Search results for: network intrusion prevention

Authors: Chao Li, Lei Zhou, Ruiqi Ren, Dan Li, Yali Wang, Daxin Ni, Zijian Feng, Qun Li

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Background: In 2016, yellow fever had been first ever discovered in China, soon after the yellow fever epidemic occurred in Angola. After the discovery, China had promptly made the national protocol of control and prevention and strengthened the surveillance on passenger and vector. In this study, a descriptive analysis was conducted to summarize China’s experiences of response towards this import epidemic, in the hope of providing experiences on prevention and control of yellow fever and other similar imported infectious diseases in the future. Methods: The imported cases were discovered and reported by General Administration of Quality Supervision, Inspection and Quarantine (AQSIQ) and several hospitals. Each clinically diagnosed yellow fever case was confirmed by real-time reverse transcriptase polymerase chain reaction (RT–PCR). The data of the imported yellow fever cases were collected by local Centers for Disease Control and Prevention (CDC) through field investigations soon after they received the reports. Results: A total of 11 imported cases from Angola were reported in China, during Angola’s yellow fever outbreak. Six cases were discovered by the AQSIQ, among which two with mild symptom were initiative declarations at the time of entry. Except for one death, the remaining 10 cases all had recovered after timely and proper treatment. All cases are Chinese, and lived in Luanda, the capital of Angola. 73% were retailers (8/11) from Fuqing city in Fujian province, and the other three were labors send by companies. 10 cases had experiences of medical treatment in Luanda after onset, among which 8 cases visited the same local Chinese medicine hospital (China Railway four Bureau Hospital). Among the 11 cases, only one case had an effective vaccination. The result of emergency surveillance for mosquito density showed that only 14 containers of water were found positive around places of three cases, and the Breteau Index is 15. Conclusions: Effective response was taken to control and prevent the outbreak of yellow fever in China after discovering the imported cases. However, though the similar origin of Chinese in Angola has provided an easy access for disease detection, information sharing, health education and vaccination on yellow fever; these conveniences were overlooked during previous disease prevention methods. Besides, only one case having effective vaccination revealed the inadequate capacity of immunization service in China. These findings will provide suggestions to improve China’s capacity to deal with not only yellow fever but also other similar imported diseases in China.

Keywords: yellow fever, first import, China, suggestion

Procedia PDF Downloads 172

Authors: Adamu Buba

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As West Africa gradually recovers from the cost of more than a decade of civil conflicts in Liberia, Sierra Leone and Côte d’Ivoire, ECOWAS has continued to place prominence on peace consolidation and the prevention of conflict in the West African Sub-Region. Of particular interest is the recent political uprising in Mali and in this regard, ECOWAS has been repositioning their existing machineries to manage this dispensation more effectively by improving their early warning and mediation capabilities. This paper firstly seeks among other things to provide a conceptual understanding of what security sector governance is, and also highlight the overview of ECOWAS and to closely examine the benchmark for assessing progress in the promotion of democracy and political governance within the sub-region as well as conflict prevention techniques. The views and analysis expressed in this paper is based on secondary method of data collection alone and do not in any way represent the views of the institutions or their representatives involved in this assignment in one way or the other.

Keywords: challenges, security, governance, ecowas, response

Procedia PDF Downloads 234

Authors: Waraporn Boonchieng, Ekkarat Boonchieng, Sivaporn Aungwattana, Decha Tamdee, Wongamporn Pinyavong

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Drug addiction represents one of the most important public health issues in both developed and developing countries. The purpose of this study was to develop a drug abuse health information in a community in Northern Thailand using developmental research design. The developmental researchers performed four phases to develop drug abuse health information, including 1) synthesizing knowledge related to drug abuse prevention and identifying the components of drug abuse health information; 2) developing the system in mobile application and website; 3) implementing drug abuse health information in the rural community; and 4) evaluating the feasibility of drug abuse health information. Data collection involved both qualitative and quantitative procedures. The qualitative data and quantitative data were analyzed using content analysis and descriptive statistics, respectively. The findings of this study showed that drug abuse health information consisted of five sections, including drug-related prevention knowledge for teens, drug-related knowledge for adults and professionals, the database for drug dependence treatment centers, self-administered questionnaires, and supportive counseling sections. First, in drug-related prevention knowledge for teens, the developmental researchers designed four infographics and animation to provide drug-related prevention knowledge, including types of illegal drugs, causes of drug abuse, consequences of drug abuse, drug abuse diagnosis and treatment, and drug abuse prevention. Second, in drug-related knowledge for adults and professionals, the developmental researchers developed many documents in a form of PDF file to provide drug-related knowledge, including types of illegal drugs, causes of drug abuse, drug abuse prevention, and relapse prevention guideline. Third, database for drug dependence treatment centers included the place, direction map, operation time, and the way for contacting all drug dependence treatment centers in Thailand. Fourth, self-administered questionnaires comprised preventive drugs behavior questionnaire, drug abuse knowledge questionnaire, the stages of change readiness and treatment eagerness to drug use scale, substance use behaviors questionnaire, tobacco use behaviors questionnaire, stress screening, and depression screening. Finally, for supportive counseling, the developmental researchers designed chatting box through which each user could write and send their concerns to counselors individually. Results from evaluation process showed that 651 participants used drug abuse health information via mobile application and website. Among all users, 48.8% were males and 51.2% were females. More than half (55.3%) were 15-20 years old and most of them (88.0%) were Buddhists. Most users reported ever getting knowledge related to drugs (86.1%), and drinking alcohol (94.2%) while some of them (6.9%) reported ever using tobacco. For satisfaction with using the drug abuse health information, more than half of users reflected that the contents of drug abuse health information were interesting (59%), up-to date (61%), and highly useful to their self-study (59%) at high level. In addition, half of them were satisfied with the design in terms of infographics (54%) and animation (51%). Thus, this drug abuse health information can be adopted to explore drug abuse situation and serves as a tool to prevent drug abuse and addiction among Thai community people.

Keywords: drug addiction, health informatics, big data, development research

Procedia PDF Downloads 94

Authors: Somia Bouzid, Messaoud Ramdani

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The success of any diagnosis strategy critically depends on the sensors measuring process variables. This paper presents a detection and diagnosis sensor faults method based on a Bottleneck Neural Network (BNN). The BNN approach is used as a statistical process control tool for drinking water distribution (DWD) systems to detect and isolate the sensor faults. Variable reconstruction approach is very useful for sensor fault isolation, this method is validated in simulation on a nonlinear system: actual drinking water distribution system. Several results are presented.

Keywords: fault detection, localization, PCA, NLPCA, auto-associative neural network

Procedia PDF Downloads 369

Authors: Shogo Kato, Daisuke Okamoto, Satoko Tsuru, Yoshinori Iizuka, Ryoko Shimono

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Healthcare safety has been perceived important. It is essential to prevent troubles in healthcare processes for healthcare safety. Trouble prevention is based on trouble prediction using accumulated knowledge on processes, troubles, and countermeasures. However, information on troubles has not been accumulated in hospitals in the appropriate structure, and it has not been utilized effectively to prevent troubles. In the previous study, though a detailed knowledge acquisition process for trouble prediction was proposed, the knowledgebase for countermeasures was not involved. In this paper, we aim to propose the structure of the knowledgebase for countermeasures in the knowledge acquisition process for trouble prediction in healthcare process. We first design the structure of countermeasures and propose the knowledge representation form on countermeasures. Then, we evaluate the validity of the proposal, by applying it into an actual hospital.

Keywords: trouble prevention, knowledge structure, structured knowledge, reusable knowledge

Procedia PDF Downloads 351

Authors: K. D. Sharma, M. Parida, S. S. Jain, Anju Saini, V. K. Katiyar

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Air quality models form one of the most important components of an urban air quality management plan. Various statistical modeling techniques (regression, multiple regression and time series analysis) have been used to predict air pollution concentrations in the urban environment. These models calculate pollution concentrations due to observed traffic, meteorological and pollution data after an appropriate relationship has been obtained empirically between these parameters. Artificial neural network (ANN) is increasingly used as an alternative tool for modeling the pollutants from vehicular traffic particularly in urban areas. In the present paper, an attempt has been made to model traffic air pollution, specifically CO concentration using neural networks. In case of CO concentration, two scenarios were considered. First, with only classified traffic volume input and the second with both classified traffic volume and meteorological variables. The results showed that CO concentration can be predicted with good accuracy using artificial neural network (ANN).

Keywords: air quality management, artificial neural network, meteorological variables, statistical modeling

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Authors: Fangwei Zhu, Wei Tian, Linzhuo Wang, Miao Yu

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Megaproject has long been a critical issue in project governance, for its low success rate and large impact on society. Although the extant literature on megaproject governance is vast, to our best knowledge, the lacking of a thorough literature review makes it hard for us to gain a holistic view on current scenario of megaproject governance. The study conducts a systematic literature review process to analyze the existing literatures on megaproject governance. The finding indicates that mega project governance needs to be handled at network level and forming a network level governance provides a holistic framework for governing megaproject towards sustainable development of the projects. Theoretical and practical implications, as well as future studies and limitations, were discussed.

Keywords: megaproject, governance, literature review, network

Procedia PDF Downloads 183

Authors: Mokrane Selma

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The shift towards decision making (DM) based on artificial intelligence (AI) techniques will change the way in which consumer markets and our societies function. Through AI, predictive analytics is being used by businesses to identify these patterns and major trends with the objective to improve the DM and influence future business outcomes. This paper proposes an Artificial Neural Network (ANN) approach to predict the success of telemarketing calls for selling bank long-term deposits. To validate the proposed model, we uses the bank marketing data of 41188 phone calls. The ANN attains 98.93% of accuracy which outperforms other conventional classifiers and confirms that it is credible and valuable approach for telemarketing campaign managers.

Keywords: bank telemarketing, prediction, decision making, artificial intelligence, artificial neural network

Procedia PDF Downloads 133

Authors: Nasrin Bakhshizadeh, Ashkan Forootan

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A major problem that affects the quality control of polymer in the industrial polymerization is the lack of suitable on-line measurement tools to evaluate the properties of the polymer such as melt and density indices. Controlling the polymerization in ordinary method is performed manually by taking samples, measuring the quality of polymer in the lab and registry of results. This method is highly time consuming and leads to producing large number of incompatible products. An online application for estimating melt index and density proposed in this study is a neural network based on the input-output data of the polyethylene production plant. Temperature, the level of reactors' bed, the intensity of ethylene mass flow, hydrogen and butene-1, the molar concentration of ethylene, hydrogen and butene-1 are used for the process to establish the neural model. The neural network is taught based on the actual operational data and back-propagation and Levenberg-Marquart techniques. The simulated results indicate that the neural network process model established with three layers (one hidden layer) for forecasting the density and the four layers for the melt index is able to successfully predict those quality properties.

Keywords: polyethylene, polymerization, density, melt index, neural network

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Authors: K. C. Ching

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There is growing public concern over the increasing prevalence of student suicide in Hong Kong. Some identify the problem with insufficient social support, while some attribute it to the vast fluctuations in emotional experience and the hindrances to emotion-regulation, both typical of adolescence and emerging adulthood. This study is thus designed to explore the respective effect of perceived social support and emotion self-confidence, on positive emotions and negative emotions. Fifty-seven Hong Kong higher-education students (17 males, 40 females) aged between 18 and 25 (M = 21.78) responded to an online questionnaire consisted of self-reported measures of perceived social support, emotional self-confidence, positive emotions, and negative emotions. Hierarchical regression analysis revealed that emotional self-confidence positively associated with positive emotions and negatively with negative emotions, while perceived social support positively associated with positive emotions but was not related to negative emotions. Perceived social support and emotional self-confidence both predicted positive emotions, but did not interact to predict any emotional outcome. It is concluded that students’ positive and negative emotional experiences are closely related to their emotion-regulation process. But for social support, its effect is merely protective, meaning that although perceived social support generally promotes positive emotions, it alone does not suffice to alleviate students’ negative emotions. These conclusions carry profound implications to suicide prevention practices, including that most existing suicide prevention campaigns should advance from merely fostering mutual support to directly promoting adaptive coping of emotional negativity.

Keywords: emerging adulthood, emotional self-confidence, hong kong, perceived social support, suicide prevention

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Authors: Grienggrai Rajchakit

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As we all know that coronavirus is announced as a pandemic in the world by WHO. It is speeded all over the world with few days of time. To control this spreading, every citizen maintains social distance and self-preventive measures are the best strategies. As of now, many researchers and scientists are continuing their research in finding out the exact vaccine. The machine learning model finds that the coronavirus disease behaves in an exponential manner. To abolish the consequence of this pandemic, an efficient step should be taken to analyze this disease. In this paper, a recurrent neural network model is chosen to predict the number of active cases in a particular state. To make this prediction of active cases, we need a database. The database of COVID-19 is downloaded from the KAGGLE website and is analyzed by applying a recurrent LSTM neural network with univariant features to predict the number of active cases of patients suffering from the corona virus. The downloaded database is divided into training and testing the chosen neural network model. The model is trained with the training data set and tested with a testing dataset to predict the number of active cases in a particular state; here, we have concentrated on Andhra Pradesh state.

Keywords: COVID-19, coronavirus, KAGGLE, LSTM neural network, machine learning

Procedia PDF Downloads 141

Authors: Mujtaba Roshan, John A. Schormans

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Quality of Experience (QoE) drives churn in the broadband networks industry, and good QoE plays a large part in the retention of customers. QoE is known to be affected by the Quality of Service (QoS) factors packet loss probability (PLP), delay and delay jitter caused by the network. Earlier results have shown that the relationship between these QoS factors and QoE is non-linear, and may vary from application to application. We use the network emulator Netem as the basis for experimentation, and evaluate how QoE varies as we change the emulated QoS metrics. Focusing on Video-on-Demand, we discovered that the reported QoE may differ widely for users of different age groups, and that the most demanding age group (the youngest) can require an order of magnitude lower PLP to achieve the same QoE than is required by the most widely studied age group of users. We then used a bottleneck TCP model to evaluate the capacity cost of achieving an order of magnitude decrease in PLP, and found it be (almost always) a 3-fold increase in link capacity that was required.

Keywords: network capacity, packet loss probability, quality of experience, quality of service

Procedia PDF Downloads 259

Authors: Yassine Meraihi, Dalila Acheli, Rabah Meraihi

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MANET for Mobile Ad hoc NETwork is a collection of wireless mobile nodes that communicates with each other without using any existing infrastructure, access point or centralized administration, due to the higher mobility and limited radio transmission range, routing is an important issue in ad hoc network, so in order to ensure reliable and efficient route between to communicating nodes quickly, an appropriate routing protocol is needed. In this paper, we present the performance analysis of two mobile ad hoc network routing protocols namely DSR and OLSR using NS2.34, the performance is determined on the basis of packet delivery ratio, throughput, average jitter and end to end delay with varying pause time.

Keywords: DSR, OLSR, quality of service, routing protocols, MANET

Procedia PDF Downloads 534

Authors: Ginger Egberts, Marianne Schaaphok, Fred Vermolen, Paul van Zuijlen

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A few years ago, a promising morphoelastic model was developed for the simulation of contraction formation after burn injuries. Contraction can lead to a serious reduction in physical mobility, like a reduction in the range-of-motion of joints. If this is the case in a healing burn wound, then this is referred to as a contracture that needs medical intervention. The morphoelastic model consists of a set of partial differential equations describing both a chemical part and a mechanical part in dermal wound healing. These equations are solved with the numerical finite element method (FEM). In this method, many calculations are required on each of the chosen elements. In general, the more elements, the more accurate the solution. However, the number of elements increases rapidly if simulations are performed in 2D and 3D. In that case, it not only takes longer before a prediction is available, the computation also becomes more expensive. It is therefore important to investigate alternative possibilities to generate the same results, based on the input parameters only. In this study, a surrogate neural network has been designed to mimic the results of the one-dimensional morphoelastic model. The neural network generates predictions quickly, is easy to implement, and there is freedom in the choice of input and output. Because a neural network requires extensive training and a data set, it is ideal that the one-dimensional FEM code generates output quickly. These feed-forward-type neural network results are very promising. Not only can the network give faster predictions, but it also has a performance of over 99%. It reports on the relative surface area of the wound/scar, the total strain energy density, and the evolutions of the densities of the chemicals and mechanics. It is, therefore, interesting to investigate the applicability of a neural network for the two- and three-dimensional morphoelastic model for contraction after burn injuries.

Keywords: biomechanics, burns, feasibility, feed-forward NN, morphoelasticity, neural network, relative surface area wound

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Authors: Roy B. Clariana, Ryan L. Solnosky

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Structural feedback is any form of feedback that aims to improve the quality of students’ domain-normative conceptual interrelationships. Research with structural feedback points to the potential mediating role of network graphs as feedback for tuning students’ conceptual understanding; for example, improved content knowledge and motivation were observed for undergraduate students who accessed the instructor’s networks of course content. This exploratory study uses a one-group pretest-posttest design to examine the effects of instructor-provided network feedback during lectures on students’ knowledge structure measured using a concept sorting task at the pretest and posttest. Undergraduate students in an architectural engineering course (n = 32) completed a lesson module and then an end-of-unit quiz on building with wood and wood framing. Three weeks later, as a review, students completed a sorting task that used 26 terms from that lesson, then a week later, the sorting task data were used to create a group-average network, this network along with the instructor’s expert network were added to that week’s lecture slides and were compared and discussed during class time. A week later, students completed the sorting task again. The pre and post-sorting data were rendered into pathfinder networks, and then these students’ networks were compared to five referent networks, specifically the textbook chapter network, the lecture slides network, a network of the end-of-unit quiz, the actual expert network that served as the feedback intervention, and the group-average network. Inspection of means shows that knowledge structure measures improved for all five measures from pre-to-post, becoming more like the lesson content and like the expert. Repeated measures analysis with follow-up paired samples t-tests showed pre-to-post significant increases for both the end-of-unit quiz and the expert network referents. The findings show that instructor presentation of structural feedback as networks improved or ‘tuned’ students’ knowledge structure of the lesson content. This approach only takes a few extra minutes of class time and is fairly simple to implement in ordinary classrooms, and so it has wide potential to support classroom instruction and student learning. Further research is needed to determine how critical it is to present both the group-average network along with the expert network for comparison in order to highlight group-level misconceptions, or is presenting only the expert network sufficient? If a group-level network is necessary, then a simple clicker-like classroom tool could be developed to collect sorting task data during lectures that could then immediately provide the group-average network for class discussion and reflection.

Keywords: classroom instruction, engineering education, knowledge structure, pathfinder networks, structural feedback

Procedia PDF Downloads 49

Authors: Amira Zrelli, Tahar Ezzedine

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Routing protocol may enhance the lifetime of sensor network, it has a highly importance, especially in wireless sensor network (WSN). Therefore, routing protocol has a big effect in these networks, thus the choice of routing protocol must be studied before setting up our network. In this work, we implement the routing protocol collect tree protocol (CTP) which is one of the hierarchic protocols used in structural health monitoring (SHM). Therefore, to evaluate the performance of this protocol, we choice to work with Contiki system and Cooja simulator. By throughput and RSSI evaluation of each node, we will deduce about the utility of CTP in structural monitoring system.

Keywords: CTP, WSN, SHM, routing protocol

Procedia PDF Downloads 272

Authors: M. R. Ebrahimi, B. Mahdaviani

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Conventional electric distribution systems are radial in nature, supplied at one end through a main source. These networks generally have a simple protection system usually implemented using fuses, re-closers, and over-current relays. Recently, great attention has been paid to applying Distributed energy resources (DERs) throughout electric distribution systems. Presence of such generation in a network leads to losing coordination of protection devices. Therefore, it is desired to develop an algorithm which is capable of protecting distribution systems that include DER. On the other hand smart grid brings opportunities to the power system. Fast advancement in communication and measurement techniques accelerates the development of multi agent system (MAS). So in this paper, a new approach for the protection of distribution networks in the presence of DERs is presented base on MAS. The proposed scheme has been implemented on a sample 27-bus distribution network.

Keywords: distributed energy resource, distribution network, protection, smart grid, multi agent system

Procedia PDF Downloads 589

Authors: Anika Chebrolu

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Mono-targeted drugs can be of limited efficacy against complex diseases. Recently, multi-target drug design has been approached as a promising tool to fight against these challenging diseases. However, the scope of current computational approaches for multi-target drug design is limited. DeepLig presents a de-novo drug discovery platform that uses reinforcement learning to generate and optimize novel, potent, and multitargeted drug candidates against protein targets. DeepLig’s model consists of two networks in interplay: a generative network and a predictive network. The generative network, a Stack- Augmented Recurrent Neural Network, utilizes a stack memory unit to remember and recognize molecular patterns when generating novel ligands from scratch. The generative network passes each newly created ligand to the predictive network, which then uses multiple Graph Attention Networks simultaneously to forecast the average binding affinity of the generated ligand towards multiple target proteins. With each iteration, given feedback from the predictive network, the generative network learns to optimize itself to create molecules with a higher average binding affinity towards multiple proteins. DeepLig was evaluated based on its ability to generate multi-target ligands against two distinct proteins, multi-target ligands against three distinct proteins, and multi-target ligands against two distinct binding pockets on the same protein. With each test case, DeepLig was able to create a library of valid, synthetically accessible, and novel molecules with optimal and equipotent binding energies. We propose that DeepLig provides an effective approach to design multi-targeted drug therapies that can potentially show higher success rates during in-vitro trials.

Keywords: drug design, multitargeticity, de-novo, reinforcement learning

Procedia PDF Downloads 69

Authors: E. U. Dampage, Ovindi D. Bandara, Vinushi S. Waraketiya, Samitha S. R. De Silva, Yasiru S. Gunarathne

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The knowledge of water inflow figures is paramount in decision making on the allocation for consumption for numerous purposes; irrigation, hydropower, domestic and industrial usage, and flood control. The understanding of how reservoir inflows are affected by different climatic and hydrological conditions is crucial to enable effective water management and downstream flood control. In this research, we propose a method using a Long Short Term Memory (LSTM) Artificial Neural Network (ANN) to assist the aforesaid decision-making process. The Kotmale reservoir, which is the uppermost reservoir in the Mahaweli reservoir complex in Sri Lanka, was used as the test bed for this research. The ANN uses the runoff in the Kotmale reservoir catchment area and the effect of Sea Surface Temperatures (SST) to make a forecast for seven days ahead. Three types of ANN are tested; Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and LSTM. The extensive field trials and validation endeavors found that the LSTM ANN provides superior performance in the aspects of accuracy and latency.

Keywords: convolutional neural network, CNN, inflow, long short-term memory, LSTM, multi-layer perceptron, MLP, neural network

Procedia PDF Downloads 132

Authors: Hilya Mudrika Arini, Nur Aini Masruroh, Budi Hartono

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There are more than 20 terrorist threats from 2002 to 2012 in Indonesia. Despite of this fact, preventive solution through studies in the field of national security in Indonesia has not been conducted comprehensively. This study aims to provide a preventive solution by developing prediction model of the terrorist threat in Indonesia by using Bayesian network. There are eight stages to build the model, started from literature review, build and verify Bayesian belief network to what-if scenario. In order to build the model, four experts from different perspectives are utilized. This study finds several significant findings. First, news and the readiness of terrorist group are the most influent factor. Second, according to several scenarios of the news portion, it can be concluded that the higher positive news proportion, the higher probability of terrorist threat will occur. Therefore, the preventive solution to reduce the terrorist threat in Indonesia based on the model is by keeping the positive news portion to a maximum of 38%.

Keywords: Bayesian network, decision analysis, national security system, text mining

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Authors: Tao Peng, Jing Zhao, Yanqing Xu, Jing Cai

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Due to missing/ambiguous boundaries between the prostate and neighboring structures, the presence of shadow artifacts, as well as the large variability in prostate shapes, ultrasound prostate segmentation is challenging. To handle these issues, this paper develops a hybrid method for ultrasound prostate segmentation by combining an optimized closed principal curve-based method and the evolutionary neural network; the former can fit curves with great curvature and generate a contour composed of line segments connected by sorted vertices, and the latter is used to express an appropriate map function (represented by parameters of evolutionary neural network) for generating the smooth prostate contour to match the ground truth contour. Both qualitative and quantitative experimental results showed that our proposed method obtains accurate and robust performances.

Keywords: ultrasound prostate segmentation, optimized closed polygonal segment method, evolutionary neural network, smooth mathematical model, principal curve

Procedia PDF Downloads 177

Authors: Tsu-Yin Wu, Jenni Hoffman, Lydia McMurrows, Sarah Lally

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Recent events of the Flint Water Crisis and elevated lead levels in Detroit public school water have highlighted a specific public health disparity and shown the need for better education of healthcare providers on lead education. Identifying children and pregnant women with a high risk for lead poisoning and ensuring lead testing is completed is critical. The purpose of this study is to explore the impact of an educational intervention on knowledge and confidence levels among nursing students enrolled in the prelicensure Bachelor of Science in Nursing (BSN) and Registered Nurse to BSN program (R2B). The study used both quantitative and qualitative research methods to assess the impact of multi-modal pedagogy on knowledge and confidence of lead screening and prevention among prelicensure and R2B nursing students. The students received lead poisoning and prevention content in addition to completing an e-learning module developed by the Pediatric Environmental Health Specialty Units. A total of 115 students completed the pre-and post-test instrument that consisted of demographic, lead knowledge, and confidence items. Despite the increase of total knowledge, three dimensions of lead poisoning, and confidence from pre- to post-test scores for both groups, there was no statistical significance on the increase between prelicensure and R2B students. Thematic analysis of qualitative data showed five themes from participants' learning experiences: lead exposure, signs and symptoms of lead poisoning, screening and diagnosis, prevention, and policy and statewide issues. The study is limited by a small sample and participants recalling some correct answers from the pretest, thus, scoring higher on the post-test. The results contribute to the minimally existent literature examining a critical public health concern regarding lead health exposure and prevention education of nursing students. Incorporating such content area into the nursing curriculum is essential in ensuring that such public health disparities are mitigated.

Keywords: lead poisoning, emerging public health issue, community health, nursing edducation

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Authors: Galal H. Senussi, Muamar Benisa, Sanja Vasin

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In our business field today, one of the most important issues for any enterprises is cost minimization and profit maximization. Second issue is how to develop a strong and capable model that is able to give us desired forecasting of these two issues. Many researches deal with these issues using different methods. In this study, we developed a model for multi-criteria production program optimization, integrated with Artificial Neural Network. The prediction of the production cost and profit per unit of a product, dealing with two obverse functions at same time can be extremely difficult, especially if there is a great amount of conflict information about production parameters. Feed-Forward Neural Networks are suitable for generalization, which means that the network will generate a proper output as a result to input it has never seen. Therefore, with small set of examples the network will adjust its weight coefficients so the input will generate a proper output. This essential characteristic is of the most important abilities enabling this network to be used in variety of problems spreading from engineering to finance etc. From our results as we will see later, Feed-Forward Neural Networks has a strong ability and capability to map inputs into desired outputs.

Keywords: project profitability, multi-objective optimization, genetic algorithm, Pareto set, neural networks

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Authors: Jayant Balaji Athavale, Sean Clarke

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Background: According to the WHO, every year, more than 700,000 people die by suicide, which is one person around every 45 seconds. Suicide is the fourth leading cause of death among 15 to 29-year-olds globally. The most common situations or life events that might cause suicidal thoughts are financial problems/unemployment, rejections, relationship breakups, sexual/substance abuse and mental illnesses. Mental/psychological weakness caused due to defects in one’s personality is one of the main reasons why people feel they cannot cope in such situations and contemplate suicide. A WHO Mental Health Action Plan 2013–2020 lists a 4-point strategy to enhance mental health by ‘implementing strategies for promotion and prevention in mental health.’ Methodology: With 40 years of spiritual research background, the team at the Maharshi University of Spirituality has studied the spiritual root causes that can significantly affect one’s mental health and the solutions to improve it. Results/Findings: According to spiritual science, the time and nature of death are mostly due to spiritual reasons. A person would mostly contemplate and attempt suicide when he is spiritually most vulnerable. Spiritual practice, as per universal principles, helps in protecting a person spiritually and prevents him from getting such thoughts of self-harm or acting upon them by controlling such impulses. The University has had much success in helping people to overcome the defects in their personalities, including those with suicidal thoughts, through spiritual practices such as chanting the Name of God and the Personality Defect Removal (PDR) process developed by the Author. Conclusion: If such techniques were taught in educational institutions, they could be simple yet effective self-help tools to prevent thoughts of suicide and enhance mental health and well-being.

Keywords: suicide, mental health, abuse, suicide prevention, personality defect removal

Procedia PDF Downloads 174

Authors: Yapegue Bayogo, Ahmadou Halassi Dicko, Brahima Songore

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In developing countries, the road transportation system occupies an important place because of its flexibility and the low prices of infrastructure and rolling stock. While road transport is necessary for economic development, the movement of people and their goods, it is urgent to use transportation systems that minimize carbon emissions in order to ensure sustainable development. One of the main objectives of OEDC and the Word Bank is to ensure sustainable economic’ development. This paper aims to develop a road transport network taking into account environmental impacts. The methodology adopted consists of formulating a model optimizing the flow of goods and then collecting information relating to the transport of products. Our model was tested with data on product transport in CMDT areas in the Republic of Mali. The results of our study indicate that emissions from the transport sector can be significantly reduced by minimizing the traffic volume. According to our study, optimizing the transportation network, we benefit from a significant amount of tons of CO₂.

Keywords: road transport, transport sustainability, pollution, flexibility, optimized network

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Authors: M. S. Jimah, A. C. Achuenu, M. Momodu

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Group communications over public infrastructure are prone to a lot of security issues. Existing network protocols like Protocol Independent Multicast Sparse Mode (PIM SM) and Protocol Independent Multicast Dense Mode (PIM DM) do not have inbuilt security features. Therefore, any user or node can easily access the group communication as long as the user can send join message to the source nodes, the source node then adds the user to the network group. In this research, a hybrid method of salting and hashing to encrypt information in the source and stub node was designed, and when stub nodes need to connect, they must have the appropriate key to join the group network. Object oriented analysis design (OOAD) was the methodology used, and the result shows that no extra controlled bandwidth overhead cost was added by encrypting and the hybrid model was more securing than the existing PIM SM, PIM DM and Zhang secure PIM SM.

Keywords: group communications, multicast, PIM SM, PIM DM, encryption

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Authors: Abhishek, Seema Devi, Jyoti Ohri

Abstract:

A mobile ad hoc network (MANET) represents a system of wireless mobile nodes that can self-organize freely and dynamically into arbitrary and temporary network topology. Unlike a wired network, wireless network interface has limited transmission range. Routing is the task of forwarding data packets from source to a given destination. Ad-hoc On Demand Distance Vector (AODV) routing protocol creates a path for a destination only when it required. This paper describes the implementation of AODV routing protocol using MATLAB-based Truetime simulator. In MANET's node movements are not fixed while they are random in nature. Hence intelligent techniques i.e. fuzzy and ANFIS are used to optimize the transmission range. In this paper, we compared the transmission range of AODV, fuzzy AODV and ANFIS AODV. For soft computing AODV, we have taken transmitted power and received threshold as input and transmission range as output. ANFIS gives better results as compared to fuzzy AODV.

Keywords: ANFIS, AODV, fuzzy, MANET, reactive routing protocol, routing protocol, truetime

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Authors: Qingsong Xiong, Cheng Yuan, Qingzhao Kong, Haibei Xiong

Abstract:

Deep-learning-driven approaches based on vibration responses have attracted larger attention in rapid structural condition assessment while obtaining sufficient measured training data with corresponding labels is relevantly costly and even inaccessible in practical engineering. This study proposes an integrated label propagation network for structural condition assessment, which is able to diffuse the labels from continuously-generating measurements by intact structure to those of missing labels of damage scenarios. The integrated network is embedded with damage-sensitive features extraction by deep autoencoder and pseudo-labels propagation by optimized fuzzy clustering, the architecture and mechanism which are elaborated. With a sophisticated network design and specified strategies for improving performance, the present network achieves to extends the superiority of self-supervised representation learning, unsupervised fuzzy clustering and supervised classification algorithms into an integration aiming at assessing damage conditions. Both numerical simulations and full-scale laboratory shaking table tests of a two-story building structure were conducted to validate its capability of detecting post-earthquake damage. The identifying accuracy of a present network was 0.95 in numerical validations and an average 0.86 in laboratory case studies, respectively. It should be noted that the whole training procedure of all involved models in the network stringently doesn’t rely upon any labeled data of damage scenarios but only several samples of intact structure, which indicates a significant superiority in model adaptability and feasible applicability in practice.

Keywords: autoencoder, condition assessment, fuzzy clustering, label propagation

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Authors: Gloria Patricia Manurung

Abstract:

Jakarta Bus Rapid Transit (BRT) system which was established in 2009 to reduce private vehicle usage and ease the rush hour gridlock throughout the Jakarta Greater area, has failed to achieve its purpose. With gradually increasing the number of private vehicles ownership and reduced road space by the BRT lane construction, private vehicle users intuitively invade the exclusive lane of BRT, creating local traffic along the BRT network. Invaded BRT lanes costs become the same with the road network, making BRT which is supposed to be the main public transportation in the city becoming unreliable. Efforts to guard critical lanes with preventing the invasion by allocating traffic agents at several intersections have been expended, lead to the improving congestion level along the lane. Given a set of number of traffic agents, this study uses an analytical approach to finding the best deployment strategy of traffic agent on a simplified Jakarta road network in minimizing the BRT link cost which is expected to lead to the improvement of BRT system time reliability. User-equilibrium model of traffic assignment is used to reproduce the origin-destination demand flow on the network and the optimum solution conventionally can be obtained with brute force algorithm. This method’s main constraint is that traffic assignment simulation time escalates exponentially with the increase of set of agent’s number and network size. Our proposed metaheuristic and heuristic algorithms perform linear simulation time increase and result in minimized BRT cost approaching to brute force algorithm optimization. Further analysis of the overall network link cost should be performed to see the impact of traffic agent deployment to the network system.

Keywords: traffic assignment, user equilibrium, greedy algorithm, optimization

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Authors: Maryam Kheirollahpour, Mahmoud Danaee, Amir Faisal Merican, Asma Ahmad Shariff

Abstract:

Background: The presence of nonlinearity among the risk factors of eating behavior causes a bias in the prediction models. The accuracy of estimation of eating behaviors risk factors in the primary prevention of obesity has been established. Objective: The aim of this study was to explore the potential of a hybrid model of structural equation modeling (SEM) and Artificial Neural Networks (ANN) to predict eating behaviors. Methods: The Partial Least Square-SEM (PLS-SEM) and a hybrid model (SEM-Artificial Neural Networks (SEM-ANN)) were applied to evaluate the factors affecting eating behavior patterns among university students. 340 university students participated in this study. The PLS-SEM analysis was used to check the effect of emotional eating scale (EES), body shape concern (BSC), and body appreciation scale (BAS) on different categories of eating behavior patterns (EBP). Then, the hybrid model was conducted using multilayer perceptron (MLP) with feedforward network topology. Moreover, Levenberg-Marquardt, which is a supervised learning model, was applied as a learning method for MLP training. The Tangent/sigmoid function was used for the input layer while the linear function applied for the output layer. The coefficient of determination (R²) and mean square error (MSE) was calculated. Results: It was proved that the hybrid model was superior to PLS-SEM methods. Using hybrid model, the optimal network happened at MPLP 3-17-8, while the R² of the model was increased by 27%, while, the MSE was decreased by 9.6%. Moreover, it was found that which one of these factors have significantly affected on healthy and unhealthy eating behavior patterns. The p-value was reported to be less than 0.01 for most of the paths. Conclusion/Importance: Thus, a hybrid approach could be suggested as a significant methodological contribution from a statistical standpoint, and it can be implemented as software to be able to predict models with the highest accuracy.

Keywords: hybrid model, structural equation modeling, artificial neural networks, eating behavior patterns

Procedia PDF Downloads 125