Search results for: deep convolutional features

Authors: Hyeongbok Kim, Lingling Zhao, Xiaohong Su

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Deep reinforcement learning has been applied to address various problems in robotics, such as autonomous driving and unmanned aerial vehicle. However, because of the sparse reward penalty for a collision with obstacles during the navigation mission, the agent fails to learn the optimal policy or requires a long time for convergence. Therefore, using obstacles and enemy agents, in this paper, we present a curriculum-based boost learning method to effectively train compound skills during multi-agent reinforcement learning. First, to enable the agents to solve challenging tasks, we gradually increased learning difficulties by adjusting reward shaping instead of constructing different learning environments. Then, in a benchmark environment with static obstacles and moving enemy agents, the experimental results showed that the proposed curriculum learning strategy enhanced cooperative navigation and compound collision avoidance skills in uncertain environments while improving learning efficiency.

Keywords: curriculum learning, hard exploration, multi-agent reinforcement learning, robotic navigation, sparse reward

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Authors: Francielle A. Vargas, Thiago A. S. Pardo

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According to the standard philosophical definition, lying is saying something that you believe to be false with the intent to deceive. For deception detection, the FBI trains its agents in a technique named statement analysis, which attempts to detect deception based on parts of speech (i.e., linguistics style). This method is employed in interrogations, where the suspects are first asked to make a written statement. In this poster, we model false statements using linguistics style. In order to achieve this, we methodically analyze linguistic features in a corpus of fake news in the Portuguese language. The results show that they present substantial lexical, syntactic and semantic variations, as well as punctuation and emotion distinctions.

Keywords: deception detection, linguistics style, computational linguistics, natural language processing

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Authors: A. R. Bagirzade, A. Sh. Najafova, S. M. Yessirkepova, E. S. Albert

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This article describes a text recognition method based on Optical Character Recognition (OCR). The features of the OCR method were examined using the ABBYY FineReader program. It describes automatic text recognition in images. OCR is necessary because optical input devices can only transmit raster graphics as a result. Text recognition describes the task of recognizing letters shown as such, to identify and assign them an assigned numerical value in accordance with the usual text encoding (ASCII, Unicode). The peculiarity of this study conducted by the authors using the example of the ABBYY FineReader, was confirmed and shown in practice, the improvement of digital text recognition platforms developed by Electronic Publication.

Keywords: ABBYY FineReader system, algorithm symbol recognition, OCR/ICR techniques, recognition technologies

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Authors: Thanakrit Taweesoontorn, Sarucha Yanyong, Poom Konghuayrob

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Robotic arms have gained popularity in various industries due to their accuracy and efficiency. This research proposes a method for bin-picking tasks using the Cobot, combining the YOLOv5 CNNs model for object detection and pose estimation with traditional feature detection (FAST), feature description (BRISK), and matching algorithms. By integrating these algorithms and utilizing a small-scale depth sensor camera for capturing depth and color images, the system achieves real-time object detection and accurate pose estimation, enabling the robotic arm to pick objects correctly in both position and orientation. Furthermore, the proposed method is implemented within the ROS framework to provide a seamless platform for robotic control and integration. This integration of robotics, cameras, and AI technology contributes to the development of industrial robotics, opening up new possibilities for automating challenging tasks and improving overall operational efficiency.

Keywords: robotic vision, image processing, applications of robotics, artificial intelligent

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Authors: Ilan Tamir

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Fans’ chants in sports stadium have, over the years, become an integral part of the spectator experience. While chants add color, atmosphere, and a demonstration of fans’ support for their team, chants also play a significant role in defining fans’ perceptions of their team’s identity and its differentiation from other teams. An analysis of football chants may therefore shed light on fans’ deep-seated worldviews of their own role, their team, the sport in general, and even life itself. This study, based on an analysis of Israeli football chants over years, identifies key changing and stable perceptions of football fans. Overall 94 chants collected, over a period of five decades. After a pilot study, the chants organized in two groups (one covering 1970-1999 and the other 2000-2016). The chants analyzed through qualitative content analysis in order to understand fans values as a reflection of the society. Findings point to several values that have remained stable over years, including fans’ attitudes toward their team and its rivals, and their attitude toward God. On the other hand, recently emerging phenomena such as radicalization of hatred toward the commercialization of sport reflect social and cultural changes, both in and outside the world of sport.

Keywords: sport, fans, chants, soccer

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Authors: R. Venkatesan, A. Sabari

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Developments of social networking internet scenario are recommended for the requirements of scalable, authentic, secure group communication model like multicasting. Multicasting is an inter network service that offers efficient delivery of data from a source to multiple destinations. Even though multicast has been very successful at providing an efficient and best-effort data delivery service for huge groups, it verified complex process to expand other features to multicast in a scalable way. Separately, the requirement for secure electronic information had become gradually more apparent. Since multicast applications are deployed for mainstream purpose the need to secure multicast communications will become significant.

Keywords: multicasting, scalability, security, social network

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Authors: L. Kutateldze, T. Urushadze, R. Khvedelidze, N. Zakariashvili, I. Khokhashvili, T. Sadunishvili

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Microbial cellulase/xylanase have shown their potential application in various industries including pulp and paper, textile, laundry, biofuel production, food and feed industry, brewing, and agriculture. Extremophilic micromycetes and their enzymes that are resistant to critical values of temperature and pH, and retaining enzyme activity for a long time are of great industrial interest. Among strains of microscopic fungi from the collection of S. Durmishidze Institute of Biochemistry and Biotechnology, strains isolated from different ecological niches of Southern Caucasus-active producers of cellulase/xylanase have been selected by means of screening under deep cultivation conditions. Extremophilic micromycetes and their enzymes that are resistant to critical values of temperature and pH, and retaining enzyme activity for a long time are of great industrial interest. Among strains of microscopic fungi from the collection of S. Durmishidze Institute of Biochemistry and Biotechnology, strains isolated from different ecological niches of Southern Caucasus-active producers of cellulase/xylanase have been selected by means of screening under deep cultivation conditions. Representatives of the genera Aspergillus, Penicillium and Trichoderma are outstanding by relatively high activities of these enzymes. Among the producers were revealed thermophilic strains, representatives of the genus Aspergillus-Aspergillus terreus, Aspergillus versicolor, Aspergillus wentii, also strains of Sporotrichum pulverulentum and Chaetomium thermophile. As a result of optimization of cultivation media and conditions, activities of enzymes produced by the strains have been increased by 4 -189 %. Two strains, active producers of cellulase/xylanase – Penicillium canescence E2 (mesophile) and Aspergillus versicolor Z17 (thermophile) were chosen for further studies. Cellulase/xylanase enzyme preparations from two different genera of microscopic fungi Penicillium canescence E2 and Aspergillus versicolor Z 17 were obtained with activities 220 U/g /1200 U/g and 125 U/g /940 U/g, correspondingly. Main technical characteristics were as follows: the highest enzyme activities were obtained for mesophilic strain Penicillium canescence E2 at 45-500C, while almost the same enzyme activities were fixed for the thermophilic strain Aspergillus versicolor Z 17 at temperature 60-65°C, exceeding the temperature optimum of the mesophile by 150C. Optimum pH of action of the studied cellulase/xylanases from mesophileic and thermophilic strains were similar and equaled to 4.5-5.0 It has been shown that cellulase/xylanase technical preparations from selected strains of Penicillium canescence E2 and Aspergillus versicolor Z17 hydrolyzed cellulose of untreated wheat straw to reducible sugars by 46-52%, and to glucose by 22-27%. However the thermophilic enzyme preparations from the thermophilic A.versicolor strains conducted the process at 600C higher by 100C as compared to mesophlic analogue. Rate of hydrolyses of the pretreated substrate by the same enzyme preparations to reducible sugars and glucose conducted at optimum for their action 60 and 500C was 52-61% and 29-33%, correspondingly. Thus, maximum yield of glucose and reducible sugars form untreated and pretreated wheat straw was achieved at higher temperature (600C) by enzyme preparations from thermophilic strain, which gives advantage for their industrial application.

Keywords: cellulase/xylanase, cellulose hydrolysis, microscopic fungi, thermophilic strain

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Authors: Ursula Das, Manavjit Singh Dhindsa, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

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Wildland fires, also known as forest fires or wildfires, are exhibiting an alarming surge in frequency in recent times, further adding to its perennial global concern. Forest fires often lead to devastating consequences ranging from loss of healthy forest foliage and wildlife to substantial economic losses and the tragic loss of human lives. Despite the existence of substantial literature on the detection of active forest fires, numerous potential research avenues in forest fire management, such as preventative measures and ancillary effects of forest fires, remain largely underexplored. This paper undertakes a systematic review of these underexplored areas in forest fire research, meticulously categorizing them into distinct phases, namely pre-fire, during-fire, and post-fire stages. The pre-fire phase encompasses the assessment of fire risk, analysis of fuel properties, and other activities aimed at preventing or reducing the risk of forest fires. The during-fire phase includes activities aimed at reducing the impact of active forest fires, such as the detection and localization of active fires, optimization of wildfire suppression methods, and prediction of the behavior of active fires. The post-fire phase involves analyzing the impact of forest fires on various aspects, such as the extent of damage in forest areas, post-fire regeneration of forests, impact on wildlife, economic losses, and health impacts from byproducts produced during burning. A comprehensive understanding of the three stages is imperative for effective forest fire management and mitigation of the impact of forest fires on both ecological systems and human well-being. Artificial intelligence and machine learning (AI/ML) methods have garnered much attention in the cyber-physical systems domain in recent times leading to their adoption in decision-making in diverse applications including disaster management. This paper explores the current state of AI/ML applications for managing the activities in the aforementioned phases of forest fire. While conventional machine learning and deep learning methods have been extensively explored for the prevention, detection, and management of forest fires, a systematic classification of these methods into distinct AI research domains is conspicuously absent. This paper gives a comprehensive overview of the state of forest fire research across more recent and prominent AI/ML disciplines, including big data, classical machine learning, computer vision, explainable AI, generative AI, natural language processing, optimization algorithms, and time series forecasting. By providing a detailed overview of the potential areas of research and identifying the diverse ways AI/ML can be employed in forest fire research, this paper aims to serve as a roadmap for future investigations in this domain.

Keywords: artificial intelligence, computer vision, deep learning, during-fire activities, forest fire management, machine learning, pre-fire activities, post-fire activities

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Authors: Heramb Somthankar, Anujoy Chakraborty

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This paper presents a classification study on yield prediction in semiconductor production using machine learning approaches. A complicated semiconductor production process is generally monitored continuously by signals acquired from sensors and measurement sites. A monitoring system contains a variety of signals, all of which contain useful information, irrelevant information, and noise. In the case of each signal being considered a feature, "Feature Selection" is used to find the most relevant signals. The open-source UCI SECOM Dataset provides 1567 such samples, out of which 104 fail in quality assurance. Feature extraction and selection are performed on the dataset, and useful signals were considered for further study. Afterward, common machine learning algorithms were employed to predict whether the signal yields pass or fail. The most relevant algorithm is selected for prediction based on the accuracy and loss of the ML model.

Keywords: deep learning, feature extraction, feature selection, machine learning classification algorithms, semiconductor production monitoring, signal processing, time-series analysis

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Authors: Patricia P. M. C. Lourenco, Javier Bringué Sala, Anaisa D. A. de Sena

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Almost everyone in Dramaland knows the names of big Korean stars that grace their computer screens on a roll through social media and video streaming platforms that enable awareness of Korean dramas and lifestyle at a click. A surface culture instilled with notions of belonging has redefined the meaning of friendship and challenged deep inner values. Not everyone, however, knows Chinese Dramas or their stars, which is a consequence of Dramaland's focus on Korean dramas and promoting the Korean experience. Despite a parity in terms of production quality, star power, scripts and compelling visual settings, Chinese Dramas have been playing catch up to their famous counterparts. While they might have a strong competitive soft power for international drama fans, the soft power of Korean dramas is imbued with substantial societal values that they want to share with others. Those values are portrayed in an artistic way that connects with audiences who experience loneliness in the non-virtual world contrary to the way Chinese Dramas are perceived.

Keywords: Chinese dramas, fandom, Korean dramas, participatory culture, value perception, soft power, surface culture

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Authors: Jacques M. Berner, Monja Gerber, Gillian L. Maggs-Kolling, Stuart J. Piketh

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The enigmatic melon species, Acanthosicyos horridus Welw. ex Hook. f., locally known as !nara, is endemic to the hyper-arid Namib Desert, where it thrives in sandy dune areas and dry river banks. The Namib Desert is characterized by extreme weather conditions which include high temperatures, very low rainfall, and extremely dry air. Plant and animals that have made the Namib Dessert their home are dependent on non-rainfall water inputs, like fog, dew and water vapor, for survival. Fog is believed to be the most important non-rainfall water input for most of the coastal Namib Desert and is a life line to many Namib plants and animals. It is commonly assumed that the !nara plant is adapted and dependent upon coastal fog events. The !nara plant shares many comparable adaptive features with other organisms that are known to exploit fog as a source of moisture. These include groove-like structures on the stems and the cone-like structures of thorns. These structures are believed to be the driving forces behind directional water flow that allow plants to take advantage of fog events. The !nara-fog interaction was investigated in this study to determine the dependence of !nara on these fog events, as it would illustrate strategies to benefit from non-rainfall water inputs. The direct water uptake capacity of !nara shoots was investigated through absorption tests. Furthermore, the movement and behavior of fluorescent water droplets on a !nara stem were investigated through time-lapse macrophotography. The shoot water potential was measured to investigate the effect of fog on the water status of !nara stems. These tests were used to determine whether the morphology of !nara has evolved to exploit fog as a non-rainfall water input and whether the !nara plant has adapted physiologically in response to fog. Chlorophyll a fluorescence was used to compare the photochemical efficiency of !nara plants on days with fog events to that on non-foggy days. The results indicate that !nara plants do have the ability to take advantage of fog events as commonly believed. However, the !nara plant did not exhibit visible signs of drought stress and this, together with the strong shoot water potential, indicates that these plants are reliant on permanent underground water sources. Chlorophyll a fluorescence data indicated that temperature stress and wind were some of the main abiotic factors influencing the plants’ overall vitality.

Keywords: Acanthosicyos horridus, chlorophyll a fluorescence, fog, foliar absorption, !nara

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Authors: Felix Golla

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In an era where technology increasingly intersects with education, the potential of chatbots and ChatGPT agents in enhancing student learning experiences in higher education is both significant and timely. This study explores the integration of these AI-driven tools in educational settings, emphasizing their design and functionality to meet the specific needs of students. Recognizing the gap in literature concerning student-centered AI applications in education, this research offers valuable insights into the role and efficacy of chatbots and ChatGPT agents as educational tools. Employing qualitative research methodologies, the study involved conducting semi-structured interviews with university students. These interviews were designed to gather in-depth insights into the students' experiences and expectations regarding the use of AI in learning environments. The High-Performance Cycle Model, renowned for its focus on goal setting and motivation, served as the theoretical framework guiding the analysis. This model helped in systematically categorizing and interpreting the data, revealing the nuanced perceptions and preferences of students regarding AI tools in education. The major findings of the study indicate a strong preference among students for chatbots and ChatGPT agents that offer personalized interaction, adaptive learning support, and regular, constructive feedback. These features were deemed essential for enhancing student engagement, motivation, and overall learning outcomes. Furthermore, the study revealed that students perceive these AI tools not just as passive sources of information but as active facilitators in the learning process, capable of adapting to individual learning styles and needs. In conclusion, this study underscores the transformative potential of chatbots and ChatGPT agents in higher education. It highlights the need for these AI tools to be designed with a student-centered approach, ensuring their alignment with educational objectives and student preferences. The findings contribute to the evolving discourse on AI in education, suggesting a paradigm shift towards more interactive, responsive, and personalized learning experiences. This research not only informs educators and technologists about the desirable features of educational chatbots but also opens avenues for future studies to explore the long-term impact of AI integration in academic curricula.

Keywords: chatbot design in education, high-performance cycle model application, qualitative research in AI, student-centered learning technologies

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Authors: Hamid Reza Pakzad

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In this paper, the properties of dust-ion-acoustic (DIA) shock waves in an unmagnetized dusty plasma, whose constituents are inertial ions, superthermal electrons, and stationary dust particles, are investigated by employing the reductive perturbation method. The dissipation is taken into account the kinematic viscosity among the plasma constituents. It is shown that the basic features of DIA shock waves are significantly modified by the effects of electron superthermality and ion kinematic viscosity.

Keywords: reductive perturbation method, dust ion acoustic shock wave, superthermal electron, dissipative plasmas

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Authors: Pavana Basavakumar, Devadas Bhat

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Diabetes is a disease which often goes undiagnosed until its secondary effects are noticed. Early detection of the disease is necessary to avoid serious consequences which could lead to the death of the patient. Conventional invasive tests for screening of diabetes are mostly painful, time consuming and expensive. There’s also a risk of infection involved, therefore it is very essential to develop non-invasive methods to screen and estimate the level of blood glucose. Extensive research is going on with this perspective, involving various techniques that explore optical, electrical, chemical and thermal properties of the human body that directly or indirectly depend on the blood glucose concentration. Thus, non-invasive blood glucose monitoring has grown into a vast field of research. In this project, an attempt was made to device a prototype for screening of diabetes by measuring electrical impedance of the skin and building a model to predict a patient’s condition based on the measured impedance. The prototype developed, passes a negligible amount of constant current (0.5mA) across a subject’s index finger through tetra polar silver electrodes and measures output voltage across a wide range of frequencies (10 KHz – 4 MHz). The measured voltage is proportional to the impedance of the skin. The impedance was acquired in real-time for further analysis. Study was conducted on over 75 subjects with permission from the institutional ethics committee, along with impedance, subject’s blood glucose values were also noted, using conventional method. Nonlinear regression analysis was performed on the features extracted from the impedance data to obtain a model that predicts blood glucose values for a given set of features. When the predicted data was depicted on Clarke’s Error Grid, only 58% of the values predicted were clinically acceptable. Since the objective of the project was to screen diabetes and not actual estimation of blood glucose, the data was classified into three classes ‘NORMAL FASTING’,’NORMAL POSTPRANDIAL’ and ‘HIGH’ using linear Support Vector Machine (SVM). Classification accuracy obtained was 91.4%. The developed prototype was economical, fast and pain free. Thus, it can be used for mass screening of diabetes.

Keywords: Clarke’s error grid, electrical impedance of skin, linear SVM, nonlinear regression, non-invasive blood glucose monitoring, screening device for diabetes

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Authors: Anthony Townley

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Contract negotiation is fundamental to commercial law practice. For this study, genre and discourse analytical methodology was used to examine the legal negotiation of a Merger & Acquisition (M&A) deal undertaken by legal and business professionals in English across different jurisdictions in Europe. While some of the most delicate negotiations involved in this process were carried on face-to-face or over the telephone, these were generally progressed more systematically – and on the record – in the form of emails, email attachments, and as comments and amendments recorded in successive ‘marked-up’ versions of the contracts under negotiation. This large corpus of textual data was originally obtained by the author, in 2012, for the purpose of doctoral research. For this study, the analysis is particularly concerned with the use of emails and covering letters to exchange legal advice about the negotiations. These two genres help to stabilize and progress the negotiation process and account for negotiation activities. Swalesian analysis of functional Moves and Steps was able to identify structural similarities and differences between these text types and to identify certain salient discursive features within them. The analytical findings also indicate how particular linguistic strategies are more appropriately and more effectively associated with one legal genre rather than another. The concept of intertextuality is an important dimension of contract negotiation discourse and this study also examined how the discursive relationships between the different texts influence the way that texts are constructed. In terms of materials development, the research findings can contribute to more authentic English for Legal & Business Purposes pedagogies for students and novice lawyers and business professionals. The findings can first be used to design discursive maps that provide learners with a coherent account of the intertextual nature of the contract negotiation process. These discursive maps can then function as a framework in which to present detailed findings about the textual and structural features of the text types by applying the Swalesian genre analysis. Based on this acquired knowledge of the textual nature of contract negotiation, the authentic discourse materials can then be used to provide learners with practical opportunities to role-play negotiation activities and experience professional ways of thinking and using language in preparation for the written discourse challenges they will face in this important area of legal and business practice.

Keywords: English for legal and business purposes, discourse analysis, genre analysis, intertextuality, pedagogical materials

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Authors: Clement Chimezie Aladi, Itamar Shabtai

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A review of the literature in the last few years reveals that little attention has been paid to technological affordances in e-learning designs. However, affordances are key to engaging students and enabling teachers to actualize learning goals. E-learning systems (software and artifacts) need to be designed in such a way that the features facilitate perceptions of the affordances with minimal cognition. This study aimed to fill this gap in the literature and encourage further research in this area. It provides guidelines for facilitating the perception of affordances in e-learning design and advances Technology Affordance and Constraints Theory by incorporating the affordance-based design process, the principles of multimedia learning, e-learning design philosophy, and emotional and cognitive affordances.

Keywords: e-learning, technology affrodances, affordance based design, e-learning design

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Authors: Amad Nasseef, Layan Zugail, Joud Musalli, Layan Shaikan

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Technology has become an essential part of our lives. We have also witnessed the significant emergence of artificial intelligence in so many areas. Throughout this research paper, the following will be discussed: an introduction on AI and Socratic application, we also did an overview on the application’s background and other similar applications, as for the methodology, we conducted a survey to collect results on users experience in using the Socratic application. The results of the survey strongly supported the usefulness and interest of users in the Socratic application. Finally, we concluded that Socratic is a meaningful tool for learning purposes due to it being supported by artificial intelligence, which made the application easy to use and familiar to users to deal with through a click of a button.

Keywords: Socratic, artificial intelligence, application, features

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Authors: Sandeep K. Saxena, N. Viswanadham, Ala’a H. Al-Muhtaseb

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In an attempt to facilitate effective conversion of biomass derived products into gasoline rich in aromatics and iso-paraffins, various zeolite catalysts with special features such as nano crystallite size and acidity has been synthesized and evaluated. The catalyst (NZ) exhibits highest gasoline yield of about 74wt% with aromatics and iso-paraffins as major components. The product measures Research Octane Number (RON) of about 95, which is desirable for the gasoline specifications. Moreover, considerable amount of the Liquefied Petroleum Gas (LPG) (15wt%) and light olefins (14wt%) are also formed as bi-product that adds value to the process. The study reveals the effective conversion of bio- ethanol to high-octane gasoline.

Keywords: biomass, ethanol, acetone, gasoline, zeolite

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Authors: Janaina Pereira, Barbara Gonzalez, Valentina Chitas, Teresa Molina

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Intro: The positive impact of opioid maintenance programs on the health of heroin addicts, and on public health in general, has been widely recognized, namely on the prevalence reduction of infectious diseases as HIV, and on the social reintegration of this population. Nevertheless, a part of patients in these programs cannot remain heroin abstinent, or has relapses, during the treatment. Method: Thus, this cross-sectional research aims at analyzing the relation between a set of psychological and psychosocial variables, which have been associated with the onset of heroin use, and assess if they are also associated with absence of abstinence in participants in an opioid maintenance program. A total of 62 patients, aged between 26 and 58 years old (M= 40.87, DP= 7.39) with a time in opioid maintenance program between 1 and 10 years (M= 5.42, DP= 3.05), 77.4% male and 22.6% female, participated in this research. To assess the criterion variable (heroin use) we used the mean value of positive results in urine tests during the participation in the program, weighted according to the number of months in program. The predictor variables were the coping strategies, the dispositional sensation seeking, and the existence of Posttraumatic stress disorder (PTSD). Results: The results showed that only 33.87% of the patients were totally abstinent of heroin use since the beginning of the program, and the absence of abstinence, as the number of positive heroin tests, was primarily predicted by less proactive coping, and secondarily by a higher level of sensation seeking. 16.13% of the sample fulfilled diagnosis criteria for PTSD, and 67.74 % had at least one traumatic experience throughout their lives. The total of PTSD symptoms had a positive correlation with the number of physical health problems, and with the lack of professional occupation. These results have several implications for the clinical practice in this field, and we suggest the promotion of proactive coping strategies should integrate these opioid maintenance programs, as they represent the tendency to face future events as challenges and opportunities, being positively related to positive results on several fields. The early identification of PTSD in the participants, before entering the opioid maintenance programs, would be important as it is related to negative features that hinder social reintegration, Finally, to identify individuals with a sensation seeking profile would be relevant, not only because they face a higher risk of relapse, but also because the therapeutical approaches should not ignore this dispositional feature in the alternatives they propose to the patients.

Keywords: opioid maintenance programs, proactive coping, PTSD, sensation seeking

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Authors: Mohamed Abdelkareem

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Remote sensing data contributed on predicting the prospective areas of water resources. Integration of microwave and multispectral data along with climatic, hydrologic, and geological data has been used here. In this article, Sentinel-2, Landsat-8 Operational Land Imager (OLI), Shuttle Radar Topography Mission (SRTM), Tropical Rainfall Measuring Mission (TRMM), and Advanced Land Observing Satellite (ALOS) Phased Array Type L‐band Synthetic Aperture Radar (PALSAR) data were utilized to identify the geological, hydrologic and structural features of Wadi Asyuti which represents a defunct tributary of the Nile basin, in the eastern Sahara. The image transformation of Sentinel-2 and Landsat-8 data allowed characterizing the different varieties of rock units. Integration of microwave remotely-sensed data and GIS techniques provided information on physical characteristics of catchments and rainfall zones that are of a crucial role for mapping groundwater prospective zones. A fused Landsat-8 OLI and ALOS/PALSAR data improved the structural elements that difficult to reveal using optical data. Lineament extraction and interpretation indicated that the area is clearly shaped by the NE-SW graben that is cut by NW-SE trend. Such structures allowed the accumulation of thick sediments in the downstream area. Processing of recent OLI data acquired on March 15, 2014, verified the flood potential maps and offered the opportunity to extract the extent of the flooding zone of the recent flash flood event (March 9, 2014), as well as revealed infiltration characteristics. Several layers including geology, slope, topography, drainage density, lineament density, soil characteristics, rainfall, and morphometric characteristics were combined after assigning a weight for each using a GIS-based knowledge-driven approach. The results revealed that the predicted groundwater potential zones (GPZs) can be arranged into six distinctive groups, depending on their probability for groundwater, namely very low, low, moderate, high very, high, and excellent. Field and well data validated the delineated zones.

Keywords: GIS, remote sensing, groundwater, Egypt

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Authors: Ghusen al-Kafri, Mohammed Ali Abdallah Elsageer, Ahmed Mohamed Hadya Alsdaai, Abdeimanam Salhien Salih Khalifa

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In this paper, external reinforcement to enhance a reinforced concrete structure performance has been done using externally bonded steel plate. This technique has been reported effective in enhancing the strength of reinforced concrete beam, a study to determine the effectiveness of steel plate as an external reinforcement was carried out. A total of two groups of beams and one group content five beams, each 750 mm long, 150 mm wide, and 150 mm deep were cast, strengthened and tested till failure under two point loads. One beam was act as a control beam without strengthening and other four beams were strengthened with steel plate at a different arrangement. Other group beams were strengthened with steel plate in shear zone and also strengthened at bottom as first group. The behaviours of the strengthened beams were studied through their load-deflection characteristic upon bending, cracking and mode of failure. The results confirmed that all steel plate arrangements enhanced the strength of the reinforced concrete beam, the positioning of the steel plate affect the moment carrying capacity of the beam.

Keywords: beams, bending, beflection, steel plates

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Authors: M. Khajezadeh, M. Saied Fallah Niasar, S. Ali Asli, D. Davani Davari, M. Godarzi, Y. Asgari

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This study aims to explore the joint application of both Boston and Ansoff matrices in the operational development of the product. We conduct deep analysis, by utilizing the Artificial Neural Network, to predict the position of the product in the market while the company is interested in increasing its share. The data are gathered from two industries, called hygiene and detergent. In doing so, the effort is being made by investigating the behavior of top player companies and, recommend strategic orientations. In conclusion, this combination analysis is appropriate for operational development; as well, it plays an important role in providing the position of the product in the market for both hygiene and detergent industries. More importantly, it will elaborate on the company’s strategies to increase its market share related to a combination of the Boston Consulting Group (BCG) Matrix and Ansoff Matrix.

Keywords: artificial neural network, portfolio analysis, BCG matrix, Ansoff matrix

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Authors: Mayssa Bensalah, Atef Boujelben, Mouna Baklouti, Mohamed Abid

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Image segmentation has a fundamental role in analysis and interpretation for many applications. The automated segmentation of organs and tissues throughout the body using computed imaging has been rapidly increasing. Indeed, it represents one of the most important parts of clinical diagnostic tools. In this paper, we discuss a thorough literature review of recent methods of tumour segmentation from medical images which are briefly explained with the recent contribution of various researchers. This study was followed by comparing these methods in order to define new directions to develop and improve the performance of the segmentation of the tumour area from medical images.

Keywords: features extraction, image segmentation, medical images, tumor detection

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Authors: Clarissa Delariarte

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With the aim of helping the poorest of the poor achieve quality education, Psychology students supported and served as teacher assistants to its Early Childhood Education Center in two barangays since the program began in 1999. Making use of qualitative approach, the impact of the program to 29 alumni who served as teacher assistants between 2000-2014 was assessed. Results show that the impact to the alumni is in cognitive as well as social-emotional in terms of feelings of deep satisfaction and sense of volunteerism which is being carried out in their respective workspaces. They also expressed positive feelings of inspiration, gratefulness and happiness. A wider perspective in life, being confident, creative and resourceful was also articulated as concrete impacts. It is concluded that the program had an impact on helping the helper and is a concrete manifestation of the academe being successful in its commitment of forming individuals into becoming integrated and compassionate in the service of the Church and Society. It implies that more opportunities of helping others be provided to students since, in the final analysis, is actually an opportunity of helping the helper be of better service to others.

Keywords: applied psychology, life skill, qualitative research, quality education

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Authors: V. Ziegler, F. Schneider, M. Pesch

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With the EDM264, GRIMM offers a solution for mobile short- and long-term measurements in outdoor areas and at production sites. For research as well as permanent areal observations on a near reference quality base. The model EDM264 features a powerful and robust measuring cell based on optical particle counting (OPC) principle with all the advantages that users of GRIMM's portable aerosol spectrometers are used to. The system is embedded in a compact weather-protection housing with all-weather sampling, heated inlet system, data logger, and meteorological sensor. With TSP, PM10, PM4, PM2.5, PM1, and PMcoarse, the EDM264 provides all fine dust fractions real-time, valid for outdoor applications and calculated with the proven GRIMM enviro-algorithm, as well as six additional dust mass fractions pm10, pm2.5, pm1, inhalable, thoracic and respirable for IAQ and workplace measurements. This highly versatile instrument performs real-time monitoring of particle number, particle size and provides information on particle surface distribution as well as dust mass distribution. GRIMM's EDM264 has 31 equidistant size channels, which are PSL traceable. A high-end data logger enables data acquisition and wireless communication via LTE, WLAN, or wired via Ethernet. Backup copies of the measurement data are stored in the device directly. The rinsing air function, which protects the laser and detector in the optical cell, further increases the reliability and long term stability of the EDM264 under different environmental and climatic conditions. The entire sample volume flow of 1.2 L/min is analyzed by 100% in the optical cell, which assures excellent counting efficiency at low and high concentrations and complies with the ISO 21501-1standard for OPCs. With all these features, the EDM264 is a world-leading dust monitor for precise monitoring of particulate matter and particle number concentration. This highly reliable instrument is an indispensable tool for many users who need to measure aerosol levels and air quality outdoors, on construction sites, or at production facilities.

Keywords: aerosol research, aerial observation, fence line monitoring, wild fire detection

Procedia PDF Downloads 151

Authors: Stamataki Erofili Nellie, Benardos Andreas

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Winemaking and aging in Greece has been performed so far in special facilities, designed either as above ground or shallow underground buildings. The latter are well-known in Santorini as “canaves,” dating back to the 1700s. Canaves were mainly used for wine storage and aging, although occasionally, they included a winepress to complete there the whole wine production. On the other hand, wine caves are subterranean caves of the same use as canaves in the wine manufacturing industry, but they are excavated at a much greater depth of more than 53 meters or 175 feet. Whereas canaves or a typical wine cellar is around 10 feet deep, with is equivalent to almost 3 meters. This paper discusses the advantages and the disadvantages of creating a wine cave for the vinification of a winery in Greece and the financial investment or risk that has to be taken. The data presented and analysed are given from wineries in Greece and especially from those located in Santorini island. The estimation of the cost for the excavation of the model selected as a wine cave will be compared with the financial budget of the existing premises and facilities above ground in Greek wineries. In order to show whether it is viable for a greek winery to invest in a wine cave.

Keywords: underground space use, subterranean winery, wine cave, underground winery, greece

Procedia PDF Downloads 180

Authors: Arun Sanjel, Greg Speegle

Abstract:

Every second, massive amounts of data are generated, and Data Intensive Scalable Computing (DISC) frameworks have evolved into effective tools for analyzing such massive amounts of data. Since the underlying architecture of these distributed computing platforms is often new to users, building a DISC application can often be time-consuming and prone to errors. The automated conversion of a sequential program to a DISC program will consequently significantly improve productivity. However, synthesizing a user’s intended program from an input specification is complex, with several important applications, such as distributed program synthesizing and code refactoring. Existing works such as Tyro and Casper rely entirely on deductive synthesis techniques or similar program synthesis approaches. Our approach is to develop a data-driven synthesis technique to identify sequential components and translate them to equivalent distributed operations. We emphasize using reinforcement learning and unit testing as feedback mechanisms to achieve our objectives.

Keywords: program synthesis, distributed computing, reinforcement learning, unit testing, DISC

Procedia PDF Downloads 107

Authors: Subhash Nerella, Ziyuan Guan, Azra Bihorac, Parisa Rashidi

Abstract:

Intensive care units (ICUs) provide care to critically ill patients in severe and life-threatening conditions. However, patient monitoring in the ICU is limited by the time and resource constraints imposed on healthcare providers. Many critical care indices such as mobility are still manually assessed, which can be subjective, prone to human errors, and lack granularity. Other important aspects, such as environmental factors, are not monitored at all. For example, critically ill patients often experience circadian disruptions due to the absence of effective environmental “timekeepers” such as the light/dark cycle and the systemic effect of acute illness on chronobiologic markers. Although the occurrence of delirium is associated with circadian disruption risk factors, these factors are not routinely monitored in the ICU. Hence, there is a critical unmet need to develop systems for precise and real-time assessment through novel enabling technologies. We have developed the mobility and circadian disruption quantification system (Mobi-DiQ) by augmenting biomarker and clinical data with pervasive sensing data to generate mobility and circadian cues related to mobility, nightly disruptions, and light and noise exposure. We hypothesize that Mobi-DiQ can provide accurate mobility and circadian cues that correlate with bedside clinical mobility assessments and circadian biomarkers, ultimately important for delirium risk assessment and prevention. The collected multimodal dataset consists of depth images, Electromyography (EMG) data, patient extremity movement captured by accelerometers, ambient light levels, Sound Pressure Level (SPL), and indoor air quality measured by volatile organic compounds, and the equivalent CO₂ concentration. For delirium risk assessment, the system recognizes mobility cues (axial body movement features and body key points) and circadian cues, including nightly disruptions, ambient SPL, and light intensity, as well as other environmental factors such as indoor air quality. The Mobi-DiQ system consists of three major components: the pervasive sensing system, a data storage and analysis server, and a data annotation system. For data collection, six local pervasive sensing systems were deployed, including a local computer and sensors. A video recording tool with graphical user interface (GUI) developed in python was used to capture depth image frames for analyzing patient mobility. All sensor data is encrypted, then automatically uploaded to the Mobi-DiQ server through a secured VPN connection. Several data pipelines are developed to automate the data transfer, curation, and data preparation for annotation and model training. The data curation and post-processing are performed on the server. A custom secure annotation tool with GUI was developed to annotate depth activity data. The annotation tool is linked to the MongoDB database to record the data annotation and to provide summarization. Docker containers are also utilized to manage services and pipelines running on the server in an isolated manner. The processed clinical data and annotations are used to train and develop real-time pervasive sensing systems to augment clinical decision-making and promote targeted interventions. In the future, we intend to evaluate our system as a clinical implementation trial, as well as to refine and validate it by using other data sources, including neurological data obtained through continuous electroencephalography (EEG).

Keywords: deep learning, delirium, healthcare, pervasive sensing

Procedia PDF Downloads 93

Authors: Eleni Karantzola, Athanasios Karasimos, Vasiliki Makri, Ioanna Skouvara

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Historical linguistics unveils the historical depth of languages and traces variation and change by analyzing linguistic variables over time. This field of linguistics usually deals with a closed data set that can only be expanded by the (re)discovery of previously unknown manuscripts or editions. In some cases, it is possible to use (almost) the entire closed corpus of a language for research, as is the case with the Thesaurus Linguae Graecae digital library for Ancient Greek, which contains most of the extant ancient Greek literature. However, concerning ‘dynamic’ periods when the production and circulation of texts in printed as well as manuscript form have not been fully mapped, representative samples and corpora of texts are needed. Such material and tools are utterly lacking for Early Modern Greek (16th-18th c.). In this study, the principles of the creation of EMoGReC, a pilot representative corpus of Early Modern Greek (16th-18th c.) are presented. Its design follows the fundamental principles of historical corpora. The selection of texts aims to create a representative and balanced corpus that gives insight into diachronic, diatopic and diaphasic variation. The pilot sample includes data derived from fully machine-readable vernacular texts, which belong to 4-5 different textual genres and come from different geographical areas. We develop a hierarchical linguistic annotation scheme, further customized to fit the characteristics of our text corpus. Regarding variables and their variants, we use as a point of departure the bundle of twenty-four features (or categories of features) for prose demotic texts of the 16th c. Tags are introduced bearing the variants [+old/archaic] or [+novel/vernacular]. On the other hand, further phenomena that are underway (cf. The Cambridge Grammar of Medieval and Early Modern Greek) are selected for tagging. The annotated texts are enriched with metalinguistic and sociolinguistic metadata to provide a testbed for the development of the first comprehensive set of tools for the Greek language of that period. Based on a relational management system with interconnection of data, annotations, and their metadata, the EMoGReC database aspires to join a state-of-the-art technological ecosystem for the research of observed language variation and change using advanced computational approaches.

Keywords: early modern Greek, variation and change, representative corpus, diachronic variables.

Procedia PDF Downloads 67

Authors: Rudi Kurniawan Arief

Abstract:

Manufacturing cost and setup time are the hot topics to improve in Metal Stamping industry because material and components price are always rising up while costumer requires to cut down the component price year by year. The Single Minute Exchange of Die (SMED) is one of many methods to reduce waste in stamping industry. The Japanese Quick Die Change (QDC) dies system is one of SMED systems that could reduce both of setup time and manufacturing cost. However, this system is rarely used in stamping industries. This paper will analyze how deep the QDC dies system could reduce setup time and the manufacturing cost. The research is conducted by direct observation, simulating and comparing of QDC dies system with conventional dies system. In this research, we found that the QDC dies system could save up to 35% of manufacturing cost and reduce 70% of setup times. This simulation proved that the QDC die system is effective for cost reduction but must be applied in several parallel production processes.

Keywords: press die, metal stamping, QDC system, single minute exchange die, manufacturing cost saving, SMED

Procedia PDF Downloads 170