Search results for: semisolid metals processing

Authors: Olomon Wu, Hung Lu, Nick Wilkins, Daniel Kerr, Zekeriya Aliyazicioglu, H. K. Hwang

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Multiple Input Multiple Output (MIMO) radar has received increasing attention in recent years. MIMO radar has many advantages over conventional phased array radar such as target detection, resolution enhancement, and interference suppression. In this paper, the results are presented from a simulation study of MIMO Uniformly-Spaced Linear Array (ULA) antennas. The performance is investigated under varied parameters, including varied array size, Pseudo Random (PN) sequence length, number of snapshots, and Signal to Noise Ratio (SNR). The results of MIMO are compared to a traditional array antenna.

Keywords: MIMO radar, phased array antenna, target detection, radar signal processing

Procedia PDF Downloads 540

Authors: Yayun Hsu, Henry Horng-Shing Lu

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This paper proposes a data-driven, biology-inspired neural segmentation method of 3D drosophila Brainbow images. We use Bayesian Sequential Partitioning algorithm for probabilistic modeling, which can be used to detect somas and to eliminate cross talk effects. This work attempts to develop an automatic methodology for neuron image segmentation, which nowadays still lacks a complete solution due to the complexity of the image. The proposed method does not need any predetermined, risk-prone thresholds since biological information is inherently included in the image processing procedure. Therefore, it is less sensitive to variations in neuron morphology; meanwhile, its flexibility would be beneficial for tracing the intertwining structure of neurons.

Keywords: brainbow, 3D imaging, image segmentation, neuron morphology, biological data mining, non-parametric learning

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Authors: Basma Khelfa, Antoine Tordeux, Ibrahima Ba

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We present a descriptive analysis data of lane-changing events in multi-lane roads. The data are provided from The Highway Drone Dataset (HighD), which are microscopic trajectories in highway. This paper describes and analyses the role of the different parameters and their significance. Thanks to HighD data, we aim to find the most frequent reasons that motivate drivers to change lanes. We used the programming language R for the processing of these data. We analyze the involvement and relationship of different variables of each parameter of the ego vehicle and the four vehicles surrounding it, i.e., distance, speed difference, time gap, and acceleration. This was studied according to the class of the vehicle (car or truck), and according to the maneuver it undertook (overtaking or falling back).

Keywords: autonomous driving, physical traffic model, prediction model, statistical learning process

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Authors: Weitao Lin

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To quickly extract adequate information from large-scale unstructured text data, this paper studies the representation of events in Chinese scenarios and performs the regularized abstraction. It proposes a Chinese event detection technique based on dependency parsing and rule matching. The method first performs dependency parsing on the original utterance, then performs pattern matching at the word or phrase granularity based on the results of dependent syntactic analysis, filters out the utterances with prominent non-event characteristics, and obtains the final results. The experimental results show the effectiveness of the method.

Keywords: natural language processing, Chinese event detection, rules matching, dependency parsing

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Authors: Vahid Bayrami Rad

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Ever since humans developed a collective way of life to the development of urbanization, the concern of security has always been considered one of the most important challenges of life. To protect property, locks have always been a practical tool. With the advancement of technology, the form of locks has changed from mechanical to electric. One of the most widely used fields of using artificial intelligence is its application in the technology of surveillance security systems. Currently, the technologies used in smart anti-theft door handles are one of the most potential fields for using artificial intelligence. Artificial intelligence has the possibility to learn, calculate, interpret and process by analyzing data with the help of algorithms and mathematical models and make smart decisions. We will use Arduino board to process data.

Keywords: arduino board, artificial intelligence, image processing, solenoid lock

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Authors: Yiwei Li, Mingyu Gao

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Integrating hybrid memories with heterogeneous processors could leverage heterogeneity in both compute and memory domains for better system efficiency. To ensure performance isolation, we introduce Hydrogen, a hardware architecture to optimize the allocation of hybrid memory resources to heterogeneous CPU-GPU systems. Hydrogen supports efficient capacity and bandwidth partitioning between CPUs and GPUs in both memory tiers. We propose decoupled memory channel mapping and token-based data migration throttling to enable flexible partitioning. We also support epoch-based online search for optimized configurations and lightweight reconfiguration with reduced data movements. Hydrogen significantly outperforms existing designs by 1.21x on average and up to 1.31x.

Keywords: hybrid memory, heterogeneous systems, dram cache, graphics processing units

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Authors: C. R. Ríos-Soberanis, V. M. Moo-Huchin, R. J. Estrada-Leon, E. Perez-Pacheco

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Polymers from renewable resources have attracted an increasing amount of attention over the last two decades, predominantly due to two major reasons: firstly environmental concerns, and secondly the realization that our petroleum resources are finite. Finding new uses for agricultural commodities is also an important area of research. Therefore, it is crucial to get new sources of natural materials that can be used in different applications. Ramon tree (Brosimum alicastrum) is a tropical plant that grows freely in Yucatan countryside. This paper focuses on the seeds recollection, processing and starch extraction and characterization in order to find out about its suitability as biomaterial. Results demonstrated that it has a high content of qualities to be used not only as comestible but also as an important component in polymeric blends.

Keywords: biomaterials, characterization techniques, natural resource, starch

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Authors: Tamanna Jethava, Rahul Joshi

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Frequent Item sets play an essential role in many data Mining tasks that try to find interesting patterns from the database. Typically it refers to a set of items that frequently appear together in transaction dataset. There are several mining algorithm being used for frequent item set mining, yet most do not scale to the type of data we presented with today, so called “BIG DATA”. Big Data is a collection of large data sets. Our approach is to work on the frequent item set mining over the large dataset with scalable and speedy way. Big Data basically works with Map Reduce along with HDFS is used to find out frequent item sets from Big Data on large cluster. This paper focuses on using pre-processing & mining algorithm as hybrid approach for big data over Hadoop platform.

Keywords: frequent item set mining, big data, Hadoop, MapReduce

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Authors: Vrinda P. S. Borker

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Major water pollutants are dyes from effluents of industries. Different methods have been tried to degrade or treat the effluent before it is left to the environment. In order to understand the degradation process and later apply it to effluents, solar degradation study of methylene blue (MB) and methyl red (MR), the model dyes was carried out in the presence of photo-catalysts, the oxides of cobalt oxide Co₃O₄, and copper doped cobalt oxides (Co₀.₉Cu₀.₁)₃O₄ and (Co₀.₉₅Cu₀.₀₅)₃O₄. They were prepared from oxalate complex and hydrazinated oxalate complex of cobalt as well as mix metals, copper, and cobalt. The complexes were synthesized and characterized by FTIR. Complexes were decomposed to form oxides and were characterized by XRD. They were found to be monophasic. Solar degradation of MR and MB was carried out in presence of these oxides in acidic and basic medium. Degradation was faster in alkaline medium in the presence of Co₃O₄ obtained from hydrazinated oxalate. Doping of nanomaterial oxides modifies their characteristics. Doped cobalt oxides are found to photo-decolourise MR in alkaline media efficiently. In the absence of photocatalyst, solar degradation of alkaline MR does not occur. In acidic medium, MR is minimally decolorized even in the presence of photocatalysts. The industrial textile effluent contains chemicals like NaCl and Na₂CO₃ along with the unabsorbed dye. It is reported that these two chemicals hamper the degradation of dye. The chemicals like K₂S₂O₈ and H₂O₂ are reported to enhance degradation. The solar degradation study of MB in presence of photocatalyst (Co₀.₉Cu₀.₁)₃O₄ and these four chemicals reveals that presence of K₂S₂O₈ and H₂O₂ enhances degradation. It proves that H₂O₂ generates hydroxyl ions required for degradation of dye and the sulphate anion radical being strong oxidant attacks dye molecules leading to its fragmentation rapidly. Thus addition of K₂S₂O₈ and H₂O₂ during solar degradation in presence of (Co₀.₉Cu₀.₁)₃O₄ helps to break the organic moiety efficiently.

Keywords: cobalt oxides, Cu-doped cobalt oxides, H₂O₂ in dye degradation, photo-catalyst, solar dye degradation

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Authors: Akkouche Karim, Nekmouche Aghiles, Bouzid Leyla

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This study focuses on the development of a multifunctional Expert System (ES) called post-seismic damage inspection tool (PSDIT), a powerful tool which allows the evaluation, the processing and the archiving of the collected data stock after earthquakes. PSDIT can be operated by two user types; an ordinary user (engineer, expert or architect) for the damage visual inspection and an administrative user for updating the knowledge and / or for adding or removing the ordinary user. The knowledge acquisition is driven by a hierarchical knowledge model, the Information from investigation reports and those acquired through feedback from expert / engineer questionnaires are part.

Keywords: buildings, earthquake, seismic damage, damage assessment, expert system

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Authors: Bharath R., Tharun Sai N., Bhuvan G.

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This project focuses on developing a Parallel Computing method aimed at optimizing matrix multiplication through GPU acceleration. Addressing algorithmic challenges, GPU programming intricacies, and integration issues, the project aims to enhance efficiency and scalability. The methodology involves algorithm design, GPU programming, and optimization techniques. Future plans include advanced optimizations, extended functionality, and integration with high-level frameworks. User engagement is emphasized through user-friendly interfaces, open- source collaboration, and continuous refinement based on feedback. The project's impact extends to significantly improving matrix multiplication performance in scientific computing and machine learning applications.

Keywords: matrix multiplication, parallel processing, cuda, performance boost, neural networks

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Authors: Swapnil A. Padvi, Dipak S. Dalal

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The chemistry of tetrazoles has been grown tremendously in the past few years because tetrazoles are important and useful class of heterocyclic compounds which have a widespread application such as anticancer, antimicrobial, analgesics, antibacterial, antifungal, antihypertensive, and anti-allergic drugs in medicinal chemistry. Furthermore, tetrazoles have application in material sciences as explosives, rocket propellants, and in information recording systems. In addition to this, they have a wide range of application in coordination chemistry as a ligand. Deep eutectic solvents (DES) have emerged over the current decade as a novel class of green reaction media and applied in various fields of sciences because of their unique physical and chemical properties similar to the ionic liquids such as low vapor pressure, non-volatility, high thermal stability and recyclability. In addition, the reactants of DES are cheaply available, low-toxic, and biodegradable, which makes them predominantly required for large-scale applications effectively in industrial production. Herein we report the [2+3] cycloaddition reaction of organic nitriles with sodium azide affords the corresponding 5-substituted 1H-tetrazoles in six different types of choline chloride based deep eutectic solvents under mild reaction condition. Choline chloride: ZnCl2 (1:2) showed the best results for the synthesis of 5-substituted 1 H-tetrazoles. This method reduces the disadvantages such as: the use of toxic metals and expensive reagents, drastic reaction conditions and the presence of dangerous hydrazoic acid. The approach provides environment-friendly, short reaction times, good to excellent yields; safe process and simple workup make this method an attractive and useful contribution to present green organic synthesis of 5-substituted-1H-tetrazoles. All synthesized compounds were characterized by IR, 1H NMR, 13C NMR and Mass spectroscopy. DES can be recovered and reused three times with very little loss in activity.

Keywords: click chemistry, choline chloride, green chemistry, deep eutectic solvent, tetrazoles

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Authors: Joseph Omeiza Alao

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The wobbly state of waste management and the high level of environmental irresponsibility is a threat to environmental security, which invariably endangered public health, regional groundwater systems and atmospheric condition. The dumping of waste materials in water bodies and gutters and the frequent burning of waste materials heaped at dumpsites as well depict the highest level of environmental indiscipline. These unruly human factors have compelled this study to apply four different techniques for environmental impact assessment and the possible public health risks of poor waste management in Nigeria. The techniques include a geophysical survey (resistivity data acquisition), dispatched questionnaire surveys, physiochemical water analysis and a physical survey of several dumpsites. While the resistivity data indicates high-level dumpsite leachate invading the ground soil down to the water table, the physiochemical water analysis depicts high content of BOD (401 – 711) mg/l, COD (731 – 1312) mg/l, TDS (419 – 1871) mg/l and heavy metals (0.014 – 1.971) mg/l present in the regional groundwater systems, which have altered the chemistry of the regional groundwater. The resistivity data shows that the overburdened soil layer overlaying the regional groundwater systems was very low (4.5 Ωm – 151 Ωm) as against the existing data (180 Ωm – 3500 Ωm). However, the physical surveys and the dispatched questionnaire surveys explore the depth of environmental irresponsibility among the citizen. While the imprints of gross environmental indiscipline may be absolutely irreversible, adequate knowledge of the environmental implications of careless waste disposal. After a critical examination of the current waste management strategies in Nigeria, the study suggests a future direction for environmental security and sustainability. Several influential regional factors, such as geology, climatic conditions, and hydrology, were also discussed.

Keywords: groundwater, environmental indiscipline, waste management, water analysis, leachate plumes, public health

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Authors: Hillary Onyeka Abugu, Valentine Chinakwugwo Ezea, Janefrances Ngozi Ihedioha, Nwachukwu Romanus Ekere

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The study assessed the spring water quality in Igbo-Etiti, Nigeria, for drinking and irrigation application using Physico-chemical parameters, water quality index, mineral and trace elements, pollution indices and risk assessment. Standard methods were used to determine the physicochemical properties of the spring water in rainy and dry seasons. Trace metals such as Pb, Cd, Zn and Cu were determined with atomic absorption spectrophotometer. The results showed that most of the physicochemical properties studied were within the guideline values set by Nigeria Standard for Drinking Water Quality (NSDWQ), WHO and US EPA for drinking water purposes. However, pH of all the spring water (4.27- 4.73; and 4.95- 5.73), lead (Pb) (0.01-1.08 mg/L) and cadmium (Cd) (0.01-0.15 mg/L) concentrations were above the guideline values in both seasons. This could be attributed to the lithography of the study area, which is the Nsukka formation. Leaching of lead and sulphides from the embedded coal deposits could have led to the increased lead levels and made the water acidic. Two-way ANOVA showed significant differences in most of the parameters studied in dry and rainy seasons. Pearson correlation analysis and cluster analysis showed strong significant positive and negative correlations in some of the parameters studied in both seasons. The water quality index showed that none of the spring water had excellent water status. However, one spring (Iyi Ase) had poor water status in dry season and is considered unsafe for drinking. Iyi Ase was also considered not suitable for irrigation application as predicted by most of the pollution indices, while others were generally considered suitable for irrigation application. Probable cancer and non-cancer risk assessment revealed a probable risk associated with the consumption of the spring in the Igbo-Ettiti area, Nigeria.

Keywords: water quality, pollution index, risk assessment, physico-chemical parameters

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Authors: Hanan Alshaher, Jinsheng Xu

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Large amounts of data on the web can provide valuable information. For example, product reviews help business owners measure customer satisfaction. Sentiment analysis classifies texts into two polarities: positive and negative. This paper examines movie reviews and tweets using a new term weighting scheme, called one-over-sigma (1/sigma), on benchmark datasets for sentiment classification. The proposed method aims to improve the performance of sentiment classification. The results show that 1/sigma is more accurate than the popular term weighting schemes. In order to verify if the entropy reflects the discriminating power of terms, we report a comparison of entropy values for different term weighting schemes.

Keywords: 1/sigma, natural language processing, sentiment analysis, term weighting scheme, text classification

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Authors: Alemayehu Agizew Woldeamanuel, Mekonnen Maschal Tarekegn, Raj Mohan Balakrishina

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Composting is one of the conventional techniques adopted for organic waste management, but the practice is very limited in emerging cities despite the most of the waste generated is organic. This paper aims to examine the viability of composting for organic waste management in the emerging city of Addis Ababa, Ethiopia, by addressing the composting practice, quality of compost, and application of compost in urban agriculture. The study collects data using compost laboratory testing and urban farm households’ survey and uses descriptive analysis on the state of compost production and application, physicochemical analysis of the compost samples, and regression analysis on the urban farmer’s willingness to pay for compost. The findings of the study indicated that there is composting practice at a small scale, most of the producers use unsorted feedstock materials, aerobic composting is dominantly used, and the maturation period ranged from four to ten weeks. The carbon content of the compost ranges from 30.8 to 277.1 due to the type of feedstock applied, and this surpasses the ideal proportions for C:N ratio. The total nitrogen, pH, organic matter, and moisture content are relatively optimal. The levels of heavy metals measured for Mn, Cu, Pb, Cd and Cr⁶⁺ in the compost samples are also insignificant. In the urban agriculture sector, chemical fertilizer is the dominant type of soil input in crop productions but vegetable producers use a combination of both fertilizer and other organic inputs, including compost. The willingness to pay for compost depends on income, household size, gender, type of soil inputs, monitoring soil fertility, the main product of the farm, farming method and farm ownership. Finally, this study recommends the need for collaboration among stakeholders’ along the value chain of waste, awareness creation on the benefits of composting and addressing challenges faced by both compost producers and users.

Keywords: composting, emerging city, organic waste management, urban agriculture

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Authors: Eda Kocakaya, Sebahat Seker, Dogan Argun

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The aim of this study is to determine the process of information technologies and the connections between concepts in accounting management services in Turkey. The objective of this study is to determine the adaptation and evaluation process of information technologies and the connections between concepts and differences in accounting management services in Turkey. The situation and determination of the IT applications of Accounting Management were studied. The applications of • Billing • Order Processing • Accounts Receivable/Payable Management • Contract Management • Bank Account Management Were discussed in this study. The IT applications were demonstrated and realized in actual accounting services. The sectoral representative's companies were selected, and the IT application was searched by bibliometric analysis.

Keywords: management, accounting, information technologies, adaptation

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Authors: C. Mayr, J. Periya, A. Kariminezhad

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In this paper, we developed a learning framework for the classification of vulnerable road users (VRU) by their range-Doppler signatures. The frequency-modulated continuous-wave (FMCW) radar raw data is first pre-processed to obtain robust object range-Doppler maps per coherent time interval. The complex-valued range-Doppler maps captured from our outdoor measurements are further fed into a convolutional neural network (CNN) to learn the classification. This CNN has gone through a hyperparameter optimization process for improved learning. By learning VRU range-Doppler signatures, the three classes 'pedestrian', 'dog', and 'noise' are classified with an average accuracy of almost 95%. Interestingly, this classification accuracy holds for a combined longitudinal and lateral object trajectories.

Keywords: machine learning, radar, signal processing, autonomous driving

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Authors: Vishakha Kaim, Mookan Natarajan, Sandeep Kaur-Ghumaan

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Hydrogen is one of the most abundant elements present on earth’s crust and considered to be the simplest element in existence. It is not found naturally as a gas on earth and thus has to be manufactured. Hydrogen can be produced from a variety of sources, i.e., water, fossil fuels, or biomass and it is a byproduct of many chemical processes. It is also considered as a secondary source of energy commonly referred to as an energy carrier. Though hydrogen is not widely used as a fuel, it still has the potential for greater use in the future as a clean and renewable source of energy. Electrocatalysis is one of the important source for the production of hydrogen which could contribute to this prominent challenge. Metals such as platinum and palladium are considered efficient for hydrogen production but with limited applications. As a result, a wide variety of metal complexes with earth abundant elements and varied ligand environments have been explored for the electrochemical production of hydrogen. In nature, [FeFe] hydrogenase enzyme present in DesulfoVibrio desulfuricans and Clostridium pasteurianum catalyses the reversible interconversion of protons and electrons into dihydrogen. Since the first structure for the enzyme was reported in 1990s, a range of iron complexes has been synthesized as structural and functional mimics of the enzyme active site. Mn is one of the most desirable element for sustainable catalytic transformations, immediately behind Fe and Ti. Only limited number manganese complexes have been reported in the last two decades as catalysts for proton reduction. Furthermore, redox reactions could be carried out in a facile manner, due to the capability of manganese complexes to be stable at different oxidation states. Herein are reported, four µ2-thiolate bridged manganese complexes [Mn₂(CO)₆(μ-S₂N₄C₁₄H₁₀)] 1, [Mn₂(CO)7(μ- S₂N₄C₁₄H₁₀)] 2, Mn₂(CO)₆(μ-S₄N₂C₁₄H₁₀)] 3 and [Mn₂(CO)(μ- S₄N₂C₁₄H₁₀)] 4 have been synthesized and characterized. The cyclic voltammograms of the complexes displayed irreversible reduction peaks in the range - 0.9 to -1.3 V (vs. Fc⁺/Fc in acetonitrile at 0.1 Vs⁻¹). The complexes were catalytically active towards proton reduction in the presence of trifluoroacetic acid as seen from electrochemical investigations.

Keywords: earth abundant, electrocatalytic, hydrogen, manganese

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Authors: Tassadit Issaadi Hamitouche, Nicolas Gillard, Jean Petit, Valerie Lavaste, Celine Mayousse

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Water is a fundamental resource for the industry. It is taken from the environment either from municipal distribution networks or from various natural water sources such as the sea, ocean, rivers, aquifers, etc. Once used, water is discharged into the environment, reprocessed at the plant or treatment plants. These withdrawals and discharges have a direct impact on natural water resources. These impacts can apply to the quantity of water available, the quality of the water used, or to impacts that are more complex to measure and less direct, such as the health of the population downstream from the watercourse, for example. Based on the analysis of data (meteorological, river characteristics, physicochemical substances), we wish to predict water stress episodes and anticipate prefectoral decrees, which can impact the performance of plants and propose improvement solutions, help industrialists in their choice of location for a new plant, visualize possible interactions between companies to optimize exchanges and encourage the pooling of water treatment solutions, and set up circular economies around the issue of water. The development of a system for the collection, processing, and use of data related to water resources requires the functional constraints specific to the latter to be made explicit. Thus the system will have to be able to store a large amount of data from sensors (which is the main type of data in plants and their environment). In addition, manufacturers need to have 'near-real-time' processing of information in order to be able to make the best decisions (to be rapidly notified of an event that would have a significant impact on water resources). Finally, the visualization of data must be adapted to its temporal and geographical dimensions. In this study, we set up an infrastructure centered on the TICK application stack (for Telegraf, InfluxDB, Chronograf, and Kapacitor), which is a set of loosely coupled but tightly integrated open source projects designed to manage huge amounts of time-stamped information. The software architecture is coupled with the cross-industry standard process for data mining (CRISP-DM) data mining methodology. The robust architecture and the methodology used have demonstrated their effectiveness on the study case of learning the level of a river with a 7-day horizon. The management of water and the activities within the plants -which depend on this resource- should be considerably improved thanks, on the one hand, to the learning that allows the anticipation of periods of water stress, and on the other hand, to the information system that is able to warn decision-makers with alerts created from the formalization of prefectoral decrees.

Keywords: data mining, industry, machine Learning, shortage, water resources

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Authors: Razieh Khalafi

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The brain is the information processing centre of the human body. Stimuli in the form of information are transferred to the brain and then brain makes the decision on how to respond to them. In this research, we propose a new partial differential equation which analyses the EEG signals and make a relationship between the incoming stimuli and the brain response to them. In order to test the proposed model, a set of external stimuli applied to the model and the model’s outputs were checked versus the real EEG data. The results show that this model can model the EEG signal well. The proposed model is useful not only for modelling of EEG signal in case external stimuli but it can be used for modelling of brain response in case of internal stimuli.

Keywords: brain, stimuli, partial differential equation, response, EEG signal

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Authors: Ali M. Alakeel

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Assertion-based software testing has been shown to be a promising tool for generating test cases that reveal program faults. Because the number of assertions may be very large for industry-size programs, one of the main concerns to the applicability of assertion-based testing is the amount of search time required to explore a large number of assertions. This paper presents a new approach for assertions exploration during the process of Assertion-Based software testing. Our initial exterminations with the proposed approach show that the performance of Assertion-Based testing may be improved, therefore, making this approach more efficient when applied on programs with large number of assertions.

Keywords: software testing, assertion-based testing, program assertions, generating test

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Authors: Kamalpreet Kaur, Harjit Pal Singh, Vikas Khullar

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In Wireless Sensor Network (WSN), the main limitation is generally inimitable energy consumption during processing of the sensor nodes. Cluster head (CH) election is one of the main issues that can reduce the energy consumption. Therefore, discovering energy saving routing protocol is the focused area for research. In this paper, fuzzy-based energy aware routing protocol is presented, which enhances the stability and network lifetime of the network. Fuzzy logic ensures the well-organized selection of CH by taking four linguistic variables that are concentration, energy, centrality, and distance to base station (BS). The results show that the proposed protocol shows better results in requisites of stability and throughput of the network.

Keywords: ERA, fuzzy logic, network model, WSN

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Authors: Shweta Hoyani, Charlie Oommen

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HAN-based liquid propellants are perceived as potential substitute for hydrazine in space propulsion. Storage stability for long service life in orbit is one of the key concerns for HAN-based monopropellants because of its reactivity with metallic and non-metallic impurities which could entrain from the surface of fuel tanks and the tubes. The end result of this reactivity directly affects the handling, performance and storability of the liquid propellant. Gaseous products resulting from the decomposition of the propellant can lead to deleterious pressure build up in storage vessels. The partial loss of an energetic component can change the ignition and the combustion behavior and alter the performance of the thruster. The effect of largely plausible metals- iron, copper, chromium, nickel, manganese, molybdenum, zinc, titanium and cadmium on the thermal decomposition mechanism of HAN has been investigated in this context. Studies involving different concentrations of metal ions and HAN at different preheat temperatures have been carried out. Effect of metal ions on the decomposition behavior of HAN has been studied earlier in the context of use of HAN as gun propellant. However the current investigation pertains to the decomposition mechanism of HAN in the context of use of HAN as monopropellant for space propulsion. Decomposition onset temperature, rate of weight loss, heat of reaction were studied using DTA- TGA and total pressure rise and rate of pressure rise during decomposition were evaluated using an in-house built constant volume batch reactor. Besides, reaction mechanism and product profile were studied using TGA-FTIR setup. Iron and copper displayed the maximum reaction. Initial results indicate that iron and copper shows sensitizing effect at concentrations as low as 50 ppm with 60% HAN solution at 80°C. On the other hand 50 ppm zinc does not display any effect on the thermal decomposition of even 90% HAN solution at 80°C.

Keywords: hydroxylammonium nitrate, monopropellant, reaction mechanism, thermal stability

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Authors: Djamila Meghraoui, Bachir Boudraa, Thouraya Meksen, M.Boudraa

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Parkinson's disease is a pathology that involves characteristic perturbations in patients’ voices. This paper describes a proposed method that aims to diagnose persons with Parkinson (PWP) by analyzing on line their voices signals. First, Thresholds signals alterations are determined by the Multi-Dimensional Voice Program (MDVP). Principal Analysis (PCA) is exploited to select the main voice principal componentsthat are significantly affected in a patient. The decision phase is realized by a Mul-tinomial Bayes (MNB) Classifier that categorizes an analyzed voice in one of the two resulting classes: healthy or PWP. The prediction accuracy achieved reaching 98.8% is very promising.

Keywords: Parkinson’s disease recognition, PCA, MDVP, multinomial Naive Bayes

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Authors: Claus N. Holm, Oliver F. Grooss, Robert A. Alphinas

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This research strives to predict the remaining product life cycle of a song on radio after it has been played for one or two months. The best results were achieved using a k-d tree to calculate the most similar songs to the test songs and use a Random Forest model to forecast radio plays. An 82.78% and 83.44% accuracy is achieved for the two time periods, respectively. This explorative research leads to over 4500 test metrics to find the best combination of models and pre-processing techniques. Other algorithms tested are KNN, MLP and CNN. The features only consist of daily radio plays and use no musical features.

Keywords: hit song science, product life cycle, machine learning, radio

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Authors: Daya Rani

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Non-metal-based compounds have emerged as promising electrodes in recent years to replace scarce and expensive transition-metals for energy storage applications. Herein, a simple electro-spinning technique followed by carbonization is used to create tetraphosphorus triselenide(P4Se3)nano-flakes encapsulated in carbon nanofiber (P4Se3@CNF) to obtain a binder-free, metal-free and flexible hybrid electrode with high electrical conductivity and cyclic stability. A remarkable capacitive performance (5.5-folds@P4Se3) of 810Fg-1/[email protected] has been obtained using P4Se3@CNF electrode with an excellent rate capability compared to pristine(P4Se3) which is further supported by theoretical calculations via intercalating graphene within bare P4Se3 flakes inducing partial charge redistribution in hetero-structure. A flexible pouch-type hybrid-supercapacitor followed by coin-cell has been manufactured offering exceptional energy-density without sacrificing power density and ultra-long durability over 35000 and 100000-cycles with capacitance-retention of 99.77% and 100%, respectively. It has been demonstrated that as-fabricated device has practical usefulness towards renewable energy harvesting and storage via integrating commercial solar cell module with supercapattery array that can enlighten the blue LED approximately for 31minutes, rotate the homemade windmill device, power Arduino and glow “INST” against 2minutes of charging. This work demonstrates a facile route towards the development of metal-free electrochemical renewable energy storage/transfer devices offering an inevitable adoption in industrial platforms.

Keywords: metal free, carbon nano-fiber, pouch-type hybrid super-capacitor, nano-flakes

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Authors: Avigail Moor, Michal Otmazgin, Hagar Tsiddon, Avivit Mahazri

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The goal of the present study was to refine sexual assault therapy through the examination of the level of agreement between survivor and therapist assessments of key recovery-promoting therapeutic interventions. This is the first study to explore the level of agreement between those who partake in the treatment process from either position. Semi structured interviews were conducted in this qualitative study with 10 survivors and 10 experienced therapists. The results document considerable concurrence between them regarding relational and trauma processing treatment components alike. Together, these reports outline key effective interventions, both common and specific in nature, concomitantly supported by both groups.

Keywords: sexual assault, rape treatment, therapist training, psychotherapy

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Authors: M.R Asghari Bejestani, Gh. R. Mohammad Khani, V.R. Nafisi

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Imagined speech recognition is one of the most interesting approaches to BCI development and a lot of works have been done in this area. Many different experiments have been designed and hundreds of combinations of feature extraction methods and classifiers have been examined. Reported classification accuracies range from the chance level to more than 90%. Based on non-stationary nature of brain signals, we have introduced 3 classification modes according to time difference in inter and intra-class samples. The modes can explain the diversity of reported results and predict the range of expected classification accuracies from the brain signal accusation procedure. In this paper, a few samples are illustrated by inspecting results of some previous works.

Keywords: brain computer interface, silent talk, imagined speech, classification, signal processing

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Authors: Ayad Asaad Ibrahim

Abstract:

Myoelectrically controlled prostheses are becoming more and more popular, for below-elbow amputation, the wrist flexor and extensor muscle group, while for above-elbow biceps and triceps brachii muscles are used for control of the prosthesis. A two site multi-function controller is presented. Two stainless steel bipolar electrode pairs are used to monitor the activities in both muscles. The detected signals are processed by new pre-whitening technique to identify the accurate tension estimation in these muscles. These estimates will activate the relevant prosthesis control signal, with a time constant of 200 msec. It is ensured that the tension states in the control muscle to activate a particular prosthesis function are similar to those used to activate normal functions in the natural hand. This facilitates easier training.

Keywords: prosthesis, biosignal processing, pre-whitening, myoelectric controller

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