Search results for: hidden geothermal

Authors: Ghaemi Reza, Abdollahi Hamid, Dashti Elham

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Cloud computing poses a diversity of challenges in data mining operation arising out of the dynamic structure of data distribution as against the use of typical database scenarios in conventional architecture. Due to immense number of users seeking data on daily basis, there is a serious security concerns to cloud providers as well as data providers who put their data on the cloud computing environment. Big data analytics use compute intensive data mining algorithms (Hidden markov, MapReduce parallel programming, Mahot Project, Hadoop distributed file system, K-Means and KMediod, Apriori) that require efficient high performance processors to produce timely results. Data mining algorithms to solve or optimize the model parameters. The challenges that operation has to encounter is the successful transactions to be established with the existing virtual machine environment and the databases to be kept under the control. Several factors have led to the distributed data mining from normal or centralized mining. The approach is as a SaaS which uses multi-agent systems for implementing the different tasks of system. There are still some problems of data mining based on cloud computing, including design and selection of data mining algorithms.

Keywords: cloud computing, data mining, computing models, cloud services

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Authors: Abdolreza Memari

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In this article in order to estimate the viscosity of crude oil,a numerical method has been used. We use this method to measure the crude oil's viscosity for 3 states: Saturated oil's viscosity, viscosity above the bubble point and viscosity under the saturation pressure. Then the crude oil's viscosity is estimated by using KHAN model and roller ball method. After that using these data that include efficient conditions in measuring viscosity, the estimated viscosity by the presented method, a radial based neural method, is taught. This network is a kind of two layered artificial neural network that its stimulation function of hidden layer is Gaussian function and teaching algorithms are used to teach them. After teaching radial based neural network, results of experimental method and artificial intelligence are compared all together. Teaching this network, we are able to estimate crude oil's viscosity without using KHAN model and experimental conditions and under any other condition with acceptable accuracy. Results show that radial neural network has high capability of estimating crude oil saving in time and cost is another advantage of this investigation.

Keywords: viscosity, Iranian crude oil, radial based, neural network, roller ball method, KHAN model

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Authors: John Lorenzo Bautista, Yoon-Joong Kim

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In this paper, a set of Filipino Phonetically Balanced Word list consisting of 250 words (PBW250) were constructed for a phoneme-level ASR system for the Filipino language. The Entropy Maximization is used to obtain phonological balance in the list. Entropy of phonemes in a word is maximized, providing an optimal balance in each word’s phonological distribution using the Add-Delete Method (PBW algorithm) and is compared to the modified PBW algorithm implemented in a dynamic algorithm approach to obtain optimization. The gained entropy score of 4.2791 and 4.2902 for the PBW and modified algorithm respectively. The PBW250 was recorded by 40 respondents, each with 2 sets data. Recordings from 30 respondents were trained to produce an acoustic model that were tested using recordings from 10 respondents using the HMM Toolkit (HTK). The results of test gave the maximum accuracy rate of 97.77% for a speaker dependent test and 89.36% for a speaker independent test.

Keywords: entropy maximization, Filipino language, Hidden Markov Model, phonetically balanced words, speech recognition

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Authors: H. Fuad Usman, M. Rafay Khan Sial, Shahzaib Hamid

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The trend of renewable energy resources has been amplified due to global warming and other environmental related complications in the 21st century. Recent research has very much emphasized on the generation of electrical power through renewable resources like solar, wind, hydro, geothermal, etc. The use of the photovoltaic cell has become very public as it is very useful for the domestic and commercial purpose overall the world. Although a single cell gives the low voltage output but connecting a number of cells in a series formed a complete module of the photovoltaic cells, it is becoming a financial investment as the use of it fetching popular. This also reduced the prices of the photovoltaic cell which gives the customers a confident of using this source for their electrical use. Photovoltaic cell gives the MPPT at single specific point of operation at a given temperature and level of solar intensity received at a given surface whereas the focal point changes over a large range depending upon the manufacturing factor, temperature conditions, intensity for insolation, instantaneous conditions for shading and aging factor for the photovoltaic cells. Two improved algorithms have been proposed in this article for the MPPT. The widely used algorithms are the ‘Incremental Conductance’ and ‘Perturb and Observe’ algorithms. To extract the maximum power from the source to the load, the duty cycle of the convertor will be effectively controlled. After assessing the previous techniques, this paper presents the improved and reformed idea of harvesting maximum power point from the photovoltaic cells. A thoroughly go through of the previous ideas has been observed before constructing the improvement in the traditional technique of MPP. Each technique has its own importance and boundaries at various weather conditions. An improved technique of implementing the use of both ‘Perturb and Observe’ and ‘Incremental Conductance’ is introduced.

Keywords: duty cycle, MPPT (Maximum Power Point Tracking), perturb and observe (P&O), photovoltaic module

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Authors: Katharina Fellnhofer

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The more information and knowledge that technology provides, the more important are profoundly human skills like intuition, the skill of using nonconscious information. As our world becomes more complex, shaken by crises, and characterized by uncertainty, time pressure, ambiguity, and rapidly changing conditions, intuition is increasingly recognized as a key human asset. However, due to methodological limitations of sample size or time frame or a lack of real-world or cross-cultural scope, precisely how to measure intuition when facing risks on a nonconscious level remains unclear. In light of the measurement challenge related to intuition’s nonconscious nature, a technique is introduced to measure intuition via hidden images as nonconscious additional information to trigger intuition. This technique has been tested in a within-subject fully online design with 62,721 real-world investment decisions made by 657 subjects in Europe and the United States. Bayesian models highlight the technique’s potential to measure skill at using nonconscious information for conscious decision making. Over the long term, solving the mysteries of intuition and mastering its use could be of immense value in personal and organizational decision-making contexts.

Keywords: cognition, intuition, investment decisions, methodology

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Authors: JaeHwan Yang, Da-Woon Jeong, Seung-Young Kho, Dong-Kyu Kim

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In terms of ITS, information on link characteristic is an essential factor for plan or operation. But in practical cases, not every link has installed sensors on it. The link that does not have data on it is called “Missing Link”. The purpose of this study is to impute data of these missing links. To get these data, this study applies the machine learning method. With the machine learning process, especially for the deep learning process, missing link data can be estimated from present link data. For deep learning process, this study uses “Recurrent Neural Network” to take time-series data of road. As input data, Dedicated Short-range Communications (DSRC) data of Dalgubul-daero of Daegu Metropolitan Area had been fed into the learning process. Neural Network structure has 17 links with present data as input, 2 hidden layers, for 1 missing link data. As a result, forecasted data of target link show about 94% of accuracy compared with actual data.

Keywords: data estimation, link data, machine learning, road network

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Authors: Parthasarathi Pramanik

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Body packing is a way of transfer drugs across the international border or any drug prohibited area. The drugs are usually hidden in body packets inside the anatomical body cavities like mouth, intestines, rectum, ear, vagina etc. Cocaine is a very common drug for body packing across the world. A 48 year old male was reported dead in his hotel after complaining of chest pain and vomiting. At autopsy, there were eighty-two white cylindrical body packs in the stomach, small and large intestines. Seals of few of the packets were opened. Toxicological examination revealed presence of cocaine in the stomach, liver, kidney and hair samples. Microscopically, presence of myocardial necrosis with interstitial oedema along with hypertrophy and fibrosis of the myocardial fibre suggested heart failure due to cocaine cardio toxicity. However, focal lymphocyte infiltration and perivascular fibrosis in the myocardium also indicated chronic cocaine toxicity of the deceased. After careful autopsy examination it was considered the victim was died due congestive heart failure secondary to acute and chronic cocaine poisoning.

Keywords: cardiac failure, cocaine, body packer, sudden death

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Authors: Shirin Manavi, Gorbanali Ebrahimi

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Man is a symbolist. He experiences living, whether he lived in the Old World or he is living in today's world. The symbol is a kind of concise expression of wishes and in the meantime, it comprises all the demands and Dos and Don'ts of a group, a people, and a nation. Our land, Iran, is also the land of symbols, explicit and hidden ones. The astronomical symbols are among these symbols that were recruited in fortune telling of cities. One of the symbols is the astronomical symbol of sagittairc which is the astrology of some cities such as Isfahan. November 22 of each year has been selected by Isfahan experts as Isfahan's Glorification Day. They have also selected the historically painted picture on the entrance of Qeysariye Bazar which has been designed based on the arch constellation. This symbol was chosen because Isfahan has been found in arch constellation on the basis of historical documentation of astrology. This study aims at the recognition, description, and analysis of the arch constellation, the symbol of Isfahan where it has been displayed once upon a time over the Tabarak castle and it is for centuries on the vertex of Isfahan's Qeysariye Bazar. This research has been done on the basis of the analytical method, but due to the particular structure of this paper, it has also benefited from the historical and descriptive methods.

Keywords: Isfahan's Qeysariye Bazar, semiotics, astrology of cities, constellation, sagittairc (Borje Ghos)

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Authors: Elmira Davasaz Tabrizi, Müşteba Sevil, Ercan Arican

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In recent decades, there has been a sharp increase in the prevalence of several unrelated chronic diseases. The use of long-term antibiotics for chronic illnesses is increasing. The antibiotic resistance occurrence and its relationship with host microbiomes are still unclear. Properties of the identifying antibodies have been the focus of chronic disease research, such as prostatitis or autoimmune. The immune system is made up of a complicated but well-organized network of cell types that constantly monitor and maintain their surroundings. The regulated homeostatic interaction between immune system cells and their surrounding environment shapes the microbial flora. Researchers believe that the disappearance of special bacterial species from our ancestral microbiota might have altered the body flora that can cause a rise in disease during the human life span. This unpleasant pattern demonstrates the importance of focusing on discovering and revealing the root causes behind the disappearance or alteration of our microbiota. In this review, we gathered the results of some studies that reveal changes in the diversity and quantity of microorganisms that may affect chronic and autoimmune diseases. Additionally, a Ph.D. thesis that is still in process as Metagenomic studies in chronic prostatitis samples is mentioned.

Keywords: metagenomic, autoimmune, prostatitis, microbiome

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Authors: Strahinja Kovačević, Jelena Vladić, Senka Vidović, Zoran Zeković, Lidija Jevrić, Sanja Podunavac Kuzmanović

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Supercritical extracts of highly valuated medicinal plant Satureja montana were prepared by application of supercritical carbon dioxide extraction in the carbon dioxide pressure range from 125 to 350 bar and temperature range from 40 to 60°C. Using GC/MS method of analysis chemical profiles (aromatic constituents) of S. montana extracts were obtained. Self-training artificial neural networks were applied to predict the retention time of the analyzed terpenes in GC/MS system. The best ANN model obtained was multilayer perceptron (MLP 11-11-1). Hidden activation was tanh and output activation was identity with Broyden–Fletcher–Goldfarb–Shanno training algorithm. Correlation measures of the obtained network were the following: R(training) = 0.9975, R(test) = 0.9971 and R(validation) = 0.9999. The comparison of the experimental and predicted retention times of the analyzed compounds showed very high correlation (R = 0.9913) and significant predictive power of the established neural network.

Keywords: ANN regression, GC/MS, Satureja montana, terpenes

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Authors: Md. Minhazul Islam, Shah Ashisul Abed Nipun, Majharul Islam, Md. Abdur Rakib Rahat, Jonayet Miah, Salsavil Kayyum, Anwar Shadaab, Faiz Al Faisal

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The target of this research is to predict Myocardial Infarction using unsupervised Machine Learning algorithms. Myocardial Infarction Prediction related to heart disease is a challenging factor faced by doctors & hospitals. In this prediction, accuracy of the heart disease plays a vital role. From this concern, the authors have analyzed on a myocardial dataset to predict myocardial infarction using some popular Machine Learning algorithms K-Means and Hierarchical Clustering. This research includes a collection of data and the classification of data using Machine Learning Algorithms. The authors collected 345 instances along with 26 attributes from different hospitals in Bangladesh. This data have been collected from patients suffering from myocardial infarction along with other symptoms. This model would be able to find and mine hidden facts from historical Myocardial Infarction cases. The aim of this study is to analyze the accuracy level to predict Myocardial Infarction by using Machine Learning techniques.

Keywords: Machine Learning, K-means, Hierarchical Clustering, Myocardial Infarction, Heart Disease

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Authors: G. Anjan Babu, G. Sumana, M. Rajasekhar

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Many inherited diseases and non-hereditary disorders are common in the development of renal cystic diseases. Polycystic kidney disease (PKD) is a disorder developed within the kidneys in which grouping of cysts filled with water like fluid. PKD is responsible for 5-10% of end-stage renal failure treated by dialysis or transplantation. New experimental models, application of molecular biology techniques have provided new insights into the pathogenesis of PKD. Researchers are showing keen interest for developing an automated system by applying computer aided techniques for the diagnosis of diseases. In this paper a multi-layered feed forward neural network with one hidden layer is constructed, trained and tested by applying back propagation learning rule for the diagnosis of PKD based on physical symptoms and test results of urinanalysis collected from the individual patients. The data collected from 50 patients are used to train and test the network. Among these samples, 75% of the data used for training and remaining 25% of the data are used for testing purpose. Furthermore, this trained network is used to implement for new samples. The output results in normality and abnormality of the patient.

Keywords: dialysis, hereditary, transplantation, polycystic, pathogenesis

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Authors: Ritesh Kumar, Gitanjali Chhetri, Mandira Bhatia, Mohit Mishra, Abhijith Bailur, Abhinav

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The prevalence of the internet and technology in our day to day lives is creating more security issues than ever. The need for protecting and providing a secure access to private and business data has led to the development of many security systems. One of the potential solutions is to employ the bio-metric authentication technique. In this paper we present an innovative biometric authentication method that utilizes a person’s EEG signal, which is acquired in response to an auditory stimulus,and transferred wirelessly to a computer that has the necessary ANN algorithm-Multi layer perceptrol neural network because of is its ability to differentiate between information which is not linearly separable.In order to determine the weights of the hidden layer we use Gaussian random weight initialization. MLP utilizes a supervised learning technique called Back propagation for training the network. The complex algorithm used for EEG classification reduces the chances of intrusion into the protected public or private data.

Keywords: EEG signal, auditory evoked potential, biometrics, multilayer perceptron neural network, back propagation rule, Gaussian random weight initialization

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Authors: Anneline Chetty

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Innovation is not always born in a sterile lab or is not always about applications and technology. Innovative solutions to community challenges can be borne out of the creativity of community members. This was proven by Professor Anil Gupta who for more than two decades scoured rural India for its hidden innovations motivated by the belief that the most powerful ideas for fighting poverty and hardship will not come from corporate research labs, but from ordinary people struggling to survive. The Ethekwini Municipality is a city in South Africa which adopted a similar approach, recognising the innovativeness of youth (students and school pupils) in its area. The intention was to make the youth a part of the solution to challenges faced by the Municipality. In this regard, five areas were selected and five groups of students were identified. Each group was sent into the community to identify challenges and engage with community leaders as well as members. Each group was tasked to come with solutions to these challenges which were to be presented at an Innovation Summit. The presented solutions were judged and the winning solution would be implemented by the Municipality. This paper, documents the experience of the students as well as the kinds of solutions that were presented. The purpose is to highlight the importance of using the ingenious minds and creativity of youth and channel their energy into becoming part of society’s solutions as opposed to being the problem

Keywords: innovation, indigenous, entrepreneurship, community

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Authors: S. Sadhu, M. Ramgopal, S. Bhattacharyya

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Natural circulation loops (NCLs) are buoyancy driven flow systems without any moving components. NCLs have vast applications in geothermal, solar and nuclear power industry where reliability and safety are of foremost concern. Due to certain favorable thermophysical properties, especially near supercritical regions, carbon dioxide can be considered as an ideal loop fluid in many applications. In the present work, a high temperature NCL that uses supercritical carbon dioxide as loop fluid is analysed. The effects of relevant design and operating variables on loop performance are studied. The system operating under steady state is modelled taking into account the axial conduction through loop fluid and loop wall, and heat transfer with surroundings. The heat source is considered to be a heater with controlled heat flux and heat sink is modelled as an end heat exchanger with water as the external cold fluid. The governing equations for mass, momentum and energy conservation are normalized and are solved numerically using finite volume method. Results are obtained for a loop pressure of 90 bar with the power input varying from 0.5 kW to 6.0 kW. The numerical results are validated against the experimental results reported in the literature in terms of the modified Grashof number (Gr_m) and Reynolds number (Re). Based on the results, buoyancy and friction dominated regions are identified for a given loop. Parametric analysis has been done to show the effect of loop diameter, loop height, ambient temperature and insulation. The results show that for the high temperature loop, heat loss to surroundings affects the loop performance significantly. Hence this conjugate heat transfer between the loop and surroundings has to be considered in the analysis of high temperature NCLs.

Keywords: conjugate heat transfer, heat loss, natural circulation loop, supercritical carbon dioxide

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Authors: Si Mon Kueh, Tom J. Kazmierski

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There are about 1% of the world population suffering from the hidden disability known as epilepsy and major developing countries are not fully equipped to counter this problem. In order to reduce the inconvenience and danger of epilepsy, different methods have been researched by using a artificial neural network (ANN) classification to distinguish epileptic waveforms from normal brain waveforms. This paper outlines the aim of achieving massive ANN parallelization through a dedicated hardware using bit-serial processing. The design of this bit-serial Neural Processing Element (NPE) is presented which implements the functionality of a complete neuron using variable accuracy. The proposed design has been tested taking into consideration non-idealities of a hardware ANN. The NPE consists of a bit-serial multiplier which uses only 16 logic elements on an Altera Cyclone IV FPGA and a bit-serial ALU as well as a look-up table. Arrays of NPEs can be driven by a single controller which executes the neural processing algorithm. In conclusion, the proposed compact NPE design allows the construction of complex hardware ANNs that can be implemented in a portable equipment that suits the needs of a single epileptic patient in his or her daily activities to predict the occurrences of impending tonic conic seizures.

Keywords: Artificial Neural Networks (ANN), bit-serial neural processor, FPGA, Neural Processing Element (NPE)

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Authors: Y. Jin, N. Zhang, X. Luo, W. Zhang

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China with annual increasing 0.5-0.6 billion squares city residence has brought in enormous energy consumption by HVAC facilities and other appliances. In this regard, governments and researchers are encouraging renewable energy like solar energy, geothermal energy using in houses. However, high cost of equipment and low energy conversion result in a very low acceptable to residents. So what’s the equilibrium point of eco-feedback to reach economic benefit and thermal comfort? That is the main question should be answered. In this paper, the objective is an on-site solar PV and heater house, which has been evaluated as a low energy building. Since HVAC system is considered as main energy consumption equipment, the residence with 24-hour monitoring system set to measure temperature, wind velocity and energy in-out value with no HVAC system for one month of summer and winter. Thermal comfort time period will be analyzed and confirmed; then the air-conditioner will be started within thermal discomfort time for the following one summer and winter month. The same data will be recorded to calculate the average energy consumption monthly for a purpose of whole day thermal comfort. Finally, two analysis work will be done: 1) Original building thermal simulation by computer at design stage with actual measured temperature after construction will be contrastive analyzed; 2) The cost of renewable energy facilities and power consumption converted to cost efficient rate to assess the feasibility of renewable energy input for residence. The results of the experiment showed that a certain deviation exists between actual measured data and simulated one for human thermal comfort, especially in summer period. Moreover, the cost-effectiveness is high for a house in targeting city Guilin now with at least 11 years of cost-covering. The conclusion proves that an eco-feedback of a low energy residence is never only consideration of its energy net value, but also the cost efficiency that is the critical factor to push renewable energy acceptable by the public.

Keywords: cost efficiency, eco-feedback, low energy residence, thermal comfort

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Authors: Neziha Jaouedi, Noureddine Boujnah, Mohamed Salim Bouhlel

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In the framework of human machine interaction systems enhancement, we focus throw this paper on human behavior analysis and action recognition. Human behavior is characterized by actions and reactions duality (movements, psychological modification, verbal and emotional expression). It’s worth noting that many information is hidden behind gesture, sudden motion points trajectories and speeds, many research works reconstructed an information retrieval issues. In our work we will focus on motion extraction, tracking and action recognition using wavelet network approaches. Our contribution uses an analysis of human subtraction by Gaussian Mixture Model (GMM) and body movement through trajectory models of motion constructed from kalman filter. These models allow to remove the noise using the extraction of the main motion features and constitute a stable base to identify the evolutions of human activity. Each modality is used to recognize a human action using wavelets of derived beta distributions approach. The proposed approach has been validated successfully on a subset of KTH and UCF sports database.

Keywords: feautures extraction, human action classifier, wavelet neural network, beta wavelet

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Authors: Maxim Glushenkov, Alexander Kronberg

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Isobaric expansion (IE) process is an alternative to conventional gas/vapor expansion accompanied by a pressure decrease typical of all state-of-the-art heat engines. The elimination of the expansion stage accompanied by useful work means that the most critical and expensive parts of ORC systems (turbine, screw expander, etc.) are also eliminated. In many cases, IE heat engines can be more efficient than conventional expansion machines. In addition, IE machines have a very simple, reliable, and inexpensive design. They can also perform all the known operations of existing heat engines and provide usable energy in a very convenient hydraulic or pneumatic form. This paper reports measurement made with the engine operating as a heat-to-shaft-power or electricity converter and a comparison of the experimental results to a thermodynamic model. Experiments were carried out at heat source temperature in the range 30–85 °C and heat sink temperature around 20 °C; refrigerant R134a was used as the engine working fluid. The pressure difference generated by the engine varied from 2.5 bar at the heat source temperature 40 °C to 23 bar at the heat source temperature 85 °C. Using a differential piston, the generated pressure was quadrupled to pump hydraulic oil through a hydraulic motor that generates shaft power and is connected to an alternator. At the frequency of about 0.5 Hz, the engine operates with useful powers up to 1 kW and an oil pumping flowrate of 7 L/min. Depending on the temperature of the heat source, the obtained efficiency was 3.5 – 6 %. This efficiency looks very high, considering such a low temperature difference (10 – 65 °C) and low power (< 1 kW). The engine’s observed performance is in good agreement with the predictions of the model. The results are very promising, showing that the engine is a simple and low-cost alternative to ORC plants and other known energy conversion systems, especially at low temperatures (< 100 °C) and low power range (< 500 kW) where other known technologies are not economic. Thus low-grade solar, geothermal energy, biomass combustion, and waste heat with a temperature above 30 °C can be involved into various energy conversion processes.

Keywords: isobaric expansion, low-grade heat, heat engine, renewable energy, waste heat recovery

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Authors: James M. Wakiru, Liliane Pintelon, Peter Muchiri, Stanley Mburu

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Maintenance performance measurement (MPM) represents an integrated aspect that considers both operational and maintenance related aspects while evaluating the effectiveness and efficiency of maintenance to ensure assets are working as they should. Three salient issues require to be addressed for an asset-intensive organization to employ an MPM-based framework to optimize maintenance. Firstly, the organization should establish important perfomance metric(s), in this case the maintenance objective(s), which they will be focuss on. The second issue entails aligning the maintenance objective(s) with maintenance optimization. This is achieved by deriving maintenance performance indicators that subsequently form an objective function for the optimization program. Lastly, the objective function is employed in an optimization program to derive maintenance decision support. In this study, we develop a framework that initially identifies the crucial maintenance performance measures, and employs them to derive maintenance decision support. The proposed framework is demonstrated in a case study of a geothermal drilling rig, where the objective function is evaluated utilizing a simulation-based model whose parameters are derived from empirical maintenance data. Availability, reliability and maintenance inventory are depicted as essential objectives requiring further attention. A simulation model is developed mimicking a drilling rig operations and maintenance where the sub-systems are modelled undergoing imperfect maintenance, corrective (CM) and preventive (PM), with the total cost as the primary performance measurement. Moreover, three maintenance spare inventory policies are considered; classical (retaining stocks for a contractual period), vendor-managed inventory with consignment stock and periodic monitoring order-to-stock (s, S) policy. Optimization results infer that the adoption of (s, S) inventory policy, increased PM interval and reduced reliance of CM actions offers improved availability and total costs reduction.

Keywords: maintenance, vendor-managed, decision support, performance, optimization

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Authors: Zhuanfang Fred Zhang, Tim C. Johnson, Yilin Fang, Chris E. Strickland

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A variety of geophysical techniques are available to image the opaque subsurface with little or no contact with the soil. It is common to conduct time-lapse surveys of different types for a given site for improved results of subsurface imaging. Regardless of the chosen survey methods, it is often a challenge to process the massive amount of survey data. The currently available software applications are generally based on the one-dimensional assumption for a desktop personal computer. Hence, they are usually incapable of imaging the three-dimensional (3D) processes/variables in the subsurface of reasonable spatial scales; the maximum amount of data that can be inverted simultaneously is often very small due to the capability limitation of personal computers. Presently, high-performance or integrating software that enables real-time integration of multi-process geophysical methods is needed. E4D-MP enables the integration and inversion of time-lapsed large-scale data surveys from geophysical methods. Using the supercomputing capability and parallel computation algorithm, E4D-MP is capable of processing data across vast spatiotemporal scales and in near real time. The main code and the modules of E4D-MP for inverting individual or combined data sets of time-lapse 3D electrical resistivity, spectral induced polarization, and gravity surveys have been developed and demonstrated for sub-surface imaging. E4D-MP provides capability of imaging the processes (e.g., liquid or gas flow, solute transport, cavity development) and subsurface properties (e.g., rock/soil density, conductivity) critical for successful control of environmental engineering related efforts such as environmental remediation, carbon sequestration, geothermal exploration, and mine land reclamation, among others.

Keywords: gravity survey, high-performance computing, sub-surface monitoring, electrical resistivity tomography

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Authors: Sotirios Longinos, Anna Loskutova, Assel Tolegenova, Assem Imanzhussip, Lei Wang

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Hot, dry rock (HDR) has substantial potential as a thermal energy source. It has been exploited by hydraulic fracturing to extract heat and generate electricity, which is a well-developed technique known for creating the enhanced geothermal systems (EGS). These days, LN2 is being tested as an environmental friendly fracturing fluid to generate densely interconnected crevices to augment heat exchange efficiency and production. This study examines experimentally the efficacy of LN2 cryogenic fracturing for granite samples in Kazakhstan with immersion method. A comparison of two different experimental models is carried out. The first mode is rock heating along with liquid nitrogen treatment (heating with freezing time), and the second mode is multiple times of heating along with liquid nitrogen treatment (heating with LN2 freezing-thawing cycles). The experimental results indicated that with multiple heating and LN2-treatment cycles, the permeability of granite first ameliorates with increasing number of cycles and later reaches a plateau after a certain number of cycles. On the other hand, density, P-wave velocity, uniaxial compressive strength, elastic modulus, and tensile strength indicate a downward trend with increasing heating and treatment cycles. The thermal treatment cycles do not seem to have an obvious effect on the Poisson’s ratio. The changing rate of granite rock properties decreases as the number of cycles increases. The deterioration of granite primarily happens within the early few cycles. The heating temperature during the cycles shows an important influence on the deterioration of granite. More specifically, mechanical deterioration and permeability amelioration become more remarkable as the heating temperature increases.LN2 fracturing generates many positives compared to conventional fracturing methods such as little water consumption, requirement of zero chemical additives, lessening of reservoir damage, and so forth. Based on the experimental observations, LN2 can work as a promising waterless fracturing fluid to stimulate hot, dry rock reservoirs.

Keywords: granite, hydraulic fracturing, liquid nitrogen, Kazakhstan

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Authors: Sayantani Neogi, Sean Taylor, Julia Budka

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This paper presents the results of the investigations of soil/sediment sequences associated with the New Kingdom town at Sai Island, Sudan. During the course of this study, geoarchaeological surveys have been undertaken in the vicinity of this Pharaonic town within the island and the soil block samples for soil micromorphological analysis were accordingly collected. The intention was to better understand the archaeological site in its environmental context and the nature of the land surface prior to the establishment of the settlement. Soil micromorphology, a very powerful geoarchaeological methodology, is concerned with the description, measurement and interpretation of soil components and pedological features at a microscopic scale. Since soil profiles themselves are archives of their own history, soil micromorphology investigates the environmental and cultural signatures preserved within buried soils and sediments. A study of the thin sections from these soils/sediments has been able to provide robust data for providing interesting insights into the various nuances of this site, for example, the nature of the topography and existent environmental condition during the time of Pharaonic site establishment. These geoarchaeological evaluations have indicated that there is a varied hidden landscape context for this pharaonic settlement, which indicates a symbiotic relationship with the Nilotic environmental system.

Keywords: geoarchaeology, New Kingdom, Nilotic environment, soil micromorphology

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Authors: Emiko Suzuki

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Although fitness is defined as the body’s ability to respond to the demand of physical activity without undue fatigue in health science, in media oftentimes physical activity is presented as means to an attractive body rather than a fit and healthy one. Of all types of media, Instagram is becoming an increasingly persuasive source of information and advice on health and fitness, where individuals conceptualize what health and fitness mean for them. However, this user-generated and unregulated platform can be problematic, as it can communicate misleading information about health and fitness and possibly leading individuals to psychological problems such as eating disorders. In fact, previous research has shown that some messages that were posted with a tag that related to inspire others to do fitness, in fact, encouraged distancing the self from the internal needs of the body. For this reason, this present study aims to explore how health and fitness are communicated on Instagram by analyzing images and texts. A content analysis of images that were labeled with particular hashtags was performed, followed by a thematic analysis of texts from the same set of images. The result shows an interesting insight about messages about how health and fitness are communicated from companies through media, then digested and further shared among communities on Instagram. The study explores how the use of visual focused way of communicating health and fitness can lead to the dehumanization of human bodies.

Keywords: Instagram, fitness, dehumanization, body image, embodiment

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Authors: Vanessa Cho, Janet Hsiao, Nigel King, Robert Anthonappa

Abstract:

Background: The personal protective equipment (PPE) requirements for health care workers (HCWs) have changed significantly during the COVID-19 pandemic. Aim: To ascertain, using eye-tracking technology, what children notice the most when seeing HCWs in various PPE. Design: A Tobii nano pro-eye-tracking camera tracked 156 children's visual attention while they viewed photographs of HCWs in various PPEs. Eye Movement analysis with Hidden Markov Models (EMHMM) was employed to analyse 624 recordings using two approaches, namely (i) data-driven where children's fixation determined the regions of interest (ROIs), and (ii) fixed ROIs where the investigators predefined the ROIs. Results: Two significant eye movement patterns, namely distributed(85.2%) and selective(14.7%), were identified(P<0.05). Most children fixated primarily on the face regardless of the different PPEs. Children fixated equally on all PPE images in the distributed pattern, while a strong preference for unmasked faces was evident in the selective pattern (P<0.01). Conclusion: Children as young as 2.5 years used a top-down visual search behaviour and demonstrated their face processing ability. Most children did not show a strong visual preference for a specific PPE, while a minority preferred PPE with distinct facial features, namely without masks and loupes.

Keywords: COVID-19, PPE, dentistry, pediatric

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Authors: Jamil Hijazi, Stirling Howieson

Abstract:

Over one-quarter of the Kingdom of Saudi Arabia’s total oil production (2.8 million barrels a day) is used for electricity generation. The built environment is estimated to consume 77% of the total energy production. Of this amount, air conditioning systems consume about 80%. Apart from considerations surrounding global warming and CO2 production it has to be recognised that oil is a finite resource and the KSA like many other oil rich countries will have to start to consider a horizon where hydro-carbons are not the dominant energy resource. The employment of hybrid ground cooling pipes in combination with black body solar collection and radiant night cooling systems may have the potential to displace a significant proportion of oil currently used to run conventional air conditioning plant. This paper presents an investigation into the viability of such hybrid systems with the specific aim of reducing carbon emissions while providing all year round thermal comfort in a typical Saudi Arabian urban housing block. At the outset air and soil temperatures were measured in the city of Jeddah. A parametric study then was carried out by computational simulation software (Design Builder) that utilised the field measurements and predicted the cooling energy consumption of both a base case and an ideal scenario (typical block retro-fitted with insulation, solar shading, ground pipes integrated with hypocaust floor slabs/ stack ventilation and radiant cooling pipes embed in floor).Initial simulation results suggest that careful ‘ecological design’ combined with hybrid radiant and ground pipe cooling techniques can displace air conditioning systems, producing significant cost and carbon savings (both capital and running) without appreciable deprivation of amenity.

Keywords: energy efficiency, ground pipe, hybrid cooling, radiative cooling, thermal comfort

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Authors: Ramesh K. Sunkaria, Puneeta Marwaha

Abstract:

Nonlinear methods of heart rate variability (HRV) analysis are becoming more popular. It has been observed that complexity measures quantify the regularity and uncertainty of cardiovascular RR-interval time series. In the present work, SampEn has been evaluated in healthy Normal Sinus Rhythm (NSR) male and female subjects for different data lengths and tolerance level r. It is demonstrated that SampEn is small for higher values of tolerance r. Also SampEn value of healthy female group is higher than that of healthy male group for short data length and with increase in data length both groups overlap each other and it is difficult to distinguish them. The SampEn gives inaccurate results by assigning higher value to female group, because male subject have more complex HRV pattern than that of female subjects. Therefore, this traditional algorithm exhibits higher complexity for healthy female subjects than for healthy male subjects, which is misleading observation. This may be due to the fact that SampEn do not account for multiple time scales inherent in the physiologic time series and the hidden spatial and temporal fluctuations remains unexplored.

Keywords: heart rate variability, normal sinus rhythm group, RR interval time series, sample entropy

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Authors: Alessandra Marcelletti, Giovanni Trovato

Abstract:

Corruption in public offices is found to be the reflection of country-specific features, however, the exact magnitude and the statistical significance of its determinants effect has not yet been identified. The paper aims to propose an estimation method to measure the impact of country fundamentals on corruption, showing that covariates could differently affect the extent of corruption across countries. Thus, we exploit a model able to take into account different factors affecting the incentive to ask or to be asked for a bribe, coherently with the use of the Corruption Perception Index. We assume that discordant results achieved in literature may be explained by omitted hidden factors affecting the agents' decision process. Moreover, assuming homogeneous covariates effect may lead to unreliable conclusions since the country-specific environment is not accounted for. We apply a Finite Mixture Model with concomitant variables to 129 countries from 1995 to 2006, accounting for the impact of the initial conditions in the socio-economic structure on the corruption patterns. Our findings confirm the hypothesis of the decision process of accepting or asking for a bribe varies with specific country fundamental features.

Keywords: Corruption, Finite Mixture Models, Concomitant Variables, Countries Classification

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Authors: Eugene Stepanov, Arkadi Ponossov

Abstract:

Ordinary differential equations with switching nonlinearities constitute a very useful tool in many applications. The solutions of such equations can usually be calculated analytically if they cross the discontinuities transversally. Otherwise, one has trajectories that slides along the discontinuity, and the calculations become less straightforward in this case. For instance, one of the problems one faces is non-uniqueness of the sliding modes. In the presentation, it is proposed to apply the theory of hybrid dynamical systems to calculate the solutions that are ‘hidden’ in the discontinuities. Roughly, one equips the underlying switched system with an explicitly designed discrete dynamical system (‘automaton’), which governs the dynamics of the switched system. This construction ‘splits’ the dynamics, which, as it is shown in the presentation, gives uniqueness of the resulting hybrid trajectories and at the same time provides explicit formulae for them. Projecting the hybrid trajectories back onto the original continuous system explains non-uniqueness of its trajectories. The automaton is designed with the help of the attractors of the specially constructed adjoint dynamical system. Several examples are provided in the presentation, which supports the efficiency of the suggested scheme. The method can be of interest in control theory, gene regulatory networks, neural field models and other fields, where switched dynamics is a part of the analysis.

Keywords: hybrid dynamical systems, singular perturbation analysis, sliding modes, switched dynamics

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Authors: Philipp Ruf, Massiwa Chabbi, Christoph Reich, Djaffar Ould-Abdeslam

Abstract:

In recent years, convolutional neural networks (CNN) have demonstrated high performance in image analysis, but oftentimes, there is only structured data available regarding a specific problem. By interpreting structured data as images, CNNs can effectively learn and extract valuable insights from tabular data, leading to improved predictive accuracy and uncovering hidden patterns that may not be apparent in traditional structured data analysis. In applying a single neural network for analyzing multimodal data, e.g., both structured and unstructured information, significant advantages in terms of time complexity and energy efficiency can be achieved. Converting structured data into images and merging them with existing visual material offers a promising solution for applying CNN in multimodal datasets, as they often occur in a medical context. By employing suitable preprocessing techniques, structured data is transformed into image representations, where the respective features are expressed as different formations of colors and shapes. In an additional step, these representations are fused with existing images to incorporate both types of information. This final image is finally analyzed using a CNN.

Keywords: CNN, image processing, tabular data, mixed dataset, data transformation, multimodal fusion

Procedia PDF Downloads 95