Search results for: sensory processing sensitivity

Authors: Sanskar, Abhinav Pal, Aryush Gupta, Sushil Kumar Mishra

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One of the critical and tedious assignments in agricultural practices is the detection of diseases on vegetation. Agricultural production is very important in today’s economy because plant diseases are common, and early detection of plant diseases is important in agriculture. Automatic detection of such early diseases is useful because it reduces control efforts in large productive farms. Using digital image processing and machine learning algorithms, this paper presents a method for plant disease detection. Detection of the disease occurs on different leaves of the plant. The proposed system for plant disease detection is simple and computationally efficient, requiring less time than learning-based approaches. The accuracy of various plant and foliar diseases is calculated and presented in this paper.

Keywords: plant diseases, machine learning, image processing, deep learning

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Authors: W. Ait Ahmed, M. Aggour, M. Naciri

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Extracting energy from biomass is an important alternative to produce different types of energy (heat, electricity, or both) assuring low pollution and better efficiency. It is a new yet reliable approach to reduce green gas emission by extracting methane from industry effluents and use it to power machinery. We focused in our project on using paper and mill effluents, treated in a UASB reactor. The methane produced is used in the factory’s power supply. The aim of this work is to develop an electronic system using Arduino platform connected to a gas sensor, to measure and display the curve of daily methane production on processing. The sensor will send the gas values in ppm to the Arduino board so that the later sends the RS232 hardware protocol. The code developed with processing will transform the values into a curve and display it on the computer screen.

Keywords: biogas, Arduino, processing, code, methane, gas sensor, program

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Authors: Jaspreet Singh, Gurvinder Singh, Prabhsimran Singh, Rajinder Singh, Prithvipal Singh, Karanjeet Singh Kahlon, Ravinder Singh Sawhney

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Morphological evaluation of Indian languages is one of the burgeoning fields in the area of Natural Language Processing (NLP). The evaluation of a language is an eminent task in the era of information retrieval and text mining. The extraction and classification of knowledge from text can be exploited for sentiment analysis and morphological evaluation. This study coalesce morphological evaluation and sentiment analysis for the task of classification of farmer suicide cases reported in Punjab state of India. The pre-processing of Punjabi text involves morphological evaluation and normalization of Punjabi word tokens followed by the training of proposed model using deep learning classification on Punjabi language text extracted from online Punjabi news reports. The class-wise accuracies of sentiment prediction for four negatively oriented classes of farmer suicide cases are 93.85%, 88.53%, 83.3%, and 95.45% respectively. The overall accuracy of sentiment classification obtained using proposed framework on 275 Punjabi text documents is found to be 90.29%.

Keywords: deep neural network, farmer suicides, morphological processing, punjabi text, sentiment analysis

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Authors: Hee-Choul Kwon, Heeyong Kwon

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Image rotation is one of main pre-processing steps for image processing or image pattern recognition. It is implemented with a rotation matrix multiplication. It requires a lot of floating point arithmetic operations and trigonometric calculations, so it takes a long time to execute. Therefore, there has been a need for a high speed image rotation algorithm without two major time-consuming operations. However, the rotated image has a drawback, i.e. distortions. We solved the problem using an augmented two-step shear transform. We compare the presented algorithm with the conventional rotation with images of various sizes. Experimental results show that the presented algorithm is superior to the conventional rotation one.

Keywords: high-speed rotation operation, image rotation, transform matrix, image processing, pattern recognition

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Authors: Mohammed Bnebaghdad, Feriel Betouche, Malika Semmani

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Image processing become more sophisticated over time due to technological advances, especially artificial intelligence (AI) technology. Currently, AI image processing is used in many areas, including surveillance, industry, science, and medicine. AI in medical image processing can help doctors diagnose diseases faster, with minimal mistakes, and with less effort. Among these diseases is malaria, which remains a major public health challenge in many parts of the world. It affects millions of people every year, particularly in tropical and subtropical regions. Early detection of malaria is essential to prevent serious complications and reduce the burden of the disease. In this paper, we propose and implement a scheme based on AI image processing to enhance malaria disease diagnosis through automated analysis of blood smear images. The scheme is based on the convolutional neural network (CNN) method. So, we have developed a model that classifies infected and uninfected single red cells using images available on Kaggle, as well as real blood smear images obtained from the Central Laboratory of Medical Biology EHS Laadi Flici (formerly El Kettar) in Algeria. The real images were segmented into individual cells using the watershed algorithm in order to match the images from the Kaagle dataset. The model was trained and tested, achieving an accuracy of 99% and 97% accuracy for new real images. This validates that the model performs well with new real images, although with slightly lower accuracy. Additionally, the model has been embedded in a Raspberry Pi4, and a graphical user interface (GUI) was developed to visualize the malaria diagnostic results and facilitate user interaction.

Keywords: medical image processing, malaria parasite, classification, CNN, artificial intelligence

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Authors: Kirsi Laurén, Tiina Seppä

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This study focuses on human-mire relations in the context of digital aestheticization and the long-standing tradition of folklore concerning mires. The study concentrates on the Patvinsuo mire in Eastern Finland and the Viiankiaapa mire in Finnish Lapland. Patvinsuo is a national park, and Viiankiaapa is a protected mire area with hiking trails and other recreational infrastructure. Perceiving the environment through digital technology can help to notice aesthetic details in nature. In addition, sharing images and texts digitally through social media adds a sense of community to the relationship with nature and, at the same time, creates a different kind of living heritage where old and new traditions meet and mingle. People visiting and camping in these areas 'self-care' themselves through recreation in nature. However, these practices and digital aestheticization can sometimes lead to the erosion of fragile mires. The research focuses on understanding the impact of digital aestheticization, such as taking digital photos, on the relationship with nature for individuals moving and working in mires. Additionally, the study aims to explore the contemporary perception of the water environment in mires and its cultural heritage, including mythical and folkloric elements. The research material consists of senso-digital walking interviews and digital recordings (audio recordings, photographs, videos) made during the mire walks, as well as archival material from the Finnish Literature Society’s Archives on mire folklore. The analysis of the material relies centrally on theories from sensory anthropology on the relationship between sensory perception and culture. The modern-day interviewees include outdoor enthusiasts spending their leisure time in mires, artists treating mires in their art, and nature experts (scientists, civil servants, and nature guides). The senso-digital walking interviews were conducted in Patvinsuo and Viiankiaapa mires on a trail chosen by the interviewees themselves. The material selected from the archive consists mainly of folk beliefs and folk poetry from the 19th and 20th centuries that express the relationship of the narrator to the mires. The interview and archival materials date from different periods and are different in character, which has to be taken into account in the analysis. However, in the analysis of both materials, particular attention is paid to the descriptions of sensations that appear in them. Analyzing the materials in parallel is limited by the fact that they date from different periods, but on the other hand, it is their different ages that make it possible to perceive the changes in the cultural heritage of mires.

Keywords: mires, living heritage, digital aestheticization, folklore, sensory anthropology

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Authors: Aasheesh Raturi, Vimal Kothiyal, P. D. Semalty

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We studied mechanical properties of Eucalyptus tereticornis using FT-NIR spectroscopy. Firstly, spectra were pre-processed to eliminate useless information. Then, prediction model was constructed by partial least squares regression. To study the influence of pre-processing on prediction of mechanical properties for NIR analysis of wood samples, we applied various pretreatment methods like straight line subtraction, constant offset elimination, vector-normalization, min-max normalization, multiple scattering. Correction, first derivative, second derivatives and their combination with other treatment such as First derivative + straight line subtraction, First derivative+ vector normalization and First derivative+ multiplicative scattering correction. The data processing methods in combination of preprocessing with different NIR regions, RMSECV, RMSEP and optimum factors/rank were obtained by optimization process of model development. More than 350 combinations were obtained during optimization process. More than one pre-processing method gave good calibration/cross-validation and prediction/test models, but only the best calibration/cross-validation and prediction/test models are reported here. The results show that one can safely use NIR region between 4000 to 7500 cm-1 with straight line subtraction, constant offset elimination, first derivative and second derivative preprocessing method which were found to be most appropriate for models development.

Keywords: FT-NIR, mechanical properties, pre-processing, PLS

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Authors: P. N. Okeke, J. N. Chikwendu

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The study evaluated the effects of varying fermentation periods on the nutrients and anti-nutrients composition of African yam bean (Sphenostylis stenocarpa) and acha (Digitaria exilis) flour blends and sensory properties of their products. The African yam bean seeds and acha grains were fermented for 24 hrs, 48 and 72 hrs, dried (sun drying) and milled into fine flour. The fermented flours were used in a ratio of 70:30 (Protein basis) to formulate composite flour for meat pie and biscuits production. Both the fermented and unfermented flours and products were analyzed for chemical composition using the standard method. The data were statistically analyzed using SPSS version 15 to determine the mean and standard deviation. The 24, 48, and 72 hrs fermentation periods increased protein (22.81, 26.15 and 24.00% respectively). The carbohydrate, ash and moisture contents of the flours were also increased as a result of fermentation (68.01-76.83, 2.26-4.88, and 8.36-13.00% respectively). The 48 hrs fermented flour blends had the highest increase in ash relative to the control (4.88%). Fermentation increased zinc, iron, magnesium and phosphorus content of the flours. Treatment drastically reduced the anti-nutrient (oxalate, saponin, tannin, phytate, and hemagglutinin) levels of the flours. Both meat pie and biscuits had increased protein relative to the control (27.36-34.28% and 23.66-25.09%). However, the protein content of the meat pie increased more than that of the biscuits. Zinc, Iron, Magnesium and phosphorus levels increased in both meat pie and biscuits. Organoleptic attributes of the products (meat pie and biscuits) were slightly lower than the control except those of the 72 hrs fermented flours.

Keywords: fermentation, African yam bean, acha, biscuits, meat-pie

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Authors: Yanhua Zhao, Chenli Rao, Dongdong Li, Chuanmin Tao

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The Chinese Centers for Disease Control and Prevention (CCDC) issued a new laboratory-based algorithm for HIV diagnosis on April 2016, which initially screens with a combination HIV-1/HIV-2 antigen/antibody fourth-generation immunoassay (IA) followed, when reactive, an HIV-1/HIV-2 undifferentiated antibody IA in duplicate. Reactive specimens with concordant results undergo supplemental tests with western blots, or HIV-1 nucleic acid tests (NATs) and non-reactive specimens with discordant results receive HIV-1 NATs or p24 antigen tests or 2-4 weeks follow-up tests. However, little data evaluating the application of the new algorithm have been reported to date. The study was to evaluate the performance of new laboratory-based HIV diagnostic algorithm in an inpatient population of Southwest China over the initial 6 months by compared with the old algorithm. Plasma specimens collected from inpatients from May 1, 2016, to October 31, 2016, are submitted to the laboratory for screening HIV infection performed by both the new HIV testing algorithm and the old version. The sensitivity and specificity of the algorithms and the difference of the categorized numbers of plasmas were calculated. Under the new algorithm for HIV diagnosis, 170 of the total 52 749 plasma specimens were confirmed as positively HIV-infected (0.32%). The sensitivity and specificity of the new algorithm were 100% (170/170) and 100% (52 579/52 579), respectively; while 167 HIV-1 positive specimens were identified by the old algorithm with sensitivity 98.24% (167/170) and 100% (52 579/52 579), respectively. Three acute HIV-1 infections (AHIs) and two early HIV-1 infections (EHIs) were identified by the new algorithm; the former was missed by old procedure. Compared with the old version, the new algorithm produced fewer WB-indeterminate results (2 vs. 16, p = 0.001), which led to fewer follow-up tests. Therefore, the new HIV testing algorithm is more sensitive for detecting acute HIV-1 infections with maintaining the ability to verify the established HIV-1 infections and can dramatically decrease the greater number of WB-indeterminate specimens.

Keywords: algorithm, diagnosis, HIV, laboratory

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Authors: Schnell Zsuzsanna

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Background: The interpretation of written texts, language processing in the visual domain, in other words, atypical reading abilities, also known as dyslexia, is an ever-growing phenomenon in today’s societies and educational communities. The much-researched problem affects cognitive abilities and, coupled with normal intelligence normally manifests difficulties in the differentiation of sounds and orthography and in the holistic processing of written words. The factors of susceptibility are varied: social, cognitive psychological, and linguistic factors interact with each other. Methods: The research will explain the psycholinguistics of dyslexia on the basis of several empirical experiments and demonstrate how domain-general abilities of inhibition, retrieval from the mental lexicon, priming, phonological processing, and visual modality transfer affect successful language processing and interpretation. Interpretation of visual stimuli is hindered, and the problem seems to be embedded in a sociocultural, psycholinguistic, and cognitive background. This makes the picture even more complex, suggesting that the understanding and resolving of the issues of dyslexia has to be interdisciplinary, aided by several disciplines in the field of humanities and social sciences, and should be researched from an empirical approach, where the practical, educational corollaries can be analyzed on an applied basis. Aim and applicability: The lecture sheds light on the applied, cognitive aspects of interpretation, social cognitive traits of language processing, the mental underpinnings of cognitive interpretation strategies in different languages (namely, Hungarian and English), offering solutions with a few applied techniques for success in foreign language learning that can be useful advice for the developers of testing methodologies and measures across ESL teaching and testing platforms.

Keywords: dyslexia, social cognition, transparency, modalities

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Authors: M. Shemis, O. Maher, G. Casterou, F. Gauffre

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Hepatitis C virus (HCV) is a major cause of chronic liver disease with a global 170 million chronic carriers at risk of developing liver cirrhosis and/or liver cancer. Egypt reports the highest prevalence of HCV worldwide. Currently, two classes of assays are used in the diagnosis and management of HCV infection. Despite the high sensitivity and specificity of the available diagnostic assays, they are time-consuming, labor-intensive, expensive, and require specialized equipment and highly qualified personal. It is therefore important for clinical and economic terms to develop a low-tech assay for the direct detection of HCV RNA with acceptable sensitivity and specificity, short turnaround time, and cost-effectiveness. Such an assay would be critical to control HCV in developing countries with limited resources and high infection rates, such as Egypt. The unique optical and physical properties of gold nanoparticles (AuNPs) have allowed the use of these nanoparticles in developing simple and rapid colorimetric assays for clinical diagnosis offering higher sensitivity and specificity than current detection techniques. The current research aims to develop a detection assay for HCV RNA using gold nanoparticles (AuNPs). Methods: 200 anti-HCV positive samples and 50 anti-HCV negative plasma samples were collected from Egyptian patients. HCV viral load was quantified using m2000rt (Abbott Molecular Inc., Des Plaines, IL). HCV genotypes were determined using multiplex nested RT- PCR. The assay is based on the aggregation of AuNPs in presence of the target RNA. Aggregation of AuNPs causes a color shift from red to blue. AuNPs were synthesized using citrate reduction method. Different sets of probes within the 5’ UTR conserved region of the HCV genome were designed, grafted on AuNPs and optimized for the efficient detection of HCV RNA. Results: The nano-gold assay could colorimetrically detect HCV RNA down to 125 IU/ml with sensitivity and specificity of 91.1% and 93.8% respectively. The turnaround time of the assay is < 30 min. Conclusions: The assay allows sensitive and rapid detection of HCV RNA and represents an inexpensive and simple point-of-care assay for resource-limited settings.

Keywords: HCV, gold nanoparticles, point of care, viral load

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Authors: S. I. Nwadioha, M. S. Odimayo, J. A. Omotayo, A. Olu Taiwo, O. E. Olabiyi

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The current study was conducted to determine the epidemiology and antibiotic sensitivity pattern of bacteria isolated from blood of septicemic patients for improved antibiotic therapy. A three-year descriptive study has been carried out at Microbiology Laboratory, Ekiti State University Teaching Hospital, Ado Ekiti, from April 2012 to April 2015. Information compiled from patients’ records includes age, sex, isolated organisms and antibiotic susceptibility patterns. Three hundred and thirteen blood cultures were collected from neonatology and pediatrics wards, Out Patients’ Department (OPD) and from other adult patients. Forty-one cultures yielded mono microbial growth (no polymicrobial growth), giving an incidence of 13.1% positive blood culture (N=41/313). There were 58.4% Gram-negative bacilli and 41.6% Gram-positive cocci in the microbial growth. Bacteria isolated were Staphylococcus aureus 34%(14/41), Klebsiella species22% (9/41), Enterococci 17%(7/41), Proteus species12%(5/41), Escherichia coli 7%(3/41) and Streptococcal pneumoniae 7%(3/41). There was a (35%) higher occurrence of septicemia in neonates than in any other age groups in the hospital. Bacterial sensitivity to 13 antibiotic agents was determined by antibiotics disc diffusion using modified Kirby Bauer’s method. Gram-positive organisms showed a higher antibiotic sensitivity ranging from 14- 100% than the Gram-negative bacteria (11-80%). Staphylococcus aureus and Klebsiella species are the most prevalent organisms. The third generation Cephalosporins (Ceftriaxone) and Floroquinolone(Levofloxacin, Ofloxacin) have proved reliable for management of these blood infections.

Keywords: blood cultures, septicemia, antibiogram, Nigeria

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Authors: Azam Fazelpour, Saeed R. Dehghani, Vlastimil Masek, Yuri S. Muzychka

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This study employs a method based on image analyses and structure information to detect accumulated ice on known structures. The icing of marine vessels and offshore structures causes significant reductions in their efficiency and creates unsafe working conditions. Image processing methods are used to measure ice loads automatically. Most image processing methods are developed based on captured image analyses. In this method, ice loads on structures are calculated by defining structure coordinates and processing captured images. A pyramidal structure is designed with nine cylindrical bars as the known structure of experimental setup. Unsymmetrical ice accumulated on the structure in a cold room represents the actual case of experiments. Camera intrinsic and extrinsic parameters are used to define structure coordinates in the image coordinate system according to the camera location and angle. The thresholding method is applied to capture images and detect iced structures in a binary image. The ice thickness of each element is calculated by combining the information from the binary image and the structure coordinate. Averaging ice diameters from different camera views obtains ice thicknesses of structure elements. Comparison between ice load measurements using this method and the actual ice loads shows positive correlations with an acceptable range of error. The method can be applied to complex structures defining structure and camera coordinates.

Keywords: camera calibration, ice detection, ice load measurements, image processing

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Authors: Nurudeen Oluwasola Lasisi

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Newcastle disease is a highly contagious disease of birds caused by a para-myxo virus. In this paper, we presented Novel quarantine-adjusted incident and linear incident of Newcastle disease model equations. We considered the dynamics of transmission and control of Newcastle disease. The existence and uniqueness of the solutions were obtained. The existence of disease-free points was shown, and the model threshold parameter was examined using the next-generation operator method. The sensitivity analysis was carried out in order to identify the most sensitive parameters of the disease transmission. This revealed that as parameters β,ω, and ᴧ increase while keeping other parameters constant, the effective reproduction number R_ev increases. This implies that the parameters increase the endemicity of the infection of individuals. More so, when the parameters μ,ε,γ,δ_1, and α increase, while keeping other parameters constant, the effective reproduction number R_ev decreases. This implies the parameters decrease the endemicity of the infection as they have negative indices. Analytical results were numerically verified by the Differential Transformation Method (DTM) and quantitative views of the model equations were showcased. We established that as contact rate (β) increases, the effective reproduction number R_ev increases, as the effectiveness of drug usage increases, the R_ev decreases and as the quarantined individual decreases, the R_ev decreases. The results of the simulations showed that the infected individual increases when the susceptible person approaches zero, also the vaccination individual increases when the infected individual decreases and simultaneously increases the recovery individual.

Keywords: disease-free equilibrium, effective reproduction number, endemicity, Newcastle disease model, numerical, Sensitivity analysis

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Authors: Sangeeta Palekar, Neeraj Rangwani, Akash Poddar, Jayu Kalambe

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The bio-medical analysis is an indispensable procedure for identifying health-related diseases like diabetes. Monitoring the glucose level in our body regularly helps us identify hyperglycemia and hypoglycemia, which can cause severe medical problems like nerve damage or kidney diseases. This paper presents a method for predicting the glucose concentration in blood samples using image processing and machine learning algorithms. The glucose solution is prepared by the glucose oxidase (GOD) and peroxidase (POD) method. An experimental database is generated based on the colorimetric technique. The image of the glucose solution is captured by the raspberry pi camera and analyzed using image processing by extracting the RGB, HSV, LUX color space values. Regression algorithms like multiple linear regression, decision tree, RandomForest, and XGBoost were used to predict the unknown glucose concentration. The multiple linear regression algorithm predicts the results with 97% accuracy. The image processing and machine learning-based approach reduce the hardware complexities of existing platforms.

Keywords: artificial intelligence glucose detection, glucose oxidase, peroxidase, image processing, machine learning

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Authors: Doyin Afolabi, Phillip Adewole, Oladipupo Sennaike

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Most well-known classifiers, including the decision tree algorithm, can make predictions on balanced datasets efficiently. However, the decision tree algorithm tends to be biased towards imbalanced datasets because of the skewness of the distribution of such datasets. To overcome this problem, this study proposes a weighted decision tree algorithm that aims to remove the bias toward the majority class and prevents the reduction of majority observations in imbalance datasets classification. The proposed weighted decision tree algorithm was tested on three imbalanced datasets- cancer dataset, german credit dataset, and banknote dataset. The specificity, sensitivity, and accuracy metrics were used to evaluate the performance of the proposed decision tree algorithm on the datasets. The evaluation results show that for some of the weights of our proposed decision tree, the specificity, sensitivity, and accuracy metrics gave better results compared to that of the ID3 decision tree and decision tree induced with minority entropy for all three datasets.

Keywords: data mining, decision tree, classification, imbalance dataset

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Authors: Chin-Chih Chiou, Tai-Yuan Su, Ti-Yu Chen, Wen-Yu Chiu, Chungyu Chen

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The purpose of this study was to investigate the effect of reaction time (RT) or balance performance as the number of stimulus-response choices increases, the amount of information processing of 0-bit and 1-bit conditions based on Hick’s law, using the dual-task design. Eighteen children (age: 9.38 ± 0.27 years old) were recruited as the participants for this study, and asked to assess RT and balance performance separately and simultaneously as following five conditions: simple RT (0-bit decision), choice RT (1-bit decision), single balance control, balance control with simple RT, and balance control with choice RT. Biodex 950-300 balance system and You-Shang response timer were used to record and analyze the postural stability and information processing speed (RT) respectively for the participants. Repeated measures one-way ANOVA with HSD post-hoc test and 2 (balance) × 2 (amount of information processing) repeated measures two-way ANOVA were used to test the parameters of balance performance and RT (α = .05). The results showed the overall stability index in the 1-bit decision was lower than in 0-bit decision, and the mean deflection in the 1-bit decision was lower than in single balance performance. Simple RTs were faster than choice RTs both in single task condition and dual task condition. It indicated that the chronometric approach of RT could use to infer the attention requirement of the secondary task. However, this study did not find that the balance performance is interfered for children by the increasing of the amount of information processing.

Keywords: capacity theory, reaction time, Hick’s law, balance

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Authors: Pranav Ajmera, Amit Kharat, Tanveer Gupte, Richa Pant, Viraj Kulkarni, Vinay Duddalwar, Purnachandra Lamghare

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The cardiothoracic ratio (CTR) is the ratio of the diameter of the heart to the diameter of the thorax. An abnormal CTR, that is, a value greater than 0.55, is often an indicator of an underlying pathological condition. The accurate prediction of an abnormal CTR from chest X-rays (CXRs) aids in the early diagnosis of clinical conditions. We propose a deep learning-based model for automatic CTR calculation that can assist the radiologist with the diagnosis of cardiomegaly and optimize the radiology flow. The study population included 1012 posteroanterior (PA) CXRs from a single institution. The Attention U-Net deep learning (DL) architecture was used for the automatic calculation of CTR. A CTR of 0.55 was used as a cut-off to categorize the condition as cardiomegaly present or absent. An observer performance test was conducted to assess the radiologist's performance in diagnosing cardiomegaly with and without artificial intelligence (AI) assistance. The Attention U-Net model was highly specific in calculating the CTR. The model exhibited a sensitivity of 0.80 [95% CI: 0.75, 0.85], precision of 0.99 [95% CI: 0.98, 1], and a F1 score of 0.88 [95% CI: 0.85, 0.91]. During the analysis, we observed that 51 out of 1012 samples were misclassified by the model when compared to annotations made by the expert radiologist. We further observed that the sensitivity of the reviewing radiologist in identifying cardiomegaly increased from 40.50% to 88.4% when aided by the AI-generated CTR. Our segmentation-based AI model demonstrated high specificity and sensitivity for CTR calculation. The performance of the radiologist on the observer performance test improved significantly with AI assistance. A DL-based segmentation model for rapid quantification of CTR can therefore have significant potential to be used in clinical workflows.

Keywords: cardiomegaly, deep learning, chest radiograph, artificial intelligence, cardiothoracic ratio

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Authors: Hassan Irshad Bhatti, Saravanan Yuvaraja, Xiaohang Li

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GaN-based devices, such as high electron mobility transistors (HEMTs), offer superior characteristics for high-power, high-frequency, and high-temperature applications [1]. However, this exceptional electrical performance is compromised by undesirable self-heating effects under high-power applications [2, 3]. Some of the issues caused by self-heating are current collapse, thermal runway and performance degradation [4, 5]. Therefore, accurate and reliable methods for measuring the temperature of individual devices on a chip are needed to monitor and control the thermal behavior of GaN-based devices [6]. Temperature measurement at the micro/nanoscale is a challenging task that requires specialized techniques such as Infrared microscopy, Raman thermometry, and thermoreflectance. Recently, micro-thermocouples (MTCs) have attracted considerable attention due to their advantages of simplicity, low cost, high sensitivity, and compatibility with standard fabrication processes [7, 8]. A micro-thermocouple is a junction of two different metal thin films, which generates a Seebeck voltage related to the temperature difference between a hot and cold zone. Integrating MTC in a device allows local temperature to be measured with high sensitivity and accuracy [9]. This work involves the fabrication and integration of micro-thermocouples (MTCs) to measure the channel temperature of GaN HEMT. Our fabricated MTC (Platinum-Chromium junction) has shown a sensitivity of 16.98 µV/K and can measure device channel temperature with high precision and accuracy. The temperature information obtained using this sensor can help improve GaN-based devices and provide thermal engineers with useful insights for optimizing their designs.

Keywords: Electrical Engineering, Thermal engineering, Power Devices, Semiconuctors

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Authors: A. Satty, H. Mwambi

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Much clinical trials data-based research are characterized by the unavoidable problem of dropout as a result of missing or erroneous values. This paper aims to review some of the various techniques to address the dropout problems in longitudinal clinical trials. The fundamental concepts of the patterns and mechanisms of dropout are discussed. This study presents five general techniques for handling dropout: (1) Deletion methods; (2) Imputation-based methods; (3) Data augmentation methods; (4) Likelihood-based methods; and (5) MNAR-based methods. Under each technique, several methods that are commonly used to deal with dropout are presented, including a review of the existing literature in which we examine the effectiveness of these methods in the analysis of incomplete data. Two application examples are presented to study the potential strengths or weaknesses of some of the methods under certain dropout mechanisms as well as to assess the sensitivity of the modelling assumptions.

Keywords: incomplete longitudinal clinical trials, missing at random (MAR), imputation, weighting methods, sensitivity analysis

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Authors: Abdelkader Elhanaoui, Mhamed Hadji, Rachid Skouri, Said Agounad

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In this paper, altera de1-SoC FPGA board technology is utilized as a distinguished tool for nondestructive characterization of an aluminum circular cylindrical shell of radius ratio b/a (a: outer radius; b: inner radius). The acoustic backscattered signal processing system has been developed using OpenMP architecture. The design is built in three blocks; it is implemented per functional block, in a heterogeneous Intel-Altera system running under Linux. The useful data to determine the performances of SoC FPGA is computed by the analytical method. The exploitation of SoC FPGA has lead to obtain the backscattering form function and resonance spectra. A0 and S0 modes of propagation in the tube are shown. The findings are then compared to those achieved from the Matlab simulation of analytical method. A good agreement has, therefore, been noted. Moreover, the detailed SoC FPGA-based system has shown that acoustic spectra are performed at up to 5 times faster than the Matlab implementation using almost the same data. This FPGA-based system implementation of processing algorithms is realized with a coefficient of correlation R and absolute error respectively about 0.962 and 5 10⁻⁵.

Keywords: OpenMP, signal processing system, acoustic backscattering, nondestructive characterization, thin tubes

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Authors: Si Mon Kueh, Tom J. Kazmierski

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There are about 1% of the world population suffering from the hidden disability known as epilepsy and major developing countries are not fully equipped to counter this problem. In order to reduce the inconvenience and danger of epilepsy, different methods have been researched by using a artificial neural network (ANN) classification to distinguish epileptic waveforms from normal brain waveforms. This paper outlines the aim of achieving massive ANN parallelization through a dedicated hardware using bit-serial processing. The design of this bit-serial Neural Processing Element (NPE) is presented which implements the functionality of a complete neuron using variable accuracy. The proposed design has been tested taking into consideration non-idealities of a hardware ANN. The NPE consists of a bit-serial multiplier which uses only 16 logic elements on an Altera Cyclone IV FPGA and a bit-serial ALU as well as a look-up table. Arrays of NPEs can be driven by a single controller which executes the neural processing algorithm. In conclusion, the proposed compact NPE design allows the construction of complex hardware ANNs that can be implemented in a portable equipment that suits the needs of a single epileptic patient in his or her daily activities to predict the occurrences of impending tonic conic seizures.

Keywords: Artificial Neural Networks (ANN), bit-serial neural processor, FPGA, Neural Processing Element (NPE)

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Authors: George Hanna Abdelmelek Henien

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Quality and safety issues are common in Ethiopia's food processing industry, which can negatively impact consumers' health and livelihoods. The country is known for its various agricultural products that are important to the economy. However, food quality and safety policies and management practices in the food processing industry have led to many health problems, foodborne illnesses and economic losses. This article aims to show the causes and consequences of food safety and quality problems in the food processing industry in Ethiopia and discuss possible solutions to solve them. One of the main reasons for food quality and safety in Ethiopia's food processing industry is the lack of adequate regulation and enforcement mechanisms. Inadequate food safety and quality policies have led to inefficiencies in food production. Additionally, the failure to monitor and enforce existing regulations has created a good opportunity for unscrupulous companies to engage in harmful practices that endanger the lives of citizens. The impact on food quality and safety is significant due to loss of life, high medical costs, and loss of consumer confidence in the food processing industry. Foodborne diseases such as diarrhoea, typhoid and cholera are common in Ethiopia, and food quality and safety play an important role in . Additionally, food recalls due to contamination or contamination often cause significant economic losses in the food processing industry. To solve these problems, the Ethiopian government began taking measures to improve food quality and safety in the food processing industry. One of the most prominent initiatives is the Ethiopian Food and Drug Administration (EFDA), which was established in 2010 to monitor and control the quality and safety of food and beverage products in the country. EFDA has implemented many measures to improve food safety, such as carrying out routine inspections, monitoring the import of food products and implementing labeling requirements. Another solution that can improve food quality and safety in the food processing industry in Ethiopia is the implementation of food safety management system (FSMS). FSMS is a set of procedures and policies designed to identify, assess and control food safety risks during food processing. Implementing a FSMS can help companies in the food processing industry identify and address potential risks before they harm consumers. Additionally, implementing an FSMS can help companies comply with current safety and security regulations. Consequently, improving food safety policy and management system in Ethiopia's food processing industry is important to protect people's health and improve the country's economy. . Addressing the root causes of food quality and safety and implementing practical solutions that can help improve the overall food safety and quality in the country, such as establishing regulatory bodies and implementing food management systems.

Keywords: food quality, food safety, policy, management system, food processing industry food traceability, industry 4.0, internet of things, block chain, best worst method, marcos

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Authors: Jean-Philippe Amat, Timothée Vergne, Aymeric Hans, Bénédicte Ferry, Pascal Hendrikx, Jackie Tapprest, Barbara Dufour, Agnès Leblond

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The surveillance of infectious diseases is necessary to describe their occurrence and help the planning, implementation and evaluation of risk mitigation activities. However, the exact number of detected cases may remain unknown whether surveillance is based on serological tests because identifying seroconversion may be difficult. Moreover, incomplete detection of cases or outbreaks is a recurrent issue in the field of disease surveillance. This study addresses these two issues. Using a viral animal disease as an example (equine viral arteritis), the goals were to establish suitable rules for identifying seroconversion in order to estimate the number of cases and outbreaks detected by a surveillance system in France between 2006 and 2013, and to assess the sensitivity of this system by estimating the total number of outbreaks that occurred during this period (including unreported outbreaks) using a capture-recapture model. Data from horses which exhibited at least one positive result in serology using viral neutralization test between 2006 and 2013 were used for analysis (n=1,645). Data consisted of the annual antibody titers and the location of the subjects (towns). A consensus among multidisciplinary experts (specialists in the disease and its laboratory diagnosis, epidemiologists) was reached to consider seroconversion as a change in antibody titer from negative to at least 32 or as a three-fold or greater increase. The number of seroconversions was counted for each town and modeled using a unilist zero-truncated binomial (ZTB) capture-recapture model with R software. The binomial denominator was the number of horses tested in each infected town. Using the defined rules, 239 cases located in 177 towns (outbreaks) were identified from 2006 to 2013. Subsequently, the sensitivity of the surveillance system was estimated as the ratio of the number of detected outbreaks to the total number of outbreaks that occurred (including unreported outbreaks) estimated using the ZTB model. The total number of outbreaks was estimated at 215 (95% credible interval CrI95%: 195-249) and the surveillance sensitivity at 82% (CrI95%: 71-91). The rules proposed for identifying seroconversion may serve future research. Such rules, adjusted to the local environment, could conceivably be applied in other countries with surveillance programs dedicated to this disease. More generally, defining ad hoc algorithms for interpreting the antibody titer could be useful regarding other human and animal diseases and zoonosis when there is a lack of accurate information in the literature about the serological response in naturally infected subjects. This study shows how capture-recapture methods may help to estimate the sensitivity of an imperfect surveillance system and to valorize surveillance data. The sensitivity of the surveillance system of equine viral arteritis is relatively high and supports its relevance to prevent the disease spreading.

Keywords: Bayesian inference, capture-recapture, epidemiology, equine viral arteritis, infectious disease, seroconversion, surveillance

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Authors: M. Fares, S. Mameri, I. Abdlani, K. Negara

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Neutron detectors in general, proportional counters gas filling based isotope 3He in particular are going to be essential for monitoring and control of certain nuclear facilities, monitoring of experimentation around neutron beams and channels nuclear research reactors, radiation protection instruments and other tools multifaceted exploration and testing of materials, etc. This work consists of a measurement campaign features two Proportional Counters 3He (3He: LND252/USA CP, CP prototype: 3He LND/DDM). This is to make a comparison study of a CP 3He LND252/USA reference one hand, and in the context of routine periodic monitoring of the characteristics of the detectors for controlling the operation especially for laboratory prototypes. In this paper, we have described the different characteristics of the detectors and the experimental protocols used. Tables of measures have been developed and the different curves were plotted. The experimental campaign at stake: 2 PC 3He were thus characterized: Their characteristics (sensitivity, energy pulse height distribution spectra, gas amplification etc.) Were identified: 01 PC 3He 1'' Type: prototype DEDIN/DDM, 01 PC 3He 1'' Type: LND252/USA.

Keywords: PC 3He, sensitivity, pulse height distribution spectra, gas amplification

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Authors: Yu-Jia Jian, Emily Chia-Yu Su, Hui-Ling Hsu, Jian-Jhih Chen

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Coronary artery is the major supplier of myocardial blood flow. When fat and cholesterol are deposit in the coronary arterial wall, narrowing and stenosis of the artery occurs, which may lead to myocardial ischemia and eventually infarction. According to the World Health Organization (WHO), estimated 740 million people have died of coronary heart disease in 2015. According to Statistics from Ministry of Health and Welfare in Taiwan, heart disease (except for hypertensive diseases) ranked the second among the top 10 causes of death from 2013 to 2016, and it still shows a growing trend. According to American Heart Association (AHA), the risk factors for coronary heart disease including: age (> 65 years), sex (men to women with 2:1 ratio), obesity, diabetes, hypertension, hyperlipidemia, smoking, family history, lack of exercise and more. We have collected a dataset of 421 patients from a hospital located in northern Taiwan who received coronary computed tomography (CT) angiography. There were 300 males (71.26%) and 121 females (28.74%), with age ranging from 24 to 92 years, and a mean age of 56.3 years. Prior to coronary CT angiography, basic data of the patients, including age, gender, obesity index (BMI), diastolic blood pressure, systolic blood pressure, diabetes, hypertension, hyperlipidemia, smoking, family history of coronary heart disease and exercise habits, were collected and used as input variables. The output variable of the prediction module is the degree of coronary artery stenosis. The output variable of the prediction module is the narrow constriction of the coronary artery. In this study, the dataset was randomly divided into 80% as training set and 20% as test set. Four machine learning algorithms, including logistic regression, stepwise regression, neural network and decision tree, were incorporated to generate prediction results. We used area under curve (AUC) / accuracy (Acc.) to compare the four models, the best model is neural network, followed by stepwise logistic regression, decision tree, and logistic regression, with 0.68 / 79 %, 0.68 / 74%, 0.65 / 78%, and 0.65 / 74%, respectively. Sensitivity of neural network was 27.3%, specificity was 90.8%, stepwise Logistic regression sensitivity was 18.2%, specificity was 92.3%, decision tree sensitivity was 13.6%, specificity was 100%, logistic regression sensitivity was 27.3%, specificity 89.2%. From the result of this study, we hope to improve the accuracy by improving the module parameters or other methods in the future and we hope to solve the problem of low sensitivity by adjusting the imbalanced proportion of positive and negative data.

Keywords: decision support, computed tomography, coronary artery, machine learning

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Authors: A. M. Bumbea, A. Musetescu, P. Ciurea, A. Bighea

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Objectives: The authors present a Parsonage Turner syndrome, a rare disease characterized by onset in apparently healthy person with shoulder and/or arm pain, sensory deficit, motor deficit. The causes are not established, could be determinate by vaccination, postoperative, immunologic disease, post traumatic etc. Methods: The authors present a woman case, 32 years old, (in 2006), no medical history, with arm pain and no other symptom. The onset was sudden with pain at very high level quantified as 10 to a 0 to 10 scale, with no response to classical analgesic and corticoids. The only drugs which can reduce the intensity of pain were oxycodone hydrochloride, 60 mg daily and pregabalinum150 mg daily. After two weeks the intensity of pain was reduced to 5. The patient started a rehabilitation program. After 6 weeks the patient associated sensory and motor deficit. We performed electromyography for upper limb that showed incomplete denervation with reduced neural transmission speed. The patient receives neurotrophic drugs and painkillers for a long period and physical and kinetic therapy. After 6 months the pain was reduced to level 2 and the patient maintained only 150 mg pregabalinum for another 6 months. Then, the evaluation showed no pain but general amiotrophy in upper limb. Results: At the evaluation in 2009, the patient developed a rheumatoid syndrome with tender and swelling joints, but no positive inflammation test, no antibodies or rheumatoid factor. After two years, in 2011 the patient develops an increase of antinuclear antibodies. This context certifies the diagnosis of lupus and the patient receives the specific therapy. Conclusions: This case is not a typical case of onset of lupus with PTS, but the onset of PTS could include the onset of an immune disease.

Keywords: lupus, arm pain, patient, swelling

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Authors: K. Shimola, M. Krishnaveni

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This paper examines vulnerability assessment of water resources in a semi-arid basin using the 4-step approach. The vulnerability assessment framework is developed to study the water resources vulnerability which includes the creation of GIS-based vulnerability maps. These maps represent the spatial variability of the vulnerability index. This paper introduces the 4-step approach to assess vulnerability that incorporates a new set of indicators. The approach is demonstrated using a framework composed of a precipitation data for (1975–2010) period, temperature data for (1965–2010) period, hydrological model outputs and the water resources GIS data base. The vulnerability assessment is a function of three components such as exposure, sensitivity and adaptive capacity. The current water resources vulnerability is assessed using GIS based spatio-temporal information. Rainfall Coefficient of Variation, monsoon onset and end date, rainy days, seasonality indices, temperature are selected for the criterion ‘exposure’. Water yield, ground water recharge, evapotranspiration (ET) are selected for the criterion ‘sensitivity’. Type of irrigation and storage structures are selected for the criterion ‘Adaptive capacity’. These indicators were mapped and integrated in GIS environment using overlay analysis. The five sub-basins, namely Arjunanadhi, Kousiganadhi, Sindapalli-Uppodai and Vallampatti Odai, fall under medium vulnerability profile, which indicates that the basin is under moderate stress of water resources. The paper also explores prioritization of sub-basinwise adaptation strategies to climate change based on the vulnerability indices.

Keywords: adaptive capacity, exposure, overlay analysis, sensitivity, vulnerability

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Authors: Yang Cao

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Analyzing the oral production of Chinese-speaking learners of English as a second language (L2), we can find a large variety of verb inflections – Why does it seem so hard for them to use consistent correct past morphologies in obligatory past contexts? Failed Functional Features Hypothesis (FFFH) attributes the rather non-target-like performance to the absence of [±past] feature in their L1 Chinese, arguing that for post puberty learners, new features in L2 are no more accessible. By contrast, Missing Surface Inflection Hypothesis (MSIH) tends to believe that all features are actually acquirable for late L2 learners, while due to the mapping difficulties from features to forms, it is hard for them to realize the consistent past morphologies on the surface. However, most of the studies are limited to the verb morphologies in finite clauses and few studies have ever attempted to figure out these learners’ performance in non-finite clauses. Additionally, it has been discussed that Chinese learners may be able to tell the finite/infinite distinction (i.e. the [±finite] feature might be selected in Chinese, even though the existence of [±past] is denied). Therefore, adopting a self-paced reading task (SPR), the current study aims to analyze the processing patterns of Chinese-speaking learners of L2 Russian, in order to find out if they are sensitive to misuse of tense morphologies in both finite and non-finite clauses and whether they are sensitive to the finite/infinite distinction presented in Russian. The study targets L2 Russian due to its systematic morphologies in both present and past tenses. A native Russian group, as well as a group of English-speaking learners of Russian, whose L1 has definitely selected both [±finite] and [±past] features, will also be involved. By comparing and contrasting performance of the three language groups, the study is going to further examine and discuss the two theories, FFFH and MSIH. Preliminary hypotheses are: a) Russian native speakers are expected to spend longer time reading the verb forms which violate the grammar; b) it is expected that Chinese participants are, at least, sensitive to the misuse of inflected verbs in non-finite clauses, although no sensitivity to the misuse of infinitives in finite clauses might be found. Therefore, an interaction of finite and grammaticality is expected to be found, which indicate that these learners are able to tell the finite/infinite distinction; and c) having selected [±finite] and [±past], English-speaking learners of Russian are expected to behave target-likely, supporting L1 transfer.

Keywords: features, finite clauses, morphosyntax, non-finite clauses, past morphologies, present morphologies, Second Language Acquisition, self-paced reading task, verb inflections

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Authors: Peng Lu, Liu Ting

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Because of the difficulty of financing from the external capital market, the reorganization and merger of private enterprises have formed a family group, seeking the help of the internal capital market to alleviate the capital demand. However, the inefficiency of the internal capital market can damage the effect it should have played, and even hinder the development of enterprises. This paper takes the "Tomorrow Group" as the research object to carry on the case analysis. After using the improved cash flow sensitivity coefficient to measure the efficiency of the internal capital market of Tomorrow Group, the inefficiency phenomenon is found. Then the analysis reveals that the reasons for its inefficiency include that the pyramidal equity structure is conducive to control, the separation of cash flow rights and control rights, the concentration of equity leads to poor balance, the abandonment of real industries and information asymmetry.

Keywords: tomorrow group, internal capital market, related-party transactions, Baotou tomorrow technology Co., LTD

Procedia PDF Downloads 137