Search results for: attention-based fully convolutional network

Authors: Krishna Pal Singh, Shailendra Kumar Gupta, Olaf Wolkenhauer

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Background: Our study aimed to identify common genes and potential targets across the four diseases, which include rheumatoid arthritis, melanoma metastasis, ulcerative colitis, and Crohn’s disease. We used a network and systems biology approach to identify the hub gene, which can act as a potential target for all four disease conditions. The regulatory network was extracted from the PPI using the MCODE module present in Cytoscape. Our objective was to investigate the significance of hub genes in these diseases using gene ontology and KEGG pathway enrichment analysis. Methods: Our methodology involved collecting disease gene-related information from DisGeNET databases and performing protein-protein interaction (PPI) network and core genes screening. We then conducted gene ontology and KEGG pathway enrichment analysis. Results: We found that IL6 plays a critical role in all disease conditions and in different pathways that can be associated with the development of all four diseases. Conclusions: The theoretical importance of our research is that we employed various systems and structural biology techniques to identify a crucial protein that could serve as a promising target for treating multiple diseases. Our data collection and analysis procedures involved rigorous scrutiny, ensuring high-quality results. Our conclusion is that IL6 plays a significant role in all four diseases, and it can act as a potential target for treating them. Our findings may have important implications for the development of novel therapeutic interventions for these diseases.

Keywords: melanoma metastasis, rheumatoid arthritis, inflammatory bowel diseases, integrated bioinformatics analysis

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Authors: Waddah Abdullah, Saleh Al-Sarem

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Electrokinetic remediation was undoubtedly proven to be one of the most efficient techniques used to clean up soils contaminated with polar charged contaminants (such as heavy metals) and non-polar organic contaminants. It can be efficiently used to clean up low permeability mud, wastewater, electroplating wastes, sludge, and marine dredging. This study presented and discussed the results of electrokinetic remediation processes to clean up soils contaminated with nickel. Two types of electrokinetics cells were used: an open cell and an advanced cylindrical cell. Two types of soils were used for this investigation; the Azraq green clay which has very low permeability taken from the eastern part of Jordan (city of Azraq) and a sandy soil having, relatively, very high permeability. The clayey soil was spiked with 500 ppm of nickel, and the sandy soil was spiked with 1500 ppm of nickel. Fully saturated and partially saturated clayey soils were used for the clean-up process. Clayey soils were tested under a direct current of 80 mA and 50 mA to study the effect of the electrical current on the remediation process. Chelating agent (Na-EDTA), disodium ethylene diamine tetraacetatic acid, was used in both types of soils to enhance the electroremediation process. The effect of carbonates presence in the contaminated soils, also, was investigated by use of sodium carbonate and calcium carbonate. pH changes in the anode and the cathode compartments were controlled by use of buffer solutions. The results of the investigation showed that for the fully saturated clayey soil spiked with nickel had an average removal efficiency of 64%, and the average removal efficiency was 46% for the unsaturated clayey soil. For the sandy soil, the average removal efficiency of Nickel was 90%. Test results showed that presence of carbonates in the remediated soils retarded the clean-up process of nickel-contaminated soils (removal efficiency was reduced from 90% to 60%). EDTA enhanced decontamination of nickel contaminated clayey and sandy soils with carbonates was studied. The average removal efficiency increased from 60% (prior to using EDTA) to more than 90% after using EDTA.

Keywords: buffer solution, EDTA, electroremediation, nickel removal efficiency

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Authors: Yi-De Liu

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The aim of this paper is to discuss how the construct of networking and social capital can be used to understand the effect events can have on the cultural sector. Based on case study, this research sought the views of those working in the cultural sector on Liverpool’s year as the European Capital of Culture (ECOC). Methodologically, this study involves literature review to prompt theoretical sensitivity, the collection of primary data via online survey (n= 42) and follow-up telephone interviews (n= 8) to explore the emerging findings in more detail. The findings point to a number of ways in which the ECOC constitutes a boost for networking and its effects on city’s cultural sector, including organisational learning, aspiration and leadership. The contributions of this study are two-fold: (1) Evaluating the long-term effects on network formation in the cultural sector following major event; (2) conceptualising the impact assessment of organisational social capital for future ECOC or similar events.

Keywords: network, social capital, cultural impact, european capital of culture

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Authors: Mustafa Mutahari, Nao Sugiki, Kojiro Matsuo

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The four decades of war, lack of job opportunities, poverty, lack of services, and natural disasters in different provinces of Afghanistan have contributed to a rapid increase in the population of Kabul, the capital city of Afghanistan. Population census has not been conducted since 1979, the first and last population census in Afghanistan. However, according to population estimations by Afghan authorities, the population of Kabul has been estimated at more than 4 million people, whereas the city was designed for two million people. Although the major transport mode of Kabul residents is public transport, responsible authorities within the country failed to supply the required means of transportation systems for the city. Besides, informal resettlement, lack of intersection control devices, presence of illegal vendors on streets, presence of illegal and unstandardized on-street parking and bus stops, driver`s unprofessional behavior, weak traffic law enforcement, and blocked roads and sidewalks have contributed to the extreme traffic congestion of Kabul. In 2018, the government of Afghanistan approved the Kabul city Urban Design Framework (KUDF), a vision towards the future of Kabul, which provides strategies and design guidance at different scales to direct urban development. Considering traffic congestion of the city and its budget limitations, the KUDF proposes a BRT route network with seven lines to reduce the traffic congestion, and it is said to facilitate more than 50% of Kabul population to benefit from this service. Based on the KUDF, it is planned to increase the BRT mode share from 0% to 17% and later to 30% in medium and long-term planning scenarios, respectively. Therefore, a detailed research study is needed to evaluate the proposed system before the implementation stage starts. The integrated land-use transport model is an effective tool to evaluate the Kabul BRT because of its future assessment capabilities that take into account the interaction between land use and transportation. This research aims to analyze and evaluate the proposed BRT route network with the application of an integrated land-use and transportation model. The research estimates the population distribution and travel behavior of Kabul within small boundary scales. The actual road network and land-use detailed data of the city are used to perform the analysis. The BRT corridors are evaluated not only considering its impacts on the spatial interactions in the city`s transportation system but also on the spatial developments. Therefore, the BRT are evaluated with the scenarios of improving the Kabul transportation system based on the distribution of land-use or spatial developments, planned development typology and population distribution of the city. The impacts of the new improved transport system on the BRT network are analyzed and the BRT network is evaluated accordingly. In addition, the research also focuses on the spatial accessibility of BRT stops, corridors, and BRT line beneficiaries, and each BRT stop and corridor are evaluated in terms of both access and geographic coverage, as well.

Keywords: accessibility, BRT, integrated land-use and transport model, travel behavior, spatial development

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Authors: Takashi Yamaguchi, Ichio Asanuma, Jong G. Park, Kenneth J. Mackin, John Mittleman

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In this paper, vessel detection using the artificial neural network is proposed in order to automatically construct the vessel detection model from the satellite imagery of day/night band (DNB) in visible infrared in the products of Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (Suomi-NPP).The goal of our research is the establishment of vessel detection method using the satellite imagery of DNB in order to monitor the change of vessel activity over the wide region. The temporal vessel monitoring is very important to detect the events and understand the circumstances within the maritime environment. For the vessel locating and detection techniques, Automatic Identification System (AIS) and remote sensing using Synthetic aperture radar (SAR) imagery have been researched. However, each data has some lack of information due to uncertain operation or limitation of continuous observation. Therefore, the fusion of effective data and methods is important to monitor the maritime environment for the future. DNB is one of the effective data to detect the small vessels such as fishery ships that is difficult to observe in AIS. DNB is the satellite sensor data of VIIRS on Suomi-NPP. In contrast to SAR images, DNB images are moderate resolution and gave influence to the cloud but can observe the same regions in each day. DNB sensor can observe the lights produced from various artifact such as vehicles and buildings in the night and can detect the small vessels from the fishing light on the open water. However, the modeling of vessel detection using DNB is very difficult since complex atmosphere and lunar condition should be considered due to the strong influence of lunar reflection from cloud on DNB. Therefore, artificial neural network was applied to learn the vessel detection model. For the feature of vessel detection, Brightness Temperature at the 3.7 μm (BT3.7) was additionally used because BT3.7 can be used for the parameter of atmospheric conditions.

Keywords: artificial neural network, day/night band, remote sensing, Suomi National Polar-orbiting Partnership, vessel detection, Visible Infrared Imaging Radiometer Suite

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Authors: Jia-Hong Lee, Mei-Yi Wu, Hsien-Tsung Kuo

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Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets － UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers.

Keywords: texture classification, texture descriptor, SIFT, SURF, ORB

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Authors: Bandreddy Anand Babu, Srinivasa Rao Mandadi, C. Pradeep Reddy, N. Ramesh Babu

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In this paper quickly illustrate the correlation investigation of Auto-Regressive Integrated Moving and Average (ARIMA) and daptive Network Based Fuzzy Inference System (ANFIS) models done by climate estimating. The climate determining is taken from University of Waterloo. The information is taken as Relative Humidity, Ambient Air Temperature, Barometric Pressure and Wind Direction utilized within this paper. The paper is carried out by analyzing the exhibitions are seen by demonstrating of ARIMA and ANIFIS model like with Sum of average of errors. Versatile Network Based Fuzzy Inference System (ANFIS) demonstrating is carried out by Mat lab programming and Auto-Regressive Integrated Moving and Average (ARIMA) displaying is produced by utilizing XLSTAT programming. ANFIS is carried out in Fuzzy Logic Toolbox in Mat Lab programming.

Keywords: ARIMA, ANFIS, fuzzy surmising tool stash, weather forecasting, MATLAB

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Authors: M. Sayyour, M. Abdulkareem, O. Osman, S. Salmeh

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Emphatic sounds in Arabic are /tˤ/, /sˤ/, /dˤ/, and /ðˤ/. They involve a secondary articulation in the pharynx area as opposed to their counterparts: /t/,/s/,/d/and /ð/. Although they are present in most Arabic dialects, some dialects have lost this class as a historical development, such as Maltese Arabic. It has been found that there is a difference in the pronunciation of these emphatic sounds between the two genders, arguing that males tend to produce more evident emphasis than females. This study builds on these studies by trying to investigate whether listeners perceive fully emphatic sounds as more masculine and less emphatic sounds as more feminine. Furthermore, the study aims to find out which is more important in this perception process: the emphatic consonant itself or the vowel following it. To test this, natural and manipulated tokens of two male and two female speakers were used. The natural tokens include words that have emphatic consonant and emphatic vowel and tokens that have plain consonant and plain vowel. The manipulated tokens include words that have emphatic consonant but central vowel and plain consonant followed by the same central vowel. These manipulated tokens allow us to see whether the consonant will still affect the perception even if the vowel is controlled. Another group of words that contained no emphatic sounds was used as a control group. The total number of tokens (natural, manipulated, and control) are 160 tokens. After that, 60 university students (30 males and 30 females) listened to these tokens and responded by choosing a specific character that they think is likely to produce each token. The characters’ descriptions are carefully written with two degrees of femininity and two degrees of masculinity. The preliminary results for the femininity level showed that the highest degree of femininity was for tokens that contain a plain consonant and a plain vowel. The lowest level of femininity was given for tokens that have fully emphatic consonant and vowel. For the manipulated tokens that contained plain consonant and central vowel, the femininity degree was high which indicates that the consonant is more important than the vowel, while for the manipulated tokens that contain emphatic consonant and a central vowel, the femininity level was higher than that for the tokens that have emphatic consonant and emphatic vowel, which indicates that the vowel is more important for the perception of emphatic consonants. These results are interpreted in light of feminist linguistic theories, linguistic expectations, performed gender and linguistic change theories.

Keywords: Emphatic sounds, gender studies, perception, sociophonetics

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Authors: Mohamed Korany, Azza Gazy, Essam Khamis, Marwa Adel, Miranda Fawzy

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Background: Two new, simple and specific methods; First, a Zero-crossing first-derivative technique and second, a chemometric-assisted spectrophotometric artificial neural network (ANN) were developed and validated in accordance with ICH guidelines. Both methods were used for the simultaneous estimation of the two closely related antioxidant nutraceuticals ; Coenzyme Q10 (Q) ; also known as Ubidecarenone or Ubiquinone-10, and Vitamin E (E); alpha-tocopherol acetate, in their pharmaceutical binary mixture. Results: For first method: By applying the first derivative, both Q and E were alternatively determined; each at the zero-crossing of the other. The D1 amplitudes of Q and E, at 285 nm and 235 nm respectively, were recorded and correlated to their concentrations. The calibration curve is linear over the concentration range of 10-60 and 5.6-70 μg mL-1 for Q and E, respectively. For second method: ANN (as a multivariate calibration method) was developed and applied for the simultaneous determination of both analytes. A training set (or a concentration set) of 90 different synthetic mixtures containing Q and E, in wide concentration ranges between 0-100 µg/mL and 0-556 µg/mL respectively, were prepared in ethanol. The absorption spectra of the training sets were recorded in the spectral region of 230–300 nm. A Gradient Descend Back Propagation ANN chemometric calibration was computed by relating the concentration sets (x-block) to their corresponding absorption data (y-block). Another set of 45 synthetic mixtures of the two drugs, in defined range, was used to validate the proposed network. Neither chemical separation, preparation stage nor mathematical graphical treatment were required. Conclusions: The proposed methods were successfully applied for the assay of Q and E in laboratory prepared mixtures and combined pharmaceutical tablet with excellent recoveries. The ANN method was superior over the derivative technique as the former determined both drugs in the non-linear experimental conditions. It also offers rapidity, high accuracy, effort and money saving. Moreover, no need for an analyst for its application. Although the ANN technique needed a large training set, it is the method of choice in the routine analysis of Q and E tablet. No interference was observed from common pharmaceutical additives. The results of the two methods were compared together

Keywords: coenzyme Q10, vitamin E, chemometry, quantitative analysis, first derivative spectrophotometry, artificial neural network

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Authors: Patience Muchini, Electdom Matandiroya, Emmanuel Mashonjowa

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A ground-end manifestation of space weather phenomena is known as geomagnetically induced currents (GICs). GICs flow along the electric power transmission cables connecting the transformers and between the grounding points of power transformers during significant geomagnetic storms. Geomagnetically induced currents have been studied in other regions and have been noted to affect the power grid network. In Zimbabwe, grid failures have been experienced, but it is yet to be proven if these failures have been due to GICs. The purpose of this paper is to characterize geomagnetically induced currents with a power grid network. This paper analyses data collected, which is geomagnetic data, which includes the Kp index, DST index, and the G-Scale from geomagnetic storms and also analyses power grid data, which includes reactive power, relay tripping, and alarms from high voltage substations and then correlates the data. This research analysis was first theoretically analyzed by studying geomagnetic parameters and then experimented upon. To correlate, MATLAB was used as the basic software to analyze the data. Latitudes of the substations were also brought into scrutiny to note if they were an impact due to the location as low latitudes areas like most parts of Zimbabwe, there are less severe geomagnetic variations. Based on theoretical and graphical analysis, it has been proven that there is a slight relationship between power system failures and GICs. Further analyses can be done by implementing measuring instruments to measure any currents in the grounding of high-voltage transformers when geomagnetic storms occur. Mitigation measures can then be developed to minimize the susceptibility of the power network to GICs.

Keywords: adverse space weather, DST index, geomagnetically induced currents, KP index, reactive power

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Authors: Ghobad Gorji, Hasan Golabi

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The goal of this paper is to describe the design of an ultra-wideband (UWB) system that is optimized for the low-rate wireless personal area network application. To this aim, we propose a system based on direct chaotic communication (DCC) technology. Based on this system, a 2-GHz wide chaotic signal is directly generated into the lower band of the UWB spectrum, i.e., 3.1–5.1 GHz. For this system, two simple modulation schemes, namely chaotic on-off keying (COOK) and differential chaos shift keying (DCSK), were studied before, and their performance was evaluated. We propose a modulation scheme, namely Double DCSK, to improve the performance of UWB DCC. Different characteristics of these systems, with Monte Carlo simulations based on the Additive White Gaussian Noise (AWGN) and the IEEE 802.15.4a standard channel models, are compared.

Keywords: UWB, DCC, IEEE 802.15.4a, COOK, DCSK

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Authors: Matthew Scott Lau

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This study addresses the research question: How effective has the Community Health Assist Scheme (CHAS) been in reducing market failure in Singapore’s healthcare sector? The CHAS policy, introduced in 2012 in Singapore, aims to improve accessibility and affordability of healthcare by offering subsidies to low and middle-income groups and elderly individuals for general practice consultations and healthcare. The investigation was undertaken by acquiring and analysing primary and secondary research data from 3 main sources, including handwritten survey responses of 334 individuals who were valid CHAS subsidy recipients (CHAS cardholders) from 5 different locations in Singapore, interview responses from two established general practitioner doctors with working knowledge of the scheme, and information from literature available online. Survey responses were analysed to determine how CHAS has affected the affordability and consumption of healthcare, and other benefits or drawbacks for CHAS users. The interview responses were used to explain the benefits of healthcare consumption and provide different perspectives on the impacts of CHAS on the various parties involved. Online sources provided useful information on changes in healthcare consumerism and Singapore’s government policies. The study revealed that CHAS has been largely effective in reducing market failure as the subsidies granted to consumers have improved the consumption of healthcare. This has allowed for the external benefits of healthcare consumption to be realized, thus reducing market failure. However, the study also revealed that CHAS cannot be fully effective in reducing market failure as the scope of CHAS prevents healthcare consumption from fully reaching the socially optimal level. Hence, the study concluded that CHAS has been effective to a large extent in reducing market failure in Singapore’s healthcare sector, albeit with some benefits to third parties yet to be realised. There are certain elements of the investigation, which may limit the validity of the conclusion, such as the means used to determine the socially optimal level of healthcare consumption, and the survey sample size.

Keywords: healthcare consumption, health economics, market failure, subsidies

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Authors: Zhaopeng Zhu, Xianzhi Song, Gensheng Li, Shuo Zhu, Shiming Duan, Xuezhe Yao

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Accurate calculation of wellbore pressure is of great significance to prevent wellbore risk during drilling. The traditional mechanism model needs a lot of iterative solving procedures in the calculation process, which reduces the calculation efficiency and is difficult to meet the demand of dynamic control of wellbore pressure. In recent years, many scholars have introduced artificial intelligence algorithms into wellbore pressure calculation, which significantly improves the calculation efficiency and accuracy of wellbore pressure. However, due to the ‘black box’ property of intelligent algorithm, the existing intelligent calculation model of wellbore pressure is difficult to play a role outside the scope of training data and overreacts to data noise, often resulting in abnormal calculation results. In this study, the multi-phase flow mechanism is embedded into the objective function of the neural network model as a constraint condition, and an intelligent prediction model of wellbore pressure under the constraint condition is established based on more than 400,000 sets of pressure measurement while drilling (MPD) data. The constraint of the multi-phase flow mechanism makes the prediction results of the neural network model more consistent with the distribution law of wellbore pressure, which overcomes the black-box attribute of the neural network model to some extent. The main performance is that the accuracy of the independent test data set is further improved, and the abnormal calculation values basically disappear. This method is a prediction method driven by MPD data and multi-phase flow mechanism, and it is the main way to predict wellbore pressure accurately and efficiently in the future.

Keywords: multiphase flow mechanism, pressure while drilling data, wellbore pressure, mechanism constraints, combined drive

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Authors: B. L. Ho, L. Shi, D. F. Wang, V. C. T. Mok

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The brain’s functional connectivity while temporally non-stationary does express consistency at a macro spatial level. The study of stable resting state connectivity patterns hence provides opportunities for identification of diseases if such stability is severely perturbed. A mathematical model replicating the brain’s spatial connections will be useful for understanding brain’s representative geometry and complements the empirical model where it falls short. Empirical computations tend to involve large matrices and become infeasible with fine parcellation. However, the proposed analytical model has no such computational problems. To improve replicability, 92 subject data are obtained from two open sources. The proposed methodology, inspired by financial theory, uses multivariate regression to find relationships of every cortical region of interest (ROI) with some pre-identified hubs. These hubs acted as representatives for the entire cortical surface. A variance-covariance framework of all ROIs is then built based on these relationships to link up all the ROIs. The result is a high level of match between model and empirical correlations in the range of 0.59 to 0.66 after adjusting for sample size; an increase of almost forty percent. More significantly, the model framework provides an intuitive way to delineate between systemic drivers and idiosyncratic noise while reducing dimensions by more than 30 folds, hence, providing a way to conduct attribution analysis. Due to its analytical nature and simple structure, the model is useful as a standalone toolkit for network dependency analysis or as a module for other mathematical models.

Keywords: functional magnetic resonance imaging, multivariate regression, network hubs, resting state functional connectivity

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Authors: L. Fridrichova, R. Knížek, V. Bajzík

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Drape is one of the important visual characteristics of the fabric. This paper is introducing an innovative method of measurement and evaluation of the drape shape of the fabric. The measuring principle is based on the possibility of multiple vertical strain of the fabric. This method more accurately simulates the real behavior of the fabric in the process of draping. The method is fully automated, so the sample can be measured by using any number of cycles in any time horizon. Using the present method of measurement, we are able to describe the viscoelastic behavior of the fabric.

Keywords: drape, drape shape, automated drapemeter, fabric

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Authors: Salim Ouchtati, Aissa Belmeguenai, Mouldi Bedda

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In this study, we propose an offline system for the recognition of the fragmented handwritten numeric chains. Firstly, we realized a recognition system of the isolated handwritten digits, in this part; the study is based mainly on the evaluation of neural network performances, trained with the gradient backpropagation algorithm. The used parameters to form the input vector of the neural network are extracted from the binary images of the isolated handwritten digit by several methods: the distribution sequence, sondes application, the Barr features, and the centered moments of the different projections and profiles. Secondly, the study is extended for the reading of the fragmented handwritten numeric chains constituted of a variable number of digits. The vertical projection was used to segment the numeric chain at isolated digits and every digit (or segment) was presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits).

Keywords: features extraction, handwritten numeric chains, image processing, neural networks

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Authors: Harshadkumar Rajgor, Jeff Butterworth

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Background: Barretts oesophagus increases the risk of developing oesophageal adenocarcinoma. Over the last 40 years, there has been a 6 fold increase in the incidence of oesophageal adenocarcinoma in the western world and the incidence rates are increasing at a greater rate than cancers of the colon, breast and lung. Endoscopic mucosal resection (EMR) is a relatively new technique being used by 2 centres in the greater midlands cancer network. EMR can be used for curative or staging purposes, for high-grade dysplasia’s and T1 tumours of the oesophagus. EMR is also suitable for those who are deemed high risk for oesophagectomy. EMR has a recurrence rate of 21% according to the Wiesbaden data. Method: A retrospective study of prospectively collected data was carried out involving 24 patients who had EMR for curative or staging purposes. Complications of residual or recurrent disease following EMR that required further treatment were investigated. Results: In 54% of cases residual or recurrent disease was suspected. 96% of patients were given clear and concise information regarding their diagnosis of high-grade dysplasia or T1 tumours. All 24 patients consulted the same specialist healthcare team. Conclusion: EMR is a safe and effective treatment for patients who have high-grade dysplasia and T1NO tumours. In 54% of cases residual or recurrent disease was suspected. Initially, only single resections were undertaken. Multiple resections are now being carried out to reduce the risk of recurrence. Complications from EMR remain low in this series and consisted of a single episode of post procedural bleeding.

Keywords: endoscopic mucosal resection, oesophageal dysplasia, T1 tumours, cancer network

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Authors: Smail Boussaadi, Hassina Aliane

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With the rapid growth of scientific literature, it is becoming increasingly challenging for researchers to keep up with the latest findings in their fields. Academic, professional networks play an essential role in connecting researchers and disseminating knowledge. To improve the user experience within these networks, we need effective article recommendation systems that provide personalized content.Current recommendation systems often rely on collaborative filtering or content-based techniques. However, these methods have limitations, such as the cold start problem and difficulty in capturing semantic relationships between articles. To overcome these challenges, we propose a new approach that combines BERTopic (Bidirectional Encoder Representations from Transformers), a state-of-the-art topic modeling technique, with community detection algorithms in a academic, professional network. Experiences confirm our performance expectations by showing good relevance and objectivity in the results.

Keywords: scientific articles, community detection, academic social network, recommender systems, neural topic model

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Authors: Rana I. Abdulghani, Amera I. Melhum

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With the abnormal growth of the usage of computers over networks and under the consideration or agreement of most of the computer security experts who said that the goal of building a secure system is never achieved effectively, all these points led to the design of the intrusion detection systems(IDS). This research adopts a comparison between two techniques for network intrusion detection, The first one used the (Particles Swarm Optimization) that fall within the field (Swarm Intelligence). In this Act, the algorithm Enhanced for the purpose of obtaining the minimum error rate by amending the cluster centers when better fitness function is found through the training stages. Results show that this modification gives more efficient exploration of the original algorithm. The second algorithm used a (Back propagation NN) algorithm. Finally a comparison between the results of two methods used were based on (NSL_KDD) data sets for the construction and evaluation of intrusion detection systems. This research is only interested in clustering the two categories (Normal and Abnormal) for the given connection records. Practices experiments result in intrude detection rate (99.183818%) for EPSO and intrude detection rate (69.446416%) for BP neural network.

Keywords: IDS, SI, BP, NSL_KDD, PSO

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Authors: Chenlu Zhang, Wanni Zhang, Florian Schaule

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Indoor environment in office buildings directly affects comfort, productivity, health, and well-being of building occupants. Wireless environmental sensor networks have been deployed in many modern offices to monitor and control the indoor environments. However, indoor environmental variables are not strong enough predictors of comfort and productivity levels of every occupant due to personal differences, both physiologically and psychologically. This study proposes human-centric sensor networks that integrate wireless environmental sensors, body area network sensors and participatory sensing technologies to collect data from both environment and human and support building operations. The sensor networks have been tested in one small-size and one medium-size office rooms with 22 participants for five months. Indoor environmental data (e.g., air temperature and relative humidity), physiological data (e.g., skin temperature and Galvani skin response), and physiological responses (e.g., comfort and self-reported productivity levels) were obtained from each participant and his/her workplace. The data results show that: (1) participants have different physiological and physiological responses in the same environmental conditions; (2) physiological variables are more effective predictors of comfort and productivity levels than environmental variables. These results indicate that the human-centric sensor networks can support human-centric building control and improve comfort and productivity in offices.

Keywords: body area network, comfort and productivity, human-centric sensors, internet of things, participatory sensing

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Authors: Ilef Ketata, Mohamed Khalil Baazaoui, Robert Fromm, Ahmad Fakhfakh, Faouzi Derbel

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The integration of wireless communication, e. g. in real-or quasi-real-time applications, is related to many challenges such as energy consumption, communication range, latency, quality of service, and reliability. To minimize the latency without increasing energy consumption, wake-up receiver (WuRx) nodes have been introduced in recent works. Low-noise amplifiers (LNAs) are introduced to improve the WuRx sensitivity but increase the supply current severely. Different WuRx approaches exist with always-on, power-gated, or duty-cycled receiver designs. This paper presents a comparative study for improving communication range and decreasing the energy consumption of wireless sensor nodes.

Keywords: wireless sensor network, wake-up receiver, duty-cycled, low-noise amplifier, envelope detector, range study

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Authors: M. Cabrera, W. Tizani, J. Ninic, F. Wang

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Open section beam to concrete-filled tubular column structures has been increasingly utilized in construction over the past few decades due to their enhanced structural performance, as well as economic and architectural advantages. However, the use of this configuration in construction is limited due to the difficulties in connecting the structural members as there is no access to the inner part of the tube to install standard bolts. Blind-bolted systems are a relatively new approach to overcome this limitation as they only require access to one side of the tubular section to tighten the bolt. The performance of these connections in concrete-filled steel tubular sections remains uncharacterized due to the complex interactions between concrete, bolt, and steel section. Over the last years, research in structural performance has moved to a more sophisticated and efficient approach consisting of machine learning algorithms to generate metamodels. This method reduces the need for developing complex, and computationally expensive finite element models, optimizing the search for desirable design variables. Metamodels generated by a data fusion approach use numerical and experimental results by combining multiple models to capture the dependency between the simulation design variables and connection performance, learning the relations between different design parameters and predicting a given output. Fully characterizing this connection will transform high-rise and multistorey construction by means of the introduction of design guidance for moment-resisting blind-bolted connections, which is currently unavailable. This paper presents a review of the steps taken to develop metamodels generated by means of artificial neural network algorithms which predict the connection stress and stiffness based on the design parameters when using Extended Hollo-Bolt blind bolts. It also provides consideration of the failure modes and mechanisms that contribute to the deformability as well as the feasibility of achieving blind-bolted rigid connections when using the blind fastener.

Keywords: blind-bolted connections, concrete-filled tubular structures, finite element analysis, metamodeling

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Authors: Ilona Ivova Anachkova

Abstract:

The analogy between the artist and the shaman as both being practitioners that more easily recognize and explore spiritual matters, and thus contribute to the society in a unique way has been implied in both Modernity and Postmodernity. The Romantic conception of the shaman as a great artist who helps common men see and understand messages of a higher consciousness has been employed throughout Modernity and is active even now. This paper deconstructs the term ‘shaman’ in the metaphorical analogy ‘artist – shaman’ that was developed more fully in Modernity in different artistic and scientific discourses. The shaman is a figure that to a certain extent adequately reflects the late modern and postmodern holistic views on the world. Such views aim at distancing from traditional religious and overly rationalistic discourses. However, the term ‘shaman’ can be well substituted by other concepts such as the priest, for example. The concept ‘shaman’ is based on modern ethnographic and historical investigations. Its later philosophical, psychological and artistic appropriations designate the role of the artist as a spiritual and cultural leader. However, the artist and the shaman are not fully interchangeable terms. The figure of the shaman in ‘primitive’ societies has performed many social functions that are now delegated to different institutions and positions. The shaman incorporates the functions of a judge, a healer. He is a link to divine entities. He is the creative, aspiring human being that has heightened sensitivity to the world in both its spiritual and material aspects. Building the metaphorical analogy between the shaman and the artist comes in many ways. Both are seen as healers of the society, having propensity towards connection to spiritual entities, or being more inclined to creativity than others. The ‘shaman’ however is a fashionable word for a spiritual person used perhaps because of the anti-traditionalist religious modern and postmodern views. The figure of the priest is associated with a too rational, theoretical and detached attitude towards spiritual matters, while the practices of the shaman and the artist are considered engaged with spirituality on a deeper existential level. The term ‘shaman’ however does not have priority of other words/figures that can explore and deploy spiritual aspects of reality. Having substituted the term ‘shaman’ in the pair ‘artist as a shaman’ with ‘the priest’ or literally ‘anybody,' we witness destruction of spiritual hierarchies and come to the view that everybody is responsible for their own spiritual and creative evolution.

Keywords: artist as a shaman, creativity, extended theory of art, functions of art, priest as an artist

Procedia PDF Downloads 222

Authors: Anil Kumar Pandey

Abstract:

A compact planar monopole antenna with dual-band operation suitable for wireless local area network (WLAN) application is presented in this paper. The antenna occupies an overall area of 18 ×12 mm². The antenna is fed by a coplanar waveguide (CPW) transmission line and it combines two folded strips, which radiates at 2.4 and 5.2 GHz. In the proposed antenna, by optimally selecting the antenna dimensions, dual-band resonant modes with a much wider impedance matching at the higher band can be produced. Prototypes of the obtained optimized design have been simulated using EM solver. The simulated results explore good dual-band operation with -10 dB impedance bandwidths of 50 MHz and 2400 MHz at bands of 2.4 and 5.2 GHz, respectively, which cover the 2.4/5.2/5.8 GHz WLAN operating bands. Good antenna performances such as radiation patterns and antenna gains over the operating bands have also been observed. The antenna with a compact size of 18×12×1.6 mm³ is designed on an FR4 substrate with a dielectric constant of 4.4.

Keywords: CPW antenna, dual-band, electromagnetic simulation, wireless local area network (WLAN)

Procedia PDF Downloads 196

Authors: Mohamed Mokhtar, Mostafa F. Shaaban

Abstract:

Among the challenges facing solar photovoltaic (PV) systems in the United Arab Emirates (UAE), dust accumulation on solar panels is considered the most severe problem that faces the growth of solar power plants. The accumulation of dust on the solar panels significantly degrades output from these panels. Hence, solar PV panels have to be cleaned manually or using costly automated cleaning methods. This paper focuses on initiating cleaning actions when required to reduce maintenance costs. The cleaning actions are triggered only when the dust level exceeds a threshold value. The amount of dust accumulated on the PV panels is estimated using an artificial neural network (ANN). Experiments are conducted to collect the required data, which are used in the training of the ANN model. Then, this ANN model will be fed by the output power from solar panels, ambient temperature, and solar irradiance, and thus, it will be able to estimate the amount of dust accumulated on solar panels at these conditions. The model was tested on different case studies to confirm the accuracy of the developed model.

Keywords: machine learning, dust, PV panels, renewable energy

Procedia PDF Downloads 132

Authors: Mathieu Bourdeau, Philippe Basset, Julien Waeytens, Elyes Nefzaoui

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As residential buildings account for a third of the overall energy consumption and greenhouse gas emissions in Europe, building energy modeling is an essential tool to reach energy efficiency goals. In the energy modeling process, calibration is a mandatory step to obtain accurate and reliable energy simulations. Nevertheless, the comparison between simulation results and the actual building energy behavior often highlights a significant performance gap. The literature discusses different origins of energy performance gaps, from building design to building operation. Then, building operation description in energy models, especially energy usages and users’ behavior, plays an important role in the reliability of simulations but is also the most accessible target for post-occupancy energy management and optimization. Therefore, the present study aims to discuss results on the calibration ofresidential building energy models using real operation data. Data are collected through a sensor network of more than 180 sensors and advanced energy meters deployed in three collective residential buildings undergoing major retrofit actions. The sensor network is implemented at building scale and in an eight-apartment sample. Data are collected for over one year and half and coverbuilding energy behavior – thermal and electricity, indoor environment, inhabitants’ comfort, occupancy, occupants behavior and energy uses, and local weather. Building energy simulations are performed using a physics-based building energy modeling software (Pleaides software), where the buildings’features are implemented according to the buildingsthermal regulation code compliance study and the retrofit project technical files. Sensitivity analyses are performed to highlight the most energy-driving building features regarding each end-use. These features are then compared with the collected post-occupancy data. Energy-driving features are progressively replaced with field data for a step-by-step calibration of the energy model. Results of this study provide an analysis of energy performance gap on an existing residential case study under deep retrofit actions. It highlights the impact of the different building features on the energy behavior and the performance gap in this context, such as temperature setpoints, indoor occupancy, the building envelopeproperties but also domestic hot water usage or heat gains from electric appliances. The benefits of inputting field data from an extensive instrumentation campaign instead of standardized scenarios are also described. Finally, the exhaustive instrumentation solution provides useful insights on the needs, advantages, and shortcomings of the implemented sensor network for its replicability on a larger scale and for different use cases.

Keywords: calibration, building energy modeling, performance gap, sensor network

Procedia PDF Downloads 146

Authors: Abdenour Bourabaa, Malika Fekih, Mohamed Saighi

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A numerical investigation of the fin efficiency and temperature distribution of an annular fin under dehumidification has been presented in this paper. The non-homogeneous second order differential equation that describes the temperature distribution from the fin base to the fin tip has been solved using the central finite difference method. The effects of variations in parameters including relative humidity, air temperature, air face velocity on temperature distribution and fin efficiency are investigated and compared with those under fully dry fin conditions. Also, the effect of fin pitch on the dimensionless temperature has been studied.

Keywords: annular fin, dehumidification, fin efficiency, heat and mass transfer, wet fin

Procedia PDF Downloads 469

Authors: Daniel Olshansky, Ramiro Rodrıguez Colmeiro

Abstract:

Relay Mining presents a scalable solution employing probabilistic mechanisms and crypto-economic incentives to estimate RPC volume usage, facilitating decentralized multitenant rate limiting. Network traffic from individual applications can be concurrently serviced by multiple RPC service providers, with costs, rewards, and rate limiting governed by a native cryptocurrency on a distributed ledger. Building upon established research in token bucket algorithms and distributed rate-limiting penalty models, our approach harnesses a feedback loop control mechanism to adjust the difficulty of mining relay rewards, dynamically scaling with network usage growth. By leveraging crypto-economic incentives, we reduce coordination overhead costs and introduce a mechanism for providing RPC services that are both geopolitically and geographically distributed.

Keywords: remote procedure call, crypto-economic, commit-reveal, decentralization, scalability, blockchain, rate limiting, token bucket

Procedia PDF Downloads 43

Authors: Amin Ranjbar, Amir Arsalan Jalili Zolfaghari, Amir Abolfazl Suratgar, Mehrdad Khajavi

Abstract:

This paper describes two stages of learning-based fault detection procedure in alternators. The procedure consists of three states of machine condition namely shortened brush, high impedance relay and maintaining a healthy condition in the alternator. The fault detection algorithm uses Wigner-Ville distribution as a feature extractor and also appropriate feature classifier. In this work, ANN (Artificial Neural Network) and also SVM (support vector machine) were compared to determine more suitable performance evaluated by the mean squared of errors criteria. Modules work together to detect possible faulty conditions of machines working. To test the method performance, a signal database is prepared by making different conditions on a laboratory setup. Therefore, it seems by implementing this method, satisfactory results are achieved.

Keywords: alternator, artificial neural network, support vector machine, time-frequency analysis, Wigner-Ville distribution

Procedia PDF Downloads 360

Authors: Teresa Smith, Farrah Jacquez

Abstract:

Health-risk behaviors (e.g., smoking, poor diet) directly increase the risk for chronic disease and morbidity. There is substantial evidence of a negative association between socioeconomic status (SES) and engagement in health-risk behaviors. However, due to the complexity of SES, researchers have suggested looking beyond this factor to fully understand the mechanisms that underlie engagement in health-risk behaviors. Stress is one plausible mechanism through which SES impacts health-risk behaviors. Currently, it remains unclear how stress occurring across the life course might impact health behaviors and explain the association between SES and these behaviors. To address the gaps in the literature, 172 adults between the ages of 18-49 were surveyed about their lifetime stress exposure, sociodemographic variables, and health-risk behaviors via an online recruitment portal, Prolific. Five major findings emerged from the current study. First, SES was negatively associated with engagement in health-risk behaviors and lifetime stress above and beyond current stress and other relevant demographics. Second, lifetime stress was significantly associated with health-risk behaviors above and beyond current stress and relevant demographic variables. Third, lifetime stress fully mediated the association between SES and health-risk behaviors above and beyond current stress and other demographics. Fourth, the severity of stress experienced emerged as the most significant lifetime stress variable that explains the relation between SES and health-risk behaviors. Fifth and finally, lower SES and experiencing financial and legal/crime stressors increased the likelihood of engaging in health-risk behaviors. The current study results align with previous research and suggest that stress occurring over the lifespan impacts the relation between SES and health-risk behaviors, which are in turn known to impact health outcomes. However, our findings move the current literature forward by providing a more nuanced understanding of the specific aspects of stress that influence this association. Specifically, the severity of stress experienced across the entire lifespan was the most important aspect of stress when examining the association between SES and health-risk behaviors. Further, individuals most at risk for engaging in health-risk behaviors are those of the lowest SES and experience financial and legal/crime stressors. These findings have the potential to inform interventions and policies aimed at addressing health-risk behaviors by providing a more sophisticated understanding of the impact of stress.

Keywords: stress, health behaviors, socioeconomic status, health

Procedia PDF Downloads 137