting experiments on a prototype, or by numerical simulations. Current simulation platforms for robotic applications do not have flexible and updated models for image rendering, being unable to reproduce complex light effects and materials. Thus, it is necessary to create a test platform that integrates sophisticated simulated applications of real environments for navigation, with data and image processing. This work proposes the development of a high-level platform for building 3D model’s environments and the test of image-based navigation algorithms for mobile robots. Techniques were used for applying texture and lighting effects in order to accurately represent the generation of rendered images regarding the real world version. The application will integrate image processing scripts, trajectory control, dynamic modeling and simulation techniques for physics representation and picture rendering with the open source 3D creation suite - Blender.

Keywords: simulation, visual navigation, mobile robot, data visualization

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Authors: Joseph Wolff, Jeffrey Eilbott

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Autism Spectrum Disorders (ASD) are a spectrum of social disorders characterized by deficits in social communication, verbal ability, and interaction that can vary in severity. In recent years, researchers have used magnetic resonance imaging (MRI) to help detect how neural patterns in individuals with ASD differ from those of neurotypical (NT) controls for classification purposes. This study analyzed the classification of ASD within males and females using functional MRI data. Functional connectivity (FC) correlations among brain regions were used as feature inputs for machine learning algorithms. Analysis was performed on 558 cases from the Autism Brain Imaging Data Exchange (ABIDE) I dataset. When trained specifically on females, the algorithm underperformed in classifying the ASD subset of our testing population. Although the subject size was relatively smaller in the female group, the manual matching of both male and female training groups helps explain the algorithm’s bias, indicating the altered sex abnormalities in functional brain networks compared to typically developing peers. These results highlight the importance of taking sex into account when considering how generalizations of findings on males with ASD apply to females.

Keywords: autism spectrum disorder, machine learning, neuroimaging, sex differences

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Authors: Ashwag O. Maghraby, Nida N. Khan, Hosnia A. Ahmed, Ghufran N. Brohi, Hind F. Assouli, Jawaher S. Melibari

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The Arabic language is one of the most important languages. Learning it is so important for many people around the world because of its religious and economic importance and the real challenge lies in practicing it without grammatical or syntactical mistakes. This research focused on detecting and correcting the syntactic mistakes of Arabic syntax according to their position in the sentence and focused on two of the main syntactical rules in Arabic: Dual and Plural. It analyzes each sentence in the text, using Stanford CoreNLP morphological analyzer and machine-learning approach in order to detect the syntactical mistakes and then correct it. A prototype of the proposed system was implemented and evaluated. It uses support vector machine (SVM) algorithm to detect Arabic grammatical errors and correct them using the rule-based approach. The prototype system has a far accuracy 81%. In general, it shows a set of useful grammatical suggestions that the user may forget about while writing due to lack of familiarity with grammar or as a result of the speed of writing such as alerting the user when using a plural term to indicate one person.

Keywords: Arabic language acquisition and learning, natural language processing, morphological analyzer, part-of-speech

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Authors: Deike Janssen

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In the scenery of the Tyrolean mountains, at an altitude of 1300 meters, stands a building. Residents and activists call it a prison. However, it is not a prison -according to authorities, it is a 'Return Counseling Facility' where migrants and refugees should be "motivated" to return "voluntary" to their countries of origin. This paper argues that the geographical location of the camp functions as a site of exclusion, isolation, and coercion where no one can decide “voluntary” to return, but where people are brought to despair to leave Austria. Through a qualitative case study, this paper documents the heavy impact of offshore detention on the mental, physical and social state of the residents and a variety of human rights problems in the centre. Different developments at the Return Counselling Facility and the law that back up the centre uncover a worrying dynamic that deliberately accepts human rights problems in order to enforce borders, a policy that disregards humanitarian, legal, and ethical stands in order to deport people at all hazards. It, therefore, can be seen as a creative and ultimate exercise of state power, which uses isolated locations to control migration. While the analysis revises the micro and macro implications of the facility and, therefore, the legal and political facets, it also sheds light on the role of the civil society, which tries to increase through constant and collective efforts the human rights efforts of the government.

Keywords: deportation, human rights, migration, refugee detention, voluntary return

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Authors: Robert L. Williamson

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A 'token economy' refers to a method of positive behaviour support whereby ‘tokens’ are delivered to students as a reward for exhibiting specific behaviours. Students later exchange tokens to ‘purchase’ items of interest. Unfortunately, implementation fidelity can be problematic as some find physical delivery of tokens while teaching difficult. This project developed and tested a prototype, iPad-based tool that enabled teachers to deliver and track tokens electronically. Using an alternating treatment design, any differences in on-task individual and/or group behaviours between the virtual versus physical token delivery systems were examined. Results indicated that while students and teachers preferred iPad-based implementation, no significant difference was found concerning on-task behaviours of students between the two methodologies. Perhaps more interesting was that the teacher found implementation of both methods problematic and suggested a second person was most effective in implementing a token economy method. This would represent a significant cost to the effective use of such a method. Further research should focus on the use of a lay volunteer regarding method implementation fidelity and associated outcomes of the method.

Keywords: positive behaviour support, inclusion, token economy, applied behaviour analysis

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Authors: Sepideh Hassanpour Khodaei

Abstract:

Background/Objectives: The purpose of this study is to evaluate the effect of mummy substances on two issues of proliferation and production of matrix protein synthesis in wound healing. Methods: The methodology used for this aim involves isolating mesenchymal stem cells and human fibroblasts procured at Pastor Institute, Iran. The cells were treated with mummy substances separately and co-cultured between ASCs and WJSCs, and fibroblasts. Proliferation was assessed by Ki67 method in monolayer conditions. Synthesis of components of extracellular matrix (ECM) such as collagen type I, type III, and fibronectin 1 (FN1) was determined by qPCR. Results: The effects of adipocyte stem cells (ASCs), Wharton Jelly Stem Cells (WJSCs), and Mummy material on fibroblast proliferation and migration were evaluated. The present finding underlined the importance of Mummy material, ASCs, and WJSCs in the proliferation and migration of fibroblast cells. Furthermore, the expression of collagen I, III, and FN1 was increased in the presence of the above material and cells. Conclusion: This study presented an effective in vitro method for the healing process. Hence, the prospect of utilizing Mummy material and stem cell-based therapies in wound healing as a therapeutic approach is promising.

Keywords: mummy material, wound healing, adipose tissue, Wharton’s jelly

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Authors: P. Viduthalai, A. K. Divakar, V. Natarajan

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Media is one of the powerful tools that manipulate the world in numerous aspects especially in the form of a communication process. For instance, the concept of the public sphere, which was earlier represented by landlords and elites has now transformed into a virtual public sphere, which is also represented by marginalized people. Unfortunately, this acquisition is still paradoxical. Though the media proliferation and its effects are humongous, still it has not been the same throughout the world. Inequality in access to media has created a technological divide among people. Finally, globalization and approach by the government towards using media for development communication has significantly changed the way in which the media reaches every nook and corner. Monarchy, oligarchy, republic and democracy together form the basis of most governments of the world. Of which, democracy is the one with the highest involvement and participation of the people. Ideally, the participation of the people is what, that keeps the democracy running. A healthy democracy is possible only when people are able to access information that makes citizens responsible and serves to check the functioning of their elected representatives. On one side the media consumption of people plays a crucial role in the formation of the public sphere, and on the other side, big media conglomerates are a serious threat to community participation, which is a goal that the media should strive for in a country like India. How different people consume these different media, differs greatly from length and breadth of the country. Another aspect of this media consumption is that it isn’t passive. People usage and consumption of media are related with the gratification that they derive from the particular media. This aspect varies from person to person and from society to society according to both internal and external factors. This article sets out from the most underlying belief that Malayali Tribes have adopted television and becomes a part of daily life and a day never passes without it especially after the introduction of Free Television Scheme by the past state government. Though they are living in hilly and socially isolated places, they too have started accessing media for understanding about the people of the plains and their culture, dictated by their interest. Many of these interests appear to have a social and psychological origin. The present research attempts to study how gratification of these needs lead Malayali Tribes to form such a virtual public sphere where they could communicate with people of the plains. Data was collected through survey method, from 300 respondents on “Exposure towards Television and their perception”. Conventional anthropological methods like unstructured interviews were also used to supplement the data collection efforts in the three taluks namely Yercaud, Pethanayankkanpalayam and Panamaraththuppatty in Salem district of TamilNadu. The results highlight the role of Television in gratifying needs of the Malayali Tribes.

Keywords: democracy, gratification, Malayali Tribes and television, virtual public sphere

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Authors: Michael Ngongeh Mombe

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This Paper seeks to investigate an assertion made by some colleagues that there is no need paying a human translator to translate their literary texts, that there are softwares such as Reverso that can be used to do the translation. The main objective of this study is to examine the veracity of this assertion using Reverso to translate a literary text without any post-editing by a human translator. The work is based on two theories: Skopos and Communicative theories of translation. The work is a documentary research where data were collected from published documents in libraries, on the internet and from the translation produced by Reverso. We made a comparative text analyses of both source and target texts in a bid to highlight the weaknesses and strengths of the software. Findings of this work revealed that those who advocate the use of only Machine translation do so in ignorance of the translation mistakes usually made by the software. From the review of all the 268 segments of translation, we found out that the translation produced by Reverso is fraught with errors. We therefore recommend the use of human translators to either do the translation of their literary texts or revise the translation produced by machine to conform to the skopos of the work. This paper is based on Reverso translation. Similar works in the near future will be based on the other translation softwares to determine their weaknesses and strengths.

Keywords: machine translation, human translator, Reverso, literary text

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Authors: Deok Hyun Han

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Objective: To explore outcomes of UventaTM metallic ureteral stent between malignant and benign ureteral obstruction. Methods: We reviewed the medical records of 90 consecutive patients who underwent Uventa stent placement for benign or malignant ureteral obstruction from December 2009 to June 2013. We evaluated the clinical outcomes, complications, and reasons and results for unexpected stent removals. Results: The median follow-up was 10.7 (0.9 – 41) months. From a total of 125 ureter units, there were 24 units with benign obstructions and 101 units with malignant obstructions. Initial technical successes were achieved in all patients. The overall success rate was 70.8% with benign obstructions and 84.2% with malignant obstructions. The major reasons for treatment failure were stent migration (12.5%) in benign and tumor progression (11.9%) in malignant obstructions. The overall complication rate was similar between benign and malignant obstructions (58.3% and 42.6%), but severe complications, which are Clavien grade 3 or more, occurred in 41.7% of benign and 6.9% of malignant obstructions. The most common complications were stent migration (25.0%) in benign obstructions and persistent pain (14.9%) in malignant obstructions. The stent removal was done in 16 units; nine units that were removed by endoscopy and seven units were by open surgery. Conclusions: In malignant ureteral obstructions, the Uventa stent showed favorable outcomes with high success rate and acceptable complication rate. However, in benign ureteral obstructions, overall success rate and complication rate were less favorable. Malignant ureteral obstruction seems to be appropriate indication of Uventa stent placement. However, in chronic diffuse benign ureteral obstructions the decision of placement of Uventa stent has to be careful.

Keywords: cause, complication, ureteral obstruction, metal stent

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Authors: Saci Abdelhakim Ferkous, Khedoudja Soudani, Smail Haddadi

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This paper presents the results of a laboratory experimental study that explores the skid resistance of asphalt concrete mixtures made of local low-performance aggregates by partially replacing sand with olive mill waste (OMW). OMW was mixed with aggregates using a dry process by replacing sand with contents of 5%, 7%, 10% and 15%. The mechanical performances of the mixtures were evaluated using the Marshall and Duriez tests. A modified accelerated polishing machine was used as polishing equipment, and a British pendulum tester (BPT) was used to test the skid resistance of the samples. Finally, texture parameter analysis was performed using scanning electron microscopy (SEM) and Mountains Map software to assess the effect of OMW on the friction coefficient evolution. Using a distinct road wheel for a modified version of an accelerated polishing machine, which is normally used to determine the polished stone value of aggregates, the results showed that the addition of OMW up to 10% conferred a better skid resistance in comparison to normal asphalt concrete. The presence of olive mill waste in the mixture until 15% guarantees a gain of 22%-29% in skid resistance after polishing compared with the reference mix. Indeed, from texture parameter analysis, it was observed that there was differential wear of the lightweight aggregates (OMW) compared to the other aggregates during the polishing process, which created a new surface microtexture that had new peaks and led to a good level of friction compared to the mixtures without OMW. In general, it was found that OMW is a promising modifier for asphalt mixtures with both engineering and economic merits.

Keywords: skid resistance, olive mill waste, polishing resistance, accelerated polishing machine, local materials, sustainable development.

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