Search results for: search algorithms

Authors: Samira-Sadat JamaliDinan, Haidar Almohri, Mohammad-Reza Nazem-Zadeh

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Lateralization of temporal lobe epilepsy (TLE) is very important for positive surgical outcomes. We propose a machine learning framework to ultimately identify the epileptogenic hemisphere for temporal lobe epilepsy (TLE) cases using magnetoencephalography (MEG) coherence source imaging (CSI) and diffusion tensor imaging (DTI). Unlike most studies that use classification algorithms, we propose an effective clustering approach to distinguish between normal and TLE cases. We apply the famous Minkowski weighted K-Means (MWK-Means) technique as the clustering framework. To overcome the problem of poor initialization of K-Means, we use particle swarm optimization (PSO) to effectively select the initial centroids of clusters prior to applying MWK-Means. We demonstrate that compared to K-means and MWK-means independently, this approach is able to improve the result of a benchmark data set.

Keywords: temporal lobe epilepsy, machine learning, clustering, magnetoencephalography

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Authors: Georges Ghazi, Marc-Henry Devillers, Ruxandra M. Botez

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Aircraft flight trajectory optimization has been identified to be a promising solution for reducing both airline costs and the aviation net carbon footprint. Nowadays, this role has been mainly attributed to the flight management system. This system is an onboard multi-purpose computer responsible for providing the crew members with the optimized flight plan from a destination to the next. To accomplish this function, the flight management system uses a variety of look-up tables to compute the optimal speed and altitude for each flight regime instantly. Because the cruise is the longest segment of a typical flight, the proposed algorithm is focused on minimizing fuel consumption for this flight phase. In this paper, a complete methodology to estimate the aircraft performance and subsequently compute the optimal speed in cruise is presented. Results showed that the obtained performance database was accurate enough to predict the flight costs associated with the cruise phase.

Keywords: Cessna Citation X, cruise speed optimization, flight cost, cost index, and golden section search

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Authors: Vahid Anari, Mina Bakhshi

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Visual search and identification of immunohistochemically stained tissue of meningioma was performed manually in pathologic laboratories to detect and diagnose the cancers type of meningioma. This task is very tedious and time-consuming. Moreover, because of cell's complex nature, it still remains a challenging task to segment cells from its background and analyze them automatically. In this paper, we develop and test a computerized scheme that can automatically identify cells in microscopic images of meningioma and classify them into positive (proliferative) and negative (normal) cells. Dataset including 150 images are used to test the scheme. The scheme uses Fuzzy C-means algorithm as a color clustering method based on perceptually uniform hue, saturation, value (HSV) color space. Since the cells are distinguishable by the human eye, the accuracy and stability of the algorithm are quantitatively compared through application to a wide variety of real images.

Keywords: positive cell, color segmentation, HSV color space, immunohistochemistry, meningioma, thresholding, fuzzy c-means

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Authors: Jennifer Kehinde, Claire O'donnell, Annmarie Grealish

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Introduction: Breastfeeding has been shown to provide numerous health benefits for both infants and mothers. The decision to breastfeed is influenced by physiological, psychological, and emotional factors. However, the importance of equipping mothers with the necessary knowledge for successful breastfeeding practice cannot be ruled out. The decline in global breastfeeding rate can be linked to lack of adequate breastfeeding education during prenatal stage.This systematic review examined the effectiveness of prenatal breastfeeding education on breastfeeding uptake postpartum. Method: This review was undertaken and reported in conformity with the Preferred Reporting Items for Systemic Reviews and Meta-Analysis statement (PRISMA) and was registered on the international prospective register for systematic reviews (PROSPERO: CRD42020213853). A PICO analysis (population, intervention, comparison, outcome) was undertaken to inform the choice of keywords in the search strategy to formulate the review question which was aimed at determining the effectiveness of prenatal breastfeeding educational programs at improving breastfeeding uptake following birth. A systematic search of five databases (Cumulative Index to Nursing and Allied Health Literature, Medline, Psych INFO, and Applied Social Sciences Index and Abstracts) were searched between January 2014 until July 2021 to identify eligible studies. Quality assessment and narrative synthesis were subsequently undertaken. Results: Fourteen studies were included. All 14 studies used different types of breastfeeding programs; eight used a combination of curriculum based breastfeeding education program, group prenatal breastfeeding counselling and one-to-one breastfeeding educational programs which were all delivered in person; four studies used web-based learning platforms to deliver breastfeeding education prenatally which were both delivered online and face to face over a period of 3 weeks to 2 months with follow-up periods ranging from 3 weeks to 6 months; one study delivered breastfeeding educational intervention using mother-to-mother breastfeeding support groups in promoting exclusive breastfeeding and one study disseminated breastfeeding education to participants based on the theory of planned behaviour. The most effective interventions were those that included both theory and hands-on demonstrations. Results showed an increase in breastfeeding uptake, breastfeeding knowledge, increase in positive attitude to breastfeeding and an increase in maternal breastfeeding self-efficacy among mothers who participated in breastfeeding educational programs during prenatal care. Conclusion: Prenatal breastfeeding education increases women’s knowledge of breastfeeding. Mothers who are knowledgeable about breastfeeding and hold a positive approach towards breastfeeding have the tendency to initiate breastfeeding and continue for a lengthened period. Findings demonstrates a general correlation between prenatal breastfeeding education and increased breastfeeding uptake postpartum. The high level of positive breastfeeding outcome inherent in all the studies can be attributed to prenatal breastfeeding education. This review provides rigorous contemporary evidence that healthcare professionals and policymakers can apply when developing effective strategies to improve breastfeeding rates and ultimately improve the health outcomes of mothers and infants.

Keywords: breastfeeding, breastfeeding programs, breastfeeding self-efficacy, prenatal breastfeedng education

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Authors: Tesfaye Dagne, Dagmawit Solomon, Firmaye Bogale, Yosef Gebreyohannes, Samson Mideksa, Mamuye Hadis, Desalegn Ararso, Ermias Woldie, Tsegaye Getachew, Sabit Ababor, Zelalem Kebede

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Globally, road traffic accident (RTA) is causing millions of deaths and injuries every year. It is one of the leading causes of death among people of all age groups and the problem is worse among young reproductive age group. Moreover the problem is increasing with an increasing number of vehicles. The majority of the problem happen in low and middle income countries (LMIC), even if the number of vehicles in these countries is low compared to their population. So, the objective of this paper is to summarize the best available evidence on interventions that can reduce road traffic accidents in low and middle income countries (LMIC). Method: A rapid evidence synthesis approach adapted from the SURE Rapid Response Service was applied to search, appraise and summarize the best available evidence on effective intervention in reducing road traffic injury. To answer the question under review, we searched for relevant studies from databases including PubMed, the Cochrane Library, TRANSPORT, Health system evidence, Epistemonikos, and SUPPORT summary. The following key terms were used for searching: Road traffic accident, RTA, Injury, Reduc*, Prevent*, Minimiz*, “Low and middle-income country”, LMIC. We found 18 articles through a search of different databases mentioned above. After screening for the titles and abstracts of the articles, four of them which satisfy the inclusion criteria were included in the final review. Then we appraised and graded the methodological quality of systematic reviews that are deemed to be highly relevant using AMSTAR. Finding: The identified interventions to reduce road traffic accidents were legislation and enforcement, public awareness/education, speed control/ rumble strips, road improvement, mandatory motorcycle helmet, graduated driver license, street lighting. Legislation and Enforcement: Legislation focusing on mandatory motorcycle helmet usage, banning cellular phone usage when driving, seat belt laws, decreasing the legal blood alcohol content (BAC) level from 0.06 g/L to 0.02 g/L bring the best result where enforcement is there. Public Awareness/Education: focusing on seat belt use, child restraint use, educational training in health centers and schools/universities, and public awareness with media through the distribution of videos, posters/souvenirs, and pamphlets are effective in the short run. Speed Control: through traffic calming bumps, or speed bumps, rumbled strips are effective in reducing accidents and fatality. Mandatory Motorcycle Helmet: is associated with reduction in mortality. Graduated driver’s license (GDL): reduce road traffic injury by 19%. Street lighting: is a low-cost intervention which may reduce road traffic accidents.

Keywords: evidence synthesis, injury, rapid review, reducing, road traffic accident

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Authors: Bekkem Chakradhar Reddy, Y. Soni Priya, Mathivanan G., L. K. Joshila Grace, N. Srinivasan, Asha P.

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Currently, there are a lot of problems related to marking attendance in schools, offices, or other places. Organizations tasked with collecting daily attendance data have numerous concerns. There are different ways to mark attendance. The most commonly used method is collecting data manually by calling each student. It is a longer process and problematic. Now, there are a lot of new technologies that help to mark attendance automatically. It reduces work and records the data. We have proposed to implement attendance marking using the latest technologies. We have implemented a system based on face identification and analyzing faces. The project is developed by gathering faces and analyzing data, using deep learning algorithms to recognize faces effectively. The data is recorded and forwarded to the host through mail. The project was implemented in Python and Python libraries used are CV2, Face Recognition, and Smtplib.

Keywords: python, deep learning, face recognition, CV2, smtplib, Dlib.

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Authors: Ursachi Andrei

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The Smart Grid Simulator is a computer software based on advanced algorithms which has as the main purpose to lower the energy bill in the most optimized price efficient way as possible for private households, companies or energy providers. It combines the energy provided by a number of solar modules and wind turbines with the consumption of one household or a cluster of nearby households and information regarding weather conditions and energy prices in order to predict the amount of energy that can be produced by renewable energy sources and the amount of energy that will be bought from the distributor for the following day. The user of the system will not only be able to minimize his expenditures on energy fractures, but also he will be informed about his hourly consumption, electricity prices fluctuation and money spent for energy bought as well as how much money he saved each day and since he installed the system. The paper outlines the algorithm that supports the Smart Grid Simulator idea and presents preliminary test results that support the discussion and implementation of the system.

Keywords: smart grid, sustainable energy, applied science, renewable energy sources

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Authors: Alaa Abdullah Altaee

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This paper presents an adaptive decision feedback equalizer (ADFE) for multi-Gbps serial links utilizing a fix-step error signal extracted from cross-points of received data symbols. The extracted signal is generated based on violation of received data symbols with minimum detection requirements at the clock and data recovery (CDR) stage. The iterations of the adaptation process search for the optimum feedback tap coefficients to maximize the data eye-opening and minimize the adaptation convergence time. The effectiveness of the proposed architecture is validated using the simulation results of a serial link designed in an IBM 130 nm 1.2V CMOS technology. The data link with variable channel lengths is analyzed using Spectre from Cadence Design Systems with BSIM4 device models.

Keywords: adaptive DFE, CMOS equalizer, error detection, serial links, timing jitter, wire-line communication

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Authors: E. O. Okan, A. Kepic, P. Williams

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Iron oxide copper gold (IOCG) deposits constitute important sources of copper and gold in Australia especially since the discovery of the supergiant Olympic Dam deposits in 1975. They are considered to be metasomatic expressions of large crustal-scale alteration events occasioned by intrusive actions and are associated with felsic igneous rocks in most cases, commonly potassic igneous magmatism, with the deposits ranging from ~2.2 –1.5 Ga in age. For the past two decades, geological, geochemical and potential methods have been used to identify the structures hosting these deposits follow up by drilling. Though these methods have largely been successful for shallow targets, at deeper depth due to low resolution they are limited to mapping only very large to gigantic deposits with sufficient contrast. As the search for ore-bodies under regolith cover continues due to depletion of the near surface deposits, there is a compelling need to develop new exploration technology to explore these deep seated ore-bodies within 1-4km which is the current mining depth range. Seismic reflection method represents this new technology as it offers a distinct advantage over all other geophysical techniques because of its great depth of penetration and superior spatial resolution maintained with depth. Further, in many different geological scenarios, it offers a greater ‘3D mapability’ of units within the stratigraphic boundary. Despite these superior attributes, no arguments for crustal scale seismic surveys have been proposed because there has not been a compelling argument of economic benefit to proceed with such work. For the seismic reflection method to be used at these scales (100’s to 1000’s of square km covered) the technical risks or the survey costs have to be reduced. In addition, as most IOCG deposits have large footprint due to its association with intrusions and large fault zones; we hypothesized that these deposits can be found by mainly looking for the seismic signatures of intrusions along prospective structures. In this study, we present two of such cases: - Olympic Dam and Vulcan iron-oxide copper-gold (IOCG) deposits all located in the Gawler craton, South Australia. Results from our 2D modelling experiments revealed that seismic reflection surveys using 20m geophones and 40m shot spacing as an exploration tool for locating IOCG deposit is possible even when hosted in very complex structures. The migrated sections were not only able to identify and trace various layers plus the complex structures but also show reflections around the edges of intrusive packages. The presences of such intrusions were clearly detected from 100m to 1000m depth range without losing its resolution. The modelled seismic images match the available real seismic data and have the hypothesized characteristics; thus, the seismic method seems to be a valid exploration tool to find IOCG deposits. We therefore propose that 2D seismic survey is viable for IOCG exploration as it can detect mineralised intrusive structures along known favourable corridors. This would help in reducing the exploration risk associated with locating undiscovered resources as well as conducting a life-of-mine study which will enable better development decisions at the very beginning.

Keywords: crustal scale, exploration, IOCG deposit, modelling, seismic surveys

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Authors: Michał Glet, Kamil Kaczyński

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Counterfeit goods and documents are a global problem, which needs more and more sophisticated methods of resolving it. Existing techniques using watermarking or embedding symbols on objects are not suitable for all use cases. To address those special needs, we created complete system allowing authentication of paper documents and physical objects with flat surface. Objects are marked using orientation independent and resistant to camera noise 2D graphic codes, named DotAuth. Based on the identifier stored in 2D code, the system is able to perform basic authentication and allows to conduct more sophisticated analysis methods, e.g., relying on augmented reality and physical properties of the object. In this paper, we present the complete architecture, algorithms and applications of the proposed system. Results of the features comparison of the proposed solution and other products are presented as well, pointing to the existence of many advantages that increase usability and efficiency in the means of protecting physical objects.

Keywords: anti-forgery, authentication, paper documents, security

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Authors: Mohammed Saleem Tariq

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The enthusiasm for gluten avoidance in a growing market is met by improvements in sensitive detection methods for analysing gluten content. Paradoxically, manufacturers employ no such systems in the production process but continue to market their product as gluten free, a significant risk posed to an undetermined coeliac population. The paper resonates with an immunological response that causes gastrointestinal scarring and villous atrophy with the conventional description of personal injury. The current developing regime in the UK however, it is discussed, has avoided creating specific rules to provide an adequate level of protection for this type of vulnerable ‘characteristic’. Due to the struggle involved with identifying an appropriate cause of action, this paper analyses whether a claim brought in misrepresentation, negligence and/or under the Consumer Protect Act 1987 could be sustained. A necessary comparison is then made with the approach adopted by the Americans with Disability Act 1990 which recognises this chronic disease as a disability. The ongoing failure to introduce a level of protection which matches that afforded to those who fall into any one of the ‘protected characteristics’ under the Equality Act 2010, is inconceivable given the outstanding level of legal vulnerability.

Keywords: coeliac, litigation, misrepresentation, negligence

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Authors: C. Sancho Salvatierra, J. M. Martinez Nieto, R. García Gonzalez-Gordon, M. I. Martinez Bellido

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Introduction: Nature could act as an asset for human health protecting against possible diseases and promoting the state of both physical and mental health. Goals: This paper aims to determine which natural elements present evidence that show positive influence on human health, on which particular aspects and how. It also aims to determine the best biomarkers to measure such influence. Method: A systematic literature review was carried out. First, a general free text search was performed in databases, such as Scopus, PubMed or PsychInfo. Secondly, a specific search was performed combining keywords in order of increasing complexity. Also the Snowballing technique was used and it was consulted in the CSIC’s (The Spanish National Research Council). Databases: Of the 130 articles obtained and reviewed, 80 referred to natural elements that influenced health. These 80 articles were classified and tabulated according to the nature elements found, the health aspects studied, the health measurement parameters used and the measurement techniques used. In this classification the results of the studies were codified according to whether they were positive, negative or neutral both for the elements of nature and for the aspects of health studied. Finally, the results of the 80 selected studies were summarized and categorized according to the elements of nature that showed the greatest positive influence on health and the biomarkers that had shown greater reliability to measure said influence. Results: Of the 80 articles studied, 24 (30.0%) were reviews and 56 (70.0%) were original research articles. Among the 24 reviews, 18 (75%) found positive results of natural elements on health, and 6 (25%) both positive and negative effects. Of the 56 original articles, 47 (83.9%) showed positive results, 3 (5.4%) both positive and negative, 4 (7.1%) negative effects, and 2 (3.6%) found no effects. The results reflect positive effects of different elements of nature on the following pathologies: diabetes, high blood pressure, stress, attention deficit hyperactivity disorder, psychotic, anxiety and affective disorders. They also show positive effects on the following areas: immune system, social interaction, recovery after illness, mood, decreased aggressiveness, concentrated attention, cognitive performance, restful sleep, vitality and sense of well-being. Among the elements of nature studied, those that show the greatest positive influence on health are forest immersion, natural views, daylight, outdoor physical activity, active transport, vegetation biodiversity, natural sounds and the green residences. As for the biomarkers used that show greater reliability to measure the effects of natural elements are the levels of cortisol (both in blood and saliva), vitamin D levels, serotonin and melatonin, blood pressure, heart rate, muscle tension and skin conductance. Conclusions: Nature is an asset for health, well-being and quality of life. Awareness programs, education and health promotion are needed based on the elements that nature brings us, which in turn generate proactive attitudes in the population towards the protection and conservation of nature. The studies related to this subject in Spain are very scarce. Aknowledgements. This study has been promoted and partially financed by the Environmental Foundation Jaime González-Gordon.

Keywords: health, green areas, nature, well-being

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Authors: Mohsen Ziaee

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In this paper, a mixed integer linear programming (MILP) model is presented to solve the flexible job shop scheduling problem (FJSP). This problem is one of the hardest combinatorial problems. The objective considered is the minimization of the makespan. The computational results of the proposed MILP model were compared with those of the best known mathematical model in the literature in terms of the computational time. The results show that our model has better performance with respect to all the considered performance measures including relative percentage deviation (RPD) value, number of constraints, and total number of variables. By this improved mathematical model, larger FJS problems can be optimally solved in reasonable time, and therefore, the model would be a better tool for the performance evaluation of the approximation algorithms developed for the problem.

Keywords: scheduling, flexible job shop, makespan, mixed integer linear programming

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Authors: Chang Liu, John Nash, Stephen D. Prior

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This paper presents the design and implementation details of a complete unmanned aerial system (UAS) based on commercial-off-the-shelf (COTS) components, focusing on safety, security, search and rescue scenarios in GPS-denied environments. In particular, the aerial platform is capable of semi-autonomously navigating through extremely low-light, GPS-denied indoor environments based on onboard sensors only, including a downward-facing optical flow camera. Besides, an additional low-cost payload camera system is developed to stream both infrared video and visible light video to a ground station in real-time, for the purpose of detecting sign of life and hidden humans. The total cost of the complete system is estimated to be $1150, and the effectiveness of the system has been tested and validated in practical scenarios.

Keywords: unmanned aerial system, commercial-off-the-shelf, extremely low-light, GPS-denied, optical flow, infrared video

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Authors: Rizwan Sultan, Hasnain Zafar

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Trauma is the leading cause of death among young population not only in USA but Pakistan as well. The high prevalence of disease should result in larger amount of data and larger number of publications resulting in exploring room for improvement in the field. We aimed to review trauma literature generated from Pakistan in journals indexed with PubMed from January 2010 to December 2014. Search using term “Trauma AND Pakistan” filtering for relevant dates and species human was done on Pubmed. The abstracts and articles were reviewed by the authors to collect data on a preformed performa. 114 articles were published from Pakistan during these 5 years. 64% articles were published in international journals. 63% articles were published in journals with impact factor less than 1. 54% articles were published from one of the four provinces of Pakistan. 64% of articles provided level 4 while 14% articles provided level 5 evidence on the topic. 55% articles discussed epidemiology in non-representative populations. Trauma literature from Pakistan is not only lacking significantly but is also of poor quality and is unable to offer conclusions on this particular subject. There is a lot of space for improvement in the upcoming years.

Keywords: trauma, literature, Pakistan, level of evidence

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Authors: M. Khal, Ab. Algouti, A. Algouti

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Water erosion is the major cause of the erosion that shapes the earth's surface. Modeling water erosion requires the use of software and GIS programs, commercial or closed source. The very high prices for commercial GIS licenses, motivates users and researchers to find open source software as relevant and applicable as the proprietary GIS. The objective of this study is the modeling of water erosion and the hydrogeological and morphophysical characterization of the Oued M'Goun watershed (southern flank of the Central High Atlas) developed by free programs of GIS. The very pertinent results are obtained by executing tasks and algorithms in a simple and easy way. Thus, the various geoscientific and geostatistical analyzes of a digital elevation model (SRTM 30 m resolution) and their combination with the treatments and interpretation of satellite imagery information allowed us to characterize the region studied and to map the area most vulnerable to water erosion.

Keywords: central High-Atlas, hydrogeology, M’Goun watershed, OpenGis, water erosion

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Authors: Elham Koohi, Silvio Jose Gumiere, Hossein Bonakdari, Saeid Homayouni

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Water used in agricultural crops can be managed by irrigation scheduling based on soil moisture levels and plant water stress thresholds. Automated irrigation scheduling limits crop physiological damage and yield reduction. Knowledge of crop water stress monitoring approaches can be effective in optimizing the use of agricultural water. Understanding the physiological mechanisms of crop responding and adapting to water deficit ensures sustainable agricultural management and food supply. This aim could be achieved by analyzing and diagnosing crop characteristics and their interlinkage with the surrounding environment. Assessments of plant functional types (e.g., leaf area and structure, tree height, rate of evapotranspiration, rate of photosynthesis), controlling changes, and irrigated areas mapping. Calculating thresholds of soil water content parameters, crop water use efficiency, and Nitrogen status make irrigation scheduling decisions more accurate by preventing water limitations between irrigations. Combining Remote Sensing (RS), the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning Algorithms (MLAs) can improve measurement accuracies and automate irrigation scheduling. This paper is a review structured by surveying about 100 recent research studies to analyze varied approaches in terms of providing high spatial and temporal resolution mapping, sensor-based Variable Rate Application (VRA) mapping, the relation between spectral and thermal reflectance and different features of crop and soil. The other objective is to assess RS indices formed by choosing specific reflectance bands and identifying the correct spectral band to optimize classification techniques and analyze Proximal Optical Sensors (POSs) to control changes. The innovation of this paper can be defined as categorizing evaluation methodologies of precision irrigation (applying the right practice, at the right place, at the right time, with the right quantity) controlled by soil moisture levels and sensitiveness of crops to water stress, into pre-processing, processing (retrieval algorithms), and post-processing parts. Then, the main idea of this research is to analyze the error reasons and/or values in employing different approaches in three proposed parts reported by recent studies. Additionally, as an overview conclusion tried to decompose different approaches to optimizing indices, calibration methods for the sensors, thresholding and prediction models prone to errors, and improvements in classification accuracy for mapping changes.

Keywords: agricultural crops, crop water stress detection, irrigation scheduling, precision agriculture, remote sensing

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Authors: Daniel R. dos Santos, Mateus S. Maldonado, Estevão J. R. Batista

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The military commanders increasingly dependent on spatial awareness, as knowing where enemy are, understanding how war battle scenarios change over time, and visualizing these trends in ways that offer insights for decision-making. Thanks to advancements in geospatial technologies and artificial intelligence algorithms, the commanders are now able to modernize military operations on a universal scale. Thus, geovisualisation has become an essential asset in the defense sector. It has become indispensable for better decisionmaking in dynamic/temporal scenarios, operation planning and management for the war field, situational awareness, effective planning, monitoring, and others. For example, a 3D visualization of war field data contributes to intelligence analysis, evaluation of postmission outcomes, and creation of predictive models to enhance decision-making and strategic planning capabilities. However, old-school visualization methods are slow, expensive, and unscalable. Despite modern technologies in generating 3D point clouds, such as LIDAR and stereo sensors, monocular depth values based on deep learning can offer a faster and more detailed view of the environment, transforming single images into visual information for valuable insights. We propose a dedicated monocular depth reconstruction approach via deep learning techniques for 3D geovisualisation of satellite images. It introduces scalability in terrain reconstruction and data visualization. First, a dataset with more than 7,000 satellite images and associated digital elevation model (DEM) is created. It is based on high resolution optical and radar imageries collected from Planet and Copernicus, on which we fuse highresolution topographic data obtained using technologies such as LiDAR and the associated geographic coordinates. Second, we developed an imagery-DEM fusion strategy that combine feature maps from two encoder-decoder networks. One network is trained with radar and optical bands, while the other is trained with DEM features to compute dense 3D depth. Finally, we constructed a benchmark with sparse depth annotations to facilitate future research. To demonstrate the proposed method's versatility, we evaluated its performance on no annotated satellite images and implemented an enclosed environment useful for Geovisualisation applications. The algorithms were developed in Python 3.0, employing open-source computing libraries, i.e., Open3D, TensorFlow, and Pythorch3D. The proposed method provides fast and accurate decision-making with GIS for localization of troops, position of the enemy, terrain and climate conditions. This analysis enhances situational consciousness, enabling commanders to fine-tune the strategies and distribute the resources proficiently.

Keywords: depth, deep learning, geovisualisation, satellite images

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Authors: Pawel Twardowski, Maciej Tabaszewski, Jakub Czyżycki

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The main goal of the work was to use machine learning to predict cutting-edge wear. The research was carried out while milling hardened steel with sintered carbide cutters at various cutting speeds. During the tests, cutting-edge wear was measured, and vibration acceleration signals were also measured. Appropriate measures were determined from the vibration signals and served as input data in the machine-learning process. Two approaches were used in this work. The first one involved a two-state classification of the cutting edge - suitable and unfit for further work. In the second approach, prediction of the cutting-edge state based on vibration signals was used. The obtained research results show that the appropriate use of machine learning algorithms gives excellent results related to monitoring cutting edge during the process.

Keywords: milling of hardened steel, tool wear, vibrations, machine learning

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Authors: Ravindra B. Patil, P. Krishnamoorthy, Shriram Sethuraman

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This work proposes a novel Gaussian Mixture Model (GMM) based approach for accurate tracking of the arterial wall and subsequent computation of the distension waveform using Radio Frequency (RF) ultrasound signal. The approach was evaluated on ultrasound RF data acquired using a prototype ultrasound system from an artery mimicking flow phantom. The effectiveness of the proposed algorithm is demonstrated by comparing with existing wall tracking algorithms. The experimental results show that the proposed method provides 20% reduction in the error margin compared to the existing approaches in tracking the arterial wall movement. This approach coupled with ultrasound system can be used to estimate the arterial compliance parameters required for screening of cardiovascular related disorders.

Keywords: distension waveform, Gaussian Mixture Model, RF ultrasound, arterial wall movement

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Authors: Gargi Phadke, Mugdha Joshi, Shamal Salunkhe

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Facial sketches are used as a crucial clue by criminal investigators for identification of suspects when the description of eyewitness or victims are only available as evidence. A forensic artist develops a sketch as per the verbal description is given by an eyewitness that shows the facial look of the culprit. In this paper, the fusion of Scale Invariant Feature Transform (SIFT) and multiscale local binary patterns (MLBP) are proposed as a feature to recognize a forensic face sketch images from a gallery of mugshot photos. This work focuses on comparative analysis of proposed scheme with existing algorithms in different challenges like illumination change and rotation condition. Experimental results show that proposed scheme can lead to better performance for the defined problem.

Keywords: SIFT feature, MLBP, PCA, face sketch

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Authors: Jessica To

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Feedback is one of the powerful levers for enhancing students’ performance. However, some students are under-engaged with feedback because they lack responsibility for feedback uptake. To resolve this conundrum, recent literature proposes feedback partnerships in which students and teachers share the power and responsibilities to co-construct feedback. During feedback co-construction, students express feedback needs to teachers, and teachers respond to individuals’ needs in return. Though this approach can increase students’ feedback ownership, its application is lagging as the field lacks conceptual clarity and implementation guide. This presentation aims to discuss the conceptual framework of feedback partnerships and feedback co-construction strategies. It identifies the components of feedback partnerships and strategies which could facilitate feedback co-construction. A systematic literature review was conducted to answer the questions. The literature search was performed using ERIC, PsycINFO, and Google Scholar with the keywords “assessment partnership”, “student as partner,” and “feedback engagement”. No time limit was set for the search. The inclusion criteria encompassed (i) student-teacher partnerships in feedback, (ii) feedback engagement in higher education, (iii) peer-reviewed publications, and (iv) English as the language of publication. Those without addressing conceptual understanding and implementation strategies were excluded. Finally, 65 publications were identified and analysed using thematic analysis. For the procedure, the texts relating to the questions were first extracted. Then, codes were assigned to summarise the ideas of the texts. Upon subsuming similar codes into themes, four themes emerged: students’ responsibilities, teachers’ responsibilities, conditions for partnerships development, and strategies. Their interrelationships were examined iteratively for framework development. Establishing feedback partnerships required different responsibilities of students and teachers during feedback co-construction. Students needed to self-evaluate performance against task criteria, identify inadequacies and communicate their needs to teachers. During feedback exchanges, they interpreted teachers’ comments, generated self-feedback through reflection, and co-developed improvement plans with teachers. Teachers had to increase students’ understanding of criteria and evaluation skills and create opportunities for students’ expression of feedback needs. In feedback dialogue, teachers responded to students’ needs and advised on the improvement plans. Feedback partnerships would be best grounded in an environment with trust and psychological safety. Four strategies could facilitate feedback co-construction. First, students’ understanding of task criteria could be increased by rubrics explanation and exemplar analysis. Second, students could sharpen evaluation skills if they participated in peer review and received teacher feedback on the quality of peer feedback. Third, provision of self-evaluation checklists and prompts and teacher modeling of self-assessment process could aid students in articulating feedback needs. Fourth, the trust could be fostered when teachers explained the benefits of feedback co-construction, showed empathy, and provided personalised comments in dialogue. Some strategies were applied in interactive cover sheets in which students performed self-evaluation and made feedback requests on a cover sheet during assignment submission, followed by teachers’ response to individuals’ requests. The significance of this presentation lies in unpacking the conceptual framework of feedback partnerships and outlining feedback co-construction strategies. With a solid foundation in theory and practice, researchers and teachers could better enhance students’ engagement with feedback.

Keywords: conceptual framework, feedback co-construction, feedback partnerships, implementation strategies

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Authors: Yogendra Sisodia

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Researchers are becoming more interested in extracting useful information from legal documents thanks to the development of large-scale language models in natural language processing (NLP), and deep learning has accelerated the creation of powerful text mining models. Legal fields like contracts benefit greatly from semantic text search since it makes it quick and easy to find related clauses. After collecting sentence embeddings, it is relatively simple to locate sentences with a comparable meaning throughout the entire legal corpus. The author of this research investigated two pre-trained language models for this task: MiniLM and Roberta, and further fine-tuned them on Legal Contracts. The author used Multiple Negative Ranking Loss for the creation of sentence transformers. The fine-tuned language models and sentence transformers showed promising results.

Keywords: legal contracts, multiple negative ranking loss, natural language inference, sentence transformers, semantic textual similarity

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Authors: Monir Ahmed

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The main purpose of this literature review was to investigate the existing quantitative clinical studies on the relationship between transcendence and psychological well-being. The primary objective of the literature review is to determine whether the existing studies adequately demonstrate the relationship between transcendence and psychological well-being, including spiritual well-being. A further objective of this literature review is to see if the ‘creatio ex nihilo’ doctrine is necessary to understand transcendence and its relationship with psychological well-being. Systematic literature review methods including studies identified from search engines, extracting data from the studies and assessing their quality for the planned review were used. The outcome of this literature review indicates that self-transcendence (STa), spiritual transcendence (STb) are positively related to psychological well-being. However, such positive relationships present limited scope for understanding transcendence and its relationship with well-being. The findings of this review support the need for further research in the area of transcendence and well-being. This literature review reveals the importance of developing a new transcendence tool for determining an individual’s ability to transcend and the relationship between his/her ability for transcendence and psychological well-being. The author of this paper proposes that the inclusion of the theological doctrine (‘creatio ex nihilo’) in understanding transcendence and psychological well-being is crucial, necessary and unavoidable.

Keywords: transcendence, psychological well-being, self-transcendence, spiritual transcendence, ‘creatio ex nihilo’

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Authors: Roshani S. Golhar, Neerajkumar S. Sathawane, Snehal Dongre

Abstract:

Background and objectives: As the, cardiovascular illnesses increasing and becoming cause of mortality worldwide, killing around lot of people each year. Arrhythmia is a type of cardiac illness characterized by a change in the linearity of the heartbeat. The goal of this study is to develop novel deep learning algorithms for successfully interpreting arrhythmia using a single second segment. Because the ECG signal indicates unique electrical heart activity across time, considerable changes between time intervals are detected. Such variances, as well as the limited number of learning data available for each arrhythmia, make standard learning methods difficult, and so impede its exaggeration. Conclusions: The proposed method was able to outperform several state-of-the-art methods. Also proposed technique is an effective and convenient approach to deep learning for heartbeat interpretation, that could be probably used in real-time healthcare monitoring systems

Keywords: electrocardiogram, ECG classification, neural networks, convolutional neural networks, portable document format

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Authors: A. Nachour, L. Ouzizi, Y. Aoura

Abstract:

Recent developments on multi-agent system have brought a new research field on image processing. Several algorithms are used simultaneously and improved in deferent applications while new methods are investigated. This paper presents a new automatic method for edge detection using several agents and many different actions. The proposed multi-agent system is based on parallel agents that locally perceive their environment, that is to say, pixels and additional environmental information. This environment is built using Vector Field Convolution that attract free agent to the edges. Problems of partial, hidden or edges linking are solved with the cooperation between agents. The presented method was implemented and evaluated using several examples on different synthetic and medical images. The obtained experimental results suggest that this approach confirm the efficiency and accuracy of detected edge.

Keywords: edge detection, medical MRImages, multi-agent systems, vector field convolution

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Authors: H. Mousavi, M. Sharifi, H. Pourvaziri

Abstract:

Redundancy Allocation Problem (RAP) is a well-known mathematical problem for modeling series-parallel systems. It is a combinatorial optimization problem which focuses on determining an optimal assignment of components in a system design. In this paper, to be more practical, we have considered the problem of redundancy allocation of series system with interval valued reliability of components. Therefore, during the search process, the reliabilities of the components are considered as a stochastic variable with a lower and upper bounds. In order to optimize the problem, we proposed a simulated annealing based on cloud theory (CBSAA). Also, the Monte Carlo simulation (MCS) is embedded to the CBSAA to handle the random variable components’ reliability. This novel approach has been investigated by numerical examples and the experimental results have shown that the CBSAA combining MCS is an efficient tool to solve the RAP of systems with interval-valued component reliabilities.

Keywords: redundancy allocation problem, simulated annealing, cloud theory, monte carlo simulation

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Authors: Kanyakorn Sujarittnetikarn, Surangkana Pipatchokchaiyo

Abstract:

The objective of this research was to study the pro-active public relations of according to the characteristic of Faculty of Management Science, Suan Sunandha Rajabhat University. The sample group for this research report was students from 4 year curriculum and continued / extended curriculum, made a random distribution proportion as follows: a group of 400 students who are working while studying and a group of non – working students. The tools used in this research were questionnaires, asking about the acknowledgement of public relations information of Faculty of Management Science in the academic year 2007. The result found that friends were the most influential in choosing the education institute. The differences of method to receive information of non-working student and working student were the entertainment magazine which was interested mostly by working students and they preferred to search the information on the website after 24:00 O’clock. However, the non-working students preferred 21:00-24:00 O’clock the most.

Keywords: development guidelines systems, faculty of management science, public relation planning, proactive public relations

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Authors: Neha Adsul, Rohit Shah

Abstract:

Background: The global history of nursing is exclusively a history of deep contradictions as it seeks to negotiate inclusion in an already gendered world. Although a powerful 'clinical gaze exists, nurses have toiled to re-negotiate and subvert the 'medical gaze' by practicing the 'therapeutic gaze' to tether back 'care into nursing practice.' This helps address the duality of the 'body' and 'mind' wherein the patient is not just limited to being an object of medical inquiry. Nevertheless, there has been a consistent effort to fit 'nursing' into being an art or an emerging science over the years. Especially with advances in hospital-based techno-centric medical practices, the boundaries between technology and nursing practices are becoming more blurred as the technical process becomes synonymous with nursing, eroding the essence of nursing care. Aim: This paper examines the history of nursing and offers insights into how gendered relations and the ideological belief of 'nursing as gendered work' have propagated to the subjugation of the nursing profession. It further aims to provide insights into the patriarchally imbibed techno-centrism that negates the gendered caregiving which lies at the crux of a nurse's work. Method: A literature search was carried out using Google Scholar, Web of Science and PubMed databases. Search words included: technology and nursing, medical technology and nursing, history of nursing, sociology and nursing and nursing care. The history of nursing is presented in a discussion that weaves together the historical events of the 'Birth of the Clinic' and the shift from 'bed-side medicine' to 'hospital-based medicine' that legitimizes exploitation of the bodies of patients to the 'medical gaze while the emergence of nursing as acquiescent to instrumental, technical, positivist and dominant views of medicine. The resultant power asymmetries, wherein in contemporary nursing, the constant struggle of nurses to juggle between being the physicians "operational right arm" to harboring that subjective understanding of the patients to refrain from de-humanizing nursing-care. Findings: The nursing profession suffers from being rendered invisible due to gendered relations having patrifocal societal roots. This perpetuates a notion rooted in the idea that emphasizes empiricism and has resulted in theoretical and epistemological fragmentation of the understanding of body and mind as separate entities. Nurses operate within this structure while constantly being at the brink of being pushed beyond the legitimate professional boundaries while being labeled as being 'unscientific' as the work does not always corroborate and align with the existing dominant positivist lines of inquiries. Conclusion: When understood in this broader context of how nursing as a practice has evolved over the years, it provides a particularly crucial testbed for understanding contemporary gender relations. Not because nurses like to live in a gendered work trap but because the gendered relations at work are written in a covert narcissistic patriarchal milieu that fails to recognize the value of intangible yet utmost necessary 'caring work in nursing. This research urges and calls for preserving and revering the humane aspect of nursing care alongside the emerging tech-savvy expectations from nursing work.

Keywords: nursing history, technocentric, power relations, scientific duality

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Authors: Marcho Setiawan Handok, Samuel S. Lemma, Abdoulaye Fofana, Naseef Mansoor

Abstract:

This paper is proposing a method to predict the outcome of the National Football League match with data from 2019 to 2022 and compare it with other popular models. The model uses open-source statistical data of each team, such as passing yards, rushing yards, fumbles lost, and scoring. Each statistical data has offensive and defensive. For instance, a data set of anticipated values for a specific matchup is created by comparing the offensive passing yards obtained by one team to the defensive passing yards given by the opposition. We evaluated the model’s performance by contrasting its result with those of established prediction algorithms. This research is using a neural network to predict the score of a National Football League match and then predict the winner of the game.

Keywords: game prediction, NFL, football, artificial neural network

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