Search results for: network distributed diagnosis

Authors: Ayesha Imtiaz, Darlina Md. Naim

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Family Nemipteridae is among the most abundantly distributed family in Malaysian fish markets due to its high contribution to landing sites of Malaysia. Using an advanced molecular approach that used two mitochondrial (Cytochrome oxidase c I and Cytochrome oxidase b) and one nuclear gene (Recombination activating gene, RAGI) to expose cryptic diversity and phylogenetic relationships among commercially important species of family Nemipteridae. Our research covered all genera (including 31 species out total 45 species) of family Nemipteridae, distributed in Malaysia. We also found certain type of geographical barriers in the South China sea that reduces dispersal and stops a few species to intermix. Northside of the South China Sea (near Vietnam) does not allow genetic diversity to mix with the Southern side of the South China sea (Sarawak) and reduces dispersal. Straits of Malacca reduce the intermixing genetic diversity of South China Sea and the Indian Ocean.

Keywords: Nemipteridae, RAG I, south east Asia, Malaysia

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Authors: Diego Aguirre, Olac Fuentes

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In this paper, we present a taxonomy of weight initialization schemes used in deep learning. We survey the most representative techniques in each class and compare them in terms of overhead cost, convergence rate, and applicability. We also introduce a new weight initialization scheme. In this technique, we perform an initial feedforward pass through the network using an initialization mini-batch. Using statistics obtained from this pass, we initialize the weights of the network, so the following properties are met: 1) weight matrices are orthogonal; 2) ReLU layers produce a predetermined number of non-zero activations; 3) the output produced by each internal layer has a unit variance; 4) weights in the last layer are chosen to minimize the error in the initial mini-batch. We evaluate our method on three popular architectures, and a faster converge rates are achieved on the MNIST, CIFAR-10/100, and ImageNet datasets when compared to state-of-the-art initialization techniques.

Keywords: deep learning, image classification, supervised learning, weight initialization

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Authors: Omojokun Gabriel Aju

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Communication network is a process of exchanging data between two or more devices via some forms of transmission medium using communication protocols. The data could be in form of text, images, audio, video or numbers which can be grouped into FTP, Email, HTTP, VOIP or Video applications. The effectiveness of such data exchange will be proved if they are accurately delivered within specified time. While some senders will not really mind when the data is actually received by the receiving device, inasmuch as it is acknowledged to have been received by the receiver. The time a data takes to get to a receiver could be very important to another sender, as any delay could cause serious problem or even in some cases rendered the data useless. The validity or invalidity of a data after delay will therefore definitely depend on the type of data (information). It is therefore imperative for the network device (such as router) to be able to differentiate among the packets which are time sensitive and those that are not, when they are passing through the same network. So, here is where the queuing disciplines comes to play, to handle network resources when such network is designed to service widely varying types of traffics and manage the available resources according to the configured policies. Therefore, as part of the resources allocation mechanisms, a router within the network must implement some queuing discipline that governs how packets (data) are buffered while waiting to be transmitted. The implementation of the queuing discipline will regulate how the packets are buffered while waiting to be transmitted. In achieving this, various queuing disciplines are being used to control the transmission of these packets, by determining which of the packets get the highest priority, less priority and which packets are dropped. The queuing discipline will therefore control the packets latency by determining how long a packet can wait to be transmitted or dropped. The common queuing disciplines are first-in-first-out queuing, Priority queuing and Weighted-fair queuing (FIFO, PQ and WFQ). This paper critically evaluates and analyse through the use of Optimized Network Evaluation Tool (OPNET) Modeller, Version 14.5 the effects of three queuing disciplines (FIFO, PQ and WFQ) on the performance of 5 different applications (FTP, HTTP, E-Mail, Voice and Video) within specified parameters using packets sent, packets received and transmission delay as performance metrics. The paper finally suggests some ways in which networks can be designed to provide better transmission performance while using these queuing disciplines.

Keywords: applications, first-in-first-out queuing (FIFO), optimised network evaluation tool (OPNET), packets, priority queuing (PQ), queuing discipline, weighted-fair queuing (WFQ)

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Authors: Goodness Onwuka, Khaled Abou-El-Hossein

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Borosilicate-crown (BK7) glass has found broad application in the optic and automotive industries and the growing demands for nanometric surface finishes is becoming a necessity in such applications. Thus, it has become paramount to optimize the parameters influencing the surface roughness of this precision lens. The research was carried out on a 4-axes Nanoform 250 precision lathe machine with an ultra-high precision grinding spindle. The experiment varied the machining parameters of feed rate, wheel speed and depth of cut at three levels for different combinations using Box Behnken design of experiment and the resulting surface roughness values were measured using a Taylor Hobson Dimension XL optical profiler. Acoustic emission monitoring technique was applied at a high sampling rate to monitor the machining process while further signal processing and feature extraction methods were implemented to generate the input to a neural network algorithm. This paper highlights the training and development of a back propagation neural network prediction algorithm through careful selection of parameters and the result show a better classification accuracy when compared to a previously developed response surface model with very similar machining parameters. Hence artificial neural network algorithms provide better surface roughness prediction accuracy in the ultra-high precision grinding of BK7 glass.

Keywords: acoustic emission technique, artificial neural network, surface roughness, ultra-high precision grinding

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Authors: Mehmet Hakan Karaata

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In this paper, we first coin a new graph theocratic problem with numerous applications. Second, we provide two algorithms for the problem. The first solution is using a brute-force techniques, whereas the second solution is based on an initial identification of the cycles in the given graph. We then provide a correctness proof of the algorithm. The applications of the problem include graph analysis, graph drawing and network structuring.

Keywords: algorithm, cycle, graph algorithm, graph theory, network structuring

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Authors: Hamed Sarbazy, Zohre Gholipour Haftkhani, Ali Safari, Pejman Hosseiniun

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Scientific and industrial progress in the past two decades has resulted in energy distribution systems based on power electronics, as an enabling technology in various industries and building management systems can be considered. Grading and standardization module power electronics systems and its use in a distributed control system, a strategy for overcoming the limitations of using this system. The purpose of this paper is to investigate strategies for scheduling and control structure of standard modules is a power electronic systems. This paper introduces the classical control methods and disadvantages of these methods will be discussed, The hierarchical control as a mechanism for distributed control structure of the classification module explains. The different levels of control and communication between these levels are fully introduced. Also continue to standardize software distribution system control structure is discussed. Finally, as an example, the control structure will be presented in a DC distribution system.

Keywords: application management, hardware management, power electronics, building blocks

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Authors: Giovana Vesentini, Fernanda Piculo, Gabriela Marini, Debora Damasceno, Angelica Barbosa, Selma Martheus, Marilza Rudge

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Problem statement: Skeletal muscle is highly adaptable, muscle fiber composition and size can respond to a variety of stimuli, such physiologic, as pregnancy, and metabolic abnormalities, as Diabetes mellitus. This study aimed to analyze the effects of pregnancy-associated diabetes on the rectus abdominis muscle (RA), and to compare this changes in rats and women. Methods: Female Wistar rats were maintained under controlled conditions and distributed in Pregnant (P) and Long-term mild pregnant diabetic (LTMd) (n=3 r/group). Diabetes in rats was induced by streptozotocin (100mg/Kg, sc) on the first day of life, for a hyperglycemic state between 120-300 mg/dL in adult life. Female rats were mated overnight, at day 21 of pregnancy were anesthetized, and killed for the harvesting of maternal RA. Pregnant women who attended the Diabetes Prenatal Care Clinic of Botucatu Medical School were distributed in Pregnant non-diabetic (Pnd) and Gestational Diabetic (GDM) (n=3 w/group). The diagnosis of GDM was established according to ADA’s criteria (2016). The harvesting of RA was during the cesarean section. Transversal cross-sections of the RA of both women and rats were analyzed by transmission electron microscopy. All procedures were approved by the Ethics Committee on Animal Experiments of the Botucatu Medical School (Protocol Number 1003/2013) and by the Botucatu Medical School Ethical Committee for Human Research in Medical Sciences (CAAE: 41570815.0.0000.5411). Results: The photomicrographs of the RA of rats revealed disorganized Z lines, thinning sarcomeres, and a usual quantity of intermyofibrillar mitochondria in the P group. The LTMd group showed swollen sarcoplasmic reticulum, dilated T tubes and areas with sarcomere disruption. The ultrastructural analysis of Pnd non-diabetic women in the RA showed well-organized myofibrils forming intact sarcomeres, organized Z lines and a normal distribution of intermyofibrillar mitochondria. The GDM group revealed increase in intermyofibrillar mitochondria, areas with sarcomere disruption and increased lipid droplets. Conclusion: Pregnancy and diabetes induce adaptations in the ultrastructure of the rectus abdominis muscle for both women and rats, changing the architectural design of these tissues. However, in rats these changes are more severe maybe because, besides the high blood glucose levels, the quadrupedal animal may suffer an excessive mechanical tension during pregnancy by gravity. Probably, these findings may suggest that these alterations are a risk factor that contributes to the development of muscle dysfunction in women with GDM and may motivate treatment strategies in these patients.

Keywords: gestational diabetes, muscle dysfunction, pregnancy, rectus abdominis

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Authors: Abdel-Monem Sayed Mohamed

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Lake Aswan is one of the largest man-made reservoirs in the world. The reservoir began to fill in 1964 and the level rose gradually, with annual irrigation cycles, until it reached a maximum water level of 181.5 m in November 1999, with a capacity of 160 km3. The filling of such large reservoir changes the stress system either through increasing vertical compressional stress by loading and/or increased pore pressure through the decrease of the effective normal stress. The resulted effect on fault zones changes stability depending strongly on the orientation of pre-existing stress and geometry of the reservoir/fault system. The main earthquake occurred on November 14, 1981, with magnitude 5.5. This event occurred after 17 years of the reservoir began to fill, along the active part of the Kalabsha fault and located not far from the High Dam. Numerous of small earthquakes follow this earthquake and continue till now. For this reason, 13 seismograph stations (radio-telemetry network short-period seismometers) were installed around the northern part of Lake Aswan. The main purpose of the network is to monitor the earthquake activity continuously within Aswan region. The data described here are obtained from the continuous record of earthquake activity and lake-water level variation through the period from 1982 to 2015. The seismicity is concentrated in the Kalabsha area, where there is an intersection of the easterly trending Kalabsha fault with the northerly trending faults. The earthquake foci are distributed in two seismic zones, shallow and deep in the crust. Shallow events have focal depths of less than 12 km while deep events extend from 12 to 28 km. Correlation between the seismicity and the water level variation in the lake provides great suggestion to distinguish the micro-earthquakes, particularly, those in shallow seismic zone in the reservoir–triggered seismicity category. The water loading is one factor from several factors, as an activating medium in triggering earthquakes. The common factors for all cases of induced seismicity seem to be the presence of specific geological conditions, the tectonic setting and water loading. The role of the water loading is as a supplementary source of earthquake events. So, the earthquake activity in the area originated tectonically (ML ≥ 4) and the water factor works as an activating medium in triggering small earthquakes (ML ≤ 3). Study of the inducing seismicity from the water level variation in Aswan Lake is of great importance and play great roles necessity for the safety of the High Dam body and its economic resources.

Keywords: Aswan lake, Aswan seismic network, seismicity, water level variation

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Authors: Adewusi Adeoluwawale, Oronti Iyabosola Busola, Akinola Iretiayo, Komolafe Olusola Aderibigbe

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The complex and radial structure of the low voltage distribution network (415V) makes it vulnerable to faults which are due to system and the environmental related factors. Besides these, the protective scheme employed on the low voltage network which is the fuse cannot be monitored remotely such that in the event of sustained fault, the utility will have to rely solely on the complaint brought by customers for loss of supply and this tends to increase the length of outages. A microcontroller based fault detection scheme is hereby developed to detect low voltage and high voltage fault conditions which are common faults on this network. Voltages below 198V and above 242V on the distribution feeders are classified and detected as low voltage and high voltages respectively. Results shows that the developed scheme produced a good response time in the detection of these faults.

Keywords: fault detection, low voltage distribution feeders, outage times, sustained faults

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Authors: Radhia Toujani, Jalel Akaichi

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Social Networks generally present a hierarchy of communities. To determine these communities and the relationship between them, detection algorithms should be applied. Most of the existing algorithms, proposed for hierarchical communities identification, are based on either agglomerative clustering or divisive clustering. In this paper, we present a hybrid hierarchical clustering approach for community detection based on both bottom-up and bottom-down clustering. Obviously, our approach provides more relevant community structure than hierarchical method which considers only divisive or agglomerative clustering to identify communities. Moreover, we performed some comparative experiments to enhance the quality of the clustering results and to show the effectiveness of our algorithm.

Keywords: agglomerative hierarchical clustering, community structure, divisive hierarchical clustering, hybrid hierarchical clustering, opinion mining, social network, social network analysis

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Authors: Chia-Yi Liu, Bo-Bin Xiao, Wen-Bin Lin, Hsiang-Ning Wu, Liang-Hsun Huang

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In order to reduce the risk of radiation damage that personnel may suffer during operations in the radiation environment, the use of automated guided vehicles to assist or replace on-site personnel in the radiation environment has become a key technology and has become an important trend. In this paper, we demonstrate our proof of concept for autonomous self-learning radiation source searcher in an unknown environment without a map. The research uses GPU version of Asynchronous Advantage Actor-Critic network (GA3C) of deep reinforcement learning to search for radiation sources. The searcher network, based on GA3C architecture, has self-directed learned and improved how search the anomalous radiation source by training 1 million episodes under three simulation environments. In each episode of training, the radiation source position, the radiation source intensity, starting position, are all set randomly in one simulation environment. The input for searcher network is the fused data from a 2D laser scanner and a RGB-D camera as well as the value of the radiation detector. The output actions are the linear and angular velocities. The searcher network is trained in a simulation environment to accelerate the learning process. The well-performance searcher network is deployed to the real unmanned vehicle, Dashgo E2, which mounts LIDAR of YDLIDAR G4, RGB-D camera of Intel D455, and radiation detector made by Institute of Nuclear Energy Research. In the field experiment, the unmanned vehicle is enable to search out the radiation source of the 18.5MBq Na-22 by itself and avoid obstacles simultaneously without human interference.

Keywords: deep reinforcement learning, GA3C, source searching, source detection

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Authors: Imen Labidi, Said Nouira

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Dung beetles Scarabaeides of Tunisia constitute a major component of soil fauna, especially in the Mediterranean region. In the first phase of the present study, an intensive investigation of this group following the gathering of all the bibliographic, museological data and based on a recent collection of 17020 specimens in 106 localities in Tunisia, allowed to confirm with certainty the presence of 94 species distributed in 43 genera, 4 families and 3 sub-families. Only 81 species distributed in 38 genres, 4 families, and 3 sub-families, have been found during our prospections. The population of dung beetles Scarabaeides is composed of 58% of Aphodiidae, 39.51% of Scarabaeidae, and 8.64% of Geotrupidae. Biogeographic affinities of the species were determined and showed that 42% of the identified species have a wide Palaearctic distribution, the endemism is very low, only 3 species are endemic to Tunisia Mecynodes demoflysi, Neobodilus marani, and Thorectes demoflysi, 29 species have a wide distribution, 35 are northern and 17 are southern species. Moreover, others are dependent on very specific Biotopes like Sisyphus schaefferi linked to the northwest of Tunisia and Scarabaeus semipunctatus related to the coastal area north of Tunisia.

Keywords: dung beetles, Tunisia, composition, biogeography

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Authors: Taha Benarbia

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The advent of automated and connected vehicles has revolutionized the transportation industry, presenting new opportunities for enhancing the efficiency, safety, and sustainability of our transportation networks. This paper explores the integration of automated and connected vehicles into a smart transportation framework, leveraging the power of deep learning techniques to optimize the overall network performance. The first aspect addressed in this paper is the deployment of automated vehicles (AVs) within the transportation system. AVs offer numerous advantages, such as reduced congestion, improved fuel efficiency, and increased safety through advanced sensing and decisionmaking capabilities. The paper delves into the technical aspects of AVs, including their perception, planning, and control systems, highlighting the role of deep learning algorithms in enabling intelligent and reliable AV operations. Furthermore, the paper investigates the potential of connected vehicles (CVs) in creating a seamless communication network between vehicles, infrastructure, and traffic management systems. By harnessing real-time data exchange, CVs enable proactive traffic management, adaptive signal control, and effective route planning. Deep learning techniques play a pivotal role in extracting meaningful insights from the vast amount of data generated by CVs, empowering transportation authorities to make informed decisions for optimizing network performance. The integration of deep learning with automated and connected vehicles paves the way for advanced transportation network optimization. Deep learning algorithms can analyze complex transportation data, including traffic patterns, demand forecasting, and dynamic congestion scenarios, to optimize routing, reduce travel times, and enhance overall system efficiency. The paper presents case studies and simulations demonstrating the effectiveness of deep learning-based approaches in achieving significant improvements in network performance metrics

Keywords: automated vehicles, connected vehicles, deep learning, smart transportation network

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Authors: Hadeer R. M. Tawfik, Rania A. K. Birry, Amani A. Saad

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Eyes are considered to be the most sensitive and important organ for human being. Thus, any eye disorder will affect the patient in all aspects of life. Cataract is one of those eye disorders that lead to blindness if not treated correctly and quickly. This paper demonstrates a model for automatic detection, classification, and grading of cataracts based on image processing techniques and artificial intelligence. The proposed system is developed to ease the cataract diagnosis process for both ophthalmologists and patients. The wavelet transform combined with 2D Log Gabor Wavelet transform was used as feature extraction techniques for a dataset of 120 eye images followed by a classification process that classified the image set into three classes; normal, early, and advanced stage. A comparison between the two used classifiers, the support vector machine SVM and the artificial neural network ANN were done for the same dataset of 120 eye images. It was concluded that SVM gave better results than ANN. SVM success rate result was 96.8% accuracy where ANN success rate result was 92.3% accuracy.

Keywords: cataract, classification, detection, feature extraction, grading, log-gabor, neural networks, support vector machines, wavelet

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Authors: Ge Zheng, Jun Peng

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Unbalanced power output from individual cells of Proton Exchange Membrane Fuel Cell (PEMFC) has direct effects on PEMFC stack performance, in particular under transient operation. In the paper, a multi-layer ANN (Artificial Neural Network) model Radial Basis Functions (RBF) has been developed for predicting cells' output profiles by applying gas supply parameters, cooling conditions, temperature measurement of individual cells, etc. The feed-forward ANN model was validated with experimental data. Influence of relevant parameters of RBF on the network accuracy was investigated. After adequate model training, the modelling results show good correspondence between actual measurements and reconstructed output profiles. Finally, after the model was used to optimize the stack output performance under steady-state and transient operating conditions, it suggested that the developed ANN control model can help PEMFC stack to have obvious improvement on power output under fast acceleration process.

Keywords: proton exchange membrane fuel cell, PEMFC, artificial neural network, ANN, cell output profile, transient

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Authors: Yashvi Italiya

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Dysthymia (DD) is a much-overlooked soft mood disorder and mostly confused with other forms of chronic depression. This research paper gives a spotlight to the DD prevailing in men and women. It also focuses on one of the comorbidities of Dysthymia, i.e., Anxiety. The comorbidities, hurdles in diagnosis, the ubiquity of the disorder, and the relation of Anxiety and DD are briefly described. Gender was the main focus here because the researcher of this paper found it as a research gap while doing the literature review. The study was done through secondary data obtained primarily from a questionnaire having Alpha 0.891 reliability. T-test method of data analysis was used to test the hypotheses. The result shows that the researcher failed to accept alternative hypothesis 1 (M1 > M2), while the alternative hypothesis 2 (M1 > M2) was accepted. The ratio of DD in women (M1) is not higher than that of men (M2) (hypothesis 1). But, women are more anxious than men (hypothesis 2). It was found that comorbid Anxiety is more widespread in one gender. It further plays a significant role in mixing up the symptoms. It was concluded that the dividing line between Dysthymia and MDD is still unclear for an accurate diagnosis. There is an essential need for spreading knowledge concerning the differences between the symptoms of DD and MDD so that the actual disorder can be identified, and proper help can be received from/provided by professionals.

Keywords: anxiety, comorbidity, dysthymia, gender, MDD

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Authors: S. Sudhakar, Geetha Manjunath, Siva Teja Kakileti, Himanshu Madhu

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Diagnosis of breast cancer at early stages has seen better clinical and survival outcomes. Survival rates in developing countries like India are very low due to accessibility and affordability issues of screening tests such as Mammography. In addition, Mammography is not much effective in younger women with dense breasts. This leaves a gap in current screening methods. Thermalytix is a new technique for detecting breast abnormality in a non-contact, non-invasive way. It is an AI-enabled computer-aided diagnosis solution that automates interpretation of high resolution thermal images and identifies potential malignant lesions. The solution is low cost, easy to use, portable and is effective in all age groups. This paper presents the results of a retrospective comparative analysis of Thermalytix over Mammography and Clinical Breast Examination for breast cancer screening. Thermalytix was found to have better sensitivity than both the tests, with good specificity as well. In addition, Thermalytix identified all malignant patients without palpable lumps.

Keywords: breast cancer screening, radiology, thermalytix, artificial intelligence, thermography

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Authors: Saeedeh Barzegar Khalilsaraei, Manoocheher Kelarestaghi, Farshad Eshghi

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The vehicle color, as a prominent and stable feature, helps to identify a vehicle more accurately. As a result, vehicle color recognition is of great importance in intelligent transportation systems. Unlike conventional methods which use only a single Convolutional Neural Network (CNN) for feature extraction or classification, in this paper, four CNNs, with different architectures well-performing in different classes, are trained to extract various features from the input image. To take advantage of the distinct capability of each network, the multiple outputs are combined using a stack generalization algorithm as an ensemble technique. As a result, the final model performs better than each CNN individually in vehicle color identification. The evaluation results in terms of overall average accuracy and accuracy variance show the proposed method’s outperformance compared to the state-of-the-art rivals.

Keywords: Vehicle Color Recognition, Ensemble Algorithm, Stack Generalization, Convolutional Neural Network

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Authors: Nureni Asafe Yekini, Olawale Nasiru Lawal, Bola Dada, Gabriel Adeyemi Okunlola

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This study is focused on the strengths and challenges associated with using the online social network as a rapidly evolving medium in marketing tourism services and businesses among the youths in Nigeria. The paper examines the Nigerian tourists’ attitude, mainly towards three aspects: application of Internet for travel and tourism; usage of online social networks in sharing travel and tourism experiences; and trust in electronic-media for marketing tourism businesses and services. The aim of this research is to determine the level of application of internet tools in marketing tourism businesses and services in Nigeria. This study reports an empirical analysis based on data obtained from a survey among 1004 Nigerian tourists. The outcome confirms the research hypothesis and points to crucial importance of introducing online social network site for marketing tourism businesses and services in Nigeria, and increasing the awareness for Nigeria as a tourist destination. Moreover, the paper strongly recommends the use of online social network as a tool for marketing tourism businesses and services, and the need for identifying effective framework for application of ICT tools in marketing tourism businesses and services in Nigeria at large.

Keywords: tourism business, internet, online social networks, tourism services, ICT

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Authors: Marjan Golmaryami, Marzieh Behzadi

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Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.

Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange

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Authors: Amber Shahi, Ayesha Tazeen, Abdus Samad, Shama Parveen

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Nanoparticles (NPs) are tiny particles. According to the International Organization for Standardization, the size range of NPs is in the nanometer range (1-100 nm). They show distinct properties that are not shown by larger particles of the same material. NPs are currently being used in different fields due to their unique physicochemical nature. NPs are a boon for medical sciences, environmental sciences, electronics, and textile industries. However, there is growing concern about their potential adverse effects on human health. This poster presents a comprehensive review of the current literature on the pros and cons of NPs on human health. The poster will discuss the various types of interactions of NPs with biological systems. There are a number of beneficial uses of NPs in the field of health and environmental welfare. NPs are very useful in disease diagnosis, antimicrobial action, and the treatment of diseases like Alzheimer’s. They can also cross the blood-brain barrier, making them capable of treating brain diseases. Additionally, NPs can target specific tumors and be used for cancer treatment. To treat environmental health, NPs also act as catalytic converters to reduce pollution from the environment. On the other hand, NPs also have some negative impacts on the human body, such as being cytotoxic and genotoxic. They can also affect the reproductive system, such as the testis and ovary, and sexual behavior. The poster will further discuss the routes of exposure of NPs. The poster will conclude with a discussion of the current regulations and guidelines on the use of NPs in various applications. It will highlight the need for further research and the development of standardized toxicity testing methods to ensure the safe use of NPs in various applications. When using NPs in diagnosis and treatment, we should also take into consideration their safe concentration in the body. Overall, this poster aims to provide a comprehensive overview of the pros and cons of NPs on human health and to promote awareness and understanding of the potential risks and benefits associated with their use.

Keywords: disease diagnosis, human health, nanoparticles, toxicity testing

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Authors: Ali Saleh Al-Rafedah, Ahmed Mohammed Al-Quthami, Tariq Jalal Al-Ashawi, Talal Nasser Motar Al-Enez

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Introduction: Barodontalgia is essentially dental pain provoked by changes in atmospheric pressure which usually disappear when the affected person reaches normal pressure zone. Barodontalgia has been recognized as a potential cause of aircrew-member vertigo and sudden incapacitation, which could jeopardize the safety of flight. Objective: The current study aimed to investigate the incidence of this phenomena among aircrews in Kingdom of Saudi Arabia. It also aimed to assess the knowledge of dental interns toward this phenomena. Material and Method: A 120 questionnaire consists of 17 questions were distributed to different of Aircrews working in commercial and governmental centers in different areas of KSA. Another questionnaire also distributed to 240 interns in different institutes in KSA. Results: Out of 120 questionnaire distributed to aircrews, 48 has been returned back (40%) and the participants were mainly pilots. The results showed that about 33% of the participants had this pain at least once during flying and the incidence of this pain was not associated with any age group. Most of the pain experience were during descending and at altitude between 10.000-20.000 feet (63%). The pain completely relieved after landing in most of the cases. Regarding pain scores, the majority of the participants reported moderate scores of severity (%65) and about 85% of them had visited the physician or dentist to investigate the existing oral problem. Among dental interns in KSA, our finding indicated lack of knowledge regarding this phenomena since only 23 % of the participants have an idea about this phenomena. Conclusion and recommendation: The incidence of Barodontalgia among aircrews in Saudi Arabia is considerably high and further studies should be carried out for better understanding of this phenomena. Significant lack of knowledge among dental interns about the Barodontalgia has been highlighted and inclusion of it in the teaching of clinical and preclinical curriculum is recommended.

Keywords: Barodontalgia/dental, atmospheric pressure, incapacitation, Saudi Arabia

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Authors: Susan Scott, Dina Hanna, Bassel Zebian, Gary Ruiz, Sreena Das

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Although the number of cases of TB in England has fallen over the last 4 years, it remains an important public health burden with 1 in 20 cases dying annually. The vast majority of cases present in non-UK born individuals with social risk factors. We present a case of non-pulmonary TB presenting in a healthy child born in the UK to professional parents. We present a case of a healthy 10 year old boy who developed acute back pain during school PE. Over the next 5 months, he was seen by various health and allied professionals with worsening back pain and kyphosis. He became increasing unsteady and for the 10 days prior to admission to our hospital, he developed fevers. He was admitted to his local hospital for tonsillitis where he suffered two falls on account of his leg weakness. A spinal X-ray revealed a pathological fracture and gibbus formation. He was transferred to our unit for further management. On arrival, the patient had lower motor neurone signs of his left leg. He underwent spinal fixture, laminectomy and decompression. Microbiology samples taken intra-operatively confirmed Mycobacterium Tuberculosis. He had a positive Mantoux and T-spot and treatment were commenced. There was no evidence of immune compromise. The patient was born in the UK, had a BCG scar and his only travel history had been two years prior to presentation when he travelled to the Phillipines for a short holiday. The patient continues to have issues around neuropathic pain, mobility, pill burden and mild liver side effects from treatment. Discussion: There is a paucity of case reports on spinal TB in paediatrics and diagnosis is often difficult due to the non-specific symptomatology. Although prognosis on treatment is good, a delayed diagnosis can have devastating consequences. This case highlights the continued need for higher index of suspicion and diagnosis in a world with changing patterns of migration and increase global travel. Surgical intervention is limited to the most serious cases to minimise further neurological damage and improve prognosis. There remains the need for a multi-disciplinary approach to deal with challenges of treatment and rehabilitation.

Keywords: tuberculosis, non-pulmonary TB, public health burden, diagnostic challenge

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Authors: Namrata Mishra

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Health of a person plays a vital role in the collective health of his community and hence the well-being of the society as a whole. But, in today’s fast paced technology driven world, health issues are increasingly being associated with human behaviors – their lifestyle. Social networks have tremendous impact on the health behavior of individuals. Many researchers have used social network analysis to understand human behavior that implicates their social and economic environments. It would be interesting to use a similar analysis to understand human behaviors that have health implications. This paper focuses on concepts of those behavioural analyses that have health implications using social networks analysis and provides possible algorithmic approaches. The results of these approaches can be used by the governing authorities for rolling out health plans, benefits and take preventive measures, while the pharmaceutical companies can target specific markets, helping health insurance companies to better model their insurance plans.

Keywords: breadth first search, directed graph, health behaviors, social network analysis

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Authors: Shin-Pin Tseng

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Nowadays, numerous spectral amplitude coding (SAC) fiber-optic code-division-multiple-access (FO-CDMA) techniques were appealing due to their capable of providing moderate security and relieving the effects of multiuser interference (MUI). Nonetheless, the performance of the previous network is degraded due to fixed in-phase cross-correlation (IPCC) value. Based on the above problems, a new SAC FO-CDMA network using partial M-sequence (PMS) code is presented in this study. Because the proposed PMS code is originated from M-sequence code, the system using the PMS code could effectively suppress the effects of MUI. In addition, two-code keying (TCK) scheme can applied in the proposed SAC FO-CDMA network and enhance the whole network performance. According to the consideration of system flexibility, simple optical encoders/decoders (codecs) using fiber Bragg gratings (FBGs) were also developed. First, we constructed a diagram of the SAC FO-CDMA network, including (N/2-1) optical transmitters, (N/2-1) optical receivers, and one N×N star coupler for broadcasting transmitted optical signals to arrive at the input port of each optical receiver. Note that the parameter N for the PMS code was the code length. In addition, the proposed SAC network was using superluminescent diodes (SLDs) as light sources, which then can save a lot of system cost compared with the other FO-CDMA methods. For the design of each optical transmitter, it is composed of an SLD, one optical switch, and two optical encoders according to assigned PMS codewords. On the other hand, each optical receivers includes a 1 × 2 splitter, two optical decoders, and one balanced photodiode for mitigating the effect of MUI. In order to simplify the next analysis, the some assumptions were used. First, the unipolarized SLD has flat power spectral density (PSD). Second, the received optical power at the input port of each optical receiver is the same. Third, all photodiodes in the proposed network have the same electrical properties. Fourth, transmitting '1' and '0' has an equal probability. Subsequently, by taking the factors of phase‐induced intensity noise (PIIN) and thermal noise, the corresponding performance was displayed and compared with the performance of the previous SAC FO-CDMA networks. From the numerical result, it shows that the proposed network improved about 25% performance than that using other codes at BER=10-9. This is because the effect of PIIN was effectively mitigated and the received power was enhanced by two times. As a result, the SAC FO-CDMA network using PMS codes has an opportunity to apply in applications of the next-generation optical network.

Keywords: spectral amplitude coding, SAC, fiber-optic code-division multiple-access, FO-CDMA, partial M-sequence, PMS code, fiber Bragg grating, FBG

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Authors: Birsel Canan Demirbag, Kıymet Yesilcicek Calik, Hacer Kobya Bulut

Abstract:

Objective: This study was conducted to evaluate the problems of women over 65 with incontinence diagnosis. Methods: This descriptive study was conducted with women over 65 with incontinence diagnosis in four Family Health Centers in a city in Eastern Black Sea region between November 1, and December 20, 2015. 203, 107, 178, 180 women over 65 were registered in these centers and 262 had incontinence diagnosis at least once and had an ongoing complaint. 177 women were volunteers for the study. During home visits and using face-to-face survey methodology, participants were given socio-demographic characteristics survey, Sandvik severity scale, Incontinence Quality of Life Scale, Urogenital Distress Inventory and a questionnaire including challenges experienced due to incontinence developed by the researcher. Data were analyzed with SPSS program using percentages, numbers, Chi-square, Man-Whitney U and t test with 95% confidence interval and a significance level p <0.05. Findings: 67 ± 1.4 was the mean age, 2.05 ± 0.04 was parity, 44.5 ± 2.12 was menopause age, 66.3% were primary school graduates, 45.7% had deceased spouse, 44.4% lived in a large family, 67.2% had their own room, 77.8% had income, 89.2% could meet self- care, 73.2% had a diagnosis of mixed incontinence, 87.5% suffered for 6-20 years % 78.2 had diuretics, antidepressants and heart medicines, 20.5% had urinary fecal cases, 80.5% had bladder training at least once, 90.1% didn’t have bladder diary calendar/control training programs, 31.1% had hysterectomy for prolapse, 97.1'i% was treated with lower urinary tract infection at least once, 66.3% saw a doctor to get drug in the last three months, 76.2 could not go out alone, 99.2 % had at least one chronic disease, 87.6 % had constipation complain, 2.9% had chronic cough., 45.1% fell due to a sudden rise for toilet. Incontinence Impact Questionnaire Average score was (QOL) 54.3 ± 21.1, Sandvik score was 12.1 ± 2.5, Urogenital Distress Inventory was 47.7 ± 9.2. Difficulties experienced due to incontinence were 99.5% feeling of unhappiness, 67.1% constant feeling of urine smell due to failing to change briefs frequently, % 87.2 move away from social life, 89.7 unable to use pad, 99.2% feeling of disturbing households / other individuals, 87.5% feel dizziness/fall due to sudden rise, 87.4% feeling of others’ imperceptions about the situation, % 94.3 insomnia, 78.2 lack of assistance, 84.7% couldn’t afford urine protection briefs. Results: With this study, it was found out that there were a lot of unsolved issues at individual and community level affecting the life quality of women with incontinence. In accordance with this common problem in women, to facilitate daily life it is obvious that regular home care training programs at institutional level in our country will be effective.

Keywords: health problems, incontinence, incontinence quality of life questionnaire, old age, urinary urogenital distress inventory, Sandviken severity, women

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Authors: Debneil Saha Roy

Abstract:

Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast hori­zon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks.

Keywords: convolutional neural network, deep learning, long short term memory, multi-layer perceptron

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Authors: Raed Hanandeh

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This empirical study examines how knowledge sharing behaviors mediate the effect Technology-driven strategy on virtual organization success in Jordanian tourism sector firms. The results reveal that Social network ties are positively related to web knowledge seeking, web knowledge contributing and interactive system, but negatively related to accidental knowledge leakage. Furthermore, all types of knowledge sharing behavior are positively related to virtual organization success. Data collected from 23 firms. The total number of questionnaires mailed, 250 questionnaires were delivered. 214 were considered valid out of 241 Responses were received. The findings provide evidence that knowledge sharing behavior play a mediating role between Social network ties and virtual organization success and show that, web knowledge seeking, web knowledge contributing and interactive system playing an important impact on virtual organization success through knowledge sharing behaviors.

Keywords: social network ties, virtual organization success, knowledge sharing behaviors, web knowledge

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Authors: Varun Agarwal

Abstract:

Introduction: With the advent of personalized medicine, histopathological review of whole slide images (WSIs) for cancer diagnosis presents an exceedingly time-consuming, complex task. Specifically, detecting metastatic regions in WSIs of sentinel lymph node biopsies necessitates a full-scanned, holistic evaluation of the image. Thus, digital pathology, low-level image manipulation algorithms, and machine learning provide significant advancements in improving the efficiency and accuracy of WSI analysis. Using Camelyon16 data, this paper proposes a deep learning pipeline to automate and ameliorate breast cancer metastasis localization and WSI classification. Methodology: The model broadly follows five stages -region of interest detection, WSI partitioning into image tiles, convolutional neural network (CNN) image-segment classifications, probabilistic mapping of tumor localizations, and further processing for whole WSI classification. Transfer learning is applied to the task, with the implementation of Inception-ResNetV2 - an effective CNN classifier that uses residual connections to enhance feature representation, adding convolved outputs in the inception unit to the proceeding input data. Moreover, in order to augment the performance of the transfer learning CNN, a stack of convolutional denoising autoencoders (CDAE) is applied to produce embeddings that enrich image representation. Through a saliency-detection algorithm, visual training segments are generated, which are then processed through a denoising autoencoder -primarily consisting of convolutional, leaky rectified linear unit, and batch normalization layers- and subsequently a contrast-normalization function. A spatial pyramid pooling algorithm extracts the key features from the processed image, creating a viable feature map for the CNN that minimizes spatial resolution and noise. Results and Conclusion: The simplified and effective architecture of the fine-tuned transfer learning Inception-ResNetV2 network enhanced with the CDAE stack yields state of the art performance in WSI classification and tumor localization, achieving AUC scores of 0.947 and 0.753, respectively. The convolutional feature retention and compilation with the residual connections to inception units synergized with the input denoising algorithm enable the pipeline to serve as an effective, efficient tool in the histopathological review of WSIs.

Keywords: breast cancer, convolutional neural networks, metastasis mapping, whole slide images

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Authors: Yu-Chu Yeh, WeiChin Hsu, Chih-Yen Chang

Abstract:

Substitution, combination, modification, putting to other uses, elimination, and rearrangement (SCAMPER) has been regarded as an effective technique that provides a structured way to help people to produce creative ideas and solutions. Although some neuroscience studies regarding creativity training have been conducted, no study has focused on SCAMPER. This study therefore aimed at examining whether the learning of SCAMPER through video tutorials would result in alternations of neural activation. Thirty college students were randomly assigned to the experimental group or the control group. The experimental group was requested to watch SCAMPER videos, whereas the control group was asked to watch natural-scene videos which were regarded as neutral stimulating materials. Each participant was brain scanned in a Functional magnetic resonance imaging (fMRI) machine while undertaking a creativity test before and after watching the videos. Furthermore, a two-way ANOVA was used to analyze the interaction between groups (the experimental group; the control group) and tasks (C task; M task; X task). The results revealed that the left precuneus significantly activated in the interaction of groups and tasks, as well as in the main effect of group. Furthermore, compared with the control group, the experimental group had greater activation in the default mode network (left precuneus and left inferior parietal cortex) and the motor network (left postcentral gyrus and left supplementary area). The findings suggest that the SCAMPER training may facilitate creativity through the stimulation of the default mode network and the motor network.

Keywords: creativity, default mode network, neural activation, SCAMPER

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