Search results for: metabolic networks

Authors: Joyraj Chakraborty, Geoffrey Ottoy, Jean-Pierre Goemaere, Lieven De Strycker

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Sensor swarms can be a cost-effectieve and more user-friendly alternative for location based service systems in different application like health-care. To increase the lifetime of such swarm networks, the energy consumption should be scaled to the required localization accuracy. In this paper we have investigated some parameter for energy model that couples localization accuracy to energy-related sensor parameters such as signal length,Bandwidth and sample frequency. The goal is to use the model for the localization of undetermined environmental sounds, by means of wireless acoustic sensors. we first give an overview of TDOA-based localization together with the primary sources of TDOA error (including reverberation effects, Noise). Then we show that in localization, the signal sample rate can be under the Nyquist frequency, provided that enough frequency components remain present in the undersampled signal. The resulting localization error is comparable with that of similar localization systems.

Keywords: sensor swarms, localization, wireless sensor swarms, scalable energy

Procedia PDF Downloads 423

Authors: Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu

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Causal inference from observational data is receiving wide applications in many fields. However, unidentifiable situations, where causal effects cannot be uniquely computed from observational data, pose critical barriers to applying causal inference to complicated real applications. In this paper, we develop a bounding method for estimating the average causal effect (ACE) under unidentifiable situations due to hidden confounders. We propose to parameterize the unknown exogenous random variables and structural equations of a causal model using neural networks and implicit generative models. Then, with an adversarial learning framework, we search the parameter space to explicitly traverse causal models that agree with the given observational distribution and find those that minimize or maximize the ACE to obtain its lower and upper bounds. The proposed method does not make any assumption about the data generating process and the type of the variables. Experiments using both synthetic and real-world datasets show the effectiveness of the method.

Keywords: average causal effect, hidden confounding, bound estimation, generative adversarial learning

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Authors: S. M. Abdullah Al Mamun

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Human Computer Interaction (HCI) is an excellent area for the researchers to make daily life more simple and fast. Necessary hardware equipments for any BCI are generally expensive and not affordable for most of the people. Emotiv is one of the solutions for this problem, which can provide electroencephalograph (EEG) signal and explain the brain activities. BCI virtual speller was one of the important applications for the people who have lost their hand or speaking ability because of diseases or unexpected accident. In this paper, a matrix speller has been designed for the first time for Bengali speaking people around the world. Bengali is one of the most commonly spoken languages. Among them, a lot of disabled person will be able to express their desire in their mother tongue. This application is also usable for the social networks and daily life communications. For this virtual keyboard, the well-known matrix speller method with column flashing is applied and controlled by single Emokey only. Emokey is a great feature which translates emotional state for application inputs. In this paper, it is presented that the ITR (Information Transfer Rate) were 29.4 bits/min and typing speed achieved up to 7.43 char/per min.

Keywords: brain computer interface, Emotiv EPOC, EEG, virtual keyboard, matrix speller

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Authors: Montaceur Zaghdoud, Mohamed Moussaoui, Jalel Akaichi

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Frequent subgraph mining refers usually to graph matching and it is widely used in when analyzing big data with large graphs. A lot of research works dealt with structural exact or inexact graph matching but a little attention is paid to semantic matching when graph vertices and/or edges are attributed and typed. Therefore, it seems very interesting to integrate background knowledge into the analysis and that extracted frequent subgraphs should become more pruned by applying a new semantic filter instead of using only structural similarity in graph matching process. Consequently, this paper focuses on developing a new hybrid approximate structuralsemantic graph matching to discover a set of frequent subgraphs. It uses simultaneously an approximate structural similarity function based on graph edit distance function and a possibilistic vertices similarity function based on affinity function. Both structural and semantic filters contribute together to prune extracted frequent set. Indeed, new hybrid structural-semantic frequent subgraph mining approach searches will be suitable to be applied to several application such as community detection in social networks.

Keywords: approximate graph matching, hybrid frequent subgraph mining, graph mining, possibility theory

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Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

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Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

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Authors: Akshaya P., Priyanjali Prabhkaran

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The paper examines the formulation of strategies to develop a comprehensive model of city level water utility management to addressing urban sanitation issues. The water is prime life sustaining natural resources and nature’s gifts to all living beings on the earth multiple urban sanitation issues are addressed in the supply of water in a city. Many of these urban sanitation issues are linked to population expansion and economic inequity. Increased usage of water and the development caused water scarcity. The lack of water supply results increases the chance of unhygienic situations in the cities. In this study, the urban sanitation issues are identified with respect to water supply and utility management. The study compared based on their best practices and initiatives. From this, best practices and initiatives identify suitable sustainable measures to address water supply issues in the city level. The paper concludes with the listed provision that should be considered suitable measures for water supply and utility management in city level to address the urban sanitation issues.

Keywords: water, benchmarking water supply, water supply networks, water supply management

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Authors: Rasha A.Ibrahim El-Ghazawy, Ashraf M. El-Saeed, Heusin El-Shafey, M. Abdel-Raheim, Maher A. El-Sockary

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Two thermotropic liquid crystalline curing agents based on abietic acid with different mesogens (LCC1 and LCC2) were synthesized for producing thermally stable liquid crystal networks suitable for high performance epoxy coatings. Differential scanning calorimetry (DSC) and polarized optical microscope (POM) was used to identify the liquid crystal phase transformation temperatures and texture, respectively. POM micro graphs for both LCCs revealing cholesteric texture. A multifunctional epoxy resin with two abietic acid moieties was also synthesized. Dynamic mechanical (DMA) and thermogravimetric (TGA) analyses show that the fully bio-based cured epoxies by either LCCs possess high glass transition temperature (Tg), high modulus (G`) and improved thermal stability. The chemical structure of the synthesized LCCs and epoxy resin was investigated through FTIR and 1HNMR spectroscopic techniques.

Keywords: abietic acid, dynamic mechanical analysis, epoxy resin, liquid crystal, thermo gravimetric analysis

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Authors: Ahmed Al-Adaileh, Souheil Khaddaj

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Outcomes of exhaustion of natural resources started influencing each spirit on this planet. Energy is an essential factor in this aspect. To restore the circumstance to the appropriate track, all attempts must focus on two fundamental branches: producing electricity from clean and renewable reserves and decreasing the overall unnecessary consumption of energy. The focal point of this paper will be on lessening the power consumption in the household's segment. This paper is an attempt to give a clear understanding of a framework called Reduction of Energy Consumption in Household Sector (RECHS) and how it should help householders to reduce their power consumption by substituting their household appliances, turning-off the appliances when stand-by modus is detected, and scheduling their appliances operation periods. Technically, the framework depends on utilizing Z-Wave compatible plug-ins which will be connected to the usual house devices to gauge and control them remotely and semi-automatically. The suggested framework underpins numerous quality characteristics, for example, integrability, scalability, security and adaptability.

Keywords: smart energy management systems, internet of things, wireless mesh networks, microservices, cloud computing, big data

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Authors: Francesco Buccafurri, Celeste Romolo

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Nowadays phishing is the most frequent starting point of cyber-attack vectors. Phishing is implemented both via email and social network messages. While a wide scientific literature exists which addresses the problem of contrasting email spam-phishing, no specific countermeasure has been so far proposed for phishing included into private messages of social network platforms. Unfortunately, the problem is severe. This paper proposes an approach against social network phishing, based on a non invasive collaborative information-sharing approach which leverages blockchain. The detection method works by filtering candidate messages, by distilling them by means of a distance-preserving hash function, and by publishing hashes over a public blockchain through a trusted smart contract (thus avoiding denial of service attacks). Phishing detection exploits social information embedded into social network profiles to identify similar messages belonging to disjoint contexts. The main contribution of the paper is to introduce a new approach to contrasting the problem of social network phishing, which, despite its severity, received little attention by both research and industry.

Keywords: phishing, social networks, information sharing, blockchain

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Authors: Z. C. Dlamini, R. L. S. Langa, A. I. Okoh, O. A. Aiyegoro

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The indiscriminate usage of antibiotics in swine production have consequential outcomes; such as development of bacterial resistance to prophylactic antibiotics and possibility of antibiotic residues in animal products. The use of probiotics appears to be the most effective procedure with positive metabolic nutritional implications. The aim of this study was to investigate the efficacy of probiotic bacteria (Lactobacillus reuteri ZJ625, Lactobacillus reuteri VB4, Lactobacillus salivarius ZJ614 and Streptococcus salivarius NBRC13956) administered as direct-fed microorganisms in weaned piglets. 45 weaned piglets blocked by weight were dived into 5 treatments groups: diet with antibiotic, diet with no-antibiotic and no probiotic, and diet with probiotic and diet with combination of probiotics. Piglets performance was monitored during the trials. Faecal and Ileum samples were collected for microbial count analysis. Blood samples were collected from pigs at the end of the trial, for analysis of haematological, biochemical and IgG stimulation. The data was analysed by Split-Plot ANOVA using SAS statistically software (SAS 9.3) (2003). The difference was observed between treatments for daily weight and feed conversion ratio. No difference was observed in analysis of faecal samples in regards with bacterial counts, difference was observed in ileums samples with enteric bacteria colony forming unit being lower in P2 treatment group as compared with lactic acid and total bacteria. With exception of globulin and albumin, biochemistry blood parameters were not affected, likewise for haematology, only basophils and segmented neutrophils were differed by having higher concentration in NC treatment group as compared with other treatment groups. Moreover, in IgG stimulation analysis, difference was also observed, with P2 treatment group having high concentration of IgG in P2 treatment group as compared to other groups. The results of this study suggest that probiotics have a beneficial effect on growth performances, blood parameters and IgG stimulation of pigs, most effective when they are administered in synergy form. This means that it is most likely that these probiotics will offer a significant benefit in pig farming by reducing risk of morbidity and mortality and produce quality meat that is more affordable to poorer communities, and thereby enhance South African pig industry’s economy. In addition, these results indicate that there is still more research need to be done on probiotics in regards with, i.e. dosage, shelf life and mechanism of action.

Keywords: antibiotics, biochemistry, haematology, IgG-stimulation, microbial count, probiotics

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Authors: M. S. Alatas, H. D. Umucalilar

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SARA is a common and serious metabolic disorder in early lactation in dairy cattle and in finishing beef cattle, caused by diets with high inclusion of cereal grain. This experiment was performed to determine the efficacy of Megasphaera elsdenii, a major lactate-utilizing bacterium in prevention/treatment of SARA in vivo. In vivo experimentation, it was used eight ruminally cannulated rams and it was applied the rapid adaptation with the mixture of grain based on wheat (%80 wheat, %20 barley) and barley (%80 barley, %20 wheat). During the systematic adaptation, it was followed the probability of SARA formation by being measured the rumen pH with two hours intervals after and before feeding. After being evaluated the data, it was determined the ruminal pH ranged from 5,2-5,6 on the condition of feeding with 60 percentage of grain mixture based on barley and wheat, that assured the definite form of subacute acidosis. In four days SARA period, M. elsdenii (1010 cfu ml-1) was inoculated during the first two days. During the SARA period, it was observed the decrease of feed intake with M. elsdenii inoculation. Inoculation of M. elsdenii was caused to differentiation of rumen pH (P < 0,0001), while it was found the pH level approximately 5,55 in animals applied the inoculation, it was 5,63 pH in other animals. It was observed that total VFA with the bacterium inoculation tended to change in terms of grain feed (P < 0,07). It increased with the effect of total VFA inoculation in barley based diet, but it was more stabilized in wheat based diet. Bacterium inoculation increased the ratio of propionic acid (18,33%-21,38%) but it caused to decrease the butyric acid, and acetic/propionic acid. During the rapid adaptation, the concentration of lactic acid in the rumen liquid increased depending upon grain level (P<0,0001). On the other hand bacterium inoculation did not have an effect on concentration of lactic acid. M. elsdenii inoculation did not affect ruminal ammonia concentration. In the group that did not apply inoculation, the level of ruminal ammonia concentration was higher than the others applied inoculation. M. elsdenii inoculation did not changed protozoa count in barley-based diet whereas it decreased in wheat-based diet. In the period of SARA, it was observed that the level of blood glucose, lactate and hematocrit increased greatly after inoculation (P < 0,0001). When it is generally evaluated, it is seen that M. elsdenii inoculation has not a positive impact on rumen parameters. Therefore, to reveal the full impact of the inoculation with different strains, feedstuffs and animal groups, further research is required.

Keywords: In vivo, Subactute ruminal acidosis, Megasphaera elsdenii, Rumen fermentation

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Authors: Yana Snegireva

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Carbonate reservoirs are known for their heterogeneity, resulting from various geological processes such as diagenesis and fracturing. These complexities may cause great challenges in understanding fluid flow behavior and predicting the production performance of naturally fractured reservoirs. The investigation of carbonate reservoirs is crucial, as many petroleum reservoirs are naturally fractured, which can be difficult due to the complexity of their fracture networks. This can lead to geological uncertainties, which are important for global petroleum reserves. The problem outlines the key challenges in carbonate reservoir modeling, including the accurate representation of fractures and their connectivity, as well as capturing the impact of fractures on fluid flow and production. Traditional reservoir modeling techniques often oversimplify fracture networks, leading to inaccurate predictions. Therefore, there is a need for a modern approach that can capture the complexities of carbonate reservoirs and provide reliable predictions for effective reservoir management and production optimization. The modern approach to carbonate reservoir modeling involves the utilization of the hybrid fracture modeling approach, including the discrete fracture network (DFN) method and implicit fracture network, which offer enhanced accuracy and reliability in characterizing complex fracture systems within these reservoirs. This study focuses on the application of the hybrid method in the Nepsko-Botuobinskaya anticline of the Eastern Siberia field, aiming to prove the appropriateness of this method in these geological conditions. The DFN method is adopted to model the fracture network within the carbonate reservoir. This method considers fractures as discrete entities, capturing their geometry, orientation, and connectivity. But the method has significant disadvantages since the number of fractures in the field can be very high. Due to limitations in the amount of main memory, it is very difficult to represent these fractures explicitly. By integrating data from image logs (formation micro imager), core data, and fracture density logs, a discrete fracture network (DFN) model can be constructed to represent fracture characteristics for hydraulically relevant fractures. The results obtained from the DFN modeling approaches provide valuable insights into the East Siberia field's carbonate reservoir behavior. The DFN model accurately captures the fracture system, allowing for a better understanding of fluid flow pathways, connectivity, and potential production zones. The analysis of simulation results enables the identification of zones of increased fracturing and optimization opportunities for reservoir development with the potential application of enhanced oil recovery techniques, which were considered in further simulations on the dual porosity and dual permeability models. This approach considers fractures as separate, interconnected flow paths within the reservoir matrix, allowing for the characterization of dual-porosity media. The case study of the East Siberia field demonstrates the effectiveness of the hybrid model method in accurately representing fracture systems and predicting reservoir behavior. The findings from this study contribute to improved reservoir management and production optimization in carbonate reservoirs with the use of enhanced and improved oil recovery methods.

Keywords: carbonate reservoir, discrete fracture network, fracture modeling, dual porosity, enhanced oil recovery, implicit fracture model, hybrid fracture model

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Authors: Asmir Gogic, Aljo Mujcic, Sandra Ibric, Nermin Suljanovic

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Ubiquity of natural disasters during last few decades have risen serious questions towards the prediction of such events and human safety. Every disaster regardless its proportion has a precursor which is manifested as a disruption of some environmental parameter such as temperature, humidity, pressure, vibrations and etc. In order to anticipate and monitor those changes, in this paper we propose an overall system for disaster prediction and monitoring, based on wireless sensor network (WSN). Furthermore, we introduce a modified and simplified WSN routing protocol built on the top of the trickle routing algorithm. Routing algorithm was deployed using the bluetooth low energy protocol in order to achieve low power consumption. Performance of the WSN network was analyzed using a real life system implementation. Estimates of the WSN parameters such as battery life time, network size and packet delay are determined. Based on the performance of the WSN network, proposed system can be utilized for disaster monitoring and prediction due to its low power profile and mesh routing feature.

Keywords: bluetooth low energy, disaster prediction, mesh routing protocols, wireless sensor networks

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Authors: Apurva Gupta, Surendra Singh

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Spirulina spp., as a promising source of many commercially valuable products, is grown photo autotrophically in open ponds and raceways on a large scale. However, the economic exploitation in an open system seems to have been limited because of lack of multiple stress-tolerant strains. The present study aims to isolate a stable stress tolerant mutant of Spirulina platensis with improved growth rate and enhanced potential to produce its commercially valuable bioactive compounds. N-methyl-n'-nitro-n-nitrosoguanidine (NTG) at 250 μg/mL (concentration permitted 1% survival) was employed for chemical mutagenesis to generate random mutants and screened against NaCl. In a preliminary experiment, wild type S. platensis was treated with NaCl concentrations from 0.5-1.5 M to calculate its LC₅₀. Mutagenized colonies were then screened for tolerance at 0.8 M NaCl (LC₅₀), and the surviving colonies were designated as NaCl tolerant mutants of S. platensis. The mutant cells exhibited 1.5 times improved growth against NaCl stress as compared to the wild type strain in control conditions. This might be due to the ability of the mutant cells to protect its metabolic machinery against inhibitory effects of salt stress. Salt stress is known to adversely affect the rate of photosynthesis in cyanobacteria by causing degradation of the pigments. Interestingly, the mutant cells were able to protect its photosynthetic machinery and exhibited 4.23 and 1.72 times enhanced accumulation of Chl a and phycobiliproteins, respectively, which resulted in enhanced rate of photosynthesis (2.43 times) and respiration (1.38 times) against salt stress. Phycocyanin production in mutant cells was observed to enhance by 1.63 fold. Nitrogen metabolism plays a vital role in conferring halotolerance to cyanobacterial cells by influx of nitrate and efflux of Na+ ions from the cell. The NaCl tolerant mutant cells took up 2.29 times more nitrate as compared to the wild type and efficiently reduce it. Nitrate reductase and nitrite reductase activity in the mutant cells also improved by 2.45 and 2.31 times, respectively against salt stress. From these preliminary results, it could be deduced that enhanced nitrogen uptake and its efficient reduction might be a reason for adaptive and halotolerant behavior of the S. platensis mutant cells. Also, the NaCl tolerant mutant of S. platensis with significant improved growth and phycocyanin accumulation compared to the wild type can be commercially promising.

Keywords: chemical mutagenesis, NaCl tolerant mutant, nitrogen metabolism, photosynthetic machinery, phycocyanin

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Authors: Umar Albalawi, Sang C. Suh, Jinoh Kim

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As internet continues to expand its usage with an enormous number of applications, cyber-threats have significantly increased accordingly. Thus, accurate detection of malicious traffic in a timely manner is a critical concern in today’s Internet for security. One approach for intrusion detection is to use Machine Learning (ML) techniques. Several methods based on ML algorithms have been introduced over the past years, but they are largely limited in terms of detection accuracy and/or time and space complexity to run. In this work, we present a novel method for intrusion detection that incorporates a set of supervised learning algorithms. The proposed technique provides high accuracy and outperforms existing techniques that simply utilizes a single learning method. In addition, our technique relies on partial flow information (rather than full information) for detection, and thus, it is light-weight and desirable for online operations with the property of early identification. With the mid-Atlantic CCDC intrusion dataset publicly available, we show that our proposed technique yields a high degree of detection rate over 99% with a very low false alarm rate (0.4%).

Keywords: intrusion detection, supervised learning, traffic classification, computer networks

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Authors: Shayan Mohajer Hamidi

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Federated Learning (FL) has emerged as a promising paradigm for training machine learning models on distributed data without the need for centralized data aggregation. In the realm of wireless communications, FL has the potential to leverage the vast amounts of data generated by wireless devices to improve model performance and enable intelligent applications. However, the fairness aspect of FL in wireless communications remains largely unexplored. This abstract presents an idea for fair federated learning in wireless communications, addressing the challenges of imbalanced data distribution, privacy preservation, and resource allocation. Firstly, the proposed approach aims to tackle the issue of imbalanced data distribution in wireless networks. In typical FL scenarios, the distribution of data across wireless devices can be highly skewed, resulting in unfair model updates. To address this, we propose a weighted aggregation strategy that assigns higher importance to devices with fewer samples during the aggregation process. By incorporating fairness-aware weighting mechanisms, the proposed approach ensures that each participating device's contribution is proportional to its data distribution, thereby mitigating the impact of data imbalance on model performance. Secondly, privacy preservation is a critical concern in federated learning, especially in wireless communications where sensitive user data is involved. The proposed approach incorporates privacy-enhancing techniques, such as differential privacy, to protect user privacy during the model training process. By adding carefully calibrated noise to the gradient updates, the proposed approach ensures that the privacy of individual devices is preserved without compromising the overall model accuracy. Moreover, the approach considers the heterogeneity of devices in terms of computational capabilities and energy constraints, allowing devices to adaptively adjust the level of privacy preservation to strike a balance between privacy and utility. Thirdly, efficient resource allocation is crucial for federated learning in wireless communications, as devices operate under limited bandwidth, energy, and computational resources. The proposed approach leverages optimization techniques to allocate resources effectively among the participating devices, considering factors such as data quality, network conditions, and device capabilities. By intelligently distributing the computational load, communication bandwidth, and energy consumption, the proposed approach minimizes resource wastage and ensures a fair and efficient FL process in wireless networks. To evaluate the performance of the proposed fair federated learning approach, extensive simulations and experiments will be conducted. The experiments will involve a diverse set of wireless devices, ranging from smartphones to Internet of Things (IoT) devices, operating in various scenarios with different data distributions and network conditions. The evaluation metrics will include model accuracy, fairness measures, privacy preservation, and resource utilization. The expected outcomes of this research include improved model performance, fair allocation of resources, enhanced privacy preservation, and a better understanding of the challenges and solutions for fair federated learning in wireless communications. The proposed approach has the potential to revolutionize wireless communication systems by enabling intelligent applications while addressing fairness concerns and preserving user privacy.

Keywords: federated learning, wireless communications, fairness, imbalanced data, privacy preservation, resource allocation, differential privacy, optimization

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Authors: Hen Friman

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Various categories of aqueous solutions are discharged within residential, institutional, commercial, and industrial structures. To safeguard public health and preserve the environment, it is imperative to subject wastewater to treatment processes that eliminate pathogens (such as bacteria and viruses), nutrients (such as nitrogen and phosphorus), and other compounds. Failure to address untreated sewage accumulation can result in an array of adverse consequences. Israel exemplifies a special case in wastewater management. Appropriate wastewater treatment significantly benefits sectors such as agriculture, tourism, horticulture, and industry. Nevertheless, untreated sewage in settlements lacking proper sewage collection or transportation networks remains an ongoing and substantial threat. Notably, the process of wastewater treatment entails substantial energy consumption. Consequently, this study explores the integration of solar energy as a renewable power source within the wastewater treatment framework. By incorporating renewable energy sources into the process, costs can be minimized, and decentralized facilities can be established even in areas lacking adequate infrastructure for traditional treatment methods.

Keywords: renewable energy, solar energy, innovative, wastewater treatment

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Authors: Prajamitra Bhuyan

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Transport operators have a range of intervention options available to improve or enhance their networks. But often such interventions are made in the absence of sound evidence on what outcomes may result. Cycling superhighways were promoted as a sustainable and healthy travel mode which aims to cut traffic congestion. The estimation of the impacts of the cycle superhighways on congestion is complicated due to the non-random assignment of such intervention over the transport network. In this paper, we analyse the causal effect of cycle superhighways utilising pre-innervation and post-intervention information on traffic and road characteristics along with socio-economic factors. We propose a modeling framework based on the propensity score and outcome regression model. The method is also extended to doubly robust set-up. Simulation results show the superiority of the performance of the proposed method over existing competitors. The method is applied to analyse a real dataset on the London transport network, and the result would help effective decision making to improve network performance.

Keywords: average treatment effect, confounder, difference-in-difference, intelligent transportation system, potential outcome

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Authors: Stefanie Siebenhütter

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This paper aims to reveal criterions involved in the process of identity-forming among multilingual minority language speakers in Northeastern Thailand and in the capital Bangkok. Using sociolinguistic interviews and questionnaires, it is asked which factors are important for speakers and how they define their identity by their interactions socially as well as linguistically. One key question to answer is how sociolinguistic factors may force or diminish the process of forming social identity of multilingual minority speakers. However, the motivation for specific language use is rarely overt to the speaker’s themselves as well as to others. Therefore, identifying the intentions included in the process of identity construction is to approach by scrutinizing speaker’s behavior and attitudes. Combining methods used in sociolinguistics and social psychology allows uncovering the tools for identity construction that ethnic Kui uses to range themselves within a multilingual setting. By giving an overview of minority speaker’s language use in context of the specific border near multilingual situation and asking how speakers construe identity within this spatial context, the results exhibit some of the subtle and mostly unconscious criterions involved in the ongoing process of identity construction.

Keywords: social identity, identity construction, minority language, multilingualism, social networks, social boundaries

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Authors: Stefano Zappacosta, Cristiano Bove, Maria Carmela Marinelli, Paola di Lauro, Katarina Spasenovic, Lorenzo Ostano, Giuseppe Aiello, Marco Pietrosanto

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This paper proposes a novel neural network approach for assessing flood hazard mapping. The core of the model is a machine learning component fed by frequency ratios, namely statistical correlations between flood event occurrences and a selected number of topographic properties. The proposed hybrid model can be used to classify four different increasing levels of hazard. The classification capability was compared with the flood hazard mapping River Basin Plans (PAI) designed by the Italian Institute for Environmental Research and Defence, ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale). The study area of Piemonte, an Italian region, has been considered without loss of generality. The frequency ratios may be used as a standalone block to model the flood hazard mapping. Nevertheless, the mixture with a neural network improves the classification power of several percentage points, and may be proposed as a basic tool to model the flood hazard map in a wider scope.

Keywords: flood modeling, hazard map, neural networks, hydrogeological risk, flood risk assessment

Procedia PDF Downloads 182

Authors: A. Gramza-Michałowska, J. Skręty, M. Antczak, J. Kobus-Cisowska, D. Kmiecik, J. Korczak, Kulczyński Bartosz

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Background: Modern consumer highly appreciates the correlation between eating habits and health. Intensified research showed many proofs confirming that food besides its basic nutritional function, possess also significant prophylactic and therapeutic potential. Preventive potential of selected food is commonly used as improvement factor of patients life standard. World Health Organization indicates that diabetes (Diabetes mellitus) and obesity are two of the most common and dangerous diseases. Diet therapy is an element of diabetes education program and a part of healing process, allowing maintaining and remaining the optimal metabolic state of the system. It must be remembered that diabetes treatment should be individualized to each patient. One of highly recommended vegetable for diabetes is asparagus (Asparagus officinalis L.), low calories common plant, growing in European countries. Objective: To propose the technology of unsweetened muesli production with addition of new components, we investigated the effects of selected vegetable addition on antioxidative capacity and consumer’s acceptance of muesli as representative of breakfast product. Methods: Muesli was formulated from a composition of oat flakes, flaxseed, bran, carrots, broccoli and asparagus. Basic composition of muesli was evaluated as content of protein, lipids, fatty acid composition, ash, selected minerals and caloricity. Antioxidant capacity of muesli was evaluated with use radical scavenging methods (DPPH, ABTS), ORAC value and PCL - photochemiluminescence antiradical potential. Proposed muesli as new product was also characterized with sensory analysis, which included color, scent, taste, consistency and overall acceptance of a product. Results: Results showed that addition of freeze-dried asparagus into muesli allowed to lower the fat content and caloricity of a product according to the base product. No significant loss in antioxidant potential was evaluated, also the sensory value of a product was not negative. Conclusion: Designed muesli would be an answer for obese people looking for healthy snack during the daytime. Results showed that product with asparagus addition would be accepted by the consumers and because of its antidiabetic potential could be a n important factor in prevention of diabetes or obesity. Financial support by the UE Project no PO IG 01.01.00.00-061/09

Keywords: muesli, vegetables, asparagus, antioxidant potential, lipids

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Authors: Abe D. Desta

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This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking.

Keywords: artificial intelligence, computer vision, deep learning, fast-regional convolutional neural networks, feature extraction, vehicle tracking

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Authors: Manverpreet Kaur, Amarpreet Singh

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The demand of Vehicular ad hoc networks is increasing day by day, due to offering the various applications and marvelous benefits to VANET users. Clustering in VANETs is most important to overcome the connectivity problems of VANETs. In this paper, we proposed a new clustering technique Enhanced cluster based connectivity maintenance in vehicular ad hoc network. Our objective is to form long living clusters. The proposed approach is grouping the vehicles, on the basis of the longest list of neighbors to form clusters. The cluster formation and cluster head selection process done by the RSU that may results it reduces the chances of overhead on to the network. The cluster head selection procedure is the vehicle which has closest speed to average speed will elect as a cluster Head by the RSU and if two vehicles have same speed which is closest to average speed then they will be calculate by one of the new parameter i.e. distance to their respective destination. The vehicle which has largest distance to their destination will be choosing as a cluster Head by the RSU. Our simulation outcomes show that our technique performs better than the existing technique.

Keywords: VANETs, clustering, connectivity, cluster head, intelligent transportation system (ITS)

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Authors: Eugene Stepanov, Arkadi Ponossov

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Many mathematical models used in biological and other applications are ill-posed. The reason for that is the nature of differential equations, where the nonlinearities are assumed to be step functions, which is done to simplify the analysis. Prominent examples are switched systems arising from gene regulatory networks and neural field equations. This simplification leads, however, to theoretical and numerical complications. In the presentation, it is proposed to apply the theory of hybrid feedback controls to regularize the problem. Roughly speaking, one attaches a finite state control (‘automaton’), which follows the trajectories of the original system and governs its dynamics at the points of ill-posedness. The construction of the automaton is based on the classification of the attractors of the specially designed adjoint dynamical system. This ‘hybridization’ is shown to regularize the original switched system and gives rise to efficient hybrid numerical schemes. Several examples are provided in the presentation, which supports the suggested analysis. The method can be of interest in other applied fields, where differential equations contain step-like nonlinearities.

Keywords: hybrid feedback control, ill-posed problems, singular perturbation analysis, step-like nonlinearities

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Authors: Monica Racha, Siva Chandrasekaran, Alex Stojcevski

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The engineering programs focus on promoting students' personal and professional development by ensuring that students acquire technical and professional competencies during four-year studies. The traditional engineering laboratory provides an opportunity for students to "practice by doing," and laboratory facilities aid them in obtaining insight and understanding of their discipline. Due to rapid technological advancements and the current COVID-19 outbreak, the traditional labs were transforming into virtual learning environments. Aim: To better understand the limitations of the physical laboratory, this research study aims to use a Machine Learning (ML) algorithm that interfaces with the Augmented Reality HoloLens and predicts the image behavior to classify and detect the electronic components. The automated electronic components error classification and detection automatically detect and classify the position of all components on a breadboard by using the ML algorithm. This research will assist first-year undergraduate engineering students in conducting laboratory practices without any supervision. With the help of HoloLens, and ML algorithm, students will reduce component placement error on a breadboard and increase the efficiency of simple laboratory practices virtually. Method: The images of breadboards, resistors, capacitors, transistors, and other electrical components will be collected using HoloLens 2 and stored in a database. The collected image dataset will then be used for training a machine learning model. The raw images will be cleaned, processed, and labeled to facilitate further analysis of components error classification and detection. For instance, when students conduct laboratory experiments, the HoloLens captures images of students placing different components on a breadboard. The images are forwarded to the server for detection in the background. A hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm will be used to train the dataset for object recognition and classification. The convolution layer extracts image features, which are then classified using Support Vector Machine (SVM). By adequately labeling the training data and classifying, the model will predict, categorize, and assess students in placing components correctly. As a result, the data acquired through HoloLens includes images of students assembling electronic components. It constantly checks to see if students appropriately position components in the breadboard and connect the components to function. When students misplace any components, the HoloLens predicts the error before the user places the components in the incorrect proportion and fosters students to correct their mistakes. This hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm automating electronic component error classification and detection approach eliminates component connection problems and minimizes the risk of component damage. Conclusion: These augmented reality smart glasses powered by machine learning provide a wide range of benefits to supervisors, professionals, and students. It helps customize the learning experience, which is particularly beneficial in large classes with limited time. It determines the accuracy with which machine learning algorithms can forecast whether students are making the correct decisions and completing their laboratory tasks.

Keywords: augmented reality, machine learning, object recognition, virtual laboratories

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Authors: Manohar Salla, Mark S. Butler, Ruby Pelingon, Geraldine Kaeslin, Daniel E. Croker, Janet C. Reid, Jong Min Baek, Paul V. Bernhardt, Elizabeth M. J. Gillam, Matthew A. Cooper, Avril A. B. Robertson

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MCC950 is a potent and selective inhibitor of the NOD-like receptor pyrin domain-containing protein 3 (NLRP3) inflammasome that shows early promise for treatment of inflammatory diseases. The identification of major metabolites of lead molecule is an important step during drug development process. It provides an information about the metabolically labile sites in the molecule and thereby helping medicinal chemists to design metabolically stable molecules. To identify major metabolites of MCC950, the compound was incubated with human liver microsomes and subsequent analysis by (+)- and (−)-QTOF-ESI-MS/MS revealed a major metabolite formed due to hydroxylation on 1,2,3,5,6,7-hexahydro-s-indacene moiety of MCC950. This major metabolite can lose two water molecules and three possible regioisomers were synthesized. Co-elution of major metabolite with each of the synthesized compounds using HPLC-ESI-SRM-MS/MS revealed the structure of the metabolite (±) N-((1-hydroxy-1,2,3,5,6,7-hexahydro-s-indacen-4-yl)carbamoyl)-4-(2-hydroxypropan-2-yl)furan-2-sulfonamide. Subsequent synthesis of individual enantiomers and coelution in HPLC-ESI-SRM-MS/MS using a chiral column revealed the metabolite was R-(+)- N-((1-hydroxy-1,2,3,5,6,7-hexahydro-s-indacen-4-yl)carbamoyl)-4-(2-hydroxypropan-2-yl)furan-2-sulfonamide. To study the possible cytochrome P450 enzyme(s) responsible for the formation of major metabolite, MCC950 was incubated with a panel of cytochrome P450 enzymes. The result indicated that CYP1A2, CYP2A6, CYP2B6, CYP2C9, CYP2C18, CYP2C19, CYP2J2 and CYP3A4 are most likely responsible for the formation of the major metabolite. The biological activity of the major metabolite and the other synthesized regioisomers was also investigated by screening for for NLRP3 inflammasome inhibitory activity and cytotoxicity. The major metabolite had 170-fold less inhibitory activity (IC50-1238 nM) than MCC950 (IC50-7.5 nM). Interestingly, one regioisomer had shown nanomolar inhibitory activity (IC50-232 nM). However, no evidence of cytotoxicity was observed with any of these synthesized compounds when tested in human embryonic kidney 293 cells (HEK293) and human liver hepatocellular carcinoma G2 cells (HepG2). These key findings give an insight into the SAR of the hexahydroindacene moiety of MCC950 and reveal a metabolic soft spot which could be blocked by chemical modification.

Keywords: Cytochrome P450, inflammasome, MCC950, metabolite, microsome, NLRP3

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Authors: Rohit Kamal, Devaki Perumal Rajaram, Rajam Krishna, Sai Kumar Pindagiri, Silas Danielraj

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Introduction: According to WHO, Global health observatory at least 2.8 million people die each year because of obesity and overweight. This is mainly because of the adverse metabolic effects of obesity and overweight on blood pressure, lipid profile especially cholesterol and insulin resistance. To achieve optimum health WHO has set the BMI in the range of 18.5 to 24.9 kg/m2. Due to modernization of life style, physical exercise in the form of work is no longer a possibility and hence an effective way to burn out calories to achieve the optimum BMI is the need of the hour. Studies have shown that exercising for more than 60 minutes /day helps to maintain the weight and to reduce the weight exercise should be done for 90 minutes a day. Moderate exercise for about 30 min is essential for burning up of calories. People with low endurance fail to perform even the low intensity exercise for minimal time. Hence, it is necessary to find out some effective method to increase the endurance time. Methodology: This study was approved by the Institutional Ethical committee of our college. After getting written informed consent, 25 apparently healthy males between the age group 18-20 years were selected. Subjects are with muscular disorder, subjects who are Hypertensive, Diabetes, Smokers, Alcoholics, taking drugs affecting the muscle strength. To determine the endurance time: Maximum voluntary contraction (MVC) was measured by asking the participants to squeeze the hand grip dynamometer as hard as possible and hold it for 3 seconds. This procedure was repeated thrice and the average of the three reading was taken as the maximum voluntary contraction. The participant was then asked to squeeze the dynamometer and hold it at 70% of the maximum voluntary contraction while hearing fast tempo music which was played for about ten minutes then the participant was asked to relax for ten minutes and was made to hold the hand grip dynamometer at 70% of the maximum voluntary contraction while hearing slow tempo music. To avoid the bias of getting habituated to the procedure the order of hearing for the fast and slow tempo music was changed. The time for which they can hold it at 70% of MVC was determined by using a stop watch and that was taken as the endurance time. Results: The mean value of the endurance time during fast and slow tempo music was compared in all the subjects. The mean MVC was 34.92 N. The mean endurance time was 21.8 (16.3) seconds with slow tempo music which was more then with fast tempo music with which the mean endurance time was 20.6 (11.7) seconds. The preference was more for slow tempo music then for fast tempo music. Conclusion: Music when played during exercise by some unknown mechanism helps to increase the endurance time by alleviating the symptoms of lactic acid accumulation.

Keywords: endurance time, fast tempo music, maximum voluntary contraction, slow tempo music

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Authors: R. Bernaoui, P. Ohly

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Our research shows the use of the mobile phone that consolidates the relationship between veterinarians, and that between breeders and veterinarians. On the other hand it asserts that the tool in question is a means of economic development. The results of our survey reveal a positive return to the veterinary community, which shows that the mobile phone has become an effective means of sustainable development through the transfer of a rapid and punctual information inheritance via social networks; including many Internet applications. Our results show that almost all veterinarians use the mobile phone for interprofessional communication. We therefore believe that the use of the mobile phone by livestock operators has greatly improved the working conditions, just as the use of this tool contributes to a better management of the exploitation as long as it allows limit travel but also save time. These results show that we are witnessing a growth in the use of mobile telephony technologies that impact is as much in terms of sustainable development. Allowing access to information, especially technical information, the mobile phone, and Information and Communication of Technology (ICT) in general, give livestock sector players not only security, by limiting losses, but also an efficiency that allows them a better production and productivity.

Keywords: algeria, breeder-veterinarian, digital heritage, networking

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Authors: Ratneev Kaur, Tajinder Kaur, Anupam Kaur

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Introduction: Polycystic Ovary Syndrome (PCOS) is a heterogenous disorder of endocrine system among women of reproductive age. PCOS is characterized by hyperandrogenism, anovulation, polycystic ovaries, hirsutism, obesity, and hyperinsulinemia. Several pathways are implicated in its etiology including the metabolic pathway of steroid hormone synthesis regulatory pathways. PCOS is an androgen excess disorder, genes operating in steroidogenesis may alter pathogenesis of PCOS. The cytochrome P450scc is a cholesterol side chain cleavage enzyme coded by CYP11A1 gene and catalyzes conversion of cholesterol to pregnenolone, the initial and rate-limiting step in steroid hormone synthesis. It is postulated that polymorphisms in this gene may play an important role in the regulation of CYP11A1 expression and leading to increased or decreased androgen production. The present study will be the first study from north India to best of our knowledge, to analyse the association of CYP11A1 (rs11632698) polymorphism in women suffering from PCOS. Methodology: The present study was approved by ethical committee of Guru Nanak Dev University in consistent with declaration of Helsinki. A total of 300 samples (150 PCOS cases and 150 controls) were recruited from Hartej hospital, for the present study. Venous blood sample (3ml) was withdrawn from women diagnosed with PCOS by doctor, according to Rotterdam 2003 criteria and from healthy age matched controls only after informed consent and detailed filled proforma. For molecular genetics analysis, blood was stored in EDTA vials. After DNA isolation by organic method, PCR-RFLP approach was used for genotyping and association analysis of rs11632698 polymorphism. Statistical analysis was done to check for significance of selected polymorphism with PCOS. Results: In 150 PCOS cases, the frequency of AA, AG and GG genotype was found to be 48%, 35%, and 13% compared to 62%, 27% and 8% in 150 controls. The major allele (A) and minor allele (G) frequency was 68% and 32% in cases and 78% and 22% in controls. Minor allele frequency was higher in cases as compared to controls, as well as the distribution of genotype was observed to be statistically significant (ᵡ²=6.525, p=0.038). Odds ratio in dominant, co-dominant and recessive models observed was 1.81 (p=0.013), 1.54 (p=0.012) and 1.77 (p=0.132) respectively. Conclusion: The present study showed statistically significant association of rs11632698 with PCOS (p=0.038) in North Indian women.

Keywords: polycystic ovary syndrome, CYP11A1, rs11632698, hyperandrogenism

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Authors: Valeria T. Dilcheva, Svetlozara L. Petkova, Ivelin Vladov

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Trichinellosis is a food-borne parasitic disease caused by nematodes of the genus Trichinella which are zoonotic parasites with cosmopolitan distribution and major socio-economic importance. Human infection is acquired through consumption of undercooked meat from domestic or wild animal. Penetration of Trichinella larvae into striated skeletal muscle cells results in ultrastructural and metabolic changes. Migration of larvae causes the typical symptoms and signs of the disease. The severity of the symptoms depends on the number of ingested Trichinella larvae and the immune response of the host. Eosinophilia is present, with few exceptions, in most cases of human trichinellosis, inasmuch as it is the earliest and most important host response. Even in human asymptomatic cases, increases in eosinophilia of up to 15% have been observed. Eosinophilia appears at an early stage of infection between the second and fifth weeks of infection. By 2005 it was considered that only two species of Trichinella genus were found in the country. After routine trichinelloscopy procedure disseminated single muscle larvae in samples of wild boars and badger were PCR-identified as T. pseudospiralis. The study aimed to observed hematological changes occurring during experimentally induced infection with Trichinella spiralis, T. britovi and T. pseudospiralis in mice. We performed hematological blood profile, tracking 15 blood indicators. In statistical analysis made by Two-way ANOVA, there were significant differences of HGB, MCHC, PLT, Lymph%, Gran% in all three types of trichinellosis compared to control animals. Capsule-forming T. spiralis showed statistically significant differences in HGB, MCHC, Lymph% and PLT compared to the other two species. Non capsule-forming T. pseudospiralis showed statistically significant differences in Lymph%, Gran% relative to the control and in Gran% relative to T. spiralis. It appears rather substantial the process of capsule formation for prolonged immune response and retention of high content of percentage of lymphocytes(Lymph%) and low of granulocyte(Gran%) in T. pseudospiralis, which is contrary to studies for T. spiralis and eosinophilia. Studies and analyzes of some specific blood profile parameters can provide additional data in favor of early diagnosis and adequate treatment as well as provide a better understanding of acute and chronic trichinosis.

Keywords: hematological test, T. britovi, T. spiralis, T. pseudospiralis

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