Search results for: real estate pricing

Authors: Ahmed ElAzab, Amira M. Idrees, Mahmoud A. Mahmoud, Hesham Hefny

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Social networking sites such as Twitter and Facebook attracts over 500 million users across the world, for those users, their social life, even their practical life, has become interrelated. Their interaction with social networking has affected their life forever. Accordingly, social networking sites have become among the main channels that are responsible for vast dissemination of different kinds of information during real time events. This popularity in Social networking has led to different problems including the possibility of exposing incorrect information to their users through fake accounts which results to the spread of malicious content during life events. This situation can result to a huge damage in the real world to the society in general including citizens, business entities, and others. In this paper, we present a classification method for detecting fake accounts on Twitter. The study determines the minimized set of the main factors that influence the detection of the fake accounts on Twitter, then the determined factors have been applied using different classification techniques, a comparison of the results for these techniques has been performed and the most accurate algorithm is selected according to the accuracy of the results. The study has been compared with different recent research in the same area, this comparison has proved the accuracy of the proposed study. We claim that this study can be continuously applied on Twitter social network to automatically detect the fake accounts, moreover, the study can be applied on different Social network sites such as Facebook with minor changes according to the nature of the social network which are discussed in this paper.

Keywords: fake accounts detection, classification algorithms, twitter accounts analysis, features based techniques

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Authors: Meriem Bahij, Ahmed Nafidi, Boujemâa Achchab, Sílvio M. A. Gama, José A. O. Matos

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Stochastic modeling concerns the use of probability to model real-world situations in which uncertainty is present. Therefore, the purpose of stochastic modeling is to estimate the probability of outcomes within a forecast, i.e. to be able to predict what conditions or decisions might happen under different situations. In the present study, we present a model of a stochastic diffusion process based on the bi-Weibull distribution function (its trend is proportional to the bi-Weibull probability density function). In general, the Weibull distribution has the ability to assume the characteristics of many different types of distributions. This has made it very popular among engineers and quality practitioners, who have considered it the most commonly used distribution for studying problems such as modeling reliability data, accelerated life testing, and maintainability modeling and analysis. In this work, we start by obtaining the probabilistic characteristics of this model, as the explicit expression of the process, its trends, and its distribution by transforming the diffusion process in a Wiener process as shown in the Ricciaardi theorem. Then, we develop the statistical inference of this model using the maximum likelihood methodology. Finally, we analyse with simulated data the computational problems associated with the parameters, an issue of great importance in its application to real data with the use of the convergence analysis methods. Overall, the use of a stochastic model reflects only a pragmatic decision on the part of the modeler. According to the data that is available and the universe of models known to the modeler, this model represents the best currently available description of the phenomenon under consideration.

Keywords: diffusion process, discrete sampling, likelihood estimation method, simulation, stochastic diffusion process, trends functions, bi-parameters weibull density function

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Authors: Elham Taghizadeh, Mostafa Abedzadeh, Mostafa Setak

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Logistics network is expected that opened facilities work continuously for a long time horizon without any failure; but in real world problems, facilities may face disruptions. This paper studies a reliable joint inventory location problem to optimize cost of facility locations, customers’ assignment, and inventory management decisions when facilities face failure risks and doesn’t work. In our model we assume when a facility is out of work, its customers may be reassigned to other operational facilities otherwise they must endure high penalty costs associated with losing service. For defining the model closer to real world problems, the model is proposed based on p-median problem and the facilities are considered to have limited capacities. We define a new binary variable (Z_is) for showing that customers are not assigned to any facilities. Our problem involve a bi-objective model; the first one minimizes the sum of facility construction costs and expected inventory holding costs, the second one function that mention for the first one is minimizes maximum expected customer costs under normal and failure scenarios. For solving this model we use NSGAII and MOSS algorithms have been applied to find the pareto- archive solution. Also Response Surface Methodology (RSM) is applied for optimizing the NSGAII Algorithm Parameters. We compare performance of two algorithms with three metrics and the results show NSGAII is more suitable for our model.

Keywords: joint inventory-location problem, facility location, NSGAII, MOSS

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Authors: Prashansa Singh, Rohit Bajaj, Manjot Kaur

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In the modern agricultural landscape, precision farming has emerged as a pivotal strategy for enhancing crop yield and optimizing resource utilization. This paper introduces an innovative Crop Recommendation System (CRS) that leverages the Internet of Things (IoT) technology and the Message Queuing Telemetry Transport (MQTT) protocol to collect critical environmental and soil data via sensors deployed across agricultural fields. The system is designed to address the challenges of real-time data acquisition, efficient data transmission, and dynamic crop recommendation through the application of advanced Artificial Intelligence (AI) and Machine Learning (ML) models. The CRS architecture encompasses a network of sensors that continuously monitor environmental parameters such as temperature, humidity, soil moisture, and nutrient levels. This sensor data is then transmitted to a central MQTT server, ensuring reliable and low-latency communication even in bandwidth-constrained scenarios typical of rural agricultural settings. Upon reaching the server, the data is processed and analyzed by AI/ML models trained to correlate specific environmental conditions with optimal crop choices and cultivation practices. These models consider historical crop performance data, current agricultural research, and real-time field conditions to generate tailored crop recommendations. This implementation gets 99% accuracy.

Keywords: Iot, MQTT protocol, machine learning, sensor, publish, subscriber, agriculture, humidity

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Authors: Khaled Elbehiery, Hussam Elbehiery

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The success of education is dependent on evolution and adaptation, while the traditional system has worked before, one type of education evolved with the digital age is virtual education that has influenced efficiency in today’s learning environments. Virtual learning has indeed proved its efficiency to overcome the drawbacks of the physical environment such as time, facilities, location, etc., but despite what it had accomplished, the educational system over all is not adequate for being a productive system yet. Earning a degree is not anymore enough to obtain a career job; it is simply missing the skills and creativity. There are always two sides of a coin; a college degree or a specialized certificate, each has its own merits, but having both can put you on a successful IT career path. For many of job-seeking individuals across world to have a clear meaningful goal for work and education and positively contribute the community, a productive correlation and cooperation among employers, universities alongside with the individual technical skills is a must for generations to come. Fortunately, the proposed research “Entrepreneur Universal Education System” is an evolution to meet the needs of both employers and students, in addition to gaining vital and real-world experience in the chosen fields is easier than ever. The new vision is to empower the education to improve organizations’ needs which means improving the world as its primary goal, adopting universal skills of effective thinking, effective action, effective relationships, preparing the students through real-world accomplishment and encouraging them to better serve their organization and their communities faster and more efficiently.

Keywords: virtual education, academic degree, certificates, internship, amazon web services, Microsoft Azure, Google Cloud Platform, hybrid models

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Authors: Shafait Hussain Ali

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Since its outbreak in China, the Covid_19 19 disease has been caused by the corona virus SARS N coyote 2. Italy was the first Western country to be severely affected, and the first country to take drastic measures to control the disease. In start of December 2019, the sudden outbreaks of the Coronary Virus Disease was caused by a new Corona 2 virus (SARS-CO2) of acute respiratory syndrome in china city Wuhan. The World Health Organization declared the epidemic a public health emergency of international concern on January 30, 2020,. On February 14, 2020, 49,053 laboratory-confirmed deaths and 1481 deaths have been reported worldwide. The threat of the disease has forced most of the governments to implement various control measures. Therefore it becomes necessary to analyze the Italian data very carefully, in particular to investigates and to find out the present condition and the number of infected persons in the form of positive cases, death, hospitalized or some other features of infected persons will clear in simple form. So used such a model that will clearly shows the real facts and figures and also understandable to every readable person which can get some real benefit after reading it. The model used must includes(total positive cases, current positive cases, hospitalized patients, death, recovered peoples frequency rates ) all features that explains and clear the wide range facts in very simple form and helpful to administration of that country.

Keywords: machine learning tools and techniques, rapid miner tool, Naive-Bayes algorithm, predictions

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Authors: Revoti Prasad Bora, Saurabh Yadav, Nikita Katyal

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In the context of educational data mining, the use case of extracting information from images containing both text and diagrams is of high importance. Hence, document analysis requires the extraction of diagrams from such images and processes the text and diagrams separately. To the author’s best knowledge, none among plenty of approaches for extracting tables, figures, etc., suffice the need for real-time processing with high accuracy as needed in multiple applications. In the education domain, diagrams can be of varied characteristics viz. line-based i.e. geometric diagrams, chemical bonds, mathematical formulas, etc. There are two broad categories of approaches that try to solve similar problems viz. traditional computer vision based approaches and deep learning approaches. The traditional computer vision based approaches mainly leverage connected components and distance transform based processing and hence perform well in very limited scenarios. The existing deep learning approaches either leverage YOLO or faster-RCNN architectures. These approaches suffer from a performance-accuracy tradeoff. This paper proposes a U-Net based architecture that formulates the diagram extraction as a segmentation problem. The proposed method provides similar accuracy with a much faster extraction time as compared to the mentioned state-of-the-art approaches. Further, the segmentation mask in this approach allows the extraction of diagrams of irregular shapes.

Keywords: computer vision, deep-learning, educational data mining, faster-RCNN, figure extraction, image segmentation, real-time document analysis, text extraction, U-Net, YOLO

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Authors: Ting-Yu Lin, Han-Liang Lin

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This article is about the urban form of visualization by Cityengine. City is composed of different domains, and each domain has its own fabric because of arrangement. For example, a neighborhood unit contains fabrics such as schools, street networks, residential and commercial spaces. Therefore, studying urban morphology can help us understand the urban form in planning process. Streets, plots, and buildings seem as urban fabrics, and they configure urban form. Traditionally, urban morphology usually discussed single parameter, which is building type, ignoring other parameters such as streets and plots. However, urban space is three-dimensional, instead of two-dimensional. People perceive urban space by their visualization. Therefore, using visualization can fill the gap between two dimensions and three dimensions. Hence, the study of urban morphology will strengthen the understanding of whole appearance of a city. Cityengine is a software which can edit, analyze and monitor the data and visualize the result for GIS, a common tool to analyze data and display the map for urban plan and urban design. Cityengine can parameterize the data of streets, plots and building types and visualize the result in three-dimensional way. The research will reappear the real urban form by visualizing. We can know whether the urban form can be parameterized and the parameterized result can match the real urban form. Then, visualizing the result by software in three dimension to analyze the rule of urban form. There will be three stages of the research. It will start with a field survey of Tainan East District in Taiwan to conclude the relationships between urban fabrics of street networks, plots and building types. Second, to visualize the relationship, it will turn the relationship into codes which Cityengine can read. Last, Cityengine will automatically display the result by visualizing.

Keywords: Cityengine, urban fabric, urban morphology, visual simulation

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Authors: Boroka Gács, Tibor Szolcsányi, Árpad Csathó

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Background: Individuals frequently show habituation to repeated noxious heat. However, given the defensive function of human pain processing, it is reasonable to assume that individuals imagine that they would become increasingly sensitive to repeated thermal pain stimuli. To the best of the authors' knowledge, no previous studies have, however, been addressed to this assumption. Therefore, in the current study, we investigated how healthy human individuals imagine the intensity of repeated thermal pain stimulations, and compared this with the intensity ratings given after physically induced thermal pain trials. Methods: Healthy participants (N = 20) gave pain intensity ratings in two conditions: imagined and real thermal pain. In the real pain condition thermal pain stimuli of two intensities (minimal and moderate pain) were delivered in four consecutive trials. The duration of the peak temperature was 20s, and stimulation was always delivered to the same location. In each trial, participants rated the pain intensity twice, 5s and 15s after the onset of the peak temperature. In the imagined pain condition, participants were subjected to a reference pain stimulus and then asked to imagine and rate the same sequence of stimulations as in the induced pain condition. Results: Ratings of imagined pain and physically induced pain followed opposite courses over repeated stimulation: Ratings of imagined pain indicated sensitization whereas ratings for physically induced pain indicated habituation. The findings were similar for minimal and moderate pain intensities. Conclusions: The findings suggest that, rather than habituating to pain, healthy individuals imagine that they would become increasingly sensitive to repeated thermal pain stimuli.

Keywords: habituation, imagined pain, pain perception, thermal stimulation

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Authors: Claire Biasco, Thaier Hayajneh

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A smart city is the integration of digital technologies in urban environments to enhance the quality of life. Smart cities capture real-time information from devices, sensors, and network data to analyze and improve city functions such as traffic analysis, public safety, and environmental impacts. Current smart cities face controversy due to their reliance on real-time data tracking and surveillance. Internet of Things (IoT) devices and blockchain technology are converging to reshape smart city infrastructure away from its centralized model. Connecting IoT data to blockchain applications would create a peer-to-peer, decentralized model. Furthermore, blockchain technology powers the ability for IoT device data to shift from the ownership and control of centralized entities to individuals or communities with Decentralized Autonomous Organizations (DAOs). In the context of smart cities, DAOs can govern cyber-physical systems to have a greater influence over how urban services are being provided. This paper will explore how the core components of a smart city now apply to DAOs. We will also analyze different definitions of DAOs to determine their most important aspects in relation to smart cities. Both categorizations will provide a solid foundation to conduct a cybersecurity assessment of DAOs in smart cities. It will identify the benefits and risks of adopting DAOs as they currently operate. The paper will then provide several mitigation methods to combat cybersecurity risks of DAO integrations. Finally, we will give several insights into what challenges will be faced by DAO and blockchain spaces in the coming years before achieving a higher level of maturity.

Keywords: blockchain, IoT, smart city, DAO

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Authors: Amir Saber Gharamaleki, Beitollah Alipour, Zeinab Faghfoori, Ahmad YariKhosroushahi

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Colorectal cancer (CRC) is one of the most prevalent cancers and intestinal microbial community plays an important role in colorectal tumorigenesis. Probiotics have recently been assessed as effective anti-proliferative agents and thus this study was performed to examine whether CRC undergo apoptosis by treating with isolated Iranian native dairy yeast, Kluyveromyces marxianus YAS, secretion metabolites. The cytotoxicity assessments on cells (HT-29, Caco-2) were accomplished through 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay as well as qualitative DAPI (4',6-diamidino-2-phenylindole staining) and quantitative (flow cytometry assessments) evaluations of apoptosis. To evaluate the main mechanism of apoptosis, Real time PCR method was applied. Kluyveromyces marxianus YAS secretions (IC50) showed significant cytotoxicity against HT-29 and Caco-2 cancer cell lines (66.57 % and 66.34 % apoptosis) similar to 5-Fluorouracil (5-FU) while apoptosis only was developed in 27.57 % of KDR normal cells. The prophylactic effects of Kluyveromyces marxianus (PTCC 5195), as a reference yeast, was not similar to Kluyveromyces marxianus YAS indicating strain dependency of bioactivities on CRC disease prevention. Based on real time PCR results, the main cytotoxicity is related to apoptosis phenomenon and the core related mechanism is depended on the overexpression of BAX, CASP 9, CASP 8 and CASP 3 inducing apoptosis genes. However, several investigations should be conducted to precisely determine the effective compounds to be used as anticancer therapeutics in the future.

Keywords: anticancer, anti-proliferative, apoptosis, cytotoxicity, yeast

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Authors: Amita Jain, Shantanu Prakash, Suruchi Shukla

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Introduction: Acute encephalitis syndrome (AES) is a complex multi etiology syndrome posing a great public health problem in the northern part of India. Japanese encephalitis (JE) virus is an established etiology of AES in this region. Recently, Scrub typhus (ST) is being recognized as an emerging aetiology of AES in JE endemic belt. This study was conducted to establish the direct evidence of Central nervous system invasion by Orientia tsutsugamushi leading to AES. Methodology: A total of 849 cases with clinical diagnosis of AES were enrolled from six districts (Deoria and its adjoining area) of the traditional north Indian Japanese encephalitis (JE) belt. Serum and Cerebrospinal fluid samples were collected and tested for major agent causing acute encephalitis. AES cases either positive for anti-ST IgM antibodies or negative for all tested etiologies were investigated for ST-DNA by real-time PCR. Results: Of these 505 cases, 250 patients were laboratory confirmed for O. tsutsugamushi infection either by anti-ST IgM antibodies positivity (n=206) on serum sample or by ST-DNA detection by real-time PCR assay on CSF sample (n=2) or by both (n=42).Total 29 isolate could be sequenced for 56KDa gene. Conclusion: All the strains were found to cluster with Gilliam strains. The majority of the isolates showed a 97–99% sequence similarity with Thailand and Cambodian strains. Gilliam strain of O.tsusugamushi is an emerging as one of the major aetiologies leading to AES in northern part of India.

Keywords: acute encephalitis syndrome, O. tsutsugamushi, Gilliam strain, North India, cerebrospinal fluid

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Authors: Mohd Noah A. Rahman, Afzaal H. Seyal, Sharul T. Tajuddin, Hartiny Md Azmi

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Taking into consideration the real time situation specifically the higher academic institutions, small, medium to large companies, public to private sectors and the remaining sectors, do experience the inventory or asset shrinkages due to theft, loss or even inventory tracking errors. This happening is due to a zero or poor security systems and measures being taken and implemented in their organizations. Henceforth, implementing the Radio Frequency Identification (RFID) technology into any manual or existing web-based system or web application can simply deter and will eventually solve certain major issues to serve better data retrieval and data access. Having said, this manual or existing system can be enhanced into a mobile-based system or application. In addition to that, the availability of internet connections can aid better services of the system. Such involvement of various technologies resulting various privileges to individuals or organizations in terms of accessibility, availability, mobility, efficiency, effectiveness, real-time information and also security. This paper will look deeper into the integration of mobile devices with RFID technologies with the purpose of asset tracking and control. Next, it is to be followed by the development and utilization of MongoDB as the main database to store data and its association with RFID technology. Finally, the development of a web based system which can be viewed in a mobile based formation with the aid of Hypertext Preprocessor (PHP), MongoDB, Hyper-Text Markup Language 5 (HTML5), Android, JavaScript and AJAX programming language.

Keywords: RFID, asset tracking system, MongoDB, NoSQL

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Authors: Prodipta Bhowmik, Poulami Saha, Preety Mehra, Yogesh Soni, Triloki Nath Jha

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In India, the smart city concept is growing day by day. So, for smart city development, a better traffic management and monitoring system is a very important requirement. Nowadays, road accidents increase due to more vehicles on the road. Reckless driving is mainly responsible for a huge number of accidents. So, an efficient traffic management system is required for all kinds of roads to control the traffic speed. The speed limit varies from road to road basis. Previously, there was a radar system but due to high cost and less precision, the radar system is unable to become favorable in a traffic management system. Traffic management system faces different types of problems every day and it has become a researchable topic on how to solve this problem. This paper proposed a computer vision and machine learning-based automated system for multiple vehicle detection, tracking, and speed estimation of vehicles using image processing. Detection of vehicles and estimating their speed from a real-time video is tough work to do. The objective of this paper is to detect vehicles and estimate their speed as accurately as possible. So for this, a real-time video is first captured, then the frames are extracted from that video, then from that frames, the vehicles are detected, and thereafter, the tracking of vehicles starts, and finally, the speed of the moving vehicles is estimated. The goal of this method is to develop a cost-friendly system that can able to detect multiple types of vehicles at the same time.

Keywords: OpenCV, Haar Cascade classifier, DLIB, YOLOV3, centroid tracker, vehicle detection, vehicle tracking, vehicle speed estimation, computer vision

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Authors: William Albeiro Alvarez, Gloria Patricia Jaramillo, Ivan Reinaldo Sarmiento

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Understanding the behavior of individuals and the different human factors that influence the choice when faced with a complex system such as transportation is one of the most complicated aspects of measuring in the components that constitute the modeling of route choice due to that various behaviors and driving mode directly or indirectly affect the choice. During the last two decades, with the development of information and communications technologies, new data collection techniques have emerged such as GPS, geolocation with mobile phones, apps for choosing the route between origin and destination, individual service transport applications among others, where an interest has been generated to improve discrete choice models when considering the incorporation of these developments as well as psychological factors that affect decision making. This paper implements a discrete choice model that proposes and estimates a hybrid model that integrates route choice models and latent variables based on the observation on the route of a sample of public taxi drivers from the city of Medellín, Colombia in relation to its behavior, personality, socioeconomic characteristics, and driving mode. The set of choice options includes the routes generated by the individual service transport applications versus the driver's choice. The hybrid model consists of measurement equations that relate latent variables with measurement indicators and utilities with choice indicators along with structural equations that link the observable characteristics of drivers with latent variables and explanatory variables with utilities.

Keywords: behavior choice model, human factors, hybrid model, real time data

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Authors: Duanghathai Saipinta, Tanittian Panyamongkol, Witaya Suriyasathaporn

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Animal management aims to provide a suitable environment for animals allowing maximal productivity in those animals. Prevention of disease is an important part of animal management. Foot-and-mouth disease (FMD) is a highly contagious viral disease in cattle and is an economically important animal disease worldwide. Monitoring the FMD virus in farms is useful management for the prevention of the FMD outbreak. A recent publication indicated collection samples from nasal swabs can be used for monitoring FMD in symptomatic cows. Therefore, the objectives of this study were to determine the FMD virus in asymptomatic dairy cattle using nasal swab samples during the absence of an FMD outbreak. The study was conducted from December 2020 to June 2021 using 185 asymptomatic signs of FMD dairy cattle in Chiang Mai Province, Thailand. By random cow selection, nasal mucosal swabs were used to collect samples from the selected cows and then were to evaluate the presence of FMD viruses using the real-time rt-PCR assay. In total, 4.9% of dairy cattle detected FMD virus, including 2 dairy farms in Mae-on (8 samples; 9.6%) and 1 farm in the Chai-Prakan district (1 sample; 1.2%). Interestingly, both farms in Mae-on were the outbreak of the FMD after this detection for 6 months. This indicated that the FMD virus presented in asymptomatic cattle might relate to the subsequent outbreak of FMD. The outbreak demonstrates the presence of the virus in the environment. In conclusion, monitoring of FMD can be performed by nasal swab collection. Further investigation is needed to show whether the FMD virus presented in asymptomatic FMD cattle could be the cause of the subsequent FMD outbreak or not.

Keywords: cattle, foot-and-mouth disease, nasal swab, real-time rt-PCR assay

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Authors: Jeh Patel, Chandrahas Paidi, Ahmed Hambaba

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Traditional methods for analyzing athlete movement often lack the detail and immediacy required for optimal training. This project aims to address this limitation by developing a Real-time human pose estimation system specifically designed to enhance athlete training across various sports. This system leverages the power of convolutional neural networks (CNNs) to provide a comprehensive and immediate analysis of an athlete’s movement patterns during training sessions. The core architecture utilizes dilated convolutions to capture crucial long-range dependencies within video frames. Combining this with the robust encoder-decoder architecture to further refine pose estimation accuracy. This capability is essential for precise joint localization across the diverse range of athletic poses encountered in different sports. Furthermore, by quantifying movement efficiency, power output, and range of motion, the system provides data-driven insights that can be used to optimize training programs. Pose estimation data analysis can also be used to develop personalized training plans that target specific weaknesses identified in an athlete’s movement patterns. To overcome the limitations posed by outdoor environments, the project employs strategies such as multi-camera configurations or depth sensing techniques. These approaches can enhance pose estimation accuracy in challenging lighting and occlusion scenarios, where pose estimation accuracy in challenging lighting and occlusion scenarios. A dataset is collected From the labs of Martin Luther King at San Jose State University. The system is evaluated through a series of tests that measure its efficiency and accuracy in real-world scenarios. Results indicate a high level of precision in recognizing different poses, substantiating the potential of this technology in practical applications. Challenges such as enhancing the system’s ability to operate in varied environmental conditions and further expanding the dataset for training were identified and discussed. Future work will refine the model’s adaptability and incorporate haptic feedback to enhance the interactivity and richness of the user experience. This project demonstrates the feasibility of an advanced pose detection model and lays the groundwork for future innovations in assistive enhancement technologies.

Keywords: computer vision, deep learning, human pose estimation, U-NET, CNN

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Authors: Isabella Maurizio

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This research analyses the pronunciation of Hebrew consonants 'bgdkpt' in II- III C. E. in Palestine, through the confrontation of two kinds of data: the fragments of transliteration of Old Testament in the Greek alphabet, from the second column of Origen’s synopsis, called Hexapla, and Hebrew names transliterated in Greek documents, especially epigraphs. Origen is a very important author, not only for his bgdkpt theological and exegetic works: the Hexapla, synoptic six columns for a critical edition of Septuaginta, has a relevant role in attempting to reconstruct the pronunciation of Hebrew language before Masoretic punctuation. For this reason, at the beginning, it is important to analyze the column in order to study phonetic and linguistic phenomena. Among the most problematic data, there is the evidence from bgdkpt consonants, always represented as Greek aspirated graphemes. This transcription raised the question if their pronunciation was the only spirant, and consequently, the double one, that is, the stop/spirant contrast, was introduced by Masoretes. However, the phonetic and linguistic examination of the column alone is not enough to establish a real pronunciation of language: this paper is significant because a confrontation between the second column’s transliteration and Hebrew names found in Greek documents epigraphic ones mainly, is achieved. Palestine in II - III was a bilingual country: Greek and Aramaic language lived together, the first one like the official language, the second one as the principal mean of communication between people. For this reason, Hebrew names are often found in Greek documents of the same geographical area: a deep examination of bgdkpt’s transliteration can help to understand better which the real pronunciation of these consonants was, or at least it allows to evidence a phonetic tendency. As a consequence, the research considers the contemporary documents to Origen and the previous ones: the first ones testify a specific stadium of pronunciation, the second ones reflect phonemes’ evolution. Alexandrian documents are also examined: Origen was from there, and the influence of Greek language, spoken in his native country, must be considered. The epigraphs have another implication: they are totally free from morphological criteria, probably used by Origen in his column, because of their popular origin. Thus, a confrontation between the hexaplaric transliteration and Hebrew names is absolutely required, in Hexapla’s studies: first of all, it can be the second clue of a pronunciation already noted in the column; then because, for documents’ specific nature, it has more probabilities to be real, reflecting a daily use of language. The examination of data shows a general tendency to employ the aspirated graphemes for bgdkpt consonants’ transliteration. This probably means that they were closer to Greek aspirated consonants rather than to the plosive ones. The exceptions are linked to a particular status of the name, i.e. its history and origin. In this way, this paper gives its contribution to onomastic studies, too: indeed, the research may contribute to verify the diffusion and the treatment of Jewish names in Hellenized world and in the koinè language.

Keywords: bgdkpt consonants, Greek epigraphs, Jewish names, origen's Hexapla

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Authors: Kelly F. Delgado-De Agrela, Sonia E. Ruiz, Marco A. Santos-Santiago

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An approach is proposed for the design of regular buildings equipped with non-linear viscous dissipating devices. The approach is based on a direct-displacement seismic design method which satisfies seismic performance objectives. The global system involved is formed by structural regular moment frames capable of supporting gravity and lateral loads with elastic response behavior plus a set of non-linear viscous dissipating devices which reduce the structural seismic response. The dampers are characterized by two design parameters: (1) a positive real exponent α which represents the non-linearity of the damper, and (2) the damping coefficient C of the device, whose constitutive force-velocity law is given by F=Cvᵃ, where v is the velocity between the ends of the damper. The procedure is carried out using a substitute structure. Two limits states are verified: serviceability and near collapse. The reduction of the spectral ordinates by the additional damping assumed in the design process and introduced to the structure by the viscous non-linear dampers is performed according to a damping reduction factor. For the design of the non-linear damper system, the real velocity is considered instead of the pseudo-velocity. The proposed design methodology is applied to an 8-story steel moment frame building equipped with non-linear viscous dampers, located in intermediate soil zone of Mexico City, with a dominant period Tₛ = 1s. In order to validate the approach, nonlinear static analyses and nonlinear time history analyses are performed.

Keywords: based design, direct-displacement based design, non-linear viscous dampers, performance design

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Authors: Tom Wanyama

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We have developed a weight-based parts inventory monitoring system utilizing the Industrial Internet of Things (IIoT) to enhance operational efficiencies in manufacturing. The system addresses various challenges, including eliminating downtimes caused by stock-outs, preventing human errors in parts delivery and product assembly, and minimizing motion waste by reducing unnecessary worker movements. The system incorporates custom QR codes for simplified inventory tracking and retrieval processes. The generated data serves a dual purpose by enabling real-time optimization of parts flow within manufacturing facilities and facilitating retroactive optimization of stock levels for informed decision-making in inventory management. The pilot implementation at SEPT Learning Factory successfully eradicated data entry errors, optimized parts delivery, and minimized workstation downtimes, resulting in a remarkable increase of over 10% in overall equipment efficiency across all workstations. Leveraging the IIoT features, the system seamlessly integrates information into the process control system, contributing to the enhancement of product quality. This approach underscores the importance of effective tracking of parts inventory in manufacturing to achieve transparency, improved inventory control, and overall profitability. In the broader context, our inventory monitoring system aligns with the evolving focus on optimizing supply chains and maintaining well-managed warehouses to ensure maximum efficiency in the manufacturing industry.

Keywords: industrial Internet of things, industrial systems integration, inventory monitoring, inventory control in manufacturing

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Authors: Anton V. Bourdine, Vladimir A. Burdin, Oleg R. Delmukhametov

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This work presents a fast and simple method for the design of large core silica optical fibers with differential mode delay (DMD) management. Some results are reported concerned with refractive index profile optimization for 42 µm core 16-LP-mode optical fiber for next-generation optical networks. Here special refractive index profile form provides total DMD reducing over all mode staff under desired enhanced mode effective area. Method for the simulation of 'real manufactured' few-mode optical fiber (FMF) core geometry differing from the desired optimized structure by core non-symmetrical ellipticity and refractive index profile deviation including local fluctuations is proposed. Results of the following analysis of optimized FMF with inserted geometry distortions performed by earlier on developed modification of rigorous mixed finite-element method showed strong DMD degradation that requires additional higher-order mode management. In addition, this work also presents a method for design mode division multiplexer channel precision spatial positioning scheme at FMF core end that provides one of the potentiality solutions of described DMD degradation problem concerned with 'distorted' core geometry due to features of optical fiber manufacturing techniques.

Keywords: differential mode delay, few-mode optical fibers, nonlinear Shannon limit, optical fiber non-circularity, ‘real manufactured’ optical fiber core geometry simulation, refractive index profile optimization

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Authors: Taha Anjamrooz, Sareh Rajabi, Hasan Mahmmud, Ghassan Abulebdeh

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Water availability, as well as water demand, are not uniformly distributed in time and space. Numerous extra-large water diversion projects are launched in Amazon to alleviate water scarcities. This research utilizes statistical analysis to examine the temporal and spatial features of 40 extra-large water diversion projects in Amazon. Using a network analysis method, the correlation between seven major basins is measured, while the impact analysis method is employed to explore the associated economic, environmental, and social impacts. The study unearths that the development of water diversion in Amazon has witnessed four stages, from a preliminary or initial period to a phase of rapid development. It is observed that the length of water diversion channels and the quantity of water transferred have amplified significantly in the past five decades. As of 2015, in Amazon, more than 75 billion m³ of water was transferred amidst 12,000 km long channels. These projects extend over half of the Amazon Area. The River Basin E is currently the most significant source of transferred water. Through inter-basin water diversions, Amazon gains the opportunity to enhance the Gross Domestic Product (GDP) by 5%. Nevertheless, the construction costs exceed 70 billion US dollars, which is higher than any other country. The average cost of transferred water per unit has amplified with time and scale but reduced from western to eastern Amazon. Additionally, annual total energy consumption for pumping exceeded 40 billion kilowatt-hours, while the associated greenhouse gas emissions are assessed to be 35 million tons. Noteworthy to comprehend that ecological problems initiated by water diversion influence the River Basin B and River Basin D. Due to water diversion, more than 350 thousand individuals have been relocated, away from their homes. In order to enhance water diversion sustainability, four categories of innovative measures are provided for decision-makers: development of water tradeoff projects strategies, improvement of integrated water resource management, the formation of water-saving inducements, and pricing approach, and application of ex-post assessment.

Keywords: sustainability, water trade-off projects, environment, Amazon

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Authors: Pavel A. Strizhak, Geniy V. Kuznetsov, Ivan S. Voytkov, Dmitri V. Antonov

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This paper presents the results of experiments carried out at a specialized test site for establishing macroscopic patterns of heat and mass transfer processes at localizing model combustion sources of ground forest fires with the use of barrier lines in the form of a wetted lay of material in front of the zone of flame burning and thermal decomposition. The experiments were performed using needles, leaves, twigs, and mixtures thereof. The dimensions of the model combustion source and the ranges of heat release correspond well to the real conditions of ground forest fires. The main attention is paid to the complex analysis of the effect of dispersion of water aerosol (concentration and size of droplets) used to form the barrier line. It is shown that effective conditions for localization and subsequent suppression of flame combustion and thermal decomposition of forest fuel can be achieved by creating a group of barrier lines with different wetting width and depth of the material. Relative indicators of the effectiveness of one and combined barrier lines were established, taking into account all the main characteristics of the processes of suppressing burning and thermal decomposition of forest combustible materials. We performed the prediction of the necessary and sufficient parameters of barrier lines (water volume, width, and depth of the wetted lay of the material, specific irrigation density) for combustion sources with different dimensions, corresponding to the real fire extinguishing practice.

Keywords: forest fire, barrier water lines, pyrolysis front, flame front

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Authors: Rafael Castilho Carvalho, Edjair de Souza Mota

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As the largest contributor to the biogeochemical functioning of the Earth system, the Amazon Rainforest has the greatest biodiversity on the planet, harboring about 15% of all the world's flora. Recognition and preservation are the focus of research that seeks to mitigate drastic changes, especially anthropic ones, which irreversibly affect this biome. Functional and low-cost monitoring alternatives to reduce these impacts are a priority, such as those using technologies such as Low Power Wide Area Networks (LPWAN). Promising, reliable, secure and with low energy consumption, LPWAN can connect thousands of IoT devices, and in particular, LoRa is considered one of the most successful solutions to facilitate forest monitoring applications. Despite this, the forest environment, in particular the Amazon Rainforest, is a challenge for these technologies, requiring work to identify and validate the use of technology in a real environment. To investigate the feasibility of deploying LPWAN in remote water quality monitoring of rivers in the Amazon Region, a LoRa-based test bed consisting of a Lora transmitter and a LoRa receiver was set up, both parts were implemented with Arduino and the LoRa chip SX1276. The experiment was carried out at the Federal University of Amazonas, which contains one of the largest urban forests in Brazil. There are several springs inside the forest, and the main goal is to collect water quality parameters and transmit the data through the forest in real time to the gateway at the uni. In all, there are nine water quality parameters of interest. Even with a high collection frequency, the amount of information that must be sent to the gateway is small. However, for this application, the battery of the transmitter device is a concern since, in the real application, the device must run without maintenance for long periods of time. With these constraints in mind, parameters such as Spreading Factor (SF) and Coding Rate (CR), different antenna heights, and distances were tuned to better the connectivity quality, measured with RSSI and loss rate. A handheld spectrum analyzer RF Explorer was used to get the RSSI values. Distances exceeding 200 m have soon proven difficult to establish communication due to the dense foliage and high humidity. The optimal combinations of SF-CR values were 8-5 and 9-5, showing the lowest packet loss rates, 5% and 17%, respectively, with a signal strength of approximately -120 dBm, these being the best settings for this study so far. The rains and climate changes imposed limitations on the equipment, and more tests are already being conducted. Subsequently, the range of the LoRa configuration must be extended using a mesh topology, especially because at least three different collection points in the same water body are required.

Keywords: IoT, LPWAN, LoRa, coverage, loss rate, forest

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Authors: Kristina Horvat Blazinovic

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In the context of a work of art, one can talk about the idea-concept-term-intention expressed by the artist by using various forms of repetition (external, material, visible repetition). Such repetitions of elements (images in space or moving visual and sound images in time) suggest a "covert", "latent" ("dressed") repetition – i.e., "hidden", "latent" term-intention-idea. Repeating in this way reveals a "deeper truth" that the viewer needs to decode and which is hidden "under" the technical manifestation of the multiplied images. It is not only images, sounds, and screens that are repeated - something else is repeated through them as well, even if, in some cases, the very idea of repetition is repeated. This paper examines serial images and single-channel or multi-channel artwork in the field of video/film art and video installations, which in a way implies the concept of repetition and multiplication. Moving or static images and screens (as multi-screens) are repeated in time and space. The categories of the real and the symbolic partly refer to the Lacan registers of reality, i.e., the Imaginary - Symbolic – Real trinity that represents the orders within which human subjectivity is established. Authors such as Bruce Nauman, VALIE EXPORT, Ragnar Kjartansson, Wolf Vostell, Shirin Neshat, Paul Sharits, Harun Farocki, Dalibor Martinis, Andy Warhol, Douglas Gordon, Bill Viola, Frank Gillette, and Ira Schneider, and Marina Abramovic problematize, in different ways, the concept and procedures of multiplication - repetition, but not in the sense of "copying" and "repetition" of reality or the original, but of repeated repetitions of the simulacrum. Referential works of art are often connected by the theme of the traumatic. Repetitions of images and situations are a response to the traumatic (experience) - repetition itself is a symptom of trauma. On the other hand, repeating and multiplying traumatic images results in a new traumatic effect or cancels it. Reflections on repetition as a temporal and spatial phenomenon are in line with the chapters that link philosophical considerations of space and time and experience temporality with their manifestation in works of art. The observations about time and the relation of perception and memory are according to Henry Bergson and his conception of duration (durée) as "quality of quantity." The video works intended to be displayed as a video loop, express the idea of infinite duration ("pure time," according to Bergson). The Loop wants to be always present - to fixate in time. Wholeness is unrecognizable because the intention is to make the effect infinitely cyclic. Reflections on time and space end with considerations about the occurrence and effects of time and space intervals as places and moments "between" – the points of connection and separation, of continuity and stopping - by reference to the "interval theory" of Soviet filmmaker DzigaVertov. The scale of opportunities that can be explored in interval mode is wide. Intervals represent the perception of time and space in the form of pauses, interruptions, breaks (e.g., emotional, dramatic, or rhythmic) denote emptiness or silence, distance, proximity, interstitial space, or a gap between various states.

Keywords: video installation, performance, repetition, multi-screen, real and symbolic, loop, video art, interval, video time

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Authors: Periklis Brakatsoulas

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Recent empirical research shows a growing interest in investment decision-making under market anomalies that contradict the rational paradigm. Momentum is undoubtedly one of the most robust anomalies in the empirical asset pricing research and remains surprisingly lucrative ever since first documented. Although predominantly phenomena identified across equities, momentum premia are now evident across various asset classes. Yet few many attempts are made so far to provide traders a diversified portfolio of strategies across different assets and markets. Moreover, literature focuses on patterns from past returns rather than mechanisms to signal future price directions prior to momentum runs. The aim of this paper is to develop a diversified portfolio approach to price distortion signals using daily position data on stocks, credit derivatives, and bonds. An algorithm allocates assets periodically, and new investment tactics take over upon price momentum signals and across different ranking groups. We focus on momentum life cycles, trend reversals, and price acceleration signals. The main effort here concentrates on the density, time span and maturity of momentum phenomena to identify consistent patterns over time and measure the predictive power of buy-sell signals generated by these anomalies. To tackle this, we propose a two-stage modelling process. First, we generate forecasts on core macroeconomic drivers. Secondly, satellite models generate market risk forecasts using the core driver projections generated at the first stage as input. Moreover, using a combination of the ARFIMA and FIGARCH models, we examine the dependence of consecutive observations across time and portfolio assets since long memory behavior in volatilities of one market appears to trigger persistent volatility patterns across other markets. We believe that this is the first work that employs evidence of volatility transmissions among derivatives, equities, and bonds to identify momentum life cycle patterns.

Keywords: forecasting, long memory, momentum, returns

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Authors: Samart Homthong, Wutthipong Moungnoi

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Critical success factors (CSFs) and the criteria to measure project success have received much attention over the decades and are among the most widely researched topics in the context of project management. However, although there have been extensive studies on the subject by different researchers, to date, there has been little agreement on the CSFs. The aim of this study is to identify the CSFs that influence the performance of construction projects, and determine their relative importance for different objectives across five stages in the project life cycle. A considerable literature review was conducted that resulted in the identification of 179 individual factors. These factors were then grouped into nine major categories. A questionnaire survey was used to collect data from three groups of respondents: client representatives, consultants, and contractors. Out of 164 questionnaires distributed, 93 were returned, yielding a response rate of 56.7%. Using the mean score, relative importance index, and weighted average method, the top 10 critical factors for each category were identified. The agreement of survey respondents on those categorised factors were analysed using Spearman’s rank correlation. A one-way analysis of variance was then performed to determine whether the mean scores among the various groups of respondents were statistically significant. The findings indicate the most CSFs in each category in procurement phase are: proper procurement programming of materials (time), stability in the price of materials (cost), and determining quality in the construction (quality). They are then followed by safety equipment acquisition and maintenance (health and safety), budgeting allowed in a contractual arrangement for implementing environmental management activities (environment), completeness of drawing documents (productivity), accurate measurement and pricing of bill of quantities (risk management), adequate communication among the project team (human resource), and adequate cost control measures (client satisfaction). An understanding of CSFs would help all interested parties in the construction industry to improve project performance. Furthermore, the results of this study would help construction professionals and practitioners take proactive measures for effective project management.

Keywords: critical success factors, procurement phase, project life cycle, project performance

Procedia PDF Downloads 184

Authors: Aura Alejandra Burbano Patino, Mariela Agotegaray, Veronica Lassalle, Fernanda Horst

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Water pollution is of worldwide concern due to its importance as an essential resource for life. Industrial and urbanistic growth are anthropogenic activities that have caused an increase of undesirable compounds in water. In the last decade, emerging pollutants have become of great interest since, at very low concentrations (µg/L and ng/L), they exhibit a hazardous effect on wildlife, aquatic ecosystems, and human organisms. One group of emerging pollutants that are a matter of study are pharmaceuticals. Their high consumption rate and their inappropriate disposal have led to their detection in wastewater treatment plant influent, effluent, surface water, and drinking water. In consequence, numerous technologies have been developed to efficiently treat these pollutants. Adsorption appears like an easy and cost-effective technology. One of the most used adsorbents of emerging pollutants removal is carbon-based materials such as hydrochars. This study aims to use a magnetic hydrochar nanocomposite to be employed as an adsorbent for diclofenac removal. Kinetics models and the adsorption efficiency in real water samples were analyzed. For this purpose, a magnetic hydrochar nanocomposite was synthesized through the hydrothermal carbonization (HTC) technique hybridized to co-precipitation to add the magnetic component into the hydrochar, based on iron oxide nanoparticles. The hydrochar was obtained from sunflower husk residue as the precursor. TEM, TGA, FTIR, Zeta potential as a function of pH, DLS, BET technique, and elemental analysis were employed to characterize the material in terms of composition and chemical structure. Adsorption kinetics were carried out in distilled water and real water at room temperature, pH of 5.5 for distilled water and natural pH for real water samples, 1:1 adsorbent: adsorbate dosage ratio, contact times from 10-120 minutes, and 50% dosage concentration of DCF. Results have demonstrated that magnetic hydrochar presents superparamagnetic properties with a saturation magnetization value of 55.28 emu/g. Besides, it is mesoporous with a surface area of 55.52 m²/g. It is composed of magnetite nanoparticles incorporated into the hydrochar matrix, as can be proven by TEM micrographs, FTIR spectra, and zeta potential. On the other hand, kinetic studies were carried out using DCF models, finding percent removal efficiencies up to 85.34% after 80 minutes of contact time. In addition, after 120 minutes of contact time, desorption of emerging pollutants from active sites took place, which indicated that the material got saturated after that t time. In real water samples, percent removal efficiencies decrease up to 57.39%, ascribable to a possible mechanism of competitive adsorption of organic or inorganic compounds, ions for active sites of the magnetic hydrochar. The main suggested adsorption mechanism between the magnetic hydrochar and diclofenac include hydrophobic and electrostatic interactions as well as hydrogen bonds. It can be concluded that the magnetic hydrochar nanocomposite could be valorized into a by-product which appears as an efficient adsorbent for DCF removal as a model emerging pollutant. These results are being complemented by modifying experimental variables such as pollutant’s initial concentration, adsorbent: adsorbate dosage ratio, and temperature. Currently, adsorption assays of other emerging pollutants are being been carried out.

Keywords: environmental remediation, emerging pollutants, hydrochar, magnetite nanoparticles

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Authors: F. Yılmaz, Y. Saylan, A. Derazshamshir, S. Atay, A. Denizli

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Quartz crystal microbalance (QCM), high-resolution mass-sensing technique, measures changes in mass on oscillating quartz crystal surface by measuring changes in oscillation frequency of crystal in real time. Protein adsorption techniques via hydrophobic interaction between protein and solid support, called hydrophobic interaction chromatography (HIC), can be favorable in many cases. Some nanoparticles can be effectively applied for HIC. HIC takes advantage of the hydrophobicity of proteins by promoting its separation on the basis of hydrophobic interactions between immobilized hydrophobic ligands and nonpolar regions on the surface of the proteins. Lysozyme is found in a variety of vertebrate cells and secretions, such as spleen, milk, tears, and egg white. Its common applications are as a cell-disrupting agent for extraction of bacterial intracellular products, as an antibacterial agent in ophthalmologic preparations, as a food additive in milk products and as a drug for treatment of ulcers and infections. Lysozyme has also been used in cancer chemotherapy. The aim of this study is the synthesis of hydrophobic nanoparticles for Lysozyme detection. For this purpose, methacryoyl-L-phenylalanine was chosen as a hydrophobic matrix. The hydrophobic nanoparticles were synthesized by micro-emulsion polymerization method. Then, hydrophobic QCM nanosensor was characterized by Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, atomic force microscopy (AFM) and zeta size analysis. Hydrophobic QCM nanosensor was tested for real-time detection of Lysozyme from aqueous solution. The kinetic and affinity studies were determined by using Lysozyme solutions with different concentrations. The responses related to a mass (Δm) and frequency (Δf) shifts were used to evaluate adsorption properties.

Keywords: nanosensor, HIC, lysozyme, QCM

Procedia PDF Downloads 349

Authors: Lemar Blake, Chris Oura, Ayanna C. N. Phillips Savage

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Improved DNA sequencing technology has greatly enhanced bacterial identification, especially for organisms that are difficult to culture. Mycobacteriosis with consistent hyphema, bilateral exophthalmia, open mouth gape and ocular lesions, were observed in various fish populations at the School of Veterinary Medicine, Aquaculture/Aquatic Animal Health Unit. Objective: To identify the species of Mycobacterium that is affecting aquarium fish at the School of Veterinary Medicine, Aquaculture/Aquatic Animal Health Unit. Method: A total of 13 fish samples were collected and analyzed via: Ziehl-Neelsen, conventional polymerase chain reaction (PCR) and real-time PCR. These tests were carried out simultaneously for confirmation. The following combination of conventional primers: 16s rRNA (564 bp), rpoB (396 bp), sod (408 bp) were used. Concatenation of the gene fragments was carried out to phylogenetically classify the organism. Results: Acid fast non-branching bacilli were detected in all samples from homogenized internal organs. All 13 acid fast samples were positive for Mycobacterium via real-time PCR. Partial gene sequences using all three primer sets were obtained from two samples and demonstrated a novel strain. A strain 99% related to Mycobacterium marinum was also confirmed in one sample, using 16srRNA and rpoB genes. The two novel strains were clustered with the rapid growers and strains that are known to affect humans. Conclusions: Phylogenetic analysis demonstrated two novel Mycobacterium strains with the potential of being zoonotic and one strain 99% related to Mycobacterium marinum.

Keywords: polymerase chain reaction, phylogenetic, DNA sequencing, zoonotic

Procedia PDF Downloads 144