Search results for: network security

Authors: Hamidreza Ghazvinian, Khosro Ghazvinian, Touba Khodaiean

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The importance of evaporation estimation in water resources and agricultural studies is undeniable. Pan evaporation are used as an indicator to determine the evaporation of lakes and reservoirs around the world due to the ease of interpreting its data. In this research, intelligent models were investigated in estimating pan evaporation on a daily basis. Shahroud and Mayamey were considered as the studied cities. These two cities are located in Semnan province in Iran. The mentioned cities have dry weather conditions that are susceptible to high evaporation potential. Meteorological data of 11 years of synoptic stations of Shahrood and Mayamey cities were used. The intelligent models used in this study are Artificial Neural Network (ANN), Least Squares Support Vector Machine (LSSVM), and M5 tree models. Meteorological parameters of minimum and maximum air temperature (Tmax, Tmin), wind speed (WS), sunshine hours (SH), air pressure (PA), relative humidity (RH) as selected input data and evaporation data from pan (EP) to The output data was considered. 70% of data is used at the education level, and 30 % of the data is used at the test level. Models used with explanation coefficient evaluation (R2) Root of Mean Squares Error (RMSE) and Mean Absolute Error (MAE). The results for the two Shahroud and Mayamey stations showed that the above three models' operations are rather appropriate.

Keywords: pan evaporation, intelligent methods, shahroud, mayamey

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Authors: Emelia Noronha, Milind Malshe

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The phatic function of communication is a vital element of any conversation. This research paper looks into this function with respect to personal blogs maintained by Indian bloggers. This paper is a study into the phenomenon of phatic communication maintained by bloggers through their blogs. Based on a linguistic analysis of the posts of twenty eight Indian bloggers, writing in English, studied over a period of three years, the study indicates that though the blogging phenomenon is not conversational in the same manner as face-to-face communication, it does make ample provision for feedback that is conversational in nature. Ordinary day to day offline conversations use conventionalized phatic utterances; those on the social media are in a perpetual mode of innovation and experimentation in order to sustain contact with its readers. These innovative methods and means are the focus of this study. Though the personal blogger aims to chronicle his/her personal life through the blog, the socializing function is crucial to these bloggers. In comparison to the western personal blogs which focus on the presentation of the ‘bounded individual self’, we find Indian personal bloggers engage in the presentation of their ‘social selves’. These bloggers yearn to reach out to the readers on the internet and the phatic function serves to initiate, sustain and renew social ties on the blogosphere thereby consolidating the social network of readers and bloggers.

Keywords: personal blogs, phatic, social-selves, blog readers

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Authors: Olivia Borgue, Massimo Panarotto, Ola Isaksson

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This article presents a neural networks-based strategy for reducing the need for support structures when designing for additive manufacturing (AM). Additive manufacturing is a relatively new and immature industrial technology, and the information to make confident decisions when designing for AM is limited. This lack of information impacts especially the early stages of engineering design, for instance, it is difficult to actively consider the support structures needed for manufacturing a part. This difficulty is related to the challenge of designing a product geometry accounting for customer requirements, manufacturing constraints and minimization of support structure. The approach presented in this article proposes an automatized geometry modification technique for reducing the use of the support structures while designing for AM. This strategy starts with a neural network-based strategy for shape recognition to achieve product classification, using an STL file of the product as input. Based on the classification, an automatic part geometry modification based on MATLAB© is implemented. At the end of the process, the strategy presents different geometry modification alternatives depending on the type of product to be designed. The geometry alternatives are then evaluated adopting a QFD-like decision support tool.

Keywords: additive manufacturing, engineering design, geometry modification optimization, neural networks

Procedia PDF Downloads 247

Authors: Enido Fabiano de Ramos, Ieda Kanashiro Makiya, Francisco I. Giocondo Cesar

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This study presents an analysis of the Digital Twin concept applied to the academic environment, focusing on the development of a Digital Twin Smart Campus Framework. Using bibliometric analysis methodologies and literature review, the research investigates the evolution and applications of the Digital Twin in educational contexts, comparing these findings with the advances of Industry 4.0. It was identified gaps in the existing literature and highlighted the need to adapt Digital Twin principles to meet the specific demands of a smart campus. By integrating Industry 4.0 concepts such as automation, Internet of Things, and real-time data analytics, we propose an innovative framework for the successful implementation of the Digital Twin in academic settings. The results of this study provide valuable insights for university campus managers, allowing for a better understanding of the potential applications of the Digital Twin for operations, security, and user experience optimization. In addition, our framework offers practical guidance for transitioning from a digital campus to a digital twin smart campus, promoting innovation and efficiency in the educational environment. This work contributes to the growing literature on Digital Twins and Industry 4.0, while offering a specific and tailored approach to transforming university campuses into smart and connected spaces, high demanded by Society 5.0 trends. It is hoped that this framework will serve as a basis for future research and practical implementations in the field of higher education and educational technology.

Keywords: smart campus, digital twin, industry 4.0, education trends, society 5.0

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Authors: Melodi Mbuyi Mata, Blessing Olamide Taiwo, Afolabi Ayodele David

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The purpose of this research is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo state aggregate quarries. In addition, an artificial neural network (ANN) model and multivariate predicting models for granite profitability were developed in the study. A formal survey questionnaire was used to collect data for the study. The data extracted from the case study mine for this study includes granite marketing operations, royalty, production costs, and mine production information. The following methods were used to achieve the goal of this study: descriptive statistics, MATLAB 2017, and SPSS16.0 software in analyzing and modeling the data collected from granite traders in the study areas. The ANN and Multi Variant Regression models' prediction accuracy was compared using a coefficient of determination (R²), Root mean square error (RMSE), and mean square error (MSE). Due to the high prediction error, the model evaluation indices revealed that the ANN model was suitable for predicting generated profit in a typical quarry. More quarries in Nigeria's southwest region and other geopolitical zones should be considered to improve ANN prediction accuracy.

Keywords: national development, granite, profitability assessment, ANN models

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Authors: Xing Yan Fan, Xing Lin Xu, Man Yuan Chen, Pei Tzu Lee, Yu Ting Wang, Yi Xiao Cao, Rui Yao

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Pyramid sales have been a widespread issue in China. Victims who are defrauded not only lose money but damage interpersonal relationship. A deeper understanding of pyramid-sale models can be beneficial to prevent potential victims from fraud and improve the property security. The goals of this study were to detect psychological characteristics of pyramid-sale sellers, and analyse persuasive skills in pyramid organizations. A qualitative study was conducted in this study. Participants (n=6) recruited by 'snowball' sampling from present pyramid-sale sellers (n=3) and imprisoned pyramid-sale sellers (n=3). All participants accepted semi-structured interview for collecting data. Content analysis was adopted for data coding and analysis. The results indicate that pyramid organizations are used to utilize their appearance packaging and celebrity effect to strengthen the positions in participants’ mind. The status gap between pyramid-sale sellers in same organization, as well as rewards to increase reputation, are used to motivate participants in pyramid. The most significant common characteristics among all participants are that they tend to possess a high sense of belongingness within the firm. Moreover, the expression of pyramid-sale sellers on gambling mentality is expected to growth as constantly losing money. Findings suggest that the psychological characteristics of pyramid-sale sellers in accordance with Maslow’s hierarchy of needs, persuasive skills of pyramid organization confront to 'attitude-behaviour change model'. These findings have implication on 'immune education' that providing guidance for victims out of stuck and protecting ordinary people from the jeopardizing of pyramid sales.

Keywords: pyramid sales, characteristics, persuasive skills, qualitative study

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Authors: Joseph Gear, Yue Xu, Ernest Foo, Praveen Gauravaran, Zahra Jadidi, Leonie Simpson

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Deep learning methods have been seeing an increasing application to the long-standing security research goal of automatic vulnerability detection for source code. Attention, however, must still be paid to the task of producing vector representations for source code (code embeddings) as input for these deep learning models. Graphical representations of code, most predominantly Abstract Syntax Trees and Code Property Graphs, have received some use in this task of late; however, for very large graphs representing very large code snip- pets, learning becomes prohibitively computationally expensive. This expense may be reduced by intelligently pruning this input to only vulnerability-relevant information; however, little research in this area has been performed. Additionally, most existing work comprehends code based solely on the structure of the graph at the expense of the information contained by the node in the graph. This paper proposes Semantic-enhanced Code Embedding for Vulnerability Discovery (SCEVD), a deep learning model which uses semantic-based feature selection for its vulnerability classification model. It uses information from the nodes as well as the structure of the code graph in order to select features which are most indicative of the presence or absence of vulnerabilities. This model is implemented and experimentally tested using the SARD Juliet vulnerability test suite to determine its efficacy. It is able to improve on existing code graph feature selection methods, as demonstrated by its improved ability to discover vulnerabilities.

Keywords: code representation, deep learning, source code semantics, vulnerability discovery

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Authors: Maria Do Carmo Baracho De Alencar

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Back pain is considered a worldwide public health problem and has led to numerous work-related absence from work and public spending on rehabilitation, as well as difficulties in the process of professional rehabilitation and return to work. Also, the rehabilitation of workers is one of the great challenges today and for the field of Workers' Health in Brazil. Aim: To investigate the procedures related to the professional rehabilitation of insured workers affected by chronic low back pain, based on the perceptions of professional counselors. Methods: A list of related professional counselors was obtained from the Professional Rehabilitation Coordination of the Baixada Santista (SP) region, and from the Social Security National Institute of Brazil, and in which cities they worked. Semistructured and individual interview was scheduled, based on a pre-elaborated script, containing questions about procedures, experiences at work and feelings. The interviews were recorded and transcribed in full for content analysis. Results: Ten (10) professional counselors of both genders and from nine (9) cities from the Baixada Santista region participated in the study. Aged between 31 and 64 years, and time in service between 4 and 38 years. Only one of the professionals was graduaded in Psychology. Among the testimonies emerged the high demand of work, the lack of interest of companies, medical authority, the social helplessness after rehabilitation process, difficulty in assessing invisible pain, and suffering, anguish, and frustration at work, between others. Conclusion: The study contributes to reflections about the importance of interdisciplinary actions and the Psychology in the processes of professional rehabilitation and readaptation in the process of return to work.

Keywords: low back pain, rehabilitation, work, occupational health

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Authors: L. Edirisinghe, P. Mukherjee, H. Edirisinghe

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Virtual Container Yard (VCY) is a modern concept that helps to reduce the empty container repositioning cost of carriers. The concept of VCY is based on container interchange between shipping lines. Although this mechanism has been theoretically accepted by the shipping community as a feasible solution, it has not yet achieved the necessary momentum among container shipping lines (CSL). This paper investigates whether there is any legal influence on this industry myopia about the VCY. It is believed that this is the first publication that focuses on the legal aspects of container exchange between carriers. Not much literature on this subject is available. This study establishes with statistical evidence that there is a phobia prevailing in the shipping industry that exchanging containers with other carriers may lead to various legal implications. The complexity of exchange is two faceted. CSLs assume that offering a container to another carrier (obviously, a competitor in terms of commercial context) or using a container offered by another carrier may lead to undue legal implications. This research reveals that this fear is reflected through four types of perceived components, namely: shipping associate; warehouse associate; network associate; and trading associate. These components carry eighteen subcomponents that comprehensively cover the entire process of a container shipment. The statistical explanation has been supported through regression analysis; INCO terms were used to illustrate the shipping process.

Keywords: virtual container yard, legal, maritime law, inventory

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Authors: Amit Barde, Dipali Khatave, Vaishali Savale, Atharva Chavan, Sapna Wagaj, Aditya Jilla

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AgriInnoConnect Pro is an advanced agricultural automation system designed to enhance irrigation efficiency and overall farm management through IoT technology. Using MIT App Inventor, Telegram, Arduino IDE, and Firebase Console, it provides a user-friendly interface for farmers. Key hardware includes soil moisture sensors, DHT11 sensors, a 12V motor, a solenoid valve, a stepdown transformer, Smart Fencing, and AC switches. The system operates in automatic and manual modes. In automatic mode, the ESP32 microcontroller monitors soil moisture and autonomously controls irrigation to optimize water usage. In manual mode, users can control the irrigation motor via a mobile app. Telegram bots enable remote operation of the solenoid valve and electric fencing, enhancing farm security. Additionally, the system upgrades conventional devices to smart ones using AC switches, broadening automation capabilities. AgriInnoConnect Pro aims to improve farm productivity and resource management, addressing the critical need for sustainable water conservation and providing a comprehensive solution for modern farm management. The integration of smart technologies in AgriInnoConnect Pro ensures precision farming practices, promoting efficient resource allocation and sustainable agricultural development.

Keywords: agricultural automation, IoT, soil moisture sensor, ESP32, MIT app inventor, telegram bot, smart farming, remote control, firebase console

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Authors: Muganyizi Jonas Bisheko, G. Rejikumar

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Reducing post-harvest losses could be a sustainable solution to enhance the food and income security of smallholder farmers in developing countries. While various research institutions have come up with a number of innovative post-harvest technologies for reducing post-harvest losses, most of them have not been extensively adopted by smallholder farmers. Despite this gap, the synthesized information about the major constraints of post-harvest technology is scarce. This study has been conducted to fill this gap and show the implications of the findings for future post-harvest research. The developed search strategy retrieved 2201 studies. However, after excluding duplicates, title, abstract and full article screening, a total of 41 documents were identified. The major findings are: (i) there is an outstanding deficiency of systematic evidence of the effect of climate change, off-farm income and sources of post-harvest information on the adoption of improved post-harvest technologies; (ii) there is very limited information on adoption constraints pertaining to matters of policy, rules and regulations; (iii) there is very thin literature on behavioral constraints associated with limited adoption of improved post-harvest technologies; (iv) most of the studies focused on post-harvest storage technologies (47%) followed by overall post-harvest management practices (25%), processing technologies (19%) and packaging technologies (3%). Much of the information was found on Cereals (58%), especially maize (44%); (v) geographically, Sub-Saharan Africa accounted for 79% of the reviewed interventions, while South Asia occupied only 21%. The findings of this review are intended to guide various post-harvest technologists and decision-makers in addressing the challenge of huge post-harvest losses.

Keywords: constraints, post-harvest loss, post-harvest technology , smallholder farmer

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Authors: Salwa Mohammad Alawneh

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The ongoing humanitarian crises spur rapid and unpredicted refugee influxes resulting in demographic changes in cities. Regarding different urban systems are vulnerable in refugee neighborhoods. With the consequent social, economic, and spatial challenges, cities must respond with a more durable and sustainable approach based on urban resilience. The paper systematically approaches urban resilience to contribute to refugee spaces by reflecting on the overall urban systems of their neighborhoods. The research will review the urban resilience literature to develop an evaluation framework. The developed framework applies urban resilience more holistically in refugee neighborhoods and expands to the urban systems of social, economic, and spatial. However, the main highlight of this paper is the resilient spatial structure in refugee neighborhoods to face the internal and complex stress of refugee waves and their demographic changes. Finding a set of resilient spatial measurements and focusing on urban forms at a neighborhood scale provide vulnerability reduction and enhance adaptation capacity. As a model example, the paper applies these measurements and facilitates geospatial technologies to one of the refugee neighborhoods in Amman, Jordan, namely Al-Jubilee. The application in Al-Jubilee helps to demonstrate a road map towards a developmental pattern in design and planning by different decision-makers of inter-governmental and humanitarian organizations. In this regard, urban resilience improves the humanitarian assistantship of refugee settings beyond providing the essential needs. In conclusion, urban resilience responds to the different challenges of refugee neighborhoods by supporting urban stability, improving livability, and maintaining both urban functions and security.

Keywords: urban resilience of refugee, resilient urban form, refugee neighborhoods, humanitarian assistantship, refugee in Jordan

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Authors: N. Maksimentseva

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The issue of public administration in subsoil use, renewal and protection is of high importance for Ukraine since it is strongly linked to energy security of the state as well as it shall facilitate the people of Ukraine to efficiently implement its propitiatory rights towards natural resources and redistribution of national wealth. As it is stipulated in the Article 11 of the Subsoil Code of Ukraine (the Code) the authorities that administer the industry are limited to central executive bodies and local governments. In particular, it is stipulated in the Code that the Ukraine’s Cabinet of Ministers carries out public administration in geological exploration, production and protection of subsoil. Other state bodies of public administration include central public authority responsible for state environmental protection policies; central public authority in charge of implementation of state geological exploration and efficient subsoil use policies; central authority in charge of state health and safety control policies. There are also public authorities in the Autonomous Republic of Crimea; local executive bodies and other state authorities and local self-government authorities in compliance with laws of Ukraine. This article is devoted to the analysis of the legal relations in the area of public administration of subsoil use, renewal and protection in Ukraine. The main approaches to study the essence of legal relations in the named area as well as its tasks, functions and methods are analyzed. It is concluded in this article that legal relationship in the field of public administration of subsoil use, renewal and protection is characterized by specifics of its task (development of natural resources).

Keywords: legal relations, public administration, subsoil code of Ukraine, subsoil use, renewal and protection

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

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To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

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Authors: Nantakrit Yodpijit, Teppakorn Sittiwanchai

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The number of older adults is rising rapidly. The world’s population becomes aging. Falls is one of common and major health problems in the elderly. Falls may lead to acute and chronic injuries and deaths. The fall-prone individuals are at greater risk for decreased quality of life, lowered productivity and poverty, social problems, and additional health problems. A number of studies on falls prevention using fall detection system have been conducted. Many available technologies for fall detection system are laboratory-based and can incur substantial costs for falls prevention. The utilization of alternative technologies can potentially reduce costs. This paper presents the new design and development of a wearable-based fall detection system using an Accelerometer and Gyroscope as motion sensors for the detection of body orientation and movement. Algorithms are developed to differentiate between Activities of Daily Living (ADL) and falls by comparing Threshold-based values with Artificial Neural Networks (ANN). Results indicate the possibility of using the new threshold-based method with neural network algorithm to reduce the number of false positive (false alarm) and improve the accuracy of fall detection system.

Keywords: aging, algorithm, artificial neural networks (ANN), fall detection system, motion sensorsthreshold

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Authors: Giselle Maggie Fer Castañeda, Diego Iván González

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The objective of this work was to study the autoshaped choice behavior in the Donahoe, Burgos and Palmer (DBP) neural network model and analyze it under the matching law. Autoshaped choice can be viewed as a form of economic behavior defined as the preference between alternatives according to their relative outcomes. The Donahoe, Burgos and Palmer (DBP) model is a connectionist proposal that unifies operant and Pavlovian conditioning. This model has been used for more than three decades as a neurobehavioral explanation of conditioning phenomena, as well as a generator of predictions suitable for experimental testing with non-human animals and humans. The study consisted of different simulations in which, in each one, a ratio of reinforcement was established for two alternatives, and the responses (i.e., activations) in each of them were measured. Choice studies with animals have demonstrated that the data generally conform closely to the generalized matching law equation, which states that the response ratio equals proportionally to the reinforcement ratio; therefore, it was expected to find similar results with the neural networks of the Donahoe, Burgos and Palmer (DBP) model since these networks have simulated and predicted various conditioning phenomena. The results were analyzed by the generalized matching law equation, and it was observed that under some contingencies, the data from the networks adjusted approximately to what was established by the equation. Implications and limitations are discussed.

Keywords: matching law, neural networks, computational models, behavioral sciences

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Authors: Md Abdullah Al Mamun

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Background: The current situation of Ensuring WHO recommended feeding practices for infant and young children which is becoming a challenge nowadays in many developing countries, especially in areas where household food security is at risk. Because many households of the developing countries often encounter severe food insecurity hence provision of adequate child nutrition is threatened. Aim: The study aimed to assess the child feeding practices of 0-24 months childbearing mothers and explore their household food insecurity in a coastal region of Bangladesh. Methods: This study was conducted in Suborno Char (one of the coastal suburbs in Noakhali District in Bangladesh) from October 2019 to April 2020. A total of 400 mothers were selected with their children of 0-24 months following a cross sectional study sampling procedure of the population. Data were collected through a standard questionnaire and analyzed using statistical tests in SPSS version 20.0.0. Results: The frequency of exclusive breastfeeding, timely initiation of complementary feeding, and giving foods from four food groups to the children were 53.5%, 75.5%, and 22.2%, respectively. Mother's level of education showed a strong association with the child feeding practices of the mothers. Mothers of severely food insecure households showed lower odds in exclusive breastfeeding practice (COR 0.233 at 95% CI 0.083, 0.655; and AOR 0.478 at 95% CI 0.133, 1.713) than mothers of food secured households. Similar results have also been found in case of timely initiation of complementary feeding and minimum dietary diversity of the children.

Keywords: household food insecurity, exclusive breastfeeding, complementary feeding, maternal education, mothers age, household income

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Authors: Ahmed Krobba, Meriem Touzene, Mohamed Debeyche

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The wireless communications system (5G) will provide more diverse applications and higher quality services for users compared to the long-term evolution 4G (LTE). 5G uses a higher carrier frequency, which suffers from information loss in 5G coverage. Most 5G users often cannot obtain high-quality communications due to transmission channel noise and channel complexity. Physical Uplink Control Channel (PUCCH-NR: Physical Uplink Control Channel New Radio) plays a crucial role in 5G NR telecommunication technology, which is mainly used to transmit link control information uplink (UCI: Uplink Control Information. This study based of evaluating the performance of channel physical uplink control PUCCH-NR under low Signal-to-Noise Ratios with various antenna numbers reception. We propose the artificial intelligence approach based on deep neural networks (Deep Learning) to estimate the PUCCH-NR channel in comparison with this approach with different conventional methods such as least-square (LS) and minimum-mean-square-error (MMSE). To evaluate the channel performance we use the block error rate (BLER) as an evaluation criterion of the communication system. The results show that the deep neural networks method gives best performance compared with MMSE and LS

Keywords: 5G network, uplink (Uplink), PUCCH channel, NR-PUCCH channel, deep learning

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Authors: Esther M. Folarin, Evans Osabuohien, Ademola Onabote

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Agriculture constitutes an important part of development and poverty mitigation in lower-middle-income countries, like Nigeria. The level of agricultural productivity in the Nigerian economy in line with the level of demand necessary to meet the desired expectation of the Nigerian populace is threatening to meeting the standard of the United Nations (UN) Sustainable Development Goals (SDGs); This includes the SDG-2 (achieve food security through agricultural productivity). The overall objective of the study is to reveal the performance of the interaction variable in the model among other factors that help in the achievement of greater Nigerian agricultural productivity. The study makes use of Wave 4 (2018/2019) of the Living Standard Measurement Studies, Integrated Survey on Agriculture (LSMS-ISA). Qualitative analysis of the information was also used to provide complimentary answers to the quantitative analysis done in the study. The study employed human capital theory and Grossman’s theory of health Demand in explaining the relationships that exist between the variables within the model of the study. The study engages the Instrumental Variable Regression technique in achieving the broad objectives among other techniques for the other specific objectives. The estimation results show that there exists a positive relationship between female healthcare and the level of female agricultural productivity in Nigeria. In conclusion, the study emphasises the need for more provision and empowerment for greater female access to healthcare and educational attainment levels that aids higher female agricultural productivity and consequently an improvement in the total agricultural productivity of the Nigerian economy.

Keywords: agricultural productivity, education, female, healthcare, investment

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Authors: Elsayed A. Issawy, Anwar H. Radwan

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Recent crustal deformations studies in Egypt are applied on the most active areas with relation to seismic activity. Temporal gravity variations in parallel with the geodetic technique (GPS) were used to monitor recent crustal movements in Egypt since 1997. The non-tidal gravity changes were constrained by the vertical component of surface movements derived from the GPS observations. The gravity changes were used to understand the surface tectonics and geodynamic modelling of the Greater Cairo region after the occurrence of an earthquake of 1992. It was found that there is a certain relation showed by increasing of gravity values before the main seismic activity. As example, relative considerable increase of gravity values was noticed for the network between the epochs of 2000 and 2004. Otherwise, the temporal gravity variations were reported a considerable decrease in gravity values between the two campaigns of 2004 and 2007 for the same stations. This behaviour could explain by compressive deformation and strain build-up stage before the South western Cairo earthquake (July 31, 2005 with magnitude of 4.3) and the stress release stage occurred after the main event. The geodetic measurements showed that, the estimated horizontal velocities for almost of points are 5.5 mm/year in approximately NW direction.

Keywords: temporal gravity variations, geodynamics, greater Cairo, recent crustal movements, earthquakes

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Authors: Masoud Abedini, Azrul A. Mutalib

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A study of the used of additives which mixed with concrete in order to increase the strength and durability of concrete was examined to improve the quality of many aspects in the concrete. This paper presents a polypropylene (PP) fibre was added into concrete to study the dynamic response under impact load. References related to dynamic impact test for sample polypropylene fibre reinforced concrete (PPFRC) is very limited and there is no specific research and information related to this research. Therefore, the study on the dynamic impact of PPFRC using a Split Hopkinson Pressure Bar (SHPB) was done in this study. Provided samples for this study was composed of 1.0 kg/m³ PP fibres, 2.0 kg/m³ PP fibres and plain concrete as a control samples. This PP fibre contains twisted bundle non-fibrillating monofilament and fibrillating network fibres. Samples were prepared by cylindrical mould with three samples of each mix proportion, 28 days curing period and concrete grade 35 Mpa. These samples are then tested for dynamic impact by SHPB at 2 Mpa pressure under the strain rate of 10 s-1. Dynamic compressive strength results showed an increase of SC1 and SC2 samples than the control sample which is 13.22 % and 76.9 % respectively with the dynamic compressive strength of 74.5 MPa and 116.4 MPa compared to 65.8 MPa. Dynamic increased factor (DIF) shows that, sample SC2 gives higher value with 4.15 than others samples SC1 and SC3 that gives the value of 2.14 and 1.97 respectively.

Keywords: polypropylene fiber, Split Hopkinson Pressure Bar, impact load, dynamic compressive strength

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Authors: Ekaterina Donii

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Understanding interpersonal competence, social interaction and peer relationships of gifted children is a concern for specialists in the field of gifted education. To gain more in-depth knowledge concerning the social functioning of gifted children among peers, we decided to study the social abilities of gifted children in a heterogeneous academic environment. Eight gifted children (5 of age 7, 1 of age 8.5, 1 of age 9.5 and 1 of age 10), their classmates (10 of age 7-8, 12 of age 8.5-9, 16 of age 9.5-10) and teachers participated in the study. The sociometric questionnaire analysis was based on the method of Rodríguez and Morera to check the social status of the gifted children among classmates. The Instrument Observational Protocol for Interactions within the Classroom (OPINTEC-v.5) was used to assess the social interactions between the gifted students, their classmates, and the teacher within the educational context. While doing a task together, the gifted children interacted more with popular and neither popular nor gifted classmates than with rejected classmates. While spending time together, the gifted children interacted more with neither popular nor rejected classmates than with popular or rejected classmates. All gifted children chose other gifted and non-gifted classmates for interaction, established close relations and demonstrated good social abilities interacting with their classmates. The aim of this study was to examine the social interactions, social status, and social network of the gifted students in a regular classroom. The majority of the gifted children were popular among their classmates and had good social skills. We should be alert, though, for those gifted children who do have social problems, in order to help them functioning in a regular classroom.

Keywords: gifted, heterogeneous environment, sociometric status, social interactions

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Authors: Lan Zhang, Cailing Zhang

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In recent years, many face anti-spoofing algorithms have high detection accuracy when detecting 2D face anti-spoofing or 3D mask face anti-spoofing alone in the field of face anti-spoofing, but their detection performance is greatly reduced in multidimensional and cross-datasets tests. The rPPG method used for face anti-spoofing uses the unique vital information of real face to judge real faces and face anti-spoofing, so rPPG method has strong stability compared with other methods, but its detection rate of 2D face anti-spoofing needs to be improved. Therefore, in this paper, we improve an rPPG(Remote Photoplethysmography) method(MrPPG) for face anti-spoofing which through color space fusion, using the correlation of pulse signals between real face regions and background regions, and introducing the cyclic neural network (LSTM) method to improve accuracy in 2D face anti-spoofing. Meanwhile, the MrPPG also has high accuracy and good stability in face anti-spoofing of multi-dimensional and cross-data datasets. The improved method was validated on Replay-Attack, CASIA-FASD, Siw and HKBU_MARs_V2 datasets, the experimental results show that the performance and stability of the improved algorithm proposed in this paper is superior to many advanced algorithms.

Keywords: face anti-spoofing, face presentation attack detection, remote photoplethysmography, MrPPG

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Authors: J. Y. Ijato, A. Adewumi, H. O Yakubu, O. O. Olajide, B. O. Ojo, B. A. Adanikin

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Post-harvest fruit rot pathogens are one of the major factors that are responsible for food security challenges in developing countries like Nigeria. These pathogens also cause fruit food poisoning. Biocidal effects of ethanolic extracts of Khaya grandifoliola, Hyptis suaveolens, Zingiber officinale, Calophyllum inophyllum, Datura stramonium on the mycelia growth of fungal rot pathogens of pineapple fruit was investigated, the ethanolic extracts of these test plants exhibited high significant inhibitory effects on the rot pathogens, the highest ethanolic extract inhibition of Zingiber officinale was on Aspergillus flavus (38.40%) at 1.0g/ml while the least inhibitory effect was on Aspergillus fumigatus (23.10%) at 1.0g/ml, the highest ethanol extract inhibition of Datura stramonium was on Aspergillus tubingensis (24.00%) at 1.0g/ml while the least inhibitory effect was 10.00% on Colletotrichum fruticola at 1.0g/ml, the highest ethanol extract inhibition of Calophyllum inophyllum was on Trichoderma harzianum (18.50%) at 1.0g/ml while the least inhibitory effect was on Aspergillus flavus (15.00%) at 1.0g/ml, the highest ethanol extract inhibition of Hyptis suaveolens was on Aspergillus fumigatus (35.00%) at 1.0g/ml while the least inhibitory effect was on Aspergillus niger (20.00%) at 1.0g/ml, the highest ethanol extract inhibition of Khaya grandifoliola was on Aspergillus flavus (35.00%) at 1.00g/ml while the least inhibitory effect was on Aspergillus fumigates (22.00%) at 1.0g/ml, the antifungal capacity of these test plant extracts on rot causing fungi on pineapple fruit reveals the possibility of their use by farmers and fruit traders as alternative to chemical fungicide that portends great threat to human and environmental health.

Keywords: fruit rot, pathogens, plant extracts, pineapple, food poisoning

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Authors: Chengcao Sun, Shujun Li, Cuili Yang, Yongyong Xi, Liang Wang, Feng Zhang, Dejia Li

Abstract:

Recently, the long non-coding RNA (lncRNA) NEAT1 has been identified as an oncogenic gene in multiple cancer types and elevated expression of NEAT1 was tightly linked to tumorigenesis and cancer progression. However, the molecular basis for this observation has not been characterized in progression of non-small cell lung cancer (NSCLC). In our studies, we identified NEAT1 was highly expressed in NSCLC patients and was a novel regulator of NSCLC progression. Patients whose tumors had high NEAT1 expression had a shorter overall survival than patients whose tumors had low NEAT1 expression. Further, NEAT1 significantly accelerates NSCLC cell growth and metastasis in vitro and tumor growth in vivo. Additionally, by using bioinformatics study and RNA pull down combined with luciferase reporter assays, we demonstrated that NEAT1 functioned as a competing endogenous RNA (ceRNA) for has-miR-377-3p, antagonized its functions and led to the de-repression of its endogenous targets E2F3, which was a core oncogene in promoting NSCLC progression. Taken together, these observations imply that the NEAT1 modulated the expression of E2F3 gene by acting as a competing endogenous RNA, which may build up the missing link between the regulatory miRNA network and NSCLC progression.

Keywords: long non-coding RNA NEAT1, hsa-miRNA-377-3p, E2F3, non-small cell lung cancer, tumorigenesis

Procedia PDF Downloads 367

Authors: Jaleh Shoshtarian Malak, Niloofar Mohamadzadeh

Abstract:

Managing pre-hospital emergency patients requires real-time practices and efficient resource utilization. Since we are facing a distributed Network of healthcare providers, services and applications choosing the right resources and treatment protocol considering patient situation is a critical task. Delivering care to emergency patients at right time and with the suitable treatment settings can save ones live and prevent further complication. In recent years Multi Agent Systems (MAS) introduced great solutions to deal with real-time, distributed and complicated problems. In this paper we propose a multi agent based pre-hospital emergency management architecture in order to manage coordination, collaboration, treatment protocol and healthcare provider selection between different parties in pre-hospital emergency in a self-organizing manner. We used AnyLogic Agent Based Modeling (ABM) tool in order to simulate our proposed architecture. We have analyzed and described the functionality of EMS center, Ambulance, Consultation Center, EHR Repository and Quality of Care Monitoring as main collaborating agents. Future work includes implementation of the proposed architecture and evaluation of its impact on patient quality of care improvement.

Keywords: multi agent systems, pre-hospital emergency, simulation, software architecture

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Authors: Sheena Christabel Pravin, M. Palanivelan

Abstract:

Speech disfluencies are common in spontaneous speech. The primary purpose of this study was to distinguish linguistic disfluencies from stuttering disfluencies in bilingual Tamil–English (TE) speaking children. The secondary purpose was to determine whether their disfluencies are mediated by native language dominance and/or on an early onset of developmental stuttering at childhood. A detailed study was carried out to identify the prosodic and acoustic features that uniquely represent the disfluent regions of speech. This paper focuses on statistical modeling of repetitions, prolongations, pauses and interjections in the speech corpus encompassing bilingual spontaneous utterances from school going children – English and Tamil. Two classifiers including Hidden Markov Models (HMM) and the Multilayer Perceptron (MLP), which is a class of feed-forward artificial neural network, were compared in the classification of disfluencies. The results of the classifiers document the patterns of disfluency in spontaneous speech samples of school-aged children to distinguish between Children Who Stutter (CWS) and Children with Language Impairment CLI). The ability of the models in classifying the disfluencies was measured in terms of F-measure, Recall, and Precision.

Keywords: bi-lingual, children who stutter, children with language impairment, hidden markov models, multi-layer perceptron, linguistic disfluencies, stuttering disfluencies

Procedia PDF Downloads 211

Authors: 李佳翰, CHIA-HAN LEE

Abstract:

Without energy, the economy would grind to a halt. Germany's prosperity and security depend on a reliable and affordable energy supply. In recent years, Germany's energy policy has undergone major changes. Due to the sharp turn in energy, Germany cannot extend the service of nuclear power plants and can only find a rapid transition energy source: natural gas for a limited time. This study attempts to use processes of decision entrapment behavior and document analysis to explain research questions. Through primary and secondary information such as official reports, parliamentary minutes, media interview records, and speech records, the author sorted out the important events experienced by the three coalition governments (Gerhard Schröder, Angela Merkel, and Olaf Scholz) and the relationship between Nord Stream 1 and Nord Stream 2 with primary and secondary sources. Also, compare it with the processes of decision entrapment behavior, which designed in this study, and divide it into four stages to explore its key elements one by one. In this regard, the following conclusions are drawn: First, from the perspective of processes of decision entrapment behavior, Merkel’s government firmly believes that she can overcome difficulties because of her past experience in crisis management capabilities. However, the outbreak of war between Ukraine and Russia was beyond Merkel's planning. Second, in the face of the crisis, the Scholz’s government increased the import of natural gas from other countries and began to import liquefied natural gas to make up for the energy gap of Russian natural gas.

Keywords: german research, nord stream gas pipeline, energy policy, processes of decision entrapment behavior

Procedia PDF Downloads 36

Authors: Cian O'Shea, Ross O'Halloran, Peter Haigh

Abstract:

Wireless Sensor Networks (WSN) are Internet of Things (IoT) edge devices. They are becoming widely adopted in many industries, including health care, building energy management, and conditional monitoring. As the scale of WSN deployments increases, the cost and complexity of battery replacement and disposal become more significant and in time may become a barrier to adoption. Harvesting ambient energies provide a pathway to reducing dependence on batteries and in the future may lead to autonomously powered sensors. This work describes a simulation tool that enables the user to predict the battery life of a wireless sensor that utilizes energy harvesting to supplement the battery power. To create this simulator, all aspects of a typical WSN edge device were modelled including, sensors, transceiver, and microcontroller as well as the energy source components (batteries, solar cells, thermoelectric generators (TEG), supercapacitors and DC/DC converters). The tool allows the user to plug and play different pre characterized devices as well as add user-defined devices. The goal of this simulation tool is to predict the lifetime of a device and scope for extension using ambient energy sources.

Keywords: Wireless Sensor Network, IoT, edge device, simulation, solar cells, TEG, supercapacitor, energy harvesting

Procedia PDF Downloads 124

Authors: Majed Ahmadi

Abstract:

In recent years, the high penetration of different types of renewable energy sources (RESs) has affected most of the available strategies. The main motivations behind the high penetration of RESs are clean energy, modular system and easy installation. Among different types of RESs, wind turbine (WT) is an interesting choice referring to the availability of wind in almost any area. The new technologies of WT can provide energy from residential applications to wide grid connected applications. Regarding the WT, advantages such as reducing the dependence on fossil fuels and enhancing the independence and flexibility of large power grid are the most prominent. Nevertheless, the high volatile nature of wind speed injects much uncertainty in the grid that if not managed optimally can put the analyses far from the reality.the aim of this project is scrutiny and to offer proper ways for renewing distribution networks with envisage the effects of wind power plants and uncertainties related to distribution systems including wind power generating plants output rate and consumers consuming rate and also decrease the incidents of the whole network losses, amount of pollution, voltage refraction and cost extent.to solve this problem we use dual point estimate method.And algorithm used in this paper is reformed bat algorithm, which will be under exact research furthermore the results.

Keywords: order renewal, wind turbines, bat algorithm, outspread production, uncertainty

Procedia PDF Downloads 278