Search results for: decoding sequential search algorithm

Authors: Leonard Tiong, Sonit Singh, Kevin Ho Shon, Sarah Lewis

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Investigation into the use of artificial intelligence in radiology continues to develop at a rapid rate. During the coronavirus pandemic, the combination of an exponential increase in chest x-rays and unpredictable staff shortages resulted in a huge strain on the department's workload. There is a World Health Organisation estimate that two-thirds of the global population does not have access to diagnostic radiology. Therefore, there could be demand for a program that could detect acute changes in imaging compatible with infection to assist with screening. We generated a conventional neural network and tested its efficacy in recognizing changes compatible with coronavirus infection. Following ethics approval, a deidentified set of 77 normal and 77 abnormal chest x-rays in patients with confirmed coronavirus infection were used to generate an algorithm that could train, validate and then test itself. DICOM and PNG image formats were selected due to their lossless file format. The model was trained with 100 images (50 positive, 50 negative), validated against 28 samples (14 positive, 14 negative), and tested against 26 samples (13 positive, 13 negative). The initial training of the model involved training a conventional neural network in what constituted a normal study and changes on the x-rays compatible with coronavirus infection. The weightings were then modified, and the model was executed again. The training samples were in batch sizes of 8 and underwent 25 epochs of training. The results trended towards an 85.71% true positive/true negative detection rate and an area under the curve trending towards 0.95, indicating approximately 95% accuracy in detecting changes on chest X-rays compatible with coronavirus infection. Study limitations include access to only a small dataset and no specificity in the diagnosis. Following a discussion with our programmer, there are areas where modifications in the weighting of the algorithm can be made in order to improve the detection rates. Given the high detection rate of the program, and the potential ease of implementation, this would be effective in assisting staff that is not trained in radiology in detecting otherwise subtle changes that might not be appreciated on imaging. Limitations include the lack of a differential diagnosis and application of the appropriate clinical history, although this may be less of a problem in day-to-day clinical practice. It is nonetheless our belief that implementing this program and widening its scope to detecting multiple pathologies such as lung masses will greatly assist both the radiology department and our colleagues in increasing workflow and detection rate.

Keywords: artificial intelligence, COVID, neural network, machine learning

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Authors: Anupama A. P.

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The Malayalam film industry, known as Mollywood, has a rich tradition of storytelling and cultural significance within Indian cinema. Middlebrow films have emerged as a distinct influential category, particularly in the 1980s, with directors like K.G. George, who engaged with female subjectivity and drew inspiration from the ‘women’s cinema’ of the 1950s and 1960s. In recent decades, particularly post-2010, the industry has transformed significantly with a new generation of filmmakers diverging from melodrama and new wave of the past, incorporating advanced technology and modern content. This study examines the evolution and impact of Malayalam middlebrow cinema in the 21st century, focusing on post-2000 films and their influence on contemporary spectator experiences. These films appeal to a wide range of audiences without compromising on their artistic integrity, tackling social issues and personal dramas with thematic and narrative complexity. Historically, middlebrow films in Malayalam cinema have portrayed realism and addressed the socio-political climate of Kerala, blending realism with reflexivity and moving away from traditional sentimentality. This shift is evident in the new generation of Malayalam films, which present a global representation of characters and a modern treatment of individuals. To provide a comprehensive understanding of this evolution, the study analyzes a diverse selection of films such as Kerala Varma Pazhassi Raja (2009), Drishyam (2013), Maheshinte Prathikaaram (2016), Take Off (2017), and Thondimuthalum Driksakshiyum (2017) and Virus (2019) illustrating the broad thematic range and innovative narrative techniques characteristic of this genre. These films exemplify how middlebrow cinema continues to evolve, adapting to changing societal contexts and audience expectations. This research employs a theoretical methodology, drawing on cultural studies and audience reception theory, utilizing frameworks such as Bordwell’s narrative theory, Deleuze’s concept of deterritorialization, and Hall’s encoding/decoding model to analyze the changes in Malayalam middlebrow cinema and interpret the storytelling methods, spectator experience, and audience reception of these films. The findings indicate that Malayalam middlebrow cinema post-2010 offers a spectator experience that is both intellectually stimulating and broadly appealing. This study highlights the critical role of middlebrow cinema in reflecting and shaping societal values, making it a significant cultural artefact within the broader context of Indian and global cinema. By bridging entertainment with thought-provoking narratives, these films engage audiences and contribute to wider cultural discourse, making them pivotal in contemporary cinematic landscapes. To conclude, this study highlights the importance of Malayalam middle-brow cinema in influencing contemporary cinematic tastes. The nuanced and approachable narratives of post-2010 films are posited to assume an increasingly pivotal role in the future of Malayalam cinema. By providing a deeper understanding of Malayalam middlebrow cinema and its societal implications, this study enriches theoretical discourse, promotes regional cinema, and offers valuable insights into contemporary spectator experiences and the future trajectory of Malayalam cinema.

Keywords: Malayalam cinema, middlebrow cinema, spectator experience, audience reception, deterritorialization

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Authors: Ibrahim A. Adeniran, Rui F. Zhu, Man S. Wong

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Owing to the insufficiency in the spatial representativeness and continuity of in situ temperature measurements from weather stations (WS), the use of temperature measurement from WS for large-range diurnal analysis in heterogenous landscapes has been limited. This has made the accurate estimation of land surface temperature (LST) from remotely sensed data more crucial. Moreover, the study of dynamic interaction between the atmosphere and the physical surface of the Earth could be enhanced at both annual and diurnal scales by using optimal LST data derived from satellite sensors. The tradeoff between the spatial and temporal resolution of LSTs from satellite’s thermal infrared sensors (TIRS) has, however, been a major challenge, especially when high spatiotemporal LST data are recommended. It is well-known from existing literature that polar satellites have the advantage of high spatial resolution, while geostationary satellites have a high temporal resolution. Hence, this study is aimed at designing a framework for the cross-comparison of LST data from polar and geostationary satellites in a heterogeneous landscape. This could help to understand the relationship between the LST estimates from the two satellites and, consequently, their integration in diurnal LST analysis. Landsat-8 satellite data will be used as the representative of the polar satellite due to the availability of its long-term series, while the Himawari-8 satellite will be used as the data source for the geostationary satellite because of its improved TIRS. For the study area, Hong Kong Special Administrative Region (HK SAR) will be selected; this is due to the heterogeneity in the landscape of the region. LST data will be retrieved from both satellites using the Split window algorithm (SWA), and the resulting data will be validated by comparing satellite-derived LST data with temperature data from automatic WS in HK SAR. The LST data from the satellite data will then be separated based on the land use classification in HK SAR using the Global Land Cover by National Mapping Organization version3 (GLCNMO 2013) data. The relationship between LST data from Landsat-8 and Himawari-8 will then be investigated based on the land-use class and over different seasons of the year in order to account for seasonal variation in their relationship. The resulting relationship will be spatially and statistically analyzed and graphically visualized for detailed interpretation. Findings from this study will reveal the relationship between the two satellite data based on the land use classification within the study area and the seasons of the year. While the information provided by this study will help in the optimal combination of LST data from Polar (Landsat-8) and geostationary (Himawari-8) satellites, it will also serve as a roadmap in the annual and diurnal urban heat (UHI) analysis in Hong Kong SAR.

Keywords: automatic weather station, Himawari-8, Landsat-8, land surface temperature, land use classification, split window algorithm, urban heat island

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Authors: Jake Hindmarch, Edward Farley, Norman Eizenberg, Mark Midwinter

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There is a need to bring the anatomical knowledge of medical students up to the standards required by surgical specialties. Contention exists amongst anatomists, clinicians, and surgeons about the standard of anatomical knowledge medical students need. The aim of this study was to explore the standards which the Royal Australasian College of Surgeons are applying knowledge of anatomy. Furthermore, to align medical school teaching to what the surgical profession requires from graduates.: The 2018 volume of the ANZ Journal of Surgery was narrowed down to 254 articles by applying the search term “Anatomy”. The main topic was then extracted from each paper. The content of the paper was assessed for ‘novel description’ or ‘application’ of anatomical knowledge’ and classified accordingly. The majority of papers with an anatomical focus was from the general surgery specialty, which focused on surgical techniques, outcomes and management. Vascular surgery had the highest percentage of papers with a novel description and application of anatomy. Cardiothoracic and paediatric surgery had no papers with a novel description of anatomy. Finally, a novel application of anatomy was the main focus of each speciality. Firstly, a high proportion of novel applications and descriptions of anatomy are in general surgery. Secondly, vascular surgery had the largest proportion of novel application and description of anatomy, namely due to the rise of therapeutic imaging and endovascular techniques. Finally, all disciplines demonstrated a trend towards having a higher proportion of novel application of anatomical knowledge

Keywords: anatomical knowledge, anatomy, surgery, novel anatomy

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Authors: Begum Koru, Isik Aybay, Demet Celik Ulusoy

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The discipline of law is quite complex and has its own terminology. Apart from written legal rules, there is also living law, which refers to legal practice. Basic legal rules aim at the happiness of individuals in social life and have different characteristics in different branches such as public or private law. On the other hand, law is a national phenomenon. The law of one nation and the legal system applied on the territory of another nation may be completely different. People who are experts in a particular field of law in one country may have insufficient expertise in the law of another country. Today, in addition to the local nature of law, international and even supranational law rules are applied in order to protect basic human values and ensure the protection of human rights around the world. Systems that offer algorithmic solutions to legal problems using artificial intelligence (AI) tools will perhaps serve to produce very meaningful results in terms of human rights. However, algorithms to be used should not be developed by only computer experts, but also need the contribution of people who are familiar with law, values, judicial decisions, and even the social and political culture of the society to which it will provide solutions. Otherwise, even if the algorithm works perfectly, it may not be compatible with the values of the society in which it is applied. The latest developments involving the use of AI techniques in legal systems indicate that artificial law will emerge as a new field in the discipline of law. More AI systems are already being applied in the field of law, with examples such as predicting judicial decisions, text summarization, decision support systems, and classification of documents. Algorithms for legal systems employing AI tools, especially in the field of prediction of judicial decisions and decision support systems, have the capacity to create automatic decisions instead of judges. When the judge is removed from this equation, artificial intelligence-made law created by an intelligent algorithm on its own emerges, whether the domain is national or international law. In this work, the aim is to make a general analysis of this new topic. Such an analysis needs both a literature survey and a perspective from computer experts' and lawyers' point of view. In some societies, the use of prediction or decision support systems may be useful to integrate international human rights safeguards. In this case, artificial law can serve to produce more comprehensive and human rights-protective results than written or living law. In non-democratic countries, it may even be thought that direct decisions and artificial intelligence-made law would be more protective instead of a decision "support" system. Since the values of law are directed towards "human happiness or well-being", it requires that the AI algorithms should always be capable of serving this purpose and based on the rule of law, the principle of justice and equality, and the protection of human rights.

Keywords: AI and law, artificial law, protection of human rights, AI tools for legal systems

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Authors: Abdullah Alsarrani, Leandro Garcia, Ruth Hunter, Laura Dunne

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Social integration with friends has an important role in shaping adolescents’ behavior and determining their well-being. Friendship features such as companionship, trust, closeness, intimacy, and conflicts all form the concept of friendship quality. The quality of friendship relationships can either enhance or impede mental development during adolescence. Therefore, this systematic review was conducted to understand the association between friendship quality and adolescents’ mental wellbeing. The evidence was synthesized from a search of five databases (Medline, Embase, ProQuest, Scopus, and PsycINFO). Thirty-two articles out of 18801 records were included in the review. The relationship between friendship quality and depression has been investigated extensively in the literature and negative (beneficial) associations were found in twelve studies out of sixteen. Poor peer relationship was linked to loneliness in eight studies out of nine. All five studies on life satisfaction and quality of peer connection found a positive association. In five studies, optimal peer relationship was found to be associated with happiness. A positive association between friendship quality and self-esteem in four out of five applicable studies. Friendship quality was found to be correlated with subjective well-being in all of three included studies focused on this area. The review demonstrates the paramount value of promoting healthy friendship to adolescents’ subjective well-being constructs. Interventions that aim to promote subjective wellbeing among adolescents should consider the development and maintenance of healthy friendships.

Keywords: adolescents, friendship quality, peer, wellbeing

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Authors: L. Petrovičová, Ž. Balážová, Z. Gálová, M. Wójcik-Jagła, M. Rapacz

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In the present study, RAPD-PCR was used to assess genetic diversity of the rye including landrances and new rye cultivars coming from Central Europe and the Union of Soviet Socialist Republics (SUN). Five arbitrary random primers were used to determine RAPD polymorphism in the set of 38 rye genotypes. These primers amplified altogether 43 different DNA fragments with an average number of 8.6 fragments per genotypes. The number of fragments ranged from 7 (RLZ 8, RLZ 9 and RLZ 10) to 12 (RLZ 6). DI and PIC values of all RAPD markers were higher than 0.8 that generally means high level of polymorphism detected between rye genotypes. The dendrogram based on hierarchical cluster analysis using UPGMA algorithm was prepared. The cultivars were grouped into two main clusters. In this experiment, RAPD proved to be a rapid, reliable and practicable method for revealing of polymorphism in the rye cultivars.

Keywords: genetic diversity, polymorphism, RAPD markers, Secale cereale L.

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Authors: M. H. Abedi, A. Jalilvand

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The economic development benefits of wind energy may be the most tangible basis for the local and state officials’ interests. In addition to the direct salaries associated with building and operating wind projects, the wind energy industry provides indirect jobs and benefits. The optimal planning of a wind farm is one most important topic in renewable energy technology. Many methods have been implemented to optimize the cost and output benefit of wind farms, but the contribution of this paper is mentioning different types of service providers and also time of installation of wind turbines during planning horizon years. Genetic algorithm (GA) is used to optimize the problem. It is observed that an appropriate layout of wind farm can cause to minimize the different types of cost.

Keywords: renewable energy, wind farm, optimization, planning

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Authors: Hilary Modir, Kyle Dutton, Michelle Swab, Shabnam Asghari

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Since its emergence, the cutaneous manifestations of COVID-19 have been documented in the literature. However, the majority are case reports with significant limitations in appraisal quality, thus leaving the role of dermatological manifestations of COVID-19 erroneously underexplored. The primary aim of this review was to systematically examine clinical patterns of dermatological manifestations as reported in the literature. This study was designed as a systematic review of case reports. The inclusion criteria consisted of all published reports and articles regarding COVID-19 in English, from September 1st, 2019, until June 22nd, 2020. The population consisted of confirmed cases of COVID-19 with associated cutaneous signs and symptoms. Exclusion criteria included research in planning stages, protocols, book reviews, news articles, review studies, and policy analyses. With the collaboration of a librarian, a search strategy was created consisting of a mixture of keyword terms and controlled vocabulary. Electronic databases searched were MEDLINE via PubMed, EMBASE, CINAHL, Web of Science, LILACS, PsycINFO, WHO Global Literature on Coronavirus Disease, Cochrane Library, Campbell Collaboration, Prospero, WHO International Clinical Trials Registry Platform, Australian and New Zealand Clinical Trials Registry, U.S. Institutes of Health Ongoing Trials Register, AAD Registry, OSF preprints, SSRN, MedRxiV and BioRxiV. The study selection featured an initial pre-screening of titles and abstracts by one independent reviewer. Results were verified by re-examining a random sample of 1% of excluded articles. Eligible studies progressed for full-text review by two calibrated independent reviewers. Covidence was used to store and extract data, such as citation information and findings pertaining to COVID-19 and cutaneous signs and symptoms. Data analysis and summarization methodology reflect the framework proposed by PRISMA and recommendations set out by Cochrane and Joanna Brigg’s Institute for conducting systematic reviews. The Oxford Centre for Evidence-Based Medicine’s level of evidence was used to appraise the quality of individual studies. The literature search revealed a total of 1221 articles. After the abstract and full-text screening, only 95 studies met the eligibility criteria, proceeding to data extraction. Studies were divided into 58% case reports and 42% series. A total of 833 manifestations were reported in 723 confirmed COVID-19 cases. The most frequent lesions were 23% maculopapular, 15% urticarial and 13% pseudo-chilblains, with 46% of lesions reporting pruritus, 16% erythema, 14% pain, 12% burning sensation, and 4% edema. The most common lesion locations were 20% trunk, 19.5% lower limbs, and 17.7% upper limbs. The time to resolution of lesions was between one and twenty-one days. In conclusion, over half of the reported cutaneous presentations in COVID-19 positive patients were maculopapular, urticarial and pseudo-chilblains, with the majority of lesions distributed to the extremities and trunk. As this review’s sample size only contained COVID-19 confirmed cases with skin presentations, it becomes difficult to deduce the direct relationship between skin findings and COVID-19. However, it can be correlated that acute onset of skin lesions, such as chilblains-like, may be associated with or may warrant consideration of COVID-19 as part of the differential diagnosis.

Keywords: COVID-19, cutaneous manifestations, cutaneous signs, general dermatology, medical dermatology, Sars-Cov-2, skin and infectious disease, skin findings, skin manifestations

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Authors: Sadia Munir

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Preeclampsia is the contributor to the worldwide maternal mortality of approximately 100,000 deaths a year. It complicates about 10% of all pregnancies and is the first cause of maternal admission to intensive care units. Predicting preeclampsia is a major challenge in obstetrics. More importantly, no major progress has been achieved in the treatment of preeclampsia. As placenta is the main cause of the disease, the only way to treat the disease is to extract placental and deliver the baby. In developed countries, the cost of an average case of preeclampsia is estimated at £9000. Interestingly, preeclampsia may have an impact on the health of mother or infant, beyond the pregnancy. We performed a systematic search of PubMed including the combination of terms such as preeclampsia, biomarkers, treatment, hypoxia, inflammation, oxidative stress, vascular endothelial growth factor A, activin A, inhibin A, placental growth factor, transforming growth factor β-1, Nodal, placenta, trophoblast cells, microRNAs. In this review, we have summarized current knowledge on the identification of potential biomarkers for the diagnosis of preeclampsia. Although these studies show promising data in early diagnosis of preeclampsia, the current value of these factors as biomarkers, for the precise prediction of preeclampsia, has its limitation. Therefore, future studies need to be done to support some of the very promising and interesting data to develop affordable and widely available tests for early detection and treatment of preeclampsia.

Keywords: activin, biomarkers, growth factors, miroRNA

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Authors: Andrew Lock, Alastair Allen

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Fragile watermarking has been proposed as a means of adding additional security or functionality to biometric systems, particularly for authentication and tamper detection. In this paper we describe an experimental study on the effect of watermarking iris images with a particular class of fragile algorithm, reversible algorithms, and the ability to correctly perform iris recognition. We investigate two scenarios, matching watermarked images to unmodified images, and matching watermarked images to watermarked images. We show that different watermarking schemes give very different results for a given capacity, highlighting the importance of investigation. At high embedding rates most algorithms cause significant reduction in recognition performance. However, in many cases, for low embedding rates, recognition accuracy is improved by the watermarking process.

Keywords: biometrics, iris recognition, reversible watermarking, vision engineering

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Authors: Fabiana Ribeiro, Ana Moreno, Antonio Oliveira, Patricia Oliveira-Silva

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Music is present in our daily lives, and to our knowledge music is often used to change the emotions in the listeners. For this reason, the objective of this study was to explore and synthesize results examining the use and effects of music on emotion regulation in children, adults, and elderly, and clarify if the music is effective across ages to promote emotion regulation. A literature search was conducted using ISI Web of Knowledge, Pubmed, PsycINFO, and Scopus, inclusion criteria comprised children, adolescents, young, and old adults, including health population. Articles applying musical intervention, specifically musical listening, and assessing the emotion regulation directly through reports or neurophysiological measures were included in this review. Results showed age differences in the function of musical listening; initially, adolescents revealed age increments in emotional listening compared to children, and young adults in comparison to older adults, in which the first use music aiming to emotion regulation and social connection, while older adults also utilize music as emotion regulation searching for personal growth. Moreover, some of the studies showed that personal characteristics also would determine the efficiency of the emotion regulation strategy. In conclusion, it was observed that music could beneficiate all ages investigated, however, this review detected a necessity to develop adequate paradigms to explore the use of music for emotion regulation.

Keywords: music, emotion, regulation, musical listening

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Authors: Laila Alkhayat

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In this study, the researcher explored the positive and negative effects of children watching YouTube on changing social values from the perspective of parents in Kuwait. This study also explored whether any correlation exists between changed values from watching YouTube and the following variables: relationship with a child, social situation, school level, gender, and age. The researcher collected data from 286 questionnaires distributed randomly to parents in Kuwait. The results of the study show that parents face many disadvantages when dealing with children watching YouTube, such as children spending too much time in front of screens, inability to organize bedtime, and children’s social isolation. However, the researcher found some positives come from watching YouTube, such as learning new information, enabling children to search for new information, and introducing children to the culture of their society and other cultures around them. Moreover, this study found that boys are more likely to have negative viewing habits than girls. Given the results, this study shows that the biggest impact on social values from children watching YouTube is that they are preoccupied with watching YouTube and they waste time, which makes them feel disturbed, and this affects the value of time management and delays children’s sleeping times. This study concludes that watching YouTube simultaneously has negative and positive effects on changing social values, but it plays a negative role in changing social values of children from the parents’ perspective.

Keywords: YouTube, children, social value, social media effects

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Authors: Ahmed Emad Ahmed

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This paper aims to address the concept of human resources information systems (HRIS) and how to link it to new technologies such as artificial intelligence (AI) to be implemented in two human resources processes. A literature view has been collected to cover the main points related to HRIS, AI, and BC. A study case has been presented by generating a random HRIS to apply some AI operations to it. Then, an algorithm was applied to the database to complete some human resources processes, including training and performance appraisal, using a pre-trained AI model. After that, outputs and results have been presented and discussed briefly. Finally, a conclusion has been introduced to show the ability of new technologies such as AI and ML to be applied to the human resources management processes.

Keywords: human resources new technologies, HR artificial intelligence, HRIS AI models, construction AI HRIS

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Authors: Fathi Kallel, Saida Khachira, Mohamed Ben Slima, Ahmed Ben Hamida

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Speckle noise is a main characteristic of cardiac ultrasound images, it corresponding to grainy appearance that degrades the image quality. For this reason, the ultrasound images are difficult to use automatically in clinical use, then treatments are required for this type of images. Then a filtering procedure of these images is necessary to eliminate the speckle noise and to improve the quality of ultrasound images which will be then segmented to extract the necessary forms that exist. In this paper, we present the importance of the pre-treatment step for segmentation. This work is applied to cardiac ultrasound images. In a first step, a comparative study of speckle filtering method will be presented and then we use a segmentation algorithm to locate and extract cardiac structures.

Keywords: medical image processing, ultrasound images, Speckle noise, image enhancement, speckle filtering, segmentation, snakes

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Authors: Elabed Saud

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This paper introduces the new ability of Artificial Neural Networks (ANNs) in estimating speed and controlling the separately excited DC motor. The neural control scheme consists of two parts. One is the neural estimator which is used to estimate the motor speed. The other is the neural controller which is used to generate a control signal for a converter. These two neutrals are training by Levenberg-Marquardt back-propagation algorithm. ANNs are the standard three layers feed-forward neural network with sigmoid activation functions in the input and hidden layers and purelin in the output layer. Simulation results are presented to demonstrate the effectiveness of this neural and advantage of the control system DC motor with ANNs in comparison with the conventional scheme without ANNs.

Keywords: Artificial Neural Network (ANNs), excited DC motor, convenional controller, speed Controller

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Authors: Ana Paula Cavalcanti Furtado, Silvio Meira

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Context: The quality of a software product can be directly influenced by the quality of its development process. Therefore, immature or ad-hoc test processes are means that are unsuited for introducing systematic test automation, and should not be used to support improving the quality of software. Objective: In order to conduct this research, the benefits and limitations of and gaps in automating software testing had to be assessed in order to identify the best practices and to propose a strategy for systematically introducing test automation into software development processes. Method: To conduct this research, an exploratory bibliographical survey was undertaken so as to underpin the search by theory and the recent literature. After defining the proposal, two case studies were conducted so as to analyze the proposal in a real-world environment. In addition, the proposal was also assessed through a focus group with specialists in the field. Results: The proposal of a Framework for Automating Software Testing (FAST), which is a theoretical framework consisting of a hierarchical structure to introduce test automation. Conclusion: The findings of this research showed that the absence of systematic processes is one of the factors that hinder the introduction of test automation. Based on the results of the case studies, FAST can be considered as a satisfactory alternative that lies within the scope of introducing and maintaining test automation in software development.

Keywords: software process improvement, software quality, software testing, test automation

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Authors: A. Djahbar, E. Bounadja, A. Zegaoui, H. Allouache

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In this paper, vector control of a series-connected two-machine drive system fed by a single inverter (CSI/VSI) is presented. The two stator windings of both machines are connected in series while the rotors may be connected to different loads, are called series-connected two-machine drive. Appropriate phase transposition is introduced while connecting the series stator winding to obtain decoupled control the two-machines. The dynamic decoupling of each machine from the group is obtained using the vector control algorithm. The independent control is demonstrated by analyzing the characteristics of torque and speed of each machine obtained via simulation under vector control scheme. The viability of the control techniques is proved using analytically and simulation approach.

Keywords: drives, inverter, multi-phase induction machine, vector control

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Authors: Jared Beard, Ali Baheri

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As autonomous systems become more prominent in society, ensuring their safe application becomes increasingly important. This is clearly demonstrated with autonomous cars traveling through a crowded city or robots traversing a warehouse with heavy equipment. Human environments can be complex, having high dimensional state and action spaces. This gives rise to two problems. One being that analytic solutions may not be possible. The other is that in simulation based approaches, searching the entirety of the problem space could be computationally intractable, ruling out formal methods. To overcome this, approximate solutions may seek to find failures or estimate their likelihood of occurrence. One such approach is adaptive stress testing (AST) which uses reinforcement learning to induce failures in the system. The premise of which is that a learned model can be used to help find new failure scenarios, making better use of simulations. In spite of these failures AST fails to find particularly sparse failures and can be inclined to find similar solutions to those found previously. To help overcome this, multi-fidelity learning can be used to alleviate this overuse of information. That is, information in lower fidelity can simulations can be used to build up samples less expensively, and more effectively cover the solution space to find a broader set of failures. Recent work in multi-fidelity learning has passed information bidirectionally using “knows what it knows” (KWIK) reinforcement learners to minimize the number of samples in high fidelity simulators (thereby reducing computation time and load). The contribution of this work, then, is development of the bidirectional multi-fidelity AST framework. Such an algorithm, uses multi-fidelity KWIK learners in an adversarial context to find failure modes. Thus far, a KWIK learner has been used to train an adversary in a grid world to prevent an agent from reaching its goal; thus demonstrating the utility of KWIK learners in an AST framework. The next step is implementation of the bidirectional multi-fidelity AST framework described. Testing will be conducted in a grid world containing an agent attempting to reach a goal position and adversary tasked with intercepting the agent as demonstrated previously. Fidelities will be modified by adjusting the size of a time-step, with higher-fidelity effectively allowing for more responsive closed loop feedback. Results will compare the single KWIK AST learner with the multi-fidelity algorithm with respect to number of samples, distinct failure modes found, and relative effect of learning after a number of trials.

Keywords: multi-fidelity reinforcement learning, multi-fidelity simulation, safety validation, falsification

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Authors: Alexander P. Vazhenin, Mikhail M. Lavrentiev, Alexey A. Romanenko, Pavel V. Tatarintsev

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One of the problems obstructing effective tsunami modelling is the lack of information about initial wave shape at source. The existing methods; geological, sea radars, satellite images, contain an important part of uncertainty. Therefore, direct measurement of tsunami waves obtained at the deep water bottom peruse recorders is also used. In this paper we propose a new method to reconstruct the initial sea surface displacement at tsunami source by the measured signal (marigram) approximation with the help of linear combination of synthetic marigrams from the selected set of unit sources, calculated in advance. This method has demonstrated good precision and very high performance. The mathematical model and results of numerical tests are here described.

Keywords: numerical tests, orthogonal decomposition, Tsunami Initial Sea Surface Displacement

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Authors: Bronwyn S. Mthimunye, Athena S. Pedro, Nicolette V. Roman

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Inclusive education is a global concern, in the context of Brazil, Russia, India, China, and South Africa. These countries are all striving for inclusive education, as there are many children excluded from formal schooling. The need for inclusive education is imperative, given the increase in special needs diagnoses. Many children confronted with special needs are still not able to exercise their basic right to education. The aim of conducting this scoping review was to explore the policies and procedures addressing the implementation of inclusive education in Brazil, Russia, India, China, and South Africa. The studies included were published between 2006-2016 and located in Academic Search Complete, ERIC, Medline, PsycARTICLES, JSTOR, and SAGE Journals. Seven articles were included in which all of the articles reported on inclusive education and the status of implementation. The findings identified many challenges faced by Brazil, Russia, India, China, and South Africa that affect the implementation of policies and programmes. Challenges such as poor planning, resource-constrained communities, lack of professionals in schools, and the need for adequate teacher training were identified. Brazil, Russia, India, China, and South Africa are faced with many social and economic challenges, which serves as a barrier to the implementation of inclusive education.

Keywords: special needs, inclusion, education, scoping review

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Authors: Alexandre Barbosa de Almeida, Telma Woerle de Lima Soares

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Protein structure prediction is a challenging task in the bioinformatics field. The biological function of all proteins majorly relies on the shape of their three-dimensional conformational structure, but less than 1% of all known proteins in the world have their structure solved. This work proposes a deep learning model to address this problem, attempting to predict some aspects of the protein conformations. Throughout a process of multiobjective dominance, a recurrent neural network was trained to abstract the particular bias of each individual multiobjective algorithm, generating a heuristic that could be useful to predict some of the relevant aspects of the three-dimensional conformation process formation, known as protein folding.

Keywords: Ab initio heuristic modeling, multiobjective optimization, protein structure prediction, recurrent neural network

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Authors: Hao-Jun Hong, Jung-Tang Huang

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The world is gradually becoming an aging society, and with the lack of laboring forces, this phenomenon is affecting the nation’s economy growth. Although nursing centers are booming in recent years, the lack of medical resources are yet to be resolved, thus creating an innovative wearable medical device could be a vital solution. This research is focused on the design and development of a wearable device which obtains a more precise heart failure measurement than products on the market. The method used by the device is based on the sensor fusion and big data algorithm. From the test result, the modified structure of wearable device can significantly decrease the MA (Motion Artifact) and provide users a more cozy and accurate physical monitor experience.

Keywords: big data, heart failure, motion artifact, sensor fusion, wearable medical device

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Authors: Hamsini Pulugurtha, P. V. S. L. Jagadamba

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Programmed Speech to Text and Text Summarization Using Graph-based Algorithms can be utilized in gatherings to get the short depiction of the gathering for future reference. This gives signature check utilizing Siamese neural organization to confirm the personality of the client and convert the client gave sound record which is in English into English text utilizing the discourse acknowledgment bundle given in python. At times just the outline of the gathering is required, the answer for this text rundown. Thus, the record is then summed up utilizing the regular language preparing approaches, for example, solo extractive text outline calculations

Keywords: Siamese neural network, English speech, English text, natural language processing, unsupervised extractive text summarization

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Authors: Yonas Shuke Kitawa

Abstract:

Background: Although malaria incidence has substantially fallen sharply over the past few years, the rate of decline varies by district, time, and malaria type. Despite this turn-down, malaria remains a major public health threat in various districts of Ethiopia. Consequently, the present study is aimed at developing a predictive model that helps to identify the spatio-temporal variation in malaria risk by multiple plasmodium species. Methods: We propose a multivariate spatio-temporal Bayesian model to obtain a more coherent picture of the temporally varying spatial variation in disease risk. The spatial autocorrelation in such a data set is typically modeled by a set of random effects that assign a conditional autoregressive prior distribution. However, the autocorrelation considered in such cases depends on a binary neighborhood matrix specified through the border-sharing rule. Over here, we propose a graph-based optimization algorithm for estimating the neighborhood matrix that merely represents the spatial correlation by exploring the areal units as the vertices of a graph and the neighbor relations as the series of edges. Furthermore, we used aggregated malaria count in southern Ethiopia from August 2013 to May 2019. Results: We recognized that precipitation, temperature, and humidity are positively associated with the malaria threat in the area. On the other hand, enhanced vegetation index, nighttime light (NTL), and distance from coastal areas are negatively associated. Moreover, nonlinear relationships were observed between malaria incidence and precipitation, temperature, and NTL. Additionally, lagged effects of temperature and humidity have a significant effect on malaria risk by either species. More elevated risk of P. falciparum was observed following the rainy season, and unstable transmission of P. vivax was observed in the area. Finally, P. vivax risks are less sensitive to environmental factors than those of P. falciparum. Conclusion: The improved inference was gained by employing the proposed approach in comparison to the commonly used border-sharing rule. Additionally, different covariates are identified, including delayed effects, and elevated risks of either of the cases were observed in districts found in the central and western regions. As malaria transmission operates in a spatially continuous manner, a spatially continuous model should be employed when it is computationally feasible.

Keywords: disease mapping, MSTCAR, graph-based optimization algorithm, P. falciparum, P. vivax, waiting matrix

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Authors: Tian Xia, Yuan Yan Tang

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In order to obtain higher small target detection accuracy, this paper presents an effective algorithm inspired by the local contrast mechanism. The proposed method can enhance target signal and suppress background clutter simultaneously. In the first stage, a enhanced image is obtained using the proposed Weighted Laplacian of Gaussian. In the second stage, an adaptive threshold is adopted to segment the target. Experimental results on two changeling image sequences show that the proposed method can detect the bright and dark targets simultaneously, and is not sensitive to sea-sky line of the infrared image. So it is fit for IR small infrared target detection.

Keywords: small target detection, local contrast, human vision system, Laplacian of Gaussian

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Authors: Hellen L. De Castro e Silva, Ana A. Robles Aguilar, Erik Meers

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Digestates are residual by-products, rich in nutrients and trace elements, which can be used as organic fertilisers on soils. However, due to the non-digestibility of these elements and reduced dry matter during the anaerobic digestion process, metal concentrations are higher in digestates than in feedstocks, which might hamper their use as fertilisers according to the threshold values of some country policies. Furthermore, there is uncertainty regarding the required assimilated amount of these elements by some crops, which might result in their bioaccumulation. Therefore, further processing of the digestate to obtain safe fertilizing products has been recommended. This research aims to analyze the effect of applying the hydrothermal carbonization process to manure-derived digestates as a thermal treatment to reduce the bioavailability of heavy metals in mono and co-digestates derived from pig manure and maize from contaminated land in France. This study examined pig manure collected from a novel stable system (VeDoWs, province of East Flanders, Belgium) that separates the collection of pig urine and feces, resulting in a solid fraction of manure with high up-concentration of heavy metals and nutrients. Mono-digestion and co-digestion processes were conducted in semi-continuous reactors for 45 days at mesophilic conditions, in which the digestates were dried at 105 °C for 24 hours. Then, hydrothermal carbonization was applied to a 1:10 solid/water ratio to guarantee controlled experimental conditions in different temperatures (180, 200, and 220 °C) and residence times (2 h and 4 h). During the process, the pressure was generated autogenously, and the reactor was cooled down after completing the treatments. The solid and liquid phases were separated through vacuum filtration, in which the solid phase of each treatment -hydrochar- was dried and ground for chemical characterization. Different fractions (exchangeable / adsorbed fraction - F1, carbonates-bound fraction - F2, organic matter-bound fraction - F3, and residual fraction – F4) of some heavy metals (Cd, Cr, Ni, and Cr) have been determined in digestates and derived hydrochars using the modified Community Bureau of Reference (BCR) sequential extraction procedure. The main results indicated a difference in the heavy metals fractionation between digestates and their derived hydrochars; however, the hydrothermal carbonization operating conditions didn’t have remarkable effects on heavy metals partitioning between the hydrochars of the proposed treatments. Based on the estimated potential ecological risk assessment, there was one level decrease (considerate to moderate) when comparing the HMs partitioning in digestates and derived hydrochars.

Keywords: heavy metals, bioavailability, hydrothermal treatment, bio-based fertilisers, agriculture

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Authors: M. Simon, O. Peillon, R. Seijas, P. Alvarez, A. Pérez-Bellmunt

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Introduction: Myofascial trigger points (MTrPs) are defined as hyperirritable areas within taut bands of skeletal muscle and classified as either active or latent. Although they could be present in any muscle, the triceps surae is one of the most affected of the lower limb. The aim of this study was described which treatments are more used and their principal results. Study design: We performed a systematic literature search using strategies for the concepts of “Trigger Points and Gastrocnemius and Soleus not Trapezius” in Medline. Articles were screened by authors and included if they contained a rehabilitation intervention of MTrPs in healthy subjects or patients. Results: The treatments used were mostly invasive interventions and only a small part of the studies used non-invasive treatments. The methodology (time o type of intervention, characteristics of treatment, etc.) used in these treatments were frequently undefined. Overall, examination variables varied significantly among the included studies, but they were improving their parameters when the MTrPs were treated. Conclusions: There are a high variety of physical therapy treatments to improve the symptomatology of MTrPs when affect triceps surae muscle. Even so, not a single study analyzing the skeletal muscle contractile parameters (as maximal displacement or delay time) change with MTrPS therapy has been found. The treatments have to better specificity the methodology used in the futures investigation.

Keywords: fascia, myofascial trigger points, physical therapy, triceps surae

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Authors: Gadiri Sabiha

Abstract:

Introduction : IgE-dependent cow's milk protein allergy (APLV) is one of the most common allergies in children and is one of the three most common allergies observed in children under 6 years of age. Its natural evolution is most often towards healing. The objective is to determine the sensitization profile of patients allergic to cow's milk (VL). Material and method :A retrospective study carried out on a pediatric population (age < 12 years) over a period of four years (2018-2021) in the context of a suspected food allergy to cow's milk proteins carried out on 121 children aged between 8 months -12 years The search for specific IgE was carried out by immunodot (EUROLINE Pediatric; EUROIMMUN) test which allows a semi-quantitative determination of specific IgE. Results 36 patients (29.7%) had a cow's milk protein allergy (ALPV) with a slight female predominance (58.33% girls vs 41.66% boys) The main clinical signs were: acute diarrhoea; vomiting; Intense abdominal pain, and cutaneous signs (pruritus/urticaria) with respective frequencies of 72%; 58%; 44% and 19%. The 3 major and specific VL allergens identified were beta-lactoglobulin 59% caseins 51% and alpha-lactalbumin 29.7%, The profile of sensitization to LV varies according to age, in infants before 1 year of anti-casein, IgE are predominant 83.3%, followed by beta-lactoglobulin 66.66% and alpha-lactolbumin 50% Conclusion CMPA is a frequent pathology which ranks among the three most common food allergies in children. This is the first to appear, most often starting in infants under 6 months old.

Keywords: specific Ige, food allergy, cow 's milk, child

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Authors: Uzma Saqib, Mirza S. Baig

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Myeloid differentiation primary response protein 88 (MYD88) has long been considered a central player in the inflammatory pathway. Recent studies clearly suggest that it is an important therapeutic target in inflammation. On the other hand, a recent study on the interaction between the orphan nuclear receptor (Nur77) and p38α, leading to increased lipopolysaccharide-induced hyperinflammatory response, suggests this binary complex as a therapeutic target. In this study, we have designed inhibitors that can inhibit both MYD88 and Nur77 at the same time. Since both MYD88 and Nur77 are an integral part of the pathways involving lipopolysaccharide-induced activation of NF-κB-mediated inflammation, we tried to target both proteins with the same library in order to retrieve compounds having dual inhibitory properties. To perform this, we developed a homodimeric model of MYD88 and, along with the crystal structure of Nur77, screened a virtual library of compounds from the traditional Chinese medicine database containing ~61,000 compounds. We analyzed the resulting hits for their efficacy for dual binding and probed them for developing a common pharmacophore model that could be used as a prototype to screen compound libraries as well as to guide combinatorial library design to search for ideal dual-target inhibitors. Thus, our study explores the identification of novel leads having dual inhibiting effects due to binding to both MYD88 and Nur77 targets.

Keywords: drug design, Nur77, MYD88, inflammation

Procedia PDF Downloads 293