Search results for: battery diagnosis

Authors: Ali Hamza

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Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.

Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network

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Authors: Mohar Kassim, Hardy Azmir, Rahmat Sholihin Mokhtar

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The purpose of this study is to build portable application software to determine the level of cardiovascular fitness for cadet students of the National Defence University of Malaysia (NDUM). Fitness in the context of this study refers to physical fitness, specifically the cardiovascular endurance level test battery in the form of a 2.4 km run test for UPNM cadet students. This run test will be conducted to measure, test, and evaluate the performance of UPNM cadet students. All the run test results can be recorded electronically inside the portable software and will later be able to show the level of cardiovascular fitness of every cadet student according to age and gender. This software can also calculate the body mass index (BMI). Normative survey method will be used in this study through the analysis of the 2.4 km run test results. The run test scores will be classified in interval and ratio scales. Based on the findings of this study, portable application software will produced. The software will be able to directly assist the Military Training Academy (ALK), Malaysian Armed Forces (ATM), and other relevant agencies in determining the level of cardiovascular fitness among their staff. The test can be done electronically and on portable mode. The next step to be taken is to have this application patented.

Keywords: development, software, application, portable, fitness norms, cardiovascular endurance

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Authors: Leila Keshavarz Afshar, Hedieh Sajedi

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Estimation of biological brain age from MR images is a topic that has been much addressed in recent years due to the importance it attaches to early diagnosis of diseases such as Alzheimer's. In this paper, we use a 3D Convolutional Neural Network (CNN) to provide a method for estimating the biological age of the brain. The 3D-CNN model is trained by MRI data that has been normalized. In addition, to reduce computation while saving overall performance, some effectual slices are selected for age estimation. By this method, the biological age of individuals using selected normalized data was estimated with Mean Absolute Error (MAE) of 4.82 years.

Keywords: brain age estimation, biological age, 3D-CNN, deep learning, T1-weighted image, SPM, preprocessing, MRI, canny, gray matter

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Authors: Smriti Agarwal, Ashish Payal, B. V. R. Reddy

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IEEE 802.15.4 is a Low Rate Wireless Personal Area Networks (LR-WPAN) standard combined with ZigBee, which is going to enable new applications in Wireless Sensor Networks (WSNs) and Internet of Things (IoT) domain. In recent years, it has become a popular standard for WSNs. Wireless communication among sensor motes, enabled by IEEE 802.15.4 standard, is extensively replacing the existing wired technology in a wide range of monitoring and control applications. Researchers have proposed a routing framework and mechanism that interacts with the IEEE 802.15.4 standard using software platform. In this paper, we have designed and implemented MAC based routing (MBR) based on IEEE 802.15.4 standard using a hardware platform “SENSEnuts”. The experimental results include data through light and temperature sensors obtained from communication between PAN coordinator and source node through coordinator, MAC address of some modules used in the experimental setup, topology of the network created for simulation and the remaining battery power of the source node. Our experimental effort on a WSN Testbed has helped us in bridging the gap between theoretical and practical aspect of implementing IEEE 802.15.4 for WSNs applications.

Keywords: IEEE 802.15.4, routing, WSN, ZigBee

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Authors: A. Yawootti, P. Intra, P. Sardyoung, P. Phoosomma, R. Puttipattanasak, S. Leeragreephol, N. Tippayawong

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The aim of this work is to design, develop and test the low-cost implementation of a particulate air pollution sensor system for continuous monitoring of outdoors and indoors particulate air pollution at a lower cost than existing instruments. In this study, measuring electrostatic charge of particles technique via high efficiency particulate-free air filter was carried out. The developed detector consists of a PM10 impactor, a particle charger, a Faraday cup electrometer, a flow meter and controller, a vacuum pump, a DC high voltage power supply and a data processing and control unit. It was reported that the developed detector was capable of measuring mass concentration of particulate ranging from 0 to 500 µg/m3 corresponding to number concentration of particulate ranging from 106 to 1012 particles/m3 with measurement time less than 1 sec. The measurement data of the sensor connects to the internet through a GSM connection to a public cellular network. In this development, the apparatus was applied the energy by a 12 V, 7 A internal battery for continuous measurement of about 20 hours. Finally, the developed apparatus was found to be close agreement with the import standard instrument, portable and benefit for air pollution and particulate matter measurements.

Keywords: particulate, air pollution, wireless communication, sensor

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Authors: Krishan Kumar, Mohit Mittal, Pramod Kumar

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Wireless sensor network is one of the most promising communication networks for monitoring remote environmental areas. In this network, all the sensor nodes are communicated with each other via radio signals. The sensor nodes have capability of sensing, data storage and processing. The sensor nodes collect the information through neighboring nodes to particular node. The data collection and processing is done by data aggregation techniques. For the data aggregation in sensor network, clustering technique is implemented in the sensor network by implementing self-organizing feature map (SOFM) neural network. Some of the sensor nodes are selected as cluster head nodes. The information aggregated to cluster head nodes from non-cluster head nodes and then this information is transferred to base station (or sink nodes). The aim of this paper is to manage the huge amount of data with the help of SOM neural network. Clustered data is selected to transfer to base station instead of whole information aggregated at cluster head nodes. This reduces the battery consumption over the huge data management. The network lifetime is enhanced at a greater extent.

Keywords: artificial neural network, data clustering, self organization feature map, wireless sensor network

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Authors: Jonathan Gong

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: Vaibhavi Birle

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Posterior reversible encephalopathy syndrome is a rare clinic-radiological syndrome associated with acute changes in blood pressure during pregnancy. It is characterized symptomatically by headache, seizures, altered mental status, and visual blurring with radiological changes of white matter (vasogenic oedema) affecting the posterior occipital and parietal lobes of the brain. It is being increasingly recognized due to increased institutional deliveries and advances in imaging particularly magnetic resonance imaging (MRI). In spite of the increasing diagnosis the prediction of PRES and patient factors affecting susceptibility is still not clear. Hence, we conducted the retrospective study to analyse the factors associated with PRES at our tertiary centre.

Keywords: pres syndrome, eclampsia, maternal outcome, fetal outcome

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Authors: Kulkarni Rohini D.

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While emerging as plausible sources of energy generation, new technologies, including photovoltaic (PV) solar panels, home battery energy storage systems, and electric vehicles (EVs), are exacerbating the operations of power distribution networks for distribution network operators (DNOs). Renewable energy production fluctuates, stemming in over- and under-generation energy, further complicating the issue of storing excess power and using it when necessary. Though renewable sources are non-exhausting and reoccurring, power storage of generated energy is almost as paramount as to its production process. Hence, to ensure smooth and efficient power storage at different levels, Grid balancing technologies are consequently the next theme to address in the sustainable space and growth sector. But, since hydrogen batteries were used in the earlier days to achieve this balance in power grids, new, recent advancements are more efficient and capable per unit of storage space while also being distinctive in terms of their underlying operating principles. The underlying technologies of "Flow batteries," "Gravity Solutions," and "Graphene Batteries" already have entered the market and are leading the race for efficient storage device solutions that will improve and stabilize Grid networks, followed by Grid balancing technologies.

Keywords: flow batteries, grid balancing, hydrogen batteries, power storage, solar

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Authors: Monika Dmitrzak-Weglarz, Aleksandra Rajewska-Rager, Maria Skibinska, Natalia Lepczynska, Piotr Sibilski, Joanna Pawlak, Pawel Kapelski, Joanna Hauser

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Epidermal growth factor (EGF) is a well-known neurotrophic factor that involves in neuronal growth and synaptic plasticity. The proteomic research provided in order to identify novel candidate biological markers for mood disorders focused on elevated EGF serum level in patients during depression episode. However, the EGF association with mood disorder spectrum among adolescents and young adults has not been studied extensively. In this study, we aim to investigate the serum levels of EGF in adolescents and young adults during hypo/manic, depressive episodes and in remission compared to healthy control group. In our study, we involved 80 patients aged 12-24 years in 2-year follow-up study with a primary diagnosis of mood disorder spectrum, and 35 healthy volunteers matched by age and gender. Diagnoses were established according to DSM-IV-TR criteria using structured clinical interviews: K-SADS for child and adolescents, and SCID for young adults. Clinical and biological evaluations were made at baseline and euthymic mood (at 3th or 6th month of treatment and after 1 and 2 years). The Young Mania Rating Scale and Hamilton Rating Scale for Depression were used for assessment. The study protocols were approved by the relevant ethics committee. Serum protein concentration was determined by Enzyme-Linked Immunosorbent Assays (ELISA) method. Human EGF (cat. no DY 236) DuoSet ELISA kit was used (R&D Systems). Serum EGF levels were analysed with following variables: age, age under 18 and above 18 years old, sex, family history of affective disorders, drug-free vs. medicated. Shapiro-Wilk test was used to test the normality of the data. The homogeneity of variance was calculated with Levene’s test. EGF levels showed non-normal distribution and the homogeneity of variance was violated. Non-parametric tests: Mann-Whitney U test, Kruskall-Wallis ANOVA, Friedman’s ANOVA, Wilcoxon signed rank test, Spearman correlation coefficient was applied in the analyses The statistical significance level was set at p<0.05. Elevated EGF level at baseline (p=0.001) and at month 24 (p=0.02) was detected in study subjects compared with controls. Increased EGF level in women at month 12 (p=0.02) compared to men in study group have been observed. Using Wilcoxon signed rank test differences in EGF levels were detected: decrease from baseline to month 3 (p=0.014) and increase comparing: month 3 vs. 24 (p=0.013); month 6 vs. 12 (p=0.021) and vs. 24 (p=0.008). EGF level at baseline was negatively correlated with depression and mania occurrence at 24 months. EGF level at 24 months was positively correlated with depression and mania occurrence at 12 months. No other correlations of EGF levels with clinical and demographical variables have been detected. The findings of the present study indicate that EGF serum level is significantly elevated in the study group of patients compared to the controls. We also observed fluctuations in EGF levels during two years of disease observation. EGF seems to be useful as an early marker for prediction of diagnosis, course of illness and treatment response in young patients during first episode od mood disorders, which requires further investigation. Grant was founded by National Science Center in Poland no 2011/03/D/NZ5/06146.

Keywords: biological marker, epidermal growth factor, mood disorders, prediction

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Authors: Mustapha Majid

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Gone were the days when technicians/mechanics will have to spend too much time trying to identify a mechanical fault and rectify the problem. Now the emphasis is on the use of Automobile diagnosing Equipment through the use of computers and special software. An investigation conducted at Tamale Metropolis and Accra in the Northern and Greater Accra regions of Ghana, respectively. Methodology for data gathering were; questionnaires, physical observation, interviews, and newspaper. The study revealed that majority of mechanics lack computer skills which can enable them use diagnosis tools such as Exhaust Gas Analyzer, Scan Tools, Electronic Wheel Balancing machine, etc.

Keywords: diagnosing, local garages and modern garages, lack of knowledge of diagnosing posing an existential threat, training of local mechanics

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Authors: Sheema Shuja Khattak, Gule Saman, Imran Khan, Abdus Salam

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Image segmentation plays an important role in medical imaging applications. Therefore, accurate methods are needed for the successful segmentation of medical images for diagnosis and detection of various diseases. In this paper, we have used maximum entropy to achieve image segmentation. Maximum entropy has been calculated using Shannon, Renyi, and Tsallis entropies. This work has novelty based on the detection of skin lesion caused by the bite of a parasite called Sand Fly causing the disease is called Cutaneous Leishmaniasis.

Keywords: shannon, maximum entropy, Renyi, Tsallis entropy

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Authors: Surjit Angra, Gajanan Rane, Vinod Kumar, Sushma Rani

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This paper presents metallurgical investigation of failed helical gear of diesel engine gear box used in a car. The failure had occurred near the bottomland of the tooth spacing. The failed surface was studied under Scanning Electron Microscope (SEM) and also visually investigated. The images produced through SEM at various magnifications were studied. Detailed metallurgical study indicates that failure was due to foreign material inclusion which is a casting defect. Further study also revealed pitting, spalling and inter-granular fracture as the causes of gear failure.

Keywords: helical gear, scanning electron microscope, casting defect, pitting

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Authors: M. Dasgupta, S. Banerjee

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Content Based Image Retrieval (CBIR) coupled with Case Based Reasoning (CBR) is a paradigm that is becoming increasingly popular in the diagnosis and therapy planning of medical ailments utilizing the digital content of medical images. This paper presents a survey of some of the promising approaches used in the detection of abnormalities in retina images as well in mammographic screening and detection of regions of interest in MRI scans of the brain. We also describe our proposed algorithm to detect hard exudates in fundus images of the retina of Diabetic Retinopathy patients.

Keywords: case based reasoning, exudates, retina image, similarity based retrieval

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Authors: Faisal AlShammari, Abdulmajid Addali

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Monitoring the conditions of rotating machinery as bearing is important in order to improve its stability of works. Acoustic emission (AE) and vibration analysis are some of the most accomplished techniques used for this purpose. Acoustic emission has the ability to detect the initial phase of component degradation. Moreover, it has been observed that the success of vibration analysis does not take place below 100 rpm rotational speed. This because the energy generated below 100 rpm rotational speed is not detectable using conventional vibration. From this pint, this paper has presented a focused review of using acoustic emission techniques for monitoring bearings condition.

Keywords: condition monitoring, stress wave analysis, low-speed bearings, bearing defect diagnosis

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Authors: T. Mcgraw, F. N. Morin, N. Desai

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Background: Antimicrobial resistance is a critical issue in global health care and a significant contributor to increased patient morbidity and mortality. Suspected urinary tract infection (UTI) is a key area of inappropriate antibiotic prescription in pediatrics. Management patterns of infectious diseases have been shown to vary by provider type within a single setting. The aim of this study was to assess compliance with national UTI management guidelines by nurse practitioners in a pediatric emergency department (ED). Methods: This was a post-hoc analysis of a retrospective cohort study to review and evaluate visits to a tertiary care freestanding pediatric emergency department. Patients were included if they were 60 days to 36 months old and discharged with a diagnosis of UTI or ‘rule-out UTI’ between July 2015 and July 2020. Primary outcome measure was proportion of visits seen by Nurse Practitioners (NP) which were associated with national guideline compliance in the diagnosis and treatment of suspected UTI. We performed descriptive statistics and comparative analyses to determine differences in practice patterns between NPs, and physicians. Results: A total of 636 charts were reviewed, of which 402 patients met inclusion criteria. 17 patients were treated by NPs, 385 were treated by either Pediatric Emergency Medicine physicians (PEM) or non-PEM physicians. Overall, the proportion of infants receiving guideline-compliant care was 25.9% (21.8-30.4%). Of those who were prescribed antibiotics, 79.6% (74.7-83.8%) received first line guideline recommended therapy and 58.9% (53.8-63.8%) received fully compliant therapy with respect to age, dose, duration, and frequency. In patients treated by NPs, 16/17 (94%(95% CI:73.0-99.0)) required antibiotics, 15/16 (93%(95% CI: 71.7-98.9)) were treated with first line agent (cephalexin), 8/16 (50%(95% CI:28-72)) were guideline compliant of dose and duration. 5/8 (63%(95% CI:30.6-86.3)) were noncompliant for dose being too high. There was no difference in receiving guideline compliant empiric antibiotic therapy between physicians and nurse practitioners (OR: 0.837 CI: 0.302-2.69). Conclusion: In this post-hoc analysis, guideline noncompliance by nurse practitioners is common in children tested and treated for UTIs in a pediatric emergency department. Care by a Nurse Practitioner was not associated with greater rate of noncompliance than care by a Pediatric Emergency Medicine physician. Future appropriately powered studies may focus on confirming these results.

Keywords: antibiotic stewardship, infectious disease, nurse practitioner, urinary tract infection

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Authors: Benu Gupta

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The purpose of the study was to assess the impact of yogic exercise on cardiovascular exercises on selected college students of high altitude. The research was conducted on college students of high altitude in Shimla for their cardiovascular function [Blood Pressure (BP), VO2 Max (TLC) and Pulse Rate (PR)] in respect to yogic exercise. Total 139 students were randomly selected from Himachal University colleges in Shimla. The study was conducted in three phases. The subjects were identified in the first phase of research program then further in next phase they were physiologically tested, and yogic exercise battery was operated in different time frame. The entire subjects were treated with three months yogic exercise. The entire lot of students were again evaluated physiologically [(Cardiovascular measurement: Blood Pressure (BP), VO2 Max (TLC) and Pulse Rate (PR)] with standard equipments. The statistical analyses of the variance (PR, BP (SBP & DBP) and TLC) were done. The result reveals that there was a significant difference in TLC; whereas there was no significant difference in PR. For BP statistical analysis suggests no significant difference were formed. Result showed that the BP of the participants were more inclined towards normal standard BP i.e. 120-80 mmHg.

Keywords: cardiovascular function, college students, high altitude, yogic exercise

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Authors: Kefaya Qaddoum

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The aim of this study is to predict breast cancer and to construct a supportive model that will stimulate a more reliable prediction as a factor that is fundamental for public health. In this study, we utilize general regression neural networks (GRNN) to replace the normal predictions with prediction periods to achieve a reasonable percentage of confidence. The mechanism employed here utilises a machine learning system called conformal prediction (CP), in order to assign consistent confidence measures to predictions, which are combined with GRNN. We apply the resulting algorithm to the problem of breast cancer diagnosis. The results show that the prediction constructed by this method is reasonable and could be useful in practice.

Keywords: neural network, conformal prediction, cancer classification, regression

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Authors: Nesrine Berber, Hafid Haffaf, Abdel Madjid Meghabar

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In the last decade, technology has continued to grow and has changed the structure of our society. Today, new technologies including the information and communication (ICT) play a main role which importance continues to grow, now it's become indispensable to the economic, social and cultural. Thus, ICT technology has proven to be as a promising intervention in the area of road transport. The supervision model of class of train of intelligent and autonomous vehicles leads us to give some defintions about IAV and the different technologies used for communication between them. Our aim in this work is to present an hypergraph modeling a class of train of Intelligent and Autonomous Vehicles (IAV).

Keywords: intelligent transportation system, intelligent autonomous vehicles, Ad Hoc network, wireless technologies, hypergraph modeling, supervision

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Authors: Shivendra Pratap Singh, Rishabh Sharma

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The widespread adoption of mobile technology in recent years has revolutionized the way we communicate and access information. The evolution of mobile systems has been rapid and impactful, shaping our lives and changing the way we live and work. However, despite its significant influence, the history and development of mobile technology are not well understood by the general public. This research paper aims to examine the history, technology and future of mobile systems, exploring their evolution from early mobile phones to the latest smartphones and beyond. The study will analyze the technological advancements and innovations that have shaped the mobile industry, from the introduction of mobile internet and multimedia capabilities to the integration of artificial intelligence and 5G networks. Additionally, the paper will also address the challenges and opportunities facing the future of mobile technology, such as privacy concerns, battery life, and the increasing demand for high-speed internet. Finally, the paper will also provide insights into potential future developments and innovations in the mobile sector, such as foldable phones, wearable technology, and the Internet of Things (IoT). The purpose of this research paper is to provide a comprehensive overview of the history, technology, and future of mobile systems, shedding light on their impact on society and the challenges and opportunities that lie ahead.

Keywords: mobile technology, artificial intelligence, networking, iot, technological advancements, smartphones

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Authors: Zeena Dcosta, Junaid Ahmed, Ceena Denny, Nandita Shenoy

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In the field of dentistry, there are many conditions which warrant the requirement of three-dimensional imaging that can aid in diagnosis and therapeutic management. Cone beam computed tomography (CBCT) is considered highly accurate in producing a three-dimensional image of an object and provides a complete insight of various findings in the captured volume. But, most of the clinicians focus primarily on the teeth and jaws and numerous unanticipated clinically significant incidental findings may be missed out. Rapid integration of CBCT into the practice of dentistry has led to the detection of various incidental findings. However, the prevalence of these incidental findings is still unknown. Thus, the study aimed to discern the reason for referral and to identify incidental findings on the referred CBCT scans. Patient’s demographic data such as age and gender was noted. CBCT scans of multiple fields of views (FOV) were considered. The referral for CBCT scans was broadly classified into two major categories: diagnostic scan and treatment planning scan. Any finding on the CBCT volumes, other than the area of concern was recorded as incidental finding which was noted under airway, developmental, pathological, endodontics, TMJ, bone, soft tissue calcifications and others. Few of the incidental findings noted under airway were deviated nasal septum, nasal turbinate hypertrophy, mucosal thickening and pneumatization of sinus. Developmental incidental findings included dilaceration, impaction, pulp stone and gubernacular canal. Resorption of teeth and periapical pathologies were noted under pathological incidental findings. Root fracture along with over and under obturation was noted under endodontics. Incidental findings under TMJ were flattening, erosion and bifid condyle. Enostosis and exostosis were noted under bone lesions. Tonsillolth, sialolith and calcified styloid ligament were noted under soft tissue calcifications. Incidental findings under others included foreign body, fused C1- C2 vertebrae, nutrient canals, and pneumatocyst. Maxillofacial radiologists should be aware of possible incidental findings and should be vigilant about comprehensively evaluating the entire captured volume, which can help in early diagnosis of any potential pathologies that may go undetected. Interpretation of CBCT is truly an art and with the experience, we can unravel the secrets hidden in the grey shades of the radiographic image.

Keywords: cone beam computed tomography, incidental findings, maxillofacial region, radiologist

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Authors: Sangguk Ahn, Youngyu Kim, Jaheon Gu, Gyoutae Park

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In this paper, we developed a dual gas leak instrument to detect Hydrocarbon (HC) and Monoxide (CO) gases. To two kinds of gases, it is necessary to design compact structure for sensors. And then it is important to draw sensing circuits such as measuring, amplifying and filtering. After that, it should be well programmed with robust, systematic and module coding methods. In center of them, improvement of accuracy and initial response time are a matter of vital importance. To manufacture distinguished gas leak detector, we applied intrinsically safe explosion-proof structure to lithium ion battery, main circuits, a pump with motor, color LCD interfaces and sensing circuits. On software, to enhance measuring accuracy we used numerical analysis such as Lagrange and Neville interpolation. Performance test result is conducted by using standard Methane with seven different concentrations with three other products. We want raise risk prevention and efficiency of gas safe management through distributing to the field of gas safety. Acknowledgment: This study was supported by Small and Medium Business Administration under the research theme of ‘Commercialized Development of a portable intrinsically safe explosion-proof type dual gas leak detector’, (task number S2456036).

Keywords: gas leak, dual gas detector, intrinsically safe, explosion proof

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Authors: Ahmed Kiani

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The United Arab Emirates is clearly facing a multitude of challenges in curbing its greenhouse gas emissions to meet its pre-allotted framework of Kyoto protocol and COP21 targets due to its hunger for modernization, industrialization, infrastructure growth, soaring population and oil and gas activity. In this work, we focus on the bonafide zero emission electric vehicles market penetration in the country’s transport industry for emission reduction. We study the global electric vehicle market trends, the complementary battery technologies and the trends by manufacturers, emission standards across borders and prioritized advancements which will ultimately dictate the terms of future conditions for the United Arab Emirate transport industry. Based on our findings and analysis at every stage of current viability and state-of-transport-affairs, we postulate policy recommendations to local governmental entities from a supply and demand perspective covering aspects of technology, infrastructure requirements, change in power dynamics, end user incentives program, market regulators behavior and communications amongst key stakeholders. 

Keywords: electric vehicles, greenhouse gas emission reductions, market analysis, policy recommendations

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Authors: Madeleine Cox

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Objective: Discussion of diagnosis and management options for myometrial ectopic pregnancy Case: A 30 yo G1P0 presented to the emergency department with vaginal bleeding for the last 4 days. She had a positive home urine pregnancy test, confirmed with a serum HCG. When she presented for an ultrasound, there was no intrauterine pregnancy, no evidence of adnexal pregnancy, however, the anterior myometrium of the uterus was noted to be markedly abnormal. When she presented to the emergency department of a busy tertiary hospital in Queensland, she had a small amount of vaginal bleeding, was anxious but well, observations normal. Repeat blood testes demonstrated a serum HCG of 9246 IU/L, haemoglobin of 143g/L. The patient had an interesting history of a right oophorectomy and open myomectomy in another country. A repeat ultrasound again showed an abnormality within the myometrium of the uterus, which was initially reported as concerning for an AVM, or potentially invasive gestational trophoblastic disease. An MRI was organised 2 days later, which demonstrated a intramural/subserosal irregularity in the right lateral body measuring 35x38x42mm with peripheral enhancement and central cystic components, favouring a myometrial ectopic most likely at the site of previous myomectomy. Alternative diagnosis of AVM, GTD were considered less likely. After discussion with the patient, IV methotrexate was administered as an in patient 4 days after her initial presentation to emergency. After this, her HCG fell to 1236 IU/L on day 6 post treatment. Weekly reviews showed stable ultrasound appearances with a steadily dropping HCG level. A repeat MRI was performed 3 weeks after methotrexate administration which confirmed involution of the myometrial ectopic, however, showed ongoing progression of vascularity surrounding the site. Despite resolution of HCG, the patient persisted to have ongoing bleeding associated with this and went to have uterine artery embolisation. Follow up ultrasound showed resolution of abnormal vascularity and negative HCG levels. Conclusion: Myometrial ectopic pregnancies are a rare occurrence and require a multidisciplinary approach to achieve timely management for these patients. This patient was in a very well resourced setting with excellent access to Interventional Radiology and specialist Radiologists who could work together with the Obstetrics, Gynaecology, and Maternal Fetal Medicine team to provide multiple options of management which preserved her fertility. This case has a very good outcome, with the patient being referred back to our service 12 months later with an early intrauterine pregnancy.

Keywords: ectopic, pregnancy, miscarriage, gynaecology

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Authors: Annie B. V., Anu A. G., Harikumar R.

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Microgrid (MG) is a self-governing miniature section of the power system. Nowadays the majority of loads and energy storage devices are inherently in DC form. This necessitates a greater scope of research in the various types of energy storage devices in DC microgrids. In a modern power system, DC microgrid is a manageable electric power system usually integrated with renewable energy sources (RESs) and DC loads with the help of power electronic converters. The stability of the DC microgrid mainly depends on the power imbalance. Power imbalance due to the presence of intermittent renewable energy resources (RERs) is supplied by energy storage devices. Battery, supercapacitor, flywheel, etc. are some of the commonly used energy storage devices. Owing to the high energy density provided by the batteries, this type of energy storage system is mainly utilized in all sorts of hybrid energy storage systems. To minimize the stability issues, a Supercapacitor (SC) is usually interfaced with the help of a bidirectional DC/DC converter. SC can exchange power during transient conditions due to its high power density. This paper analyses the stability issues of DC microgrids with hybrid energy storage systems (HESSs) arises from a reduction in SC operating voltage due to self-discharge. The stability of DC microgrid and power management is analyzed with different control strategies.

Keywords: DC microgrid, hybrid energy storage system (HESS), power management, small signal modeling, supercapacitor

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Authors: A. Derrouazin, N. Mekkakia-M, R. Taleb, M. Helaimi, A. Benbouali

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Nowadays the use of different sources of renewable energy for the production of electricity is the concern of everyone, as, even impersonal domestic use of the electricity in isolated sites or in town. As the conventional sources of energy are shrinking, a need has arisen to look for alternative sources of energy with more emphasis on its optimal use. This paper presents design of a sustainable Hybrid Power System (PV-Wind-Storage) assisted by grid as supplementary sources applied to case study residential house, to meet its entire energy demand. A Fuzzy control system model has been developed to optimize and control flow of power from these sources. This energy requirement is mainly fulfilled from PV and Wind energy stored in batteries module for critical load of a residential house and supplemented by grid for base and peak load. The system has been developed for maximum daily households load energy of 3kWh and can be scaled to any higher value as per requirement of individual /community house ranging from 3kWh/day to 10kWh/day, as per the requirement. The simulation work, using intelligent energy management, has resulted in an optimal yield leading to average reduction in cost of electricity by 50% per day.

Keywords: photovoltaic (PV), wind turbine, battery, microcontroller, fuzzy control (FC), Matlab

Procedia PDF Downloads 645

Authors: Saleh Elawgali

Abstract:

Current spectrums of a four pole-pair, 400 kW induction machine were calculated for the cases of full symmetry and static eccentricity. The calculations involve integration of 93 electrical plus four mechanical ordinary differential equations. Electrical equations account for variable inductances affected by slotting and eccentricities. The calculations were followed by Fourier analysis of the stator currents in steady state operation. Zooms of the current spectrums, around the 50 Hz fundamental harmonic as well as of the main slot harmonic zone, were included. The spectrums included refer to both calculated and measured currents.

Keywords: diagnostic, harmonic, induction machine, spectrum

Procedia PDF Downloads 518

Authors: Melissa K. Hord, Stephen P. Whiteside

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Impairment in functioning is a requirement for diagnosing psychopathology, identifying individuals in need of treatment, and documenting improvement with treatment. Further, identifying different types of functional impairment can guide educators and treatment providers. However, most assessment tools focus on symptom severity and few measures assess impairment associated with childhood anxiety disorders. The child- and parent-report versions of the Child Sheehan Disability Scale (CSDS) are measures that may provide useful information regarding impairment. The purpose of the present study is to examine whether children diagnosed with different anxiety disorders have greater impairment in school or home functioning based on self or parent report. The sample consisted of 844 children ages 5 to 19 years of age (mean 13.43, 61% female, 90.9% Caucasian), including 281 children diagnosed with obsessive compulsive disorder (OCD), 200 with generalized anxiety disorder (GAD), 176 with social phobia, 83 with separation anxiety, 61 with anxiety not otherwise specified (NOS), 30 with panic disorder, and 13 with panic with agoraphobia. To assess whether children and parents reported greater impairment in school or home functioning, a multivariate analysis of variance was conducted. (The assumptions of independence and homogeneity of variance were checked and met). A significant difference was found, Pillai's trace = .143, F (4, 28) = 4.19, p < .001, partial eta squared = .04. Post hoc comparisons using the Tukey HSD test indicated that children report significantly greater impairment in school with panic disorder (M=5.18, SD=3.28), social phobia (M=4.95, SD=3.20), and OCD (M=4.62, SD=3.32) compared to other diagnoses; whereas parents endorse significantly greater school impairment when their child has a social phobia (M=5.70, SD=3.39) diagnosis. Interestingly, both children and parents reported greater impairment in family functioning for an OCD (child report M=5.37, SD=3.20; parent report M=5.59, SD=3.38) diagnosis compared to other anxiety diagnoses. (Additional findings for the anxiety disorders associated with less impairment will also be presented). The results of the current study have important implications for educators and treatment providers who are working with anxious children. First, understanding that differences exist in how children and parents view impairment related to childhood anxiety can help those working with these families to be more sensitive during interactions. Second, evidence suggests that difficulties in one environment do not necessarily translate to another environment, thus caregivers may benefit from careful explanation of observations obtained by educators. Third, results support the use of the CSDS measure by treatment providers to identify impairment across environments in order to more effectively target interventions.

Keywords: anxiety, childhood, impairment, school functioning

Procedia PDF Downloads 275

Authors: Maria Klitgaard Christensen, Carmen Lim, Sukanta Saha, Danielle Cannon, Finley Prentis, Oleguer Plana-Ripoll, Natalie Momen, Kim Moesgaard Iburg, John J. McGrath

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Introduction: About a third of the world’s population will develop a mental disorder over their lifetime. Having a mental disorder is a huge burden in health loss and cost for the individual, but also for society because of treatment cost, production loss and caregivers’ cost. The objective of this study is to synthesize the international published literature on the economic burden of mental disorders. Methods: Systematic literature searches were conducted in the databases PubMed, Embase, Web of Science, EconLit, NHS York Database and PsychInfo using key terms for cost and mental disorders. Searches were restricted to 1980 until May 2019. The inclusion criteria were: (1) cost-of-illness studies or cost-analyses, (2) diagnosis of at least one mental disorder, (3) samples based on the general population, and (4) outcome in monetary units. 13,640 publications were screened by their title/abstract and 439 articles were full-text screened by at least two independent reviewers. 112 articles were included from the systematic searches and 31 articles from snowball searching, giving a total of 143 included articles. Results: Information about diagnosis, diagnostic criteria, sample size, age, sex, data sources, study perspective, study period, costing approach, cost categories, discount rate and production loss method and cost unit was extracted. The vast majority of the included studies were from Western countries and only a few from Africa and South America. The disorder group most often investigated was mood disorders, followed by schizophrenia and neurotic disorders. The disorder group least examined was intellectual disabilities, followed by eating disorders. The preliminary results show a substantial variety in the used perspective, methodology, costs components and outcomes in the included studies. An online tool is under development enabling the reader to explore the published information on costs by type of mental disorder, subgroups, country, methodology, and study quality. Discussion: This is the first systematic review synthesizing the economic cost of mental disorders worldwide. The paper will provide an important and comprehensive overview over the economic burden of mental disorders, and the output from this review will inform policymaking.

Keywords: cost-of-illness, health economics, mental disorders, systematic review

Procedia PDF Downloads 128

Authors: A. Al Yousef, A. Toulon, L. Petit, S. Fraitag, F. Ruemmele, S. Hadj-Rabia, C. Bodemer

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Cutaneous Crohn’s disease (CCD) refers to an extremely rare granulomatous inflammation of the skin that is non-contiguous to the bowel tract. These cutaneous lesions can occur prior to, concurrent with, or after the gastrointestinal manifestations. In adults, CCD most frequently occurs in the setting of well-documented intestinal disease. Only 20% of cases occur prior to its development. Review of CCD in children, reveals that 86% of cases (24 of 28) occurring in patients without a known diagnosis of intestinal Crohn’s disease. Overall, the genitalia was the most commonly involved location, representing 21 of the 28 cases with 16 vulvar and 5 penile/scrotal lesions.

Keywords: Crohn’s disease, cutaneous manifestations, children, atypical axillary involvement

Procedia PDF Downloads 278