Search results for: network intrusion prevention

Authors: Maryam Heidari, James H. Jones Jr.

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Millions of online posts about different topics and products are shared on popular social media platforms. One use of this content is to provide crowd-sourced information about a specific topic, event or product. However, this use raises an important question: what percentage of information available through these services is trustworthy? In particular, might some of this information be generated by a machine, i.e., a bot, instead of a human? Bots can be, and often are, purposely designed to generate enough volume to skew an apparent trend or position on a topic, yet the consumer of such content cannot easily distinguish a bot post from a human post. In this paper, we introduce a model for social media bot detection which uses Bidirectional Encoder Representations from Transformers (Google Bert) for sentiment classification of tweets to identify topic-independent features. Our use of a Natural Language Processing approach to derive topic-independent features for our new bot detection model distinguishes this work from previous bot detection models. We achieve 94\% accuracy classifying the contents of data as generated by a bot or a human, where the most accurate prior work achieved accuracy of 92\%.

Keywords: bot detection, natural language processing, neural network, social media

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Authors: Megha Gupta, Nupur Prakash

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Identification of plant diseases has been performed using machine learning and deep learning models on the datasets containing images of healthy and diseased plant leaves. The current study carries out an evaluation of some of the deep learning models based on convolutional neural network (CNN) architectures for identification of plant diseases. For this purpose, the publicly available New Plant Diseases Dataset, an augmented version of PlantVillage dataset, available on Kaggle platform, containing 87,900 images has been used. The dataset contained images of 26 diseases of 14 different plants and images of 12 healthy plants. The CNN models selected for the study presented in this paper are AlexNet, ZFNet, VGGNet (four models), GoogLeNet, and ResNet (three models). The selected models are trained using PyTorch, an open-source machine learning library, on Google Colaboratory. A comparative study has been carried out to analyze the high degree of accuracy achieved using these models. The highest test accuracy and F1-score of 99.59% and 0.996, respectively, were achieved by using GoogLeNet with Mini-batch momentum based gradient descent learning algorithm.

Keywords: comparative analysis, convolutional neural networks, deep learning, plant disease identification

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Authors: Vito Telesca, Giuseppina A. Giorgio, M. Ragosta

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The effects of extreme heat events are increasing in recent years. Humans are forced to adjust themselves to adverse climatic conditions. The impact of weather on human health has become public health significance, especially in light of climate change and rising frequency of devasting weather events (e.g., heat waves and floods). The interest of scientific community is widely known. In particular, the associations between temperature and mortality are well studied. Weather conditions are natural factors that affect the human organism. Recent works show that the temperature threshold at which an impact is seen varies by geographic area and season. These results suggest heat warning criteria should consider local thresholds to account for acclimation to local climatology as well as the seasonal timing of a forecasted heat wave. Therefore, it is very important the problem called ‘local warming’. This is preventable with adequate warning tools and effective emergency planning. Since climate change has the potential to increase the frequency of these types of events, improved heat warning systems are urgently needed. This would require a better knowledge of the full impact of extreme heat on morbidity and mortality. The majority of researchers who analyze the associations between human health and weather variables, investigate the effect of air temperature and bioclimatic indices. These indices combine air temperature, relative humidity, and wind speed and are very important to determine the human thermal comfort. Health impact studies of weather events showed that the prevention is an essential element to dramatically reduce the impact of heat waves. The summer Italian of 2012 was characterized with high average temperatures (con un +2.3°C in reference to the period 1971-2000), enough to be considered as the second hottest summer since 1800. Italy was the first among countries in Europe which adopted tools for to predict these phenomena with 72 hours in advance (Heat Health Watch Warning System - HHWWS). Furthermore, in Italy heat alert criteria relies on the different Indexes, for example Apparent temperature, Scharlau index, Thermohygrometric Index, etc. This study examines the importance of developing public health policies that protect the most vulnerable people (such as the elderly) to extreme temperatures, highlighting the factors that confer susceptibility.

Keywords: heat waves, Italy, local warming, temperature

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Authors: Hao Mi, Ming Yang, Tian-yue Yang

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Ultrasonic infrared nondestructive testing is a kind of testing method with high speed, accuracy and localization. However, there are still some problems, such as the detection requires manual real-time field judgment, the methods of result storage and viewing are still primitive. An intelligent non-destructive detection system based on embedded linux is put forward in this paper. The hardware part of the detection system is based on the ARM (Advanced Reduced Instruction Set Computer Machine) core and an embedded linux system is built to realize image processing and defect detection of thermal images. The CLAHE algorithm and the Butterworth filter are used to process the thermal image, and then the boa server and CGI (Common Gateway Interface) technology are used to transmit the test results to the display terminal through the network for real-time monitoring and remote monitoring. The system also liberates labor and eliminates the obstacle of manual judgment. According to the experiment result, the system provides a convenient and quick solution for industrial non-destructive testing.

Keywords: remote monitoring, non-destructive testing, embedded Linux system, image processing

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Authors: Raquel Rossini, Edelvais Caldeira

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The aim of this work is to investigate the motivation for the metaphorical uses of two verb combinations: come out and take over. Drawing from cognitive semantics theories, image schemas and metaphors, it was attempted to demonstrate that: a) the metaphorical senses of both 'come out' and 'take over' extend from both the verbs and the particles central (spatial) senses in such verb combinations; and b) the particles 'out' and 'over' also contribute to the whole meaning of the verb combinations. In order to do so, a random selection of 579 concordance lines for come out and 1,412 for take over was obtained from the Corpus of Contemporary American English – COCA. One of the main procedures adopted in the present work was the establishment of verb and particle central senses. As per the research questions addressed in this study, they are as follows: a) how does the identification of trajector and landmark help reveal patterns that contribute for the identification of the semantic network of these two verb combinations?; b) what is the relationship between the schematic structures attributed to the particles and the metaphorical uses found in empirical data?; and c) what conceptual metaphors underlie the mappings from the source to the target domains? The results demonstrated that not only the lexical verbs come and take, but also the particles out and over play an important whole in the different meanings of come out and take over. Besides, image schemas and conceptual metaphors were found to be helpful in order to establish the motivations for the metaphorical uses of these linguistic structures.

Keywords: cognitive linguistics, English syntax, multi-word verbs, prepositions

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Authors: Nwachukwu M. A., Nwachukwu J. I., Anyanwu J., Emeka U., Okorondu J., Acholonu C.

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Actualization of the 17 sustainable development goals (SDGs) conceived by the United Nations in 2015 is a global challenge that may not be feasible in sub-Saharan Africa by the year 2030, except universities play a committed role. This is because; there is a need to educate the people about the concepts of sustainability and sustainable development in the region to make the desired change. Here is a sensitization paper with a model of intervention and curricular planning to allow advancement in understanding and knowledge of SDGs. This Model Center for Sustainability Studies (MCSS) will enable partnerships with institutions in Africa and in advanced nations, thereby creating a global network for sustainability studies not found in sub-Saharan Africa. MCSS will train and certify public servants, government agencies, policymakers, entrepreneurs and personnel from organizations, and students on aspects of the SDGs and sustainability science. There is a need to add sustainability knowledge into environmental education and make environmental education a compulsory course in higher institutions and a secondary school certificate exam subject in sub-Saharan Africa. MCSS has 11 training modules that can be replicated anywhere in the world.

Keywords: sustainability, higher institutions, training, SDGs, collaboration, sub-Saharan Africa

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Authors: Mustafa Sahin, İlkay Yavrucuk

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This paper presents a wind turbine envelope protection control algorithm that protects Variable Speed Variable Pitch (VSVP) wind turbines from damage during operation throughout their below and above rated regions, i.e. from cut-in to cut-out wind speed. The proposed approach uses a neural network that can adapt to turbines and their operating points. An algorithm monitors instantaneous wind and turbine states, predicts a wind speed that would push the turbine to a pre-defined envelope limit and, when necessary, realizes an avoidance action. Simulations are realized using the MS Bladed Wind Turbine Simulation Model for the NREL 5 MW wind turbine equipped with baseline controllers. In all simulations, through the proposed algorithm, it is observed that the turbine operates safely within the allowable limit throughout the below and above rated regions. Two example cases, adaptations to turbine operating points for the below and above rated regions and protections are investigated in simulations to show the capability of the proposed envelope protection system (EPS) algorithm, which reduces excessive wind turbine loads and expectedly increases the turbine service life.

Keywords: adaptive envelope protection control, limit detection and avoidance, neural networks, ultimate load reduction, wind turbine power control

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Authors: Aodai Abdul-Illah Ismail

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Even though the dramatic progress that has been made in traffic safety, but still millions of peoples get killed or injured as a result of traffic crashes, besides the huge amount of economic losses due to these crashes. So traffic safety continues to be one of the most important serious issues worldwide, and it affects everyone who uses the road network system, whether you drive, walk, cycle, or push a pram. One of the most important sides that offers promise for further progress in relation to traffic safety is related to risky groups (special population groups) who may have higher potential to be involved in accidents. Traffic safety needs of risky groups are different from each other and also from the average population. Due to the various limitations between these special groups from each other and from the average population, it is not possible to address all the issues –at the same time- raising the importance ranking among the other safety issues. This paper explains a procedure used to identify the most critical traffic safety issues of five risky groups, which include younger, older and female drivers, people with disabilities and school aged children. Multi criteria used in selecting the critical issues because the single criteria is not sufficient. Highway safety professionals were surveyed to obtain the ranking of importance among the risky groups and then to develop the final ranking among issues by applying weight for each of the criteria.

Keywords: traffic safety, risky groups, old drivers, young drivers

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Authors: M. S. Mahmoud

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This study was conducted to evaluate the manganese removal from aqueous solution using Banana peels activated carbon (BPAC). Batch experiments have been carried out to determine the influence of parameters such as pH, biosorbent dose, initial metal ion concentrations and contact times on the biosorption process. From these investigations, a significant increase in percentage removal of manganese 97.4 % is observed at pH value 5.0, biosorbent dose 0.8 g, initial concentration 20 ppm, temperature 25 ± 2 °C, stirring rate 200 rpm and contact time 2 h. The equilibrium concentration and the adsorption capacity at equilibrium of the experimental results were fitted to the Langmuir and Freundlich isotherm models; the Langmuir isotherm was found to well represent the measured adsorption data implying BPAC had heterogeneous surface. A raw groundwater samples were collected from Baharmos groundwater treatment plant network at Embaba and Manshiet Elkanater City/District-Giza, Egypt, for treatment at the best conditions that reached at first phase by BPAC. The treatment with BPAC could reduce iron and manganese value of raw groundwater by 91.4 % and 97.1 %, respectively and the effect of the treatment process on the microbiological properties of groundwater sample showed decrease of total bacterial count either at 22°C or at 37°C to 85.7 % and 82.4 %, respectively. Also, BPAC was characterized using SEM and FTIR spectroscopy.

Keywords: biosorption, banana peels, isothermal models, manganese

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Authors: Saeed A. AlGhamdi, Nabil Habli, Vinoj Somasanran

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This paper presents an application of 230 kV Variable Shunt Reactors (VSR) used to compensate reactive power of dual 90 KM subsea cables. VSR integrates an on-load tap changer (OLTC) that adjusts reactive power compensation to maintain acceptable bus voltages under variable load profile and network configuration. An automatic voltage regulator (AVR) or a power management system (PMS) that allows VSR rating to be changed in discrete steps typically controls the OLTC. Typical regulation range start as minimum as 20% up to 100% and are available for systems up to 550kV. The regulation speed is normally in the order of seconds per step and approximately a minute from maximum to minimum rating. VSR can be bus or line connected depending on line/cable length and compensation requirements. The flexible reactive compensation ranges achieved by recent VSR technologies have enabled newer facilities design to deploy line connected VSR through either disconnect switches, which saves space and cost, or through circuit breakers. Lines with VSR are typically energized with lower taps (reduced reactive compensation) to minimize or remove the presence of delayed zero crossing.

Keywords: power management, reactive power, subsea cables, variable shunt reactors

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Authors: Ram Sharan Mehta, Sanjeev Sharma

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Introduction: Haemodialysis (HD) is a mechanical process of removing waste products from the blood and replacing essential substances in patients with renal failure. First artificial kidney developed in Netherlands in 1943 AD First successful treatment of CRF reported in 1960AD, life-saving treatment begins for CRF in 1972 AD. In 1973 AD Medicare took over financial responsibility for many clients and after that method become popular. BP Koirala institute of health science is the only center outside the Kathmandu, where HD service is available. In BPKIHS PD started in Jan.1998, HD started in August 2002 till September 2003 about 278 patients received HD. Day by day the number of HD patients is increasing in BPKIHS as with institutional growth. No such type of study was conducted in past hence there is lack of valid & reliable baseline data. Hence, the investigators were interested to conduct the study on " Profile of the Renal Failure patients under Haemodialysis at B.P. Koirala Institute of Health Sciences Nepal". Objectives: The objectives of the study were: to find out the Socio-demographic characteristics of the patients, to explore the knowledge of the patients regarding disease process and Haemodialysis and to identify the problems encountered by the patients. Methods: It is a hospital-based exploratory study. The population of the study was the clients under HD and the sampling method was purposive. Fifty-four patients undergone HD during the period of 17 July 2012 to 16 July 2013 of complete one year were included in the study. Structured interview schedule was used for collect data after obtaining validity and reliability. Results: Total 54 subjects had undergone for HD, having age range of 5-75 years and majority of them were male (74%) and Hindu (93 %). Thirty-one percent illiterate, 28% had agriculture their occupation, 80% of them were from very poor community, and about 30% subjects were unaware about the disease they suffering. Majority of subjects reported that they had no complications during dialysis (61%), where as 20% reported nausea and vomiting, 9% Hypotension, 4% headache and 2%chest pain during dialysis. Conclusions: CRF leading to HD is a long battle for patients, required to make major and continuous adjustment, both physiologically and psychologically. The study suggests that non-compliance with HD regimen were common. The socio-demographic and knowledge profile will help in the management and early prevention of disease and evaluate aspects that will influence care and patients can select mode of treatment themselves properly.

Keywords: profile, haemodialysis, Nepal, patients, treatment

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Authors: Shokoufeh Abrisham, Seyed Mahmoud Hossieni, Elham Pishbin

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In this paper, a hybrid method based on the fault tree analysis (FTA) and Bayesian networks (BNs) are employed to evaluate the team performance quality of advanced cardiac life support (ACLS) procedures in emergency department. According to American Heart Association (AHA) guidelines, a category relying on staff action leading to clinical incidents and also some discussions with emergency medicine experts, a fault tree model for ACLS procedure is obtained based on the human performance. The obtained FTA model is converted into BNs, and some different scenarios are defined to demonstrate the efficiency and flexibility of the presented model of BNs. Also, a sensitivity analysis is conducted to indicate the effects of team leader presence and uncertainty knowledge of experts on the quality of ACLS. The proposed model based on BNs shows that how the results of risk analysis can be closed to reality comparing to the obtained results based on only FTA in medical procedures.

Keywords: advanced cardiac life support, fault tree analysis, Bayesian belief networks, numan performance, healthcare systems

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Authors: A. Ham, A. Kuckert-Wostheinrich

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Demographic changes, like the ageing population in European countries and shortage of nursing staff, the increasing number of people with severe cognitive impairment, and elderly socially isolated people raise important questions about who will provide long-term care for ageing citizens. Due to the so-called refugee crisis in 2015, some health care institutions for ageing citizens in Europe invited first generation immigrants to start a nursing career and providing them language skills, nursing training, and internships. The aim of this ethnographic research was to explore the social processes affecting workforce integration and how newcomers enact good care in ageing citizens in a German nursing home. By ethnographic fieldwork, 200 hours of participant observations, 25 in-depth interviews with immigrants and established staff, 2 focus groups with 6 immigrants, and 6 established staff members, data were analysed. The health care institution provided the newcomers a nursing program on psychogeriatric theory and nursing skills in the psychogeriatric field and professional oriented language skills. Courses of health prevention and theater plays accompanied the training. The knowledge learned in education could be applied in internships on the wards. Additionally, diversity and inclusivity courses were given to established personal for cultural awareness and sensitivity. They learned to develop a collegial attitude of respect and appreciation, regardless of gender, nationality, ethnicity, religion or belief, age sexual orientation, or disability and identity. The qualitative data has shown that social processes affected workforce integration, like organizational constraints, staff shortages, and a demanding workload. However, zooming in on the interactions between newcomers and residents, we noticed how they tinkered to enact good care by embodied caring, playing games, singing and dancing. By situational acting and practical wisdom in nursing care, the newcomers could meet the needs of ageing residents. Thus, when health care institutions open up nursing programs for newcomers with refugees’ backgrounds and focus on talent instead of shortcomings, we might as well stimulate the unknown competencies, attitudes, skills, and expertise of newcomers and create excellent nurses for excellent care.

Keywords: established staff, Germany, nursing, refugees

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Authors: Eun-Young Kwon, Myung-Sook Choi, Su-Jung Cho, Ji-Young Choi, So Young Kim, Youngji Han

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Overweight and obesity have been linked to a low-grade chronic inflammatory response and an increased risk of developing metabolic syndrome including insulin resistance, type 2 diabetes mellitus and certain types of cancers. Luteolin is a dietary flavonoid with anti-inflammatory, anti-oxidant, anti-cancer and anti-diabetic properties. However, little is known about the detailed mechanism associated with the effect of luteolin on inflammation-related obesity and its complications. The aim of the present study was to reveal the anti-diabetic effect of luteolin in diet-induced obesity mice using “transcriptomics” tool. Thirty-nine male C57BL/6J mice (4-week-old) were randomly divided into 3 groups and were fed normal diet, high-fat diet (HFD, 20% fat) and HFD+0.005% (w/w) luteolin for 16 weeks. Luteolin improved insulin resistance, as measured by HOMA-IR and glucose tolerance, along with preservation action of pancreatic β-cells, compared to the HFD group. Luteoiln was significantly decreased the levels of leptin and ghrelin that play a pivotal role in energy balance, and the macrophage low-grade inflammation marker sCD163 (soluble Cd antigen 163) in plasma. Activities of hepatic anti-oxidant enzymes (catalase and glutathione peroxidase) were increased, while the levels of plasma transaminase (GOT and GPT) and oxidative damage markers (hepatic mitochondria H2O2 and TBARS) were markedly decreased by luteolin supplementation. In addition, luteolin increased oxidative capacity and fatty acid utilization by presenting decrease in enzyme activities of citrate synthase, cytochrome C oxidase and β-hydroxyacyl CoA dehydrogenase and UCP3 gene expression compared to high-fat diet. Moreover, our microarray results of muscle also revealed down-regulated gene expressions associated with TCA cycle by HFD were reversed to normal level by luteolin treatment. Taken together, our results indicate that luteolin is one of bioactive components for improving insulin resistance by increasing oxidative capacity, modulating anti-oxidant metabolism and suppressing inflammatory signaling cascades in diet-induced obese mice. These results provide possible therapeutic targets for prevention and treatment of diet-induced obesity and its complications.

Keywords: anti-oxidant metabolism, diabetes, luteolin, oxidative capacity

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Authors: Nishank Raisinghani

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Efficiently predicting drug response remains a challenge in the realm of drug discovery. To address this issue, we propose four model architectures that combine graphical representation with varying positions of multiheaded self-attention mechanisms. By leveraging two types of multi-omics data, transcriptomics and genomics, we create a comprehensive representation of target cells and enable drug response prediction in precision medicine. A majority of our architectures utilize multiple transformer models, one with a graph attention mechanism and the other with a multiheaded self-attention mechanism, to generate latent representations of both drug and omics data, respectively. Our model architectures apply an attention mechanism to both drug and multiomics data, with the goal of procuring more comprehensive latent representations. The latent representations are then concatenated and input into a fully connected network to predict the IC-50 score, a measure of cell drug response. We experiment with all four of these architectures and extract results from all of them. Our study greatly contributes to the future of drug discovery and precision medicine by looking to optimize the time and accuracy of drug response prediction.

Keywords: drug discovery, transformers, graph neural networks, multiomics

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Authors: Sophia Barquete, Adriana H. Trevisan, Janaina Mascarenhas

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The circular economy (CE) aims at the cycling of resources through restorative and regenerative strategies. To achieve circularity, coordination of several actors who have different responsibilities is necessary. The interaction among multiple actors allows the connection between the CE and business ecosystem research fields. Although fundamental, the relationships between actors within an ecosystem to foster circularity are not deeply explored in the literature. The objective of this study was to identify the possibilities of cooperation, competition, or even coopetition among the members of business ecosystems that adopt circular strategies. In particular, the motivations that make these actors interact to achieve a circular economy were investigated. A systematic literature review was adopted to select business ecosystem cases that adopt circular strategies. As a result, several motivations were identified for actors to engage in relationships within ecosystems, such as sharing knowledge and infrastructure, developing products with a circular design, promoting reverse logistics, among others. The results suggest that partnerships between actors are, in fact, important for the implementation of circular strategies. In order to achieve a complete and circular solution, actors must be able to clearly understand their roles and relationships within the network so that they can establish new partnerships or reframe those already established.

Keywords: business ecosystem, circular economy, cooperation, coopetition, competition

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Authors: Jana Heimel, Franziska Drescher, Lauren Ugur, Graciela Kuchle

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Sharing platforms are a widely investigated field. However, there is a research gap with a lack of focus on ‘real’ (non-profit-orientated) sharing platforms. The research project addresses this gap by conducting an empirical study on a private peer-to-peer (P2P) network to investigate cooperative behavior from a socio-psychological perspective. In recent years the conversion from possession to accessing is increasingly influencing different sectors, particularly the traveling industry. The number of people participating in hospitality exchange platforms like Airbnb, Couchsurfing, and Warmshowers (WS) is rapidly growing. WS is an increasingly popular online community that is linking cycling tourists and locals. It builds on the idea of the “sharing economy” as a not-for-profit hospitality network for bicycle tourists. Hosts not only provide a sleeping berth and warm shower free of charge but also offer additional services to their guests, such as cooking and washing clothes for them. According to previous studies, they are motivated by the idea of promoting cultural experience and forming new friendships. Trust and reciprocity are supposed to play major roles in the success of such platforms. The objective of this research project is to analyze the reciprocity behavior within the WS community. Reciprocity is the act of giving and taking among each other. Individuals feel obligated to return a favor and often expect to increase their own chances of receiving future benefits for themselves. Consequently, the drivers that incite giving and taking, as well as the motivation for hosts and guests, are examined. Thus, the project investigates a particular tourism offer that contributes to sustainable tourism by analyzing P2P resp. cyclist-to-cyclist, C2C) tourism. C2C tourism is characterized by special hospitality and generosity. To find out what motivations drive the hosts and which determinants drive the sharing cycling economy, an empirical study has been conducted globally through an online survey. The data was gathered through the WS community and comprised responses from more than 10,000 cyclists around the globe. Next to general information mostly comprising quantitative data on bicycle tourism (year/tour distance, duration and budget), qualitative information on traveling with WS as well as hosting was collected. The most important motivations for a traveler is to explore the local culture, to save money, and to make friends. The main reasons to host a guest are to promote the use of bicycles and to make friends, but also to give back and pay forward. WS members prefer to stay with/host cyclists. The results indicate that C2C tourists share homogenous characteristics and a similar philosophy, which is crucial for building mutual trust. Members of WS are generally extremely trustful. The study promotes an ecological form of tourism by combining sustainability, regionality, health, experience and the local communities' cultures. The empirical evidence found and analyzed, despite evident limitations, enabled us to shed light, especially on the issue of motivations and social capital, and on the functioning of ‘sharing’ platforms. Final research results are intended to promote C2C tourism around the globe to further replace conventional by sustainable tourism.

Keywords: bicycle tourism, homogeneity, reciprocity, sharing economy, trust

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Authors: Colette Faucher

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In modern asymmetric conflicts, the Armed Forces generally have to intervene in countries where the internal peace is in danger. They must make the local population an ally in order to be able to deploy the necessary military actions with its support. For this purpose, psychological operations (PSYOPs) are used to shape people’s behaviors and emotions by the modification of their attitudes in acting on their perceptions. PSYOPs aim at elaborating and spreading a message that must be read, listened to and/or looked at, then understood by the info-targets in order to get from them the desired behavior. A message can generate in the info-targets, reasoned thoughts, spontaneous emotions or reflex behaviors, this effect partly depending on the means of conveyance used to spread this message. In this paper, we focus on psychological operations that generate emotions. We present a method based on the Intergroup Emotion Theory, that determines, from the characteristics of the conveyed message and of the people from the population directly reached by the means of conveyance (direct info-targets), the emotion likely to be triggered in them and we simulate the propagation of the effects of such a message on indirect info-targets that are connected to them through the social networks that structure the population.

Keywords: military psychological operations, social identity, social network, emotion propagation

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Authors: Juan Sebastián Hernández

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The Nevado del Ruíz volcano, located between the limits of the Departments of Caldas and Tolima in Colombia, presented an unstable behaviour in the course of the year 2020, this volcanic activity led to secondary effects on the crust, which is why the prediction of deformations becomes the task of geoscientists. In the course of this article, the use of tropospheric variables such as evapotranspiration, UV aerosol index, carbon monoxide, nitrogen dioxide, methane, surface temperature, among others, is used to train a set of neural networks that can predict the behaviour of the resulting phase of an unrolled interferogram with the DInSAR technique, whose main objective is to identify and characterise the behaviour of the crust based on the environmental conditions. For this purpose, variables were collected, a generalised linear model was created, and a set of neural networks was created. After the training of the network, validation was carried out with the test data, giving an MSE of 0.17598 and an associated r-squared of approximately 0.88454. The resulting model provided a dataset with good thematic accuracy, reflecting the behaviour of the volcano in 2020, given a set of environmental characteristics.

Keywords: crustal deformation, Tropomi, neural networks (ANN), volcanic activity, DInSAR

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Authors: O. C. Abbah, O. Obidoa, J. Omale

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Prostate tumor is fast becoming a leading cause of morbidity and mortality in human male adults, with 50 percent of men aged 50 years and above having histological evidence of the benign tumor. The study was set out to undertake phytochemical screening and proximate analysis of the pulp of A. muricata fruit - soursop; to determine the acute toxicity of the fruit pulp extract and its effect on male albino Wistar rats with concurrent induction of experimental benign prostate hyperplasia (BPH). Eighteen rats (average weight of 100g) were used for the lethality studies and were orally administered graded doses of aqueous extracts of the fruit pulp up to 5000 mg/kg body weight. Twenty five rats weighing 150-200g were divided into five groups of five rats each for the tumor studies. The groups included four controls – Hormone control, HC, which took Testosterone, T; and Estradiol, E2 – only, in olive oil as vehicle; Vehicle control, VC; Soursop control, SC, which received the extract only; VS, Vehicle and Soursop – and the Test group, TG (500mg/kg b.w.). All rats were dosed orally. Tumor was induced with exogenous Testosterone propionate: Estradiol valerate at 300µg: 80µg/kg b.w. (respectively) in olive oil, administered subcutaneously in the inguinal region of the rats on alternate days for 21 days. Administration of the fruit pulp at graded doses up to 5000mg/kg resulted in no lethality even after 72 hours. Results from tumor studies revealed that the administration of the fruit extracts significantly (p < 0.05) reduced the relative prostate weight of the TG compared with the HC, with values of 006±0.001 and 0.010±0.003 respectively. Treatment with vehicle, soursop and vehicle with soursop caused no significant (p>0.05) change in prostate size, with their respective relative prostate weights being 0.002±0.001, 0.004±0.002 and 0.002±0.001 compared with TG. Also, treatment with A. muricata fruit extract significantly decreased (p < 0.05) serum prostate specific antigen, PSA, in TG compared with HC, with values 0.055±0.017 and 0.194±0.068 ng/ml respectively. Furthermore, A. muricata administration displayed Testosterone boosting, Estradiol lowering and consequently testosterone-estradiol ratio increasing potential at the end of the 21 days. The preventive property of soursop against experimental BPH was corroborated by histological evidence in this study. The study concludes that A. muricata fruit holds a great potential for benign prostate tumor prevention and, possibly, management.

Keywords: annona muricata, benign prostate tumor, hormone, preventive potential, soursop

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Authors: Haiyan Wu, Ying Liu, Shaoyun Shi

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Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. Besides, predictions by neural networks are difficult to explain, which is vital in authorship attribution tasks. In this paper, we not only utilize the statistical style and content features but also take advantage of both syntactic and semantic features. Different from an end-to-end neural model, feature selection and prediction are two steps in our method. An attentive n-gram network is utilized to select useful features, and logistic regression is applied to give prediction and understandable representation of writing style. Experiments show that our extracted features can improve the state-of-the-art methods on three benchmark datasets.

Keywords: authorship attribution, attention mechanism, syntactic feature, feature extraction

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Authors: Graciela Soares Marcelli, Elison Eduardo Jardim Bierhals, Luciane Teresa Salvi, Claudineia Brazil, Rafael Haag

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The sun is a source of renewable energy, and its use as both a source of heat and light is one of the most promising energy alternatives for the future. To measure the thermal or photovoltaic systems a solar irradiation database is necessary. Brazil still has a reduced number of meteorological stations that provide frequency tests, as an alternative to the radio data platform, with reanalysis systems, quite significant. ERA-Interim is a global fire reanalysis by the European Center for Medium-Range Weather Forecasts (ECMWF). The data assimilation system used for the production of ERA-Interim is based on a 2006 version of the IFS (Cy31r2). The system includes a 4-dimensional variable analysis (4D-Var) with a 12-hour analysis window. The spatial resolution of the dataset is approximately 80 km at 60 vertical levels from the surface to 0.1 hPa. This work aims to make a comparative analysis between the ERA-Interim data and the data observed in the Solarimmetric Atlas of the State of Rio Grande do Sul, to verify its applicability in the absence of an observed data network. The analysis of the results obtained for a study region as an alternative to the energy potential of a given region.

Keywords: energy potential, reanalyses, renewable energy, solar radiation

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Authors: Tshegofatso Galekgathege

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Poor water quality is of great concern to human health as it can cause disease outbreaks. A standard practice today, in developed countries, is that people should be provided with safe-reliable drinking water, as safe drinking water is recognized as a basic human right and a cost effective measure of reducing diseases. Over 1.1 billion people worldwide lack access to a safe water supply and as a result, the majority are forced to use polluted surface or groundwater. It is widely accepted that our water supply systems are susceptible to the intentional or accidental contamination .Water quality degradation may occur anywhere in the path that water takes from the water source to the consumer. Chlorine is believed to be an effective tool in disinfecting water, but its concentration may decrease with time due to consumption by chemical reactions. This shows that we are at the risk of being infected by waterborne diseases if chlorine in water falls below the required level of 0.2-1mg/liter which should be maintained in water and some contaminants enter into the water distribution system. It is believed that the lack of adequate sanitation also contributes to the contamination of water globally. This study therefore, assesses drinking water contamination from the source to the consumer by identifying the point vulnerable to contamination from the source to the consumer in the study area .To identify the point vulnerable to contamination, water was sampled monthly from boreholes, water treatment plant, water distribution system (WDS), service reservoirs and consumer taps from all the twenty (20) villages of Palapye region. Sampled water was then taken to the laboratory for testing and analysis of microbiological and chemical parameters. Water quality analysis were then compared with Botswana drinking water quality standards (BOS32:2009) to see if they comply. Major sources of water contamination identified during site visits were the livestock which were found drinking stagnant water from leaking pipes in 90 percent of the villages. Soils structure around the area was negatively affected because of livestock movement even vegetation in the area. In conclusion microbiological parameters of water in the study area do not comply with drinking water standards, some microbiological parameters in water indicated that livestock do not only affect land degradation but also the quality of water. Chlorine has been applied to water over some years but it is not effective enough thus preventative measures have to be developed, to prevent contaminants from reaching water. Remember: Prevention is better than cure.

Keywords: land degradation, leaking systems, livestock, water contamination

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Authors: Abdulfetah Shobole, Arif Karakas, Ugur Savas Selamogullari, Mustafa Baysal

Abstract:

The concern of reducing emissions of Co2 from the fossil fuel generating units and using renewable energy sources increased in our world. Due this fact the integration ratio of wind farms to grid reached 20-30% in some part of our world. With increased integration of large MW scaled wind farms to the electric grid, the stability of the electrical system is a great concern. Thus, operators of power systems usually deman the wind turbine generators to obey the same rules as other traditional kinds of generation, such as thermal and hydro, i.e. not affect the grid stability. FACTS devices such as SVC or STATCOM are mostly installed close to the connection point of the wind farm to the grid in order to increase the stability especially during faulty conditions. In this paper wind farm with DFIG turbine type and STATCOM are dynamically modeled and simulated under three phase short circuit fault condition. The dynamic modeling is done by DigSILENT PowerFactory for the wind farm, STATCOM and the network. The simulation results show improvement of system stability near to the connection point of the STATCOM.

Keywords: DFIG wind turbine, statcom, dynamic modeling, digsilent

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Authors: Giselda Cristina Ferreira, Carlos Alberto Kamienski, Ana Lígia Scott

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The COVID-19 pandemic has enhanced the anti-vaccination movement in Brazil, supported by unscientific theories and false news and the possibility of wide communication through social networks such as Twitter, Facebook, and YouTube. The World Health Organization (WHO) classified the large volume of information on the subject against COVID-19 as an Infodemic. In this paper, we present a protocol to identify polarizing users (called polarizers) and study the profiles of Brazilian polarizers on Twitter (renamed to X some weeks ago). We analyzed polarizing interactions on Twitter (in Portuguese) to identify the main polarizers and how the conflicts they caused influenced the COVID-19 vaccination rate throughout the pandemic. This protocol uses data from this social network, graph theory, Java, and R-studio scripts to model and analyze the data. The information about the vaccination rate was obtained in a public database for the government called OpenDataSus. The results present the profiles of Twitter’s Polarizer (political position, gender, professional activity, immunization opinions). We observed that social and political events influenced the participation of these different profiles in conflicts and the vaccination rate.

Keywords: Twitter, polarization, vaccine, Brazil

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Authors: Jayanta Kumar Ghosh, Swastik Sunil Goriwale, Himangshu Sarkar

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Groundwater is one of the most valuable natural resources that society consumes for its domestic, industrial, and agricultural water supply. Its bulk and indiscriminate consumption affects the groundwater resource. Often, it has been found that the groundwater recharge rate is much lower than its demand. Thus, to maintain water and food security, it is necessary to monitor and management of groundwater storage. However, it is challenging to estimate groundwater storage (GWS) by making use of existing hydrological models. To overcome the difficulties, machine learning (ML) models are being introduced for the evaluation of groundwater level (GWL). Thus, the objective of this research work is to develop an ML-based model for the prediction of GWL. This objective has been realized through the development of an artificial neural network (ANN) model based on hydro-gravimetry. The model has been developed using training samples from field observations spread over 8 months. The developed model has been tested for the prediction of GWL in an observation well. The root means square error (RMSE) for the test samples has been found to be 0.390 meters. Thus, it can be concluded that the hydro-gravimetric-based ANN model can be used for the prediction of GWL. However, to improve the accuracy, more hydro-gravimetric parameter/s may be considered and tested in future.

Keywords: machine learning, hydro-gravimetry, ground water level, predictive model

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Authors: Yassamina Berkane, Leila Kloul, Yoann Demoli

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Quality of life, environmental impact, congestion of mobility means, and infrastructures remain significant challenges for urban mobility. Solutions like car sharing, spatial redesign, eCommerce, and autonomous vehicles will likely increase the unit veh-km and the density of cars in urban traffic, thus reducing congestion. However, the impact of such solutions is not clear for researchers. Congestion arises from growing populations that must travel greater distances to arrive at similar locations (e.g., workplaces, schools) during the same time frame (e.g., rush hours). This paper first reviews the research and application cases of urban congestion methods through recent years. Rethinking the question of time, it then investigates people’s willingness and flexibility to adapt their arrival and departure times from workplaces. We use neural networks and methods of supervised learning to apply a new methodology for predicting peoples' intentions from their responses in a questionnaire. We created and distributed a questionnaire to more than 50 companies in the Paris suburb. Obtained results illustrate that our methodology can predict peoples' intentions to reschedule their activities (work, study, commerce, etc.).

Keywords: urban mobility, decongestion, machine learning, neural network

Procedia PDF Downloads 181

Authors: Ertuğrul Yılmaz

Abstract:

One of the most important occupational groups in the food chain from farm to fork is a veterinary medicine. This occupational group, which has important duties in the prevention of many zoonotic diseases and in public health, takes place in many critical control points from soil to our kitchen. It has important duties from mycotoxins transmitted from the soil to slaughterhouses or milk processing facilities. Starting from the soil, which constitutes 70% of mycotoxin contamination, up to the TMR made from raw materials obtained from the soil, there are all critical control points from feeding to slaughterhouses and milk production enterprises. We can take the precaution of mycotoxins such as Aflatoxin B1, Ochratoxin, Zearalenone, and Fumonisin, which we encounter on farms while in the field. It has been reported that aflatoxin B1 is a casenerogen and passes into milk in studies. It is likely that many mycotoxins pose significant threats to public health and will turn out to be even more dangerous over time. Even raw material storage and TMR preparation are very important for public health. The danger of fumonisin accumulating in the liver will be understood over time. Zoonotic diseases are also explained with examples. In this study, how important veterinarians are in terms of public health is explained with examples. In the two-year mycotoxin screenings, fumonisin mycotoxin was found to be very high in corn and corn by-products, and it was determined that it accumulated in the liver for a long time and remained cornic in animals. It has been determined that mycotoxins are present in all livestock feeds, poultry feeds, and raw materials, not alone, but in double-triple form. Starting from the end, mycotoxin scans should be carried out from feed to raw materials and from raw materials to soil. In this way, we prevent the transmission of mycotoxins to animals and from animals to humans. Liver protectors such as toxin binders, beta-glucan, mannan oligosaccharides, activated carbon, prebiotics, and silymarin were used in certain proportions in the total mixed ratio, and positive results were obtained. Humidity and temperature controls of raw material silos were made at certain intervals. Necropsy was performed on animals that died as a result of mycotoxicosis, and macroscopic photographs were taken of the organs. We have determined that the mycotoxin screening in experimental animals and the feeds made without detecting the presence and amount of bacterial factors affect the results of the project to be made. For this, a series of precautionary plans have been created, starting from the production processes.

Keywords: mycotoxins, feed safety, processes, public health

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Authors: Hung Nguyen

Abstract:

With the rise of global supply chain network, the choice of supply chain orientation is critical. The alignment between cultural similarity and supply chain information exchange could help identify appropriate supply chain orientations, which would differentiate the stronger competitors and performers from the weaker ones. Through developing a taxonomy, this study examined whether the choices of action programs and manufacturing performance differ depending on the levels of attainment cultural similarity and information exchange. This study employed statistical tests on a large-scale dataset consisting of 680 manufacturing plants from various cultures and industries. Firms need to align cultural practices with the level of information exchange in order to achieve good overall business performance. There appeared to be consistent three major orientations: the Proactive, the Initiative and the Reactive. Firms are experiencing higher payoffs from various improvements are the ones successful alignment in both information exchange and cultural similarity The findings provide step-by-step decision making for supply chain information exchange and offer guidance especially for global supply chain managers. In including both cultural similarity and information exchange, this paper adds greater comprehensiveness and richness to the supply chain literature.

Keywords: culture, information exchange, supply chain orientation, similarity

Procedia PDF Downloads 348

Authors: P. S. Jagadeesh Kumar, Yang Yung, Mingmin Pan, Xianpei Li, Wenli Hu

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Glaucoma is impelled by optic nerve mutilation habitually represented as cupping and visual field injury frequently with an arcuate pattern of mid-peripheral loss, subordinate to retinal ganglion cell damage and death. Glaucoma is the second foremost cause of blindness and the chief cause of permanent blindness worldwide. Consequently, all-embracing study into the analysis and empathy of glaucoma is happening to escort deep learning based neural network intrusions to deliberate this substantial optic neuropathy. This paper advances an ophthalmic hashing based supervision of glaucoma and corneal disorders preeminent on deep graphical model. Ophthalmic hashing is a newly proposed method extending the efficacy of visual hash-coding to predict glaucoma corneal disorder matching, which is the faster than the existing methods. Deep graphical model is proficient of learning interior explications of corneal disorders in satisfactory time to solve hard combinatoric incongruities using deep Boltzmann machines.

Keywords: corneal disorders, deep Boltzmann machines, deep graphical model, glaucoma, neural networks, ophthalmic hashing

Procedia PDF Downloads 233