Search results for: body area networks

Authors: Turker Yardan, Bahattin Avci, S. Sirri Bilge, Ayhan Bozkurt

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Sinapic acid (SA) is a phenylpropanoid compound with anti-inflammatory, antioxidant, and neuroprotective activities. The purpose of this study was to characterize the possible protective effect of sinapic acid on chlorpyrifos (CPF), a common organophosphorus pesticide used worldwide, induced toxicity in rats. Forty male and female rats (240-270 g) were used in this study. Each group was composed of 5 male and 5 female rats. Sinapic acid (20 mg/kg or 40 mg/kg) or vehicle (olive oil, 1 ml ⁄ rat) were given orally for 5 days. CPF (279 mg/kg) or vehicle (peanut oil, 2 ml ⁄ kg, s.c.) was administered on the sixth day, immediately after the recording of the body weight of the animals. Twenty four hours following CPF administration body weight, body temperature and locomotor activity values were recorded before decapitation of the animals. Trunk blood, brain, and liver samples were collected for biochemical examinations. Chlorpyrifos administration decreased butyrylcholinesterase activity in blood, brain, and liver, while it increased malondialdehyde (MDA) levels and advanced oxidation protein products (AOPPs) (p < 0.01 - 0.001). Additionally, CPF administration reduced the body weight, body temperature, and locomotor activity values of the animals (p < 0.01 - 0.001). All these physiological and biochemical changes induced by CPF were reduced with the 40 mg/kg dose of SA (p < 0.05 - 0.001). Our results suggest that SA administration ameliorates CPF induced toxicity in rats, possibly by supporting the antioxidant mechanism.

Keywords: antioxidant, Chlorpyrifos, poisoning, sinapic acid

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Authors: Adeline Fontvieille, Hugo Parent-Roberge, Marie-France Langlois, Tamas Fulop, Michel Pavic, Eleonor Riesco

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Introduction: Total lean body mass is reduced during cancer treatment. This loss is called cancer cachexia and is accompanied by a progressive loss of fat mass. In older adults, these body composition changes can have a larger impact on metabolic health, physical autonomy, and cancer survival. Although currently untreatable, exercise training could reduce these effects. Hence, the objective of this pilot study is to investigate if 12 weeks of exercise training during cancer treatment can mitigate the loss of muscle mass and fat mass in older adults. Methods: A total of 40 older adults (65-80 years) with an ongoing treatment for a curable cancer are currently recruited and randomised in two groups: 1) Combined training (EX, n=20) and 2) Control group (CON, n=20). All variables are measured before and after 12 weeks of intervention: Anthropometry (weight, height, body mass index), body composition (total fat mass, visceral adipose tissue, total and appendicular muscle mass; DXA), metabolic profile (HDL-C and LDL-C, triglycerides, glucose and insulin levels). Results: Preliminary analyses revealed no impact of exercise training on appendicular muscle mass (p=0,31) and fat mass (p=0,31). Furthermore, total body weight, waist circumference, HDL-cholesterol, LDL-cholesterol, glucose and insulin levels remained unchanged (all p ≥ 0.79) after 12 weeks of training. However, statistical analyses revealed that triglyceride levels slightly increased (p=0.03), irrespective of the group. Conclusion: Preliminary analyses did not reveal any impact of aerobic and resistance exercise training on body composition in oncogeriatric patients. Furthermore, exercise training seems not efficient to prevent the cancer treatment-related triglyceride levels increase.

Keywords: muscle mass, fat mass, metabolic profile, combined training, aging, cancer

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Authors: Xianping Du, Runlu Miao, Guanjun Zhang, Libo Cao, Feng Zhu

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Current vehicle safety standards and human body injury criteria were established based on the biomechanical response of Euro-American human body, without considering the difference in the body anthropometry and injury characteristics among different races, particularly the East Asian people with smaller body size. Absence of such race specific design considerations will negatively influence the protective performance of safety products for these populations, and weaken the accuracy of injury thresholds derived. To resolve these issues, in this study, we aim to develop a race specific finite element model to simulate the impact response of the lower extremity of a 50th percentile East Asian (Chinese) male. The model was built based on medical images for the leg of an average size Chinese male and slightly adjusted based on the statistical data. The model includes detailed anatomic features and is able to simulate the muscle active force. Thirteen biomechanical tests available in the literature were used to validate its biofidelity. Using the validated model, a pedestrian-car impact accident taking place in China was re-constructed computationally. The results show that the newly developed lower leg model has a good performance in predicting dynamic response and tibia fracture pattern. An additional comparison on the fracture tolerance of the East Asian and Euro-American lower limb suggests that the current injury criterion underestimates the degree of injury of East Asian human body.

Keywords: lower limb, East Asian body characteristics, traffic accident reconstruction, finite element analysis, injury tolerance

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Authors: Olga Shapovalova, Vladimir Vinogradov

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Among many different sol – gel matrices only alumina can be successfully parenteral injected in the human body. And this is not surprising, because boehmite (aluminium oxyhydroxide) is the metal oxide approved by FDA and EMA for intravenous and intramuscular administrations, and also has been using for a longtime as adjuvant for producing of many modern vaccines. In our earlier study, it has been shown, that denaturation temperature of enzymes entrapped in sol-gel boehmite matrix increases for 30 – 60 °С with preserving of initial activity. It makes such matrices more attractive for long-term storage of non-stable drugs. In current work we present ultrasound-assisted sol-gel synthesis of nano-boehmite. This method provides bio-friendly, very stable, highly homogeneous alumina sol with using only water and aluminium isopropoxide as a precursor. Many parameters of the synthesis were studied in details: time of ultrasound treatment, US frequency, surface area, pore and nanoparticle size, zeta potential and others. Here we investigated the dependence of stability of colloidal sols and textural properties of the final composites as a function of the time of ultrasonic treatment. Chosen ultrasonic treatment time was between 30 and 180 minutes. Surface area, average pore diameter and total pore volume of the final composites were measured by surface and pore size analyzer Nova 1200 Quntachrome. It was shown that the matrices with ultrasonic treatment time equal to 90 minutes have the biggest surface area 431 ± 24 m2/g. On the other had such matrices have a smaller stability in comparison with the samples with ultrasonic treatment time equal to 120 minutes that have the surface area 390 ± 21 m2/g. It was shown that the stable sols could be formed only after 120 minutes of ultrasonic treatment, otherwise the white precipitate of boehmite is formed. We conclude that the optimal ultrasonic treatment time is 120 minutes.

Keywords: boehmite matrix, stabilisation, ultrasound-assisted sol-gel synthesis

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Authors: Z. Khoshnood, L. Khoshnood

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Pesticides and drugs used in agriculture and veterinary medicine may end up in aquatic environments and bioaccumulate in the food chain, thus causing serious problems for fauna and human health. For determination of the toxic effects of atrazine herbicide on Caspian kutum, Rutilus frisii kutum larvae, the 96-h LC50 of atrazine was measured for newly hatched larvae as 18.53 ppm. Toxicity of atrazine herbicide on Caspian kutum larvae was investigated using concentrations: 9.25 ppm, 4.62 ppm and 2.31 ppm for 7 days. Comparison of the length, weight, and condition factor showed that no significant differences between atrazine exposed and control groups. The concentration of Na+, K+, Ca2+, Mg2+ and Cl- in whole body of larvae in control and atrazine exposure groups were measured and the results showed that concentrations of all these ions is higher in atrazine exposure group than control group. It is obvious from this study that atrazine negatively affects osmoregulation process and changes ion compositions of the body even at sublethal concentration and acute exposure but have no effects on growth parameters of the body.

Keywords: atrazine, Caspian Kutum, acute toxicity, body ions, LC50

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Authors: Asma Ben Yaghlane, Mohamed Naceur Azaiez, Mehdi Mrad

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We investigate the large scale of networks in the context of network survivability under attack. We use appropriate techniques to evaluate and the attacker-based- and the defender-based-network survivability. The attacker is unaware of the operated links by the defender. Each attacked link has some pre-specified probability to be disconnected. The defender choice is so that to maximize the chance of successfully sending the flow to the destination node. The attacker however will select the cut-set with the highest chance to be disabled in order to partition the network. Moreover, we extend the problem to the case of selecting the best p paths to operate by the defender and the best k cut-sets to target by the attacker, for arbitrary integers p,k > 1. We investigate some variations of the problem and suggest polynomial-time solutions.

Keywords: defense/attack strategies, large scale, networks, partitioning a network

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Authors: Marilei Amadeu Sabino

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Before the advent of Cognitive Linguistics, metaphor was considered a stylistic issue, but now it is viewed as a critical component of everyday language and a fundamental mechanism of human conceptualizations of the world. It means that human beings' conceptual system (the way we think and act) is metaphorical in nature. Another interesting hypothesis in Cognitive Linguistics is that cognition is embodied, that is, our cognition is influenced by our experiences in the physical world: the mind is connected to the body and the body influences the mind. In this sense, it is believed that many conceptual metaphors appear to be potentially universal or near-universal, because people across the world share certain bodily experiences. In these terms, many metaphors may be identical or very similar in several languages. Thus, in this study, we analyzed some somatic (also called body) idioms of Italian and Portuguese languages, in order to investigate the proportion in which their metaphors are the same, similar or different in both languages. It was selected hundreds of Italian idioms in dictionaries and indicated their corresponding idioms in Portuguese. The analysis allowed to conclude that much of the studied expressions are really structurally, semantically and metaphorically identical or similar in both languages. We also contrasted some Portuguese and Italian somatic expressions to their corresponding English idioms to have a multilingual perspective of the issue, and it also led to the conclusion that the most common idioms based on metaphors are probably those that have to do with the human body. Although this is mere speculation and needs more study, the results found incite relevant discussions on issues that matter Foreign and Second Language Teaching and Learning, including the retention of vocabulary. The teaching of the metaphorically different body idioms also plays an important role in language learning and teaching as it will be shown in this paper. Acknowledgments: FAPESP – São Paulo State Research Support Foundation –the financial support offered (proc. n° 2017/02064-7).

Keywords: body idioms, cognitive linguistics, metaphor, vocabulary teaching and learning

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Authors: Babak Mohajeri

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In recent years, the conventional business model of ownership has been changed towards accessibility in a variety of markets. Two trends can be observed in the evolution of this rental-like business model. Firstly, the technological development that enables the emergence of new business models. These new business models increasingly become agile and flexible. For example Spotify, an online music stream company provides consumers access to over millions of music tracks, conveniently through the smartphone, tablet or computer. Similarly, Car2Go, the car sharing company accesses its members with flexible and nearby sharing cars. The second trend is the increasing communication and connections via social networks. This trend enables a shift to peer-to-peer accessibility based business models. Conventionally, companies provide access for their customers to own companies products or services. In peer-to-peer model, nonetheless, companies facilitate access and connection across their customers to use other customers owned property or skills, competencies or services .The is so-called the sharing economy business model. The aim of this study is to investigate into a new and emerging type of the sharing economy model in which role of customers and service providers may dramatically change. This new model is called Collaborative Service Networks. We propose a mechanism for Collaborative Service Networks business model. Uber and Airbnb, two successful growing companies, have been selected for our case studies and their business models are analyzed. Finally, we study the emergence of the collaborative service networks in the manufacturing domain. Our finding results to a new manufacturing paradigm called social manufacturing.

Keywords: sharing economy, collaborative service networks, social manufacturing, manufacturing development

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Authors: Nasrine Olson

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With over 70,000 scholarly publications on the topic of biosensors, an overview of the field has become a challenge. To facilitate, there are currently over 700 expert-reviews of publications on biosensors and related topics. This study focuses on these review papers in order to provide a Meta-Review of the area. This paper provides a statistical analysis and overview of biosensor-related review papers. Comprehensive searches are conducted in the Web of Science, and PubMed databases and the resulting empirical material are analyzed using bibliometric methods and tools. The study finds that the biosensor-related review papers can be categorized in five related subgroups, broadly denoted by (i) properties of materials and particles, (ii) analysis and indicators, (iii) diagnostics, (iv) pollutant and analytical devices, and (v) treatment/ application. For an easy and clear access to the findings visualization of clusters and networks of connections are presented. The study includes a temporal dimension and identifies the trends over the years with an emphasis on the most recent developments. This paper provides useful insights for those who wish to form a better understanding of the research trends in the area of biosensors.

Keywords: bibliometrics, biosensors, meta-review, statistical analysis, trends visualization

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Authors: Belkacem Selma, Boumediene Selma, Tourkia Guerzou, Abbes Labdelli

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Natural disasters have occurred and will continue to cause human and material damage. Therefore, the idea of "preventing" natural disasters will never be possible. However, their prediction is possible with the advancement of technology. Even if natural disasters are effectively inevitable, their consequences may be partly controlled. The rapid growth and progress of artificial intelligence (AI) had a major impact on the prediction of natural disasters and risk assessment which are necessary for effective disaster reduction. The Earthquakes prediction to prevent the loss of human lives and even property damage is an important factor; that is why it is crucial to develop techniques for predicting this natural disaster. This present study aims to analyze the ability of artificial neural networks (ANNs) to predict earthquakes that occur in a given area. The used data describe the problem of high energy (higher than 10^4J) seismic bumps forecasting in a coal mine using two long walls as an example. For this purpose, seismic bumps data obtained from mines has been analyzed. The results obtained show that the ANN with high accuracy was able to predict earthquake parameters; the classification accuracy through neural networks is more than 94%, and that the models developed are efficient and robust and depend only weakly on the initial database.

Keywords: earthquake prediction, ANN, seismic bumps

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Authors: Jia-Shiun Chen, Quoc-Viet Huynh

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This work develops a flexible-body dynamic model of an all-terrain vehicle (ATV), capable of recovering dynamic stresses while the ATV travels on random bumpy roads. The fatigue life of components is forecasted as well. While considering the interaction between dynamic forces and structure deformation, the proposed model achieves a highly accurate structure stress prediction and fatigue life prediction. During the simulation, stress time history of the ATV structure is retrieved for life prediction. Finally, the hot sports of the ATV frame are located, and the frame life for combined road conditions is forecasted, i.e. 25833.6 hr. If the usage of vehicle is eight hours daily, the total vehicle frame life is 8.847 years. Moreover, the reaction force and deformation due to the dynamic motion can be described more accurately by using flexible body dynamics than by using rigid-body dynamics. Based on recommendations made in the product design stage before mass production, the proposed model can significantly lower development and testing costs.

Keywords: flexible-body dynamics, veicle, dynamics, fatigue, durability

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Authors: Wheyming Tina Song, Chi-Hao Hong, Peisyuan Lin

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An important performance measure for stochastic manufacturing networks is the system reliability, defined as the probability that the production output meets or exceeds a specified demand. The system parameters include the capacity of each workstation and numbers of the conforming parts produced in each workstation. We establish that eighteen archival publications, containing twenty-one examples, provide incorrect values of the system reliability. The author recently published the Song Rule, which provides the correct analytical system-reliability value; it is, however, computationally inefficient for large networks. In this paper, we use Monte Carlo simulation (implemented in C and Flexsim) to provide estimates for the above-mentioned twenty-one examples. The simulation estimates are consistent with the analytical solution for small networks but is computationally efficient for large networks. We argue here for three advantages of Monte Carlo simulation: (1) understanding stochastic systems, (2) validating analytical results, and (3) providing estimates even when analytical and numerical approaches are overly expensive in computation. Monte Carlo simulation could have detected the published analysis errors.

Keywords: Monte Carlo simulation, analytical results, leading digit rule, standard error

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Authors: Suranath Chomcheon, Nathnarong Khajohnsaksumeth, Benchawan Wiwatanapataphee

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Carbon monoxide is one of the harmful gases which have colorless, odorless, and tasteless. Too much carbon monoxide taken into the human body causes the reduction of oxygen transportation within human body cells leading to many symptoms including headache, nausea, vomiting, loss of consciousness, and death. Carbon monoxide is considered as one of the air pollution indicators. It is mainly released as soot from the exhaust pipe of the incomplete combustion of the vehicle engine. Nowadays, the increase in vehicle usage and the slowly moving of the vehicle struck by the traffic jam has created a large amount of carbon monoxide, which accumulated in the street canyon area. In this research, we study the effect of parameters such as wind speed and aspect ratio of the height building affecting the ventilation. We consider the model of the pollutant under the Bangkok Transit System (BTS) stations in a two-dimensional geometrical domain. The convention-diffusion equation and Reynolds-averaged Navier-stokes equation is used to describe the concentration and the turbulent flow of carbon monoxide. The finite element method is applied to obtain the numerical result. The result shows that our model can describe the dispersion patterns of carbon monoxide for different wind speeds.

Keywords: air pollution, carbon monoxide, finite element, street canyon

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Authors: N. Nalini, Lokesh B. Bhajantri

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In Distributed Sensor Networks, the sensor nodes are prone to failure due to energy depletion and some other reasons. In this regard, fault tolerance of network is essential in distributed sensor environment. Energy efficiency, network or topology control and fault-tolerance are the most important issues in the development of next-generation Distributed Sensor Networks (DSNs). This paper proposes a node fault detection and recovery using Genetic Algorithm (GA) in DSN when some of the sensor nodes are faulty. The main objective of this work is to provide fault tolerance mechanism which is energy efficient and responsive to network using GA, which is used to detect the faulty nodes in the network based on the energy depletion of node and link failure between nodes. The proposed fault detection model is used to detect faults at node level and network level faults (link failure and packet error). Finally, the performance parameters for the proposed scheme are evaluated.

Keywords: distributed sensor networks, genetic algorithm, fault detection and recovery, information technology

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Authors: Chondrogianni Dimitra, Yorgos J. Stephanedes

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The impact of the economic crisis, coupled with the aging of the city's old core, is reflected in central Athens. Public and private users, residents, employees, visitors desire the quality upgrading of abandoned buildings and public spaces through environmental upgrading and sustainable mobility, and promotion of the international metropolitan character of the city. In the study, a strategy for reshaping the character and function of the historic Athenian triangle is proposed, aiming at its economic, environmental, and social sustainable development through feasible, meaningful, and non-landscaping solutions of low cost and high positive impact. Sustainable mobility is the main principle in re-planning the study area and transforming it into a “Low Pollution and Limited Vehicle Traffic Zone” is the main strategy. Τhe proposed measures include the development of pedestrian mobility networks by expanding the pedestrian roads and limited-traffic routes, of bicycle networks based on the approved Metropolitan Bicycle Route of Athens, of public transportation networks with new lines of electric mini-buses, and of new regulations for vehicle mobility in the historic triangle. In addition, complementary actions are proposed regarding the provision of Wi-Fi on fixed track media, development of applications that facilitate combined travel and provide real-time data, integration of micromobility (roller skates, Segway, Hoverboard), and its enhancement as a flexible means of personal mobility, and development of car-sharing, ride-sharing and dynamic carpooling initiatives.

Keywords: regeneration plans, sustainable mobility, environmental upgrading, athens historical triangle

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: deep learning, long short term memory, energy, renewable energy load forecasting

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Authors: Ramazan I. Kadiev, Arcady Ponosov

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Mathematical models of gene regulatory networks can in many cases be described by ordinary differential equations with switching nonlinearities, where the initial value problem is ill-posed. Several regularization methods are known in the case of deterministic networks, but the presence of stochastic noise leads to several technical difficulties. In the presentation, it is proposed to apply the methods of the stochastic singular perturbation theory going back to Yu. Kabanov and Yu. Pergamentshchikov. This approach is used to regularize the above ill-posed problem, which, e.g., makes it possible to design stable numerical schemes. Several examples are provided in the presentation, which support the efficiency of the suggested analysis. The method can also be of interest in other fields of biomathematics, where differential equations contain switchings, e.g., in neural field models.

Keywords: ill-posed problems, singular perturbation analysis, stochastic differential equations, switching nonlinearities

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Authors: L. Parisi

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Kinematic data wisely correlate vector quantities in space to scalar parameters in time to assess the degree of symmetry between the intact limb and the amputated limb with respect to a normal model derived from the gait of control group participants. Furthermore, these particular data allow a doctor to preliminarily evaluate the usefulness of a certain rehabilitation therapy. Kinetic curves allow the analysis of ground reaction forces (GRFs) to assess the appropriateness of human motion. Electromyography (EMG) allows the analysis of the fundamental lower limb force contributions to quantify the level of gait asymmetry. However, the use of this technological tool is expensive and requires patient’s hospitalization. This research work suggests overcoming the above limitations by applying artificial neural networks.

Keywords: kinetics, kinematics, cyclograms, neural networks, transtibial amputation

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Authors: Orkide Donma, Mustafa M. Donma

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Basal metabolic rate is questioned as a risk factor for weight gain. The relations between basal metabolic rate and body composition have not been cleared yet. The impact of fat mass on basal metabolic rate is also uncertain. Within this context, indices based upon total body mass as well as total body fat mass are available. In this study, the aim is to investigate the potential clinical utility of these indices in the adult population. 287 individuals, aged from 18 to 79 years, were included into the scope of the study. Based upon body mass index values, 10 underweight, 88 normal, 88 overweight, 81 obese, and 20 morbid obese individuals participated. Anthropometric measurements including height (m), and weight (kg) were performed. Body mass index, diagnostic obesity notation model assessment index I, diagnostic obesity notation model assessment index II, basal metabolic rate-to-weight ratio were calculated. Total body fat mass (kg), fat percent (%), basal metabolic rate, metabolic age, visceral adiposity, fat mass of upper as well as lower extremities and trunk, obesity degree were measured by TANITA body composition monitor using bioelectrical impedance analysis technology. Statistical evaluations were performed by statistical package (SPSS) for Windows Version 16.0. Scatterplots of individual measurements for the parameters concerning correlations were drawn. Linear regression lines were displayed. The statistical significance degree was accepted as p < 0.05. The strong correlations between body mass index and diagnostic obesity notation model assessment index I as well as diagnostic obesity notation model assessment index II were obtained (p < 0.001). A much stronger correlation was detected between basal metabolic rate and diagnostic obesity notation model assessment index I in comparison with that calculated for basal metabolic rate and body mass index (p < 0.001). Upon consideration of the associations between basal metabolic rate-to-weight ratio and these three indices, the best association was observed between basal metabolic rate-to-weight and diagnostic obesity notation model assessment index II. In a similar manner, this index was highly correlated with fat percent (p < 0.001). Being independent of the indices, a strong correlation was found between fat percent and basal metabolic rate-to-weight ratio (p < 0.001). Visceral adiposity was much strongly correlated with metabolic age when compared to that with chronological age (p < 0.001). In conclusion, all three indices were associated with metabolic age, but not with chronological age. Diagnostic obesity notation model assessment index II values were highly correlated with body mass index values throughout all ranges starting with underweight going towards morbid obesity. This index is the best in terms of its association with basal metabolic rate-to-weight ratio, which can be interpreted as basal metabolic rate unit.

Keywords: basal metabolic rate, body mass index, children, diagnostic obesity notation model assessment index, obesity

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Authors: Asma Ben Yaghlane, Mohamed Naceur Azaiez

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We consider the problem of attacks on networks. We define the concept of system survivability in networks in the presence of intelligent threats. Our setting of the problem assumes a flow to be sent from one source node to a destination node. The attacker attempts to disable the network by preventing the flow to reach its destination while the defender attempts to identify the best path-set to use to maximize the chance of arrival of the flow to the destination node. Our concept is shown to be different from the classical concept of network reliability. We distinguish two types of network survivability related to the defender and to the attacker of the network, respectively. We prove that the defender-based-network survivability plays the role of a lower bound while the attacker-based-network survivability plays the role of an upper bound of network reliability. We also prove that both concepts almost never agree nor coincide with network reliability. Moreover, we use the shortest-path problem to determine the defender-based-network survivability and the min-cut problem to determine the attacker-based-network survivability. We extend the problem to a variety of models including the minimum-spanning-tree problem and the multiple source-/destination-network problems.

Keywords: defense/attack strategies, information, networks, reliability, survivability

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Authors: Kripesh Adhikari, Hamid Bouchachia, Hammadi Nait-Charif

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Falls are one of the major causes of injury and death among elderly people aged 65 and above. A support system to identify such kind of abnormal activities have become extremely important with the increase in ageing population. Pose estimation is a challenging task and to add more to this, it is even more challenging when pose estimations are performed on challenging poses that may occur during fall. Location of the body provides a clue where the person is at the time of fall. This paper presents a vision-based tracking strategy where available joints are grouped into three different feature points depending upon the section they are located in the body. The three feature points derived from different joints combinations represents the upper region or head region, mid-region or torso and lower region or leg region. Tracking is always challenging when a motion is involved. Hence the idea is to locate the regions in the body in every frame and consider it as the tracking strategy. Grouping these joints can be beneficial to achieve a stable region for tracking. The location of the body parts provides a crucial information to distinguish normal activities from falls.

Keywords: fall detection, machine learning, deep learning, pose estimation, tracking

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Authors: Mehrshad Khosraviani, Faramarz Abbaspour Leyl Abadi

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The fundamental component of the computer network of modern information society will be considered. These networks are connected to the network of the internet generally. Due to the fact that the primary purpose of the Internet is not designed for, in recent decades, none of these networks in many of the attacks has been very important. Today, for the provision of security, different security tools and systems, including intrusion detection systems are used in the network. A common diagnosis system based on artificial immunity, the designer, the Adhasaz Foundation has been evaluated. The idea of using artificial safety methods in the diagnosis of abnormalities in computer networks it has been stimulated in the direction of their specificity, there are safety systems are similar to the common needs of m, that is non-diagnostic. For example, such methods can be used to detect any abnormalities, a variety of attacks, being memory, learning ability, and Khodtnzimi method of artificial immune algorithm pointed out. Diagnosis of the common system of education offered in this paper using only the normal samples is required for network and any additional data about the type of attacks is not. In the proposed system of positive selection and negative selection processes, selection of samples to create a distinction between the colony of normal attack is used. Copa real data collection on the evaluation of ij indicates the proposed system in the false alarm rate is often low compared to other ir methods and the detection rate is in the variations.

Keywords: artificial immune system, abnormality detection, intrusion detection, computer networks

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Authors: Zineddine Bouyahiaoui, Rabah Haoui

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The aim of this work is to analyze a flow around the axisymmetric blunt body taken into account the chemical and vibrational nonequilibrium flow. This work concerns the entry of spacecraft in the atmosphere of the planet Mars. Since the equations involved are non-linear partial derivatives, the volume method is the only way to solve this problem. The choice of the mesh and the CFL is a condition for the convergence to have the stationary solution.

Keywords: blunt body, finite volume, hypersonic flow, viscous flow

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Authors: Atin Mukherjee, Amitabha Sinha, Debesh Choudhury

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Fast Fourier transform (FFT) of large-number of samples requires larger hardware resources of field programmable gate arrays and it asks for more area as well as power. In this paper, an area efficient architecture of FFT processor is proposed, that reuses the butterfly units more than once. The FFT processor is emulated and the results are validated on Virtex-6 FPGA. The proposed architecture outperforms the conventional architecture of a N-point FFT processor in terms of area which is reduced by a factor of log_N(2) with the negligible increase of processing time.

Keywords: FFT, FPGA, resource optimization, butterfly units

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Authors: R. Schweighoffer, N. Nagy, E. Reeves, B. Liebig

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Due to aging populations, the need for seamless palliative care provision is of central interest for western societies. An essential aspect of palliative care delivery is the quality of collaboration amongst palliative care providers. Therefore, the current research is based on Bainbridge’s conceptual framework, which provides an outline for the evaluation of palliative care provision. This study is the first one to investigate the predictive validity of spatial distribution on the quantity of interaction amongst various palliative care providers. Furthermore, based on the familiarity principle, we examine whether the extent of collaboration influences the perceived quality of collaboration among palliative care providers in urban versus rural areas of Switzerland. Based on a population-representative survey of Swiss palliative care providers, the results of the current study show that professionals in densely populated areas report higher absolute numbers of interactions and are more satisfied with their collaborative practice. This indicates that palliative care providers who work in urban areas are better embedded into networks than their counterparts in more rural areas. The findings are especially important, considering that efficient collaboration is a prerequisite to achieve satisfactory patient outcomes. Conclusively, measures should be taken to foster collaboration in weakly interconnected palliative care networks.

Keywords: collaboration, healthcare networks, palliative care, Switzerland

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Authors: Tharini N. de Silva, Xiao Zhibo, Zhao Rui, Mao Kezhi

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Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.

Keywords: causal realtion extraction, relation extracton, convolutional neural network, text representation

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Authors: Lingling Shui, Shuting Xie

Abstract:

As alternatives to electronic devices, optically active structures from responsive nanomaterials offer great opportunity buildup smart functional sensors. Hereby, we report on droplet microfluidics enabled construction and application of photonic microcapsules (PMCs) for colorimetric temperature microsensors, enabling miniaturization for injectable local micro-area sensing and integration for large-area sensing. Monodispersed PMCs are produced by in-situ photopolymerization of hydrogel shells of cholesteric liquid crystal (CLC)-in-water-in-oil double emulsion droplets prepared using microfluidic devices, with controllable physical structures and chemical compositions. Constructed PMCs exhibit thermal responsive structural color according to the selective Bragg reflection of CLC’s periodical helical structures within the microdroplet’s spherical confinement. Constructed PMCs with tunable size and composition have been successfully applied for monitoring the living cell extracellular temperature via co-incubation with cell suspension, and for detecting human body temperature via a flexible device from assembled PMCs. These PMCs could be flexibly applied in either micro-environment or large-area surface, enabling wide applications for precision temperature monitoring biological activities (e.g. cells or organs), optoelectronic devices working conditions (e.g. temperature indicators under extreme conditions), and etc.

Keywords: droplet, microfluidics, assembly, soft materials, microsensor

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Authors: Thiago Spilborghs Bueno Meyer, Plinio Thomaz Aquino Junior

Abstract:

Through speech, which privileges the functional and interactive nature of the text, it is possible to ascertain the spatiotemporal circumstances, the conditions of production and reception of the discourse, the explicit purposes such as informing, explaining, convincing, etc. These conditions allow bringing the interaction between humans closer to the human-robot interaction, making it natural and sensitive to information. However, it is not enough to understand what is said; it is necessary to recognize emotions for the desired interaction. The validity of the use of neural networks for feature selection and emotion recognition was verified. For this purpose, it is proposed the use of neural networks and comparison of models, such as recurrent neural networks and deep neural networks, in order to carry out the classification of emotions through speech signals to verify the quality of recognition. It is expected to enable the implementation of robots in a domestic environment, such as the HERA robot from the RoboFEI@Home team, which focuses on autonomous service robots for the domestic environment. Tests were performed using only the Mel-Frequency Cepstral Coefficients, as well as tests with several characteristics of Delta-MFCC, spectral contrast, and the Mel spectrogram. To carry out the training, validation and testing of the neural networks, the eNTERFACE’05 database was used, which has 42 speakers from 14 different nationalities speaking the English language. The data from the chosen database are videos that, for use in neural networks, were converted into audios. It was found as a result, a classification of 51,969% of correct answers when using the deep neural network, when the use of the recurrent neural network was verified, with the classification with accuracy equal to 44.09%. The results are more accurate when only the Mel-Frequency Cepstral Coefficients are used for the classification, using the classifier with the deep neural network, and in only one case, it is possible to observe a greater accuracy by the recurrent neural network, which occurs in the use of various features and setting 73 for batch size and 100 training epochs.

Keywords: emotion recognition, speech, deep learning, human-robot interaction, neural networks

Procedia PDF Downloads 138

Authors: Sanpachai Huvanandana, Settapong Malisuwan, Soparwan Tongyuak, Prust Pannachet, Anong Phoepueak, Navneet Madan

Abstract:

Many regions of Thailand are prone to flooding due to tropical climate. A commonly increasing precipitation in this continent results in risk of flooding. Many efforts have been implemented such as drainage control system, multiple dams, and irrigation canals. In order to decide where the drainages, dams, and canal should be appropriately located, the flooding risk area should be determined. This paper is aimed to identify the appropriate features that can be used to classify the flooding risk area in Thailand. Several features have been analyzed and used to classify the area. Non-supervised clustering techniques have been used and the results have been compared with ten years average actual flooding area.

Keywords: flood area clustering, geographical information system, flood features

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Authors: Mona Elhelbawy, Mac Gray

Abstract:

Traditional wired endoscopy is an intrusive process that requires a long flexible tube to be inserted through the patient’s mouth while intravenously sedated. Only images of the upper 4 feet of stomach, colon, and rectum can be captured, leaving the remaining 20 feet of small intestines. Wireless capsule endoscopy offers a painless, non-intrusive, efficient and effective alternative to traditional endoscopy. In wireless capsule endoscopy (WCE), ingestible vitamin-pill-shaped capsules with imaging capabilities, sensors, batteries, and antennas are designed to send images of the gastrointestinal (GI) tract in real time. In this paper, we investigate the radiation performance and specific absorption rate (SAR) of a miniature conformal capsule antenna operating at the Medical Implant Communication Service (MICS) frequency band in the human body. We perform numerical simulations using the finite element method based commercial software, high-frequency structure simulator (HFSS) and the ANSYS human body model (HBM). We also investigate the in-body channel characteristics between the implantable capsule and an external antenna placed on the surface of the human body.

Keywords: IEEE 802.15.6, MICS, SAR, WCE

Procedia PDF Downloads 108