Search results for: memory optimization

Authors: Béla Kovács, Éva Bódi, Farzaneh Garousi, Szilvia Várallyay, Áron Soós, Xénia Vágó, Dávid Andrási

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In agriculture for analysis of elements in different food and food raw materials, moreover environmental samples generally flame atomic absorption spectrometers (FAAS), graphite furnace atomic absorption spectrometers (GF-AAS), inductively coupled plasma optical emission spectrometers (ICP-OES) and inductively coupled plasma mass spectrometers (ICP-MS) are routinely applied. An inductively coupled plasma mass spectrometer (ICP-MS) is capable for analysis of 70-80 elements in multielemental mode, from 1-5 cm3 volume of a sample, moreover the detection limits of elements are in µg/kg-ng/kg (ppb-ppt) concentration range. All the analytical instruments have different physical and chemical interfering effects analysing the above types of samples. The smaller the concentration of an analyte and the larger the concentration of the matrix the larger the interfering effects. Nowadays there is very important to analyse growingly smaller concentrations of elements. From the above analytical instruments generally the inductively coupled plasma mass spectrometer is capable of analysing the smallest concentration of elements. The applied ICP-MS instrument has Collision Cell Technology (CCT) also. Using CCT mode certain elements have better (smaller) detection limits with 1-3 magnitudes comparing to a normal ICP-MS analytical method. The CCT mode has better detection limits mainly for analysis of selenium, arsenic, germanium, vanadium and chromium. To elaborate an analytical method for trace elements with an inductively coupled plasma mass spectrometer the most important interfering effects (problems) were evaluated: 1) Physical interferences; 2) Spectral interferences (elemental and molecular isobaric); 3) Effect of easily ionisable elements; 4) Memory interferences. Analysing food and food raw materials, moreover environmental samples an other (new) interfering effect emerged in ICP-MS, namely the effect of various matrixes having different evaporation and nebulization effectiveness, moreover having different quantity of carbon content of food and food raw materials, moreover environmental samples. In our research work the effect of different water-soluble compounds furthermore the effect of various quantity of carbon content (as sample matrix) were examined on changes of intensity of the applied elements. So finally we could find “opportunities” to decrease or eliminate the error of the analyses of applied elements (Cr, Co, Ni, Cu, Zn, Ge, As, Se, Mo, Cd, Sn, Sb, Te, Hg, Pb, Bi). To analyse these elements in the above samples, the most appropriate inductively coupled plasma mass spectrometer is a quadrupole instrument applying a collision cell technique (CCT). The extent of interfering effect of carbon content depends on the type of compounds. The carbon content significantly affects the measured concentration (intensities) of the above elements, which can be corrected using different internal standards.

Keywords: elements, environmental and food samples, ICP-MS, interference effects

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Authors: Wei Zhang

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With the rapid development of deep learning, neural network and deep learning algorithms play a significant role in various practical applications. Due to the high accuracy and good performance, Convolutional Neural Networks (CNNs) especially have become a research hot spot in the past few years. However, the size of the networks becomes increasingly large scale due to the demands of the practical applications, which poses a significant challenge to construct a high-performance implementation of deep learning neural networks. Meanwhile, many of these application scenarios also have strict requirements on the performance and low-power consumption of hardware devices. Therefore, it is particularly critical to choose a moderate computing platform for hardware acceleration of CNNs. This article aimed to survey the recent advance in Field Programmable Gate Array (FPGA)-based acceleration of CNNs. Various designs and implementations of the accelerator based on FPGA under different devices and network models are overviewed, and the versions of Graphic Processing Units (GPUs), Application Specific Integrated Circuits (ASICs) and Digital Signal Processors (DSPs) are compared to present our own critical analysis and comments. Finally, we give a discussion on different perspectives of these acceleration and optimization methods on FPGA platforms to further explore the opportunities and challenges for future research. More helpfully, we give a prospect for future development of the FPGA-based accelerator.

Keywords: deep learning, field programmable gate array, FPGA, hardware accelerator, convolutional neural networks, CNN

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Authors: J. Martin, A. Dannenberg, G. Detrell, R. Ewald, S. Fasoulas

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Microalgae-based photobioreactors (PBR) for Life Support Systems (LSS) are currently being investigated for future space missions such as a crewed base on planets or moons. Biological components may help reducing resupply masses by closing material mass flows with the help of regenerative components. Via photosynthesis, the microalgae use CO₂, water, light and nutrients to provide oxygen and biomass for the astronauts. These capabilities could have synergies with Earth applications that tackle current problems and the developed technologies can be transferred. For example, a current worldwide discussed issue is the increased nitrate and phosphate pollution of ground water from agricultural waste waters. To investigate the potential use of a biological system based on the ability of the microalgae to extract and use nitrate and phosphate for the treatment of polluted ground water from agricultural applications, a scalable test stand is being developed. This test stand investigates the maximization of intake rates of nitrate and quantifies the produced biomass and oxygen. To minimize the required energy, for the uptake of nitrate from artificial waste water (AWW) the Flashing Light Effect (FLE) and the adaption of the illumination spectrum were realized. This paper describes the composition of the AWW, the development of the illumination unit and the possibility of non-invasive process optimization and control via the adaption of the illumination spectrum and illumination cycles. The findings were a doubling of the energy related growth rate by adapting the illumination setting.

Keywords: microalgae, illumination, nitrate uptake, flashing light effect

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Authors: Khadeeja Munawar, Shamsul Haque

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We examined the plausibility of transition theory suggesting that memories of transitional events, which give rise to a significant and persistent change in the fabric of daily life, are organized around the historically defined autobiographical periods (H-DAPs). 141 Pakistani older adults retrieved 10 autobiographical memories (AMs) each to 10 cue words. As the history of Pakistan is dominated by various political and nationwide transitional events, it was expected that the participants would recall memories with H-DAPs references. The content analysis revealed that 0.7% of memories had H-DAP references and 0.4% memories mentioned major transitional events such as War/Natural Disaster. There was a vivid reminiscence bump between 10 - 20 years of age in lifespan distribution of AMs. There were 67.9% social-focused AMs. Significantly more self-focused memories were reported by individuals who endorsed themselves as conservatives. Only a few H-DAPs were reported, although the history of Pakistan was dominated by numerous political, historical and nationwide transitional events. Memories within and outside of the bump period were mostly positive. The participants rarely used historical/political or nationwide significant events or periods to date the memories elicited. The intense and nationwide (as well as region-wise) significant historical/political events spawned across decades in the lives of participants of the present study but these events did not produce H-DAPs. The findings contradicted the previous studies on H-DAPs and transition theory. The dominance of social-focused AMs in the present study is in line with the past studies comparing the memories of collectivist and individualist cultures (i.e., European Americans vs. Asian, African and Latin-American cultures). The past empirical evidence shows that conservative values and beliefs are adopted as a coping strategy to feel secure in the face of danger when future is dominated with uncertainty and to connect to likeminded others. In the present study, conservative political ideology is somehow assisting the participants in living a stable life midst of their complex social worlds. The reminiscence bump, as well as dominance of positive memories within and outside the bump period, are in line with the narrative/identity account which states that the events and experiences during adolescence and early adulthood assimilate into a person’s lifelong narratives. Hence these events are used as identity markers and are more easily recalled later in life. Also, according to socioemotional theory and the positivity effect, the participants evaluated past events more positively as they grow up and the intensity of negative emotions decreased with time.

Keywords: autobiographical memory, historically defined autobiographical periods, narrative/identity account, Pakistan, reminiscence bump, SMS framework, transition theory

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Authors: M. Magrini, D. Moroni, G. Palazzese, G. Pieri, D. Azzarelli, A. Spada, L. Fanucci, O. Salvetti

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Railway crossings are complex entities whose optimal management cannot be addressed unless with the help of an intelligent transportation system integrating information both on train and vehicular flows. In this paper, we propose an integrated system named SIMPLE (Railway Safety and Infrastructure for Mobility applied at level crossings) that, while providing unparalleled safety in railway level crossings, collects data on rail and road traffic and provides value-added services to citizens and commuters. Such services include for example alerts, via variable message signs to drivers and suggestions for alternative routes, towards a more sustainable, eco-friendly and efficient urban mobility. To achieve these goals, SIMPLE is organized as a System of Systems (SoS), with a modular architecture whose components range from specially-designed radar sensors for obstacle detection to smart ETSI M2M-compliant camera networks for urban traffic monitoring. Computational unit for performing forecast according to adaptive models of train and vehicular traffic are also included. The proposed system has been tested and validated during an extensive trial held in the mid-sized Italian town of Montecatini, a paradigmatic case where the rail network is inextricably linked with the fabric of the city. Results of the tests are reported and discussed.

Keywords: Intelligent Transportation Systems (ITS), railway, railroad crossing, smart camera networks, radar obstacle detection, real-time traffic optimization, IoT, ETSI M2M, transport safety

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Authors: Zohreh Mohammadi, Jan Kaspar, Peter Lohmander, Robert Marusak, Harald Vacik, Ljusk Ola Eriksson

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Storms are important damaging agents in European forest ecosystems. In the latest decades, significant economic losses in European forestry occurred due to storms. This study investigates the problem of optimal harvest planning when forest stands risk to be felled by storms. One of the most applicable mathematical methods which are being used to optimize forest management is stochastic dynamic programming (SDP). This method belongs to the adaptive optimization class. Sequential decisions, such as harvest decisions, can be optimized based on sequential information about events that cannot be perfectly predicted, such as the future storms and the future states of wind protection from other forest stands. In this paper, stochastic dynamic programming is used to maximize the expected present value of the profits from an area consisting of several forest stands. The region of analysis is the Czech Republic. The harvest decisions, in a particular time period, should be simultaneously taken in all neighbor stands. The reason is that different stands protect each other from possible winds. The optimal harvest age of a particular stand is a function of wind speed and different wind protection effects. The optimal harvest age often decreases with wind speed, but it cannot be determined for one stand at a time. When we consider a particular stand, this stand also protects other stands. Furthermore, the particular stand is protected by neighbor stands. In some forest stands, it may even be rational to increase the harvest age under the influence of stronger winds, in order to protect more valuable stands in the neighborhood. It is important to integrate wind risk in forestry decision-making.

Keywords: Czech republic, forest stands, stochastic dynamic programming, wind risk

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Authors: Nova Grail S. Luminang, Gwyneth B. Gortiza, Alvin E. Haboc, Marinol K. Hate, Rhean Mitchel N. Joven, Kara Kate D. Lammao, Rosemarie M. Lambayung, Elmo Carl D. Lardizabal, Zyra B. Linggayo, Rizza Mae G. Liwag, Ronalyn O. Songcuan

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Breast cancer survivorship represents a complex and continuous journey extending beyond the completion of treatment, involving coping with physical, emotional, and psychological aspects of life post-treatment. This study aimed to explore the lived experiences of breast cancer survivors after successful treatment in Tabuk City, focusing on their post-treatment experiences, coping mechanisms, and necessary lifestyle changes. Researchers have selected Tabuk City as their research locale. Utilizing Martin Heidegger’s descriptive phenomenological design, this qualitative research included six participants, allowing for data saturation. Purposive sampling was employed to select participants. Researchers used Colaizzi’s Phenomenological Method in analyzing the data in order to achieve a reliable understanding of the participants’ experiences. The findings revealed three main themes: going through post-treatment hurdles, building resilience, and transformative wellness adjustments. Breast cancer survivors faced significant challenges, including physical adversities, emotional turmoil, limited social life, memory lapses, decreased sexual intimacy, and economic constraints. To cope, survivors adjusted their thoughts and attitudes, accepted their situation, relied on religious beliefs, and joined the support group Kalinga Cancer Care Ministry INC. Additionally, they strived to return to a normal life and embraced gratitude. Survivors made essential changes to their daily routines, modifying their diets, exploring herbal remedies, and incorporating physical activities such as walking and household chores. These adjustments helped improve their overall well-being and prevent cancer recurrence. The researchers concluded that the journey of breast cancer survivors is marked by significant challenges and inspiring resilience. The impact of breast cancer treatment extends beyond physical recovery, encompassing profound emotional and social dimensions. Despite these difficulties, survivors demonstrate remarkable strength and adaptability, making positive lifestyle changes that offer a hopeful and inspiring narrative of recovery and perseverance.

Keywords: breast cancer, lived experiences, breast cancer survivor, post-treatment hurdles, emotional turmoil

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Authors: Suleiman Hassan Otuoze, Dexter Vernon Lloyd Hunt, Ian Jefferson

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Urbanization has continued to widen the gap between demand and resources available to provide resilient and sustainable transport services in many fast-growing developing countries' cities. Transport demand management is a decision-based optimization concept for both benchmarking and ensuring efficient use of transport resources. This study assesses the service quality of infrastructure and mobility services in the Nigerian cities of Kano and Lagos through five dimensions of quality (i.e., Tangibility, Reliability, Responsibility, Safety Assurance and Empathy). The methodology adopts a hybrid AHP-SERVQUAL model applied on questionnaire surveys to gauge the quality of satisfaction and the views of experts in the field. The AHP results prioritize tangibility, which defines the state of transportation infrastructure and services in terms of satisfaction qualities and intervention decision weights in the two cities. The results recorded ‘unsatisfactory’ indices of quality of performance and satisfaction rating values of 48% and 49% for Kano and Lagos, respectively. The satisfaction indices are identified as indicators of low performances of transportation demand management (TDM) measures and the necessity to re-order priorities and take proactive steps towards infrastructure. The findings pilot a framework for comparative assessment of recognizable standards in transport services, best ethics of management and a necessity of quality infrastructure to guarantee both resilient and sustainable urban mobility.

Keywords: transportation demand management, multi-criteria decision support, transport infrastructure, service quality, sustainable transport

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Authors: Ahmed Banda, Alaa Maghrabi, Aiman Fakieh

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Terahertz (THz) range lies in the area between 0.1 to 10 THz. The process of generating and detecting THz can be done through different techniques. One of the most familiar techniques is done through a photoconductive antenna (PCA). The process of generating THz radiation at PCA includes applying a laser pump in femtosecond and DC voltage difference. However, photocurrent is generated at PCA, which its value is affected by different parameters (e.g., dielectric properties, DC voltage difference and incident power of laser pump). THz radiation is used for biomedical applications. However, different biomedical fields need new technologies to meet patients’ needs (e.g. blood-related conditions). In this work, a novel method to check the red blood cells (RBCs) concentration of a blood sample using PCA is presented. RBCs constitute 44% of total blood volume. RBCs contain Hemoglobin that transfers oxygen from lungs to body organs. Then it returns to the lungs carrying carbon dioxide, which the body then gets rid of in the process of exhalation. The configuration has been simulated and optimized using COMSOL Multiphysics. The differentiation of RBCs concentration affects its dielectric properties (e.g., the relative permittivity of RBCs in the blood sample). However, the effects of four blood samples (with different concentrations of RBCs) on photocurrent value have been tested. Photocurrent peak value and RBCs concentration are inversely proportional to each other due to the change of dielectric properties of RBCs. It was noticed that photocurrent peak value has dropped from 162.99 nA to 108.66 nA when RBCs concentration has risen from 0% to 100% of a blood sample. The optimization of this method helps to launch new products for diagnosing blood-related conditions (e.g., anemia and leukemia). The resultant electric field from DC components can not be used to count the RBCs of the blood sample.

Keywords: biomedical applications, photoconductive antenna, photocurrent, red blood cells, THz radiation

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Authors: R. Sharma, S. Kumar, C. Sharma

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A number of toxic chlorophenolic compounds are formed during pulp bleaching. The nature and concentration of these chlorophenolic compounds largely depends upon the amount and nature of bleaching chemicals used. These compounds are highly recalcitrant and difficult to remove but are partially removed by the biochemical treatment processes adopted by the paper industry. Identification and estimation of these chlorophenolic compounds has been carried out in the primary and secondary clarified effluents from the paper mill by GCMS. Twenty-six chorophenolic compounds have been identified and estimated in paper mill waste waters. Electrochemical treatment is an efficient method for oxidation of pollutants and has successfully been used to treat textile and oil waste water. Electrochemical treatment using less expensive anode material, stainless steel electrodes has been tried to study their removal. The electrochemical assembly comprised a DC power supply, a magnetic stirrer and stainless steel (316 L) electrode. The optimization of operating conditions has been carried out and treatment has been performed under optimized treatment conditions. Results indicate that 68.7% and 83.8% of cholorphenolic compounds are removed during 2 h of electrochemical treatment from primary and secondary clarified effluent respectively. Further, there is a reduction of 65.1, 60 and 92.6% of COD, AOX and color, respectively for primary clarified and 83.8%, 75.9% and 96.8% of COD, AOX and color, respectively for secondary clarified effluent. EC treatment has also been found to increase significantly the biodegradability index of wastewater because of conversion of non- biodegradable fraction into biodegradable fraction. Thus, electrochemical treatment is an efficient method for the degradation of cholorophenolic compounds, removal of color, AOX and other recalcitrant organic matter present in paper mill waste water.

Keywords: chlorophenolics, effluent, electrochemical treatment, wastewater

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Authors: Daud Mustafa Minhas, Raja Rehan Khalid, Georg Frey

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The next generation power systems are equipped with abundantly available free renewable energy resources (RES). During their low-cost operations, the price of electricity significantly reduces to a lower value, and sometimes it becomes negative. Therefore, it is recommended not to operate the traditional power plants (e.g. coal power plants) and to reduce the losses. In fact, it is not a cost-effective solution, because these power plants exhibit some shutdown and startup costs. Moreover, they require certain time for shutdown and also need enough pause before starting up again, increasing inefficiency in the whole power network. Hence, there is always a trade-off between avoiding negative electricity prices, and the startup costs of power plants. To exploit this trade-off and to increase the profit of a power plant, two main contributions are made: 1) introducing retrofit technology for state of art coal power plant; 2) proposing optimal control strategy for a power plant by exploiting different flexibility features. These flexibility features include: improving ramp rate of power plant, reducing startup time and lowering minimum load. While, the control strategy is solved as mixed integer linear programming (MILP), ensuring optimal solution for the profit maximization problem. Extensive comparisons are made considering pre and post-retrofit coal power plant having the same efficiencies under different electricity price scenarios. It concludes that if the power plant must remain in the market (providing services), more flexibility reflects direct economic advantage to the plant operator.

Keywords: discrete optimization, power plant flexibility, profit maximization, unit commitment model

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Authors: Kgomotso G. Mabena, Philiswa Nomngongo, Nomvano Mketo

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This study describes the synthesis and characterization of a magnetic cellulose gold nanocomposite (MCNC@Au) for vortex-assisted dispersive magnetic solid phase microextraction (VA-d-µ-mSPE) of organosulfur compounds (OSCs) in selected liquid fuel samples followed by high-pressure liquid chromatography (HPLC) analysis. The nanocomposite was synthesized utilizing an in-situ co-precipitation approach and characterization data from FTIR, P-XRD, TGA, TEM and SEM-EDX methods validated the formation of the desired nanocomposite. The most important parameters of the suggested VA-mSPE method were examined using the 2-level half-fractional factorial design and the central composite design in order to achieve high extraction efficiency. With pH as an insignificant parameter, the multivariate optimization results demonstrated that effective extraction was achieved when 20 mg of sorbent mass, 8 minutes of extraction time, and 20 mL sample volume were used. The acquired optimal condition will be used to assess the analytical performance of the VA-d-µ-mSPE technique. The quantitative parameters, including relative standard deviation (RSD), accuracy, precision, limit of quantification (LOQ), and limit of detection (LOD), will be determined. Additionally, real fuel oil samples, including crude oil, gasoline, diesel, and kerosene, will be subjected to the optimum and validated VA-d-µ-mSPE method in order to quantitatively determine various OSCs.

Keywords: MCNC@Au, VA-d-µ-mSPE, OSCs, HPLC.

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Authors: J. B. Frisch, D. Evandiloff, P. Martin, N. Ouizille, F. Pires

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Smart Beta strategies intend to be an asset management revolution with reference to classical cap-weighted indices. Indeed, these strategies allow a better control on portfolios risk factors and an optimized asset allocation by taking into account specific risks or wishes to generate alpha by outperforming indices called 'Beta'. Among many strategies independently used, this paper focuses on four of them: Minimum Variance Portfolio, Equal Risk Contribution Portfolio, Maximum Diversification Portfolio, and Equal-Weighted Portfolio. Their efficiency has been proven under constraints like momentum or market phenomenon, suggesting a reconsideration of cap-weighting.
 To further increase strategy return efficiency, it is proposed here to compare their strengths and weaknesses inside time intervals corresponding to specific identifiable market phases, in order to define adapted strategies depending on pre-specified situations. 
Results are presented as performance curves from different combinations compared to a benchmark. If a combination outperforms the applicable benchmark in well-defined actual market conditions, it will be preferred. It is mainly shown that such investment 'rules', based on both historical data and evolution of Smart Beta strategies, and implemented according to available specific market data, are providing very interesting optimal results with higher return performance and lower risk.
 Such combinations have not been fully exploited yet and justify present approach aimed at identifying relevant elements characterizing them.

Keywords: smart beta, minimum variance portfolio, equal risk contribution portfolio, maximum diversification portfolio, equal weighted portfolio, combinations

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Authors: Yanping Liao, Congcong He, Ruigang Zhao

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The application of magnetocardiography signals to detect cardiac electrical function is a new technology developed in recent years. The magnetocardiography signal is detected with Superconducting Quantum Interference Devices (SQUID) and has considerable advantages over electrocardiography (ECG). It is difficult to extract Magnetocardiography (MCG) signal which is buried in the noise, which is a critical issue to be resolved in cardiac monitoring system and MCG applications. In order to remove the severe background noise, the Total Variation (TV) regularization method is proposed to denoise MCG signal. The approach transforms the denoising problem into a minimization optimization problem and the Majorization-minimization algorithm is applied to iteratively solve the minimization problem. However, traditional TV regularization method tends to cause step effect and lacks constraint adaptability. In this paper, an improved TV regularization method for denoising MCG signal is proposed to improve the denoising precision. The improvement of this method is mainly divided into three parts. First, high-order TV is applied to reduce the step effect, and the corresponding second derivative matrix is used to substitute the first order. Then, the positions of the non-zero elements in the second order derivative matrix are determined based on the peak positions that are detected by the detection window. Finally, adaptive constraint parameters are defined to eliminate noises and preserve signal peak characteristics. Theoretical analysis and experimental results show that this algorithm can effectively improve the output signal-to-noise ratio and has superior performance.

Keywords: constraint parameters, derivative matrix, magnetocardiography, regular term, total variation

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Authors: Youssef Mazloum, Haytham Sayah, Maroun Nemer

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The penetration of renewable energy sources into the electric grid is significantly increasing. However, the intermittence of these sources breaks the balance between supply and demand for electricity. Hence, the importance of the energy storage technologies, they permit restoring the balance and reducing the drawbacks of intermittence of the renewable energies. This paper discusses the modeling and the cost-effectiveness of an isobaric adiabatic compressed air energy storage (IA-CAES) system. The proposed system is a combination among a compressed air energy storage (CAES) system with pumped hydro storage system and thermal energy storage system. The aim of this combination is to overcome the disadvantages of the conventional CAES system such as the losses due to the storage pressure variation, the loss of the compression heat and the use of fossil fuel sources. A steady state model is developed to perform an energy and exergy analyses of the IA-CAES system and calculate the distribution of the exergy losses in the latter system. A sensitivity analysis is also carried out to estimate the effects of some key parameters on the system’s efficiency, such as the pinch of the heat exchangers, the isentropic efficiency of the rotating machinery and the pressure losses. The conducted sensitivity analysis is a local analysis since the sensibility of each parameter changes with the variation of the other parameters. Therefore, an exergoeconomic study is achieved as well as a cost optimization in order to reduce the electricity cost produced during the production phase. The optimizer used is OmOptim which is a genetic algorithms based optimizer.

Keywords: cost-effectiveness, Exergoeconomic analysis, isobaric adiabatic compressed air energy storage (IA-CAES) system, thermodynamic modeling

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Authors: Zoran Herceg, Višnja Stulić, Anet Režek Jambrak, Tomislava Vukušić

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Electrical discharge plasma is a new non-thermal processing technique which is used for the inactivation of contaminating and hazardous microbes in liquids. Plasma is a source of different antimicrobial species including UV photons, charged particles, and reactive species such as superoxide, hydroxyl radicals, nitric oxide and ozone. Escherichia coli was studied as foodborne pathogen. The aim of this work was to examine inactivation effects of electrical discharge plasma treatment on the Escherichia coli MG 1655 in pure culture. Two types of plasma configuration and polarity were used. First configuration was with titanium wire as high voltage needle and another with medical stainless steel needle used to form bubbles in treated volume and titanium wire as high voltage needle. Model solution samples were inoculated with Escerichia coli MG 1655 and treated by electrical discharge plasma at treatment time of 5 and 10 min, and frequency of 60, 90 and 120 Hz. With the first configuration after 5 minutes of treatment at frequency of 120 Hz the inactivation rate was 1.3 log₁₀ reduction and after 10 minutes of treatment the inactivation rate was 3.0 log₁₀ reduction. At the frequency of 90 Hz after 10 minutes inactivation rate was 1.3 log₁₀ reduction. With the second configuration after 5 minutes of treatment at frequency of 120 Hz the inactivation rate was 1.2 log₁₀ reduction and after 10 minutes of treatment the inactivation rate was also 3.0 log₁₀ reduction. In this work it was also examined the formation of biofilm, nucleotide and protein leakage at 260/280 nm, before and after treatment and recuperation of treated samples. Further optimization of method is needed to understand mechanism of inactivation.

Keywords: electrical discharge plasma, escherichia coli MG 1655, inactivation, point-to-plate electrode configuration

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Authors: Jesika A. Walker, Mohammed Aswad, Guy Lacroix, Denis Cousineau

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There is a fundamental disconnect between the experience of time passing and the chronometric units by which time is quantified. Specifically, there appears to be no relationship between the passage of time judgments (PoTJs) and verbal duration estimates at short durations (e.g., < 2000 milliseconds). When a duration is longer than several minutes, however, evidence suggests that a slower feeling of time passing is predictive of overestimation. Might the length of a task moderate the relation between PoTJs and duration estimates? Similarly, the estimation paradigm (prospective vs. retrospective) and the mental effort demanded of a task (task demand) have both been found to influence duration estimates. However, only a handful of experiments have investigated these effects for tasks of long durations, and the results have been mixed. Thus, might the length of a task also moderate the effects of the estimation paradigm and task demand on duration estimates? To investigate these questions, 273 participants performed either an easy or difficult visual and memory search task for either eight or 58 minutes, under prospective or retrospective instructions. Afterward, participants provided a duration estimate in minutes, followed by a PoTJ on a Likert scale (1 = very slow, 7 = very fast). A 2 (prospective vs. retrospective) × 2 (eight minutes vs. 58 minutes) × 2 (high vs. low difficulty) between-subjects ANOVA revealed a two-way interaction between task demand and task duration on PoTJs, p = .02. Specifically, time felt faster in the more challenging task, but only in the eight-minute condition, p < .01. Duration estimates were transformed into RATIOs (estimate/actual duration) to standardize estimates across durations. An ANOVA revealed a two-way interaction between estimation paradigm and task duration, p = .03. Specifically, participants overestimated the task more if they were given prospective instructions, but only in the eight-minute task. Surprisingly, there was no effect of task difficulty on duration estimates. Thus, the demands of a task may influence ‘feeling of time’ and ‘estimation time’ differently, contributing to the existing theory that these two forms of time judgement rely on separate underlying cognitive mechanisms. Finally, a significant main effect of task duration was found for both PoTJs and duration estimates (ps < .001). Participants underestimated the 58-minute task (m = 42.5 minutes) and overestimated the eight-minute task (m = 10.7 minutes). Yet, they reported the 58-minute task as passing significantly slower on a Likert scale (m = 2.5) compared to the eight-minute task (m = 4.1). In fact, a significant correlation was found between PoTJ and duration estimation (r = .27, p <.001). This experiment thus provides evidence for a compensatory effect at longer durations, in which people underestimate a ‘slow feeling condition and overestimate a ‘fast feeling condition. The results are discussed in relation to heuristics that might alter the relationship between these two variables when conditions range from several minutes up to almost an hour.

Keywords: duration estimates, long durations, passage of time judgements, task demands

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Authors: Kulvinder Bajwa, Narsi R. Bishnoi

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Finding a sustainable alternative for today’s petrochemical industry is a major challenge facing by researchers, scientists, chemical engineers, and society at the global level. Microorganisms are considered to be sustainable feedstock for 3rd generation biofuel production. In this study, we have investigated the potential of a native bacterial strain isolated from a petrol contaminated site for the production of biodiesel. The bacterium was identified to be Rhodococcus opacus by biochemical test and 16S rRNA. Compositional analysis of bacterial biomass has been carried out by Fourier transform infrared spectroscopy (FTIR) in order to confirm lipid profile. Lipid and biomass were optimized by combination with Box Behnken design (BBD) of response surface methodology. The factors selected for the optimization of growth condition were glucose, yeast extract, and ammonium nitrate concentration. The experimental model developed through RSM in terms of effective operational factors (BBD) was found to be suitable to describe the lipid and biomass production, which indicated higher lipid and biomass with a minimum concentration of ammonium nitrate, yeast extract, and quite higher dose of glucose supplementation. Optimum results of the experiments were found to be 2.88 gL⁻¹ biomass and lipid content 38.75% at glucose 20 gL⁻¹, ammonium nitrate 0.5 gL⁻¹ and yeast extract 1.25 gL⁻¹. Furthermore, GCMS study revealed that Rhodococcus opacus has favorable fatty acid profile for biodiesel production.

Keywords: biofuel, Oleaginious bacteria, Rhodococcus opacus, FTIR, BBD, free fatty acids

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Authors: R. Khenata, T. Djied, R. Ahmed, H. Baltache, S. Bin-Omran, A. Bouhemadou

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We predict structural, phase transition as well as opto-electronic properties of the filled-tetrahedral (Nowotny-Juza) NaZnAs compound in this study. Calculations are carried out by employing the full potential (FP) linearized augmented plane wave (LAPW) plus local orbitals (lo) scheme developed within the structure of density functional theory (DFT). Exchange-correlation energy/potential (EXC/VXC) functional is treated using Perdew-Burke and Ernzerhof (PBE) parameterization for generalized gradient approximation (GGA). In addition to Trans-Blaha (TB) modified Becke-Johnson (mBJ) potential is incorporated to get better precision for optoelectronic properties. Geometry optimization is carried out to obtain the reliable results of the total energy as well as other structural parameters for each phase of NaZnAs compound. Order of the structural transitions as a function of pressure is found as: Cu2Sb type → β → α phase in our study. Our calculated electronic energy band structures for all structural phases at the level of PBE-GGA as well as mBJ potential point out; NaZnAs compound is a direct (Γ–Γ) band gap semiconductor material. However, as compared to PBE-GGA, mBJ potential approximation reproduces higher values of fundamental band gap. Regarding the optical properties, calculations of real and imaginary parts of the dielectric function, refractive index, reflectivity coefficient, absorption coefficient and energy loss-function spectra are performed over a photon energy ranging from 0.0 to 30.0 eV by polarizing incident radiation in parallel to both [100] and [001] crystalline directions.

Keywords: NaZnAs, FP-LAPW+lo, structural properties, phase transition, electronic band-structure, optical properties

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Authors: Julian Wise

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Newcrest Mining is one of the world’s top five gold and rare earth mining organizations by production, reserves and market capitalization in the world. This paper elaborates on the data acquisition processes employed by Newcrest in collaboration with Fortune 500 listed organization, Insight Enterprises, to standardize machine learning solutions which process data from over a hundred thousand distributed Internet of Things (IoT) devices located at mine sites globally. Through the utilization of software architecture cloud technologies and edge computing, the technological developments enable for standardized processes of machine learning applications to influence the strategic optimization of mineral processing. Target objectives of the machine learning optimizations include time savings on mineral processing, production efficiencies, risk identification, and increased production throughput. The data acquired and utilized for predictive modelling is processed through edge computing by resources collectively stored within a data lake. Being involved in the digital transformation has necessitated the standardization software architecture to manage the machine learning models submitted by vendors, to ensure effective automation and continuous improvements to the mineral process models. Operating at scale, the system processes hundreds of gigabytes of data per day from distributed mine sites across the globe, for the purposes of increased improved worker safety, and production efficiency through big data applications.

Keywords: mineral technology, big data, machine learning operations, data lake

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Authors: Izabella Szollosi, Klara Marton

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Aphasia can be defined as a weakness in accessing linguistic information. Accessing linguistic information is strongly related to information processing, which in turn is associated with the cognitive control system. According to the literature, a deficit in the cognitive control system interferes with language processing and contributes to non-fluent speech performance. The aim of our study was to explore this hypothesis by investigating how cognitive control interacts with language performance in participants with non-fluent aphasia. Cognitive control is a complex construct that includes working memory (WM) and the ability to resist proactive interference (PI). Based on previous research, we hypothesized that impairments in domain-general (DG) cognitive control abilities have negative effects on language processing. In contrast, better DG cognitive control functioning supports goal-directed behavior in language-related processes as well. Since stroke itself might slow down information processing, it is important to examine its negative effects on both cognitive control and language processing. Participants (N=52) in our study were individuals with non-fluent Broca’s aphasia (N = 13), with transcortical motor aphasia (N=13), individuals with stroke damage without aphasia (N=13), and unimpaired speakers (N = 13). All participants performed various computer-based tasks targeting cognitive control functions such as WM and resistance to PI in both linguistic and non-linguistic domains. Non-linguistic tasks targeted primarily DG functions, while linguistic tasks targeted more domain specific (DS) processes. The results showed that participants with Broca’s aphasia differed from the other three groups in the non-linguistic tasks. They performed significantly worse even in the baseline conditions. In contrast, we found a different performance profile in the linguistic domain, where the control group differed from all three stroke-related groups. The three groups with impairment performed more poorly than the controls but similar to each other in the verbal baseline condition. In the more complex verbal PI condition, however, participants with Broca’s aphasia performed significantly worse than all the other groups. Participants with Broca’s aphasia demonstrated the most severe language impairment and the highest vulnerability in tasks measuring DG cognitive control functions. Results support the notion that the more severe the cognitive control impairment, the more severe the aphasia. Thus, our findings suggest a strong interaction between cognitive control and language. Individuals with the most severe and most general cognitive control deficit - participants with Broca’s aphasia - showed the most severe language impairment. Individuals with better DG cognitive control functions demonstrated better language performance. While all participants with stroke damage showed impaired cognitive control functions in the linguistic domain, participants with better language skills performed also better in tasks that measured non-linguistic cognitive control functions. The overall results indicate that the level of cognitive control deficit interacts with the language functions in individuals along with the language spectrum (from severe to no impairment). However, future research is needed to determine any directionality.

Keywords: cognitive control, information processing, language performance, non-fluent aphasia

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Authors: Bokkasam Sasidhar, Ibrahim Aljasser

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The problem of finding optimal schedules for each equipment in a production process is considered, which consists of a single stage of manufacturing and which can handle different types of products, where changeover for handling one type of product to the other type incurs certain costs. The machine capacity is determined by the upper limit for the quantity that can be processed for each of the products in a set up. The changeover costs increase with the number of set ups and hence to minimize the costs associated with the product changeover, the planning should be such that similar types of products should be processed successively so that the total number of changeovers and in turn the associated set up costs are minimized. The problem of cost minimization is equivalent to the problem of minimizing the number of set ups or equivalently maximizing the capacity utilization in between every set up or maximizing the total capacity utilization. Further, the production is usually planned against customers’ orders, and generally different customers’ orders are assigned one of the two priorities – “normal” or “priority” order. The problem of production planning in such a situation can be formulated into a Multiple Arc Network (MAN) model and can be solved sequentially using the algorithm for maximizing flow along a MAN and the algorithm for maximizing flow along a MAN with priority arcs. The model aims to provide optimal production schedule with an objective of maximizing capacity utilization, so that the customer-wise delivery schedules are fulfilled, keeping in view the customer priorities. Algorithms have been presented for solving the MAN formulation of the production planning with customer priorities. The application of the model is demonstrated through numerical examples.

Keywords: scheduling, maximal flow problem, multiple arc network model, optimization

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Authors: Xiaocong Liu, Huazhen Wang, Ting He, Xiaozheng Li, Weihan Zhang, Jian Chen

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The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluate the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.

Keywords: convolutional neural network, electronic medical record, feature representation, lexical semantics, semantic decision

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Authors: Ali Abdul Kadhim, Fue Lien

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Solid particle distribution on an impingement surface has been simulated utilizing a graphical processing unit (GPU). In-house computational fluid dynamics (CFD) code has been developed to investigate a 3D turbulent impinging jet using the lattice Boltzmann method (LBM) in conjunction with large eddy simulation (LES) and the multiple relaxation time (MRT) models. This paper proposed an improvement in the LBM-cellular automata (LBM-CA) probabilistic method. In the current model, the fluid flow utilizes the D3Q19 lattice, while the particle model employs the D3Q27 lattice. The particle numbers are defined at the same regular LBM nodes, and transport of particles from one node to its neighboring nodes are determined in accordance with the particle bulk density and velocity by considering all the external forces. The previous models distribute particles at each time step without considering the local velocity and the number of particles at each node. The present model overcomes the deficiencies of the previous LBM-CA models and, therefore, can better capture the dynamic interaction between particles and the surrounding turbulent flow field. Despite the increasing popularity of LBM-MRT-CA model in simulating complex multiphase fluid flows, this approach is still expensive in term of memory size and computational time required to perform 3D simulations. To improve the throughput of each simulation, a single GeForce GTX TITAN X GPU is used in the present work. The CUDA parallel programming platform and the CuRAND library are utilized to form an efficient LBM-CA algorithm. The methodology was first validated against a benchmark test case involving particle deposition on a square cylinder confined in a duct. The flow was unsteady and laminar at Re=200 (Re is the Reynolds number), and simulations were conducted for different Stokes numbers. The present LBM solutions agree well with other results available in the open literature. The GPU code was then used to simulate the particle transport and deposition in a turbulent impinging jet at Re=10,000. The simulations were conducted for L/D=2,4 and 6, where L is the nozzle-to-surface distance and D is the jet diameter. The effect of changing the Stokes number on the particle deposition profile was studied at different L/D ratios. For comparative studies, another in-house serial CPU code was also developed, coupling LBM with the classical Lagrangian particle dispersion model. Agreement between results obtained with LBM-CA and LBM-Lagrangian models and the experimental data is generally good. The present GPU approach achieves a speedup ratio of about 350 against the serial code running on a single CPU.

Keywords: CUDA, GPU parallel programming, LES, lattice Boltzmann method, MRT, multi-phase flow, probabilistic model

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Authors: Walid Tawfik

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The interaction of high intensity ultrashort laser pulses with plasma generates many significant applications, including soft x-ray lasers, time-resolved laser induced plasma spectroscopy LIPS, and laser-driven accelerators. The development in producing of femtosecond down to ten femtosecond optical pulses has facilitates scientists with a vital tool in a variety of ultrashort phenomena, such as high field physics, femtochemistry and high harmonic generation HHG. In this research, we generate a two-octave-wide ultrashort supercontinuum pulses with an optical spectrum extending from 3.5 eV (ultraviolet) to 1.3 eV (near-infrared) using a capillary fiber filled with neon gas. These pulses are formed according to nonlinear self-phase modulation in the neon gas as a nonlinear medium. The investigations of the created pulses were made using spectral phase interferometry for direct electric-field reconstruction (SPIDER). A complete description of the output pulses was considered. The observed characterization of the produced pulses includes the beam profile, the pulse width, and the spectral bandwidth. After reaching optimization conditions, the intensity of the reconstructed pulse autocorrelation function was applied for the shorts pulse duration to achieve transform limited ultrashort pulses with durations below 6-fs energies up to 600μJ. Moreover, the effect of neon pressure variation on the pulse width was examined. The nonlinear self-phase modulation realized to be increased with the pressure of the neon gas. The observed results may lead to an advanced method to control and monitor ultrashort transit interaction in femtochemistry.

Keywords: supercontinuum, ultrafast, SPIDER, ultra-broadband

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Authors: Pia Wetzl

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The learning process of students can be optimized by combining online teaching with face-to-face sessions. So-called blended learning offers extensive flexibility as well as contact opportunities with fellow students and teachers. Furthermore, learning can be individualized and self-regulated. The aim of this article is to investigate which factors are necessary for blended learning to be successful. Semi-structured interviews were conducted with students (N = 60) and lecturers (N = 21) from different disciplines at two German universities. The questions focused on the perception of online, face-to-face and blended learning courses. In addition, questions focused on possible optimization potential and obstacles to practical implementation. The results show that on-site presence is very important for blended learning to be successful. If students do not get to know each other on-site, there is a risk of loneliness during the self-learning phases. This has a negative impact on motivation. From the perspective of the lecturers, the willingness of the students to participate in the sessions on-site is low. Especially when there is no obligation to attend, group work is difficult to implement because the number of students attending is too low. Lecturers would like to see more opportunities from the university and its administration to enforce attendance. In their view, this is the only way to ensure the success of blended learning. In addition, they see the conception of blended learning courses as requiring a great deal of time, which they are not always willing to invest. More incentives are necessary to keep the lecturers motivated to develop engaging teaching material. The study identifies factors that can help teachers conceptualize blended learning. It also provides specific implementation advice and identifies potential impacts. This catalogue has great value for the future-oriented development of courses at universities. Future studies could test its practical use.

Keywords: blended learning, higher education, teachers, student learning, qualitative research

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Authors: Luis Guiliana, Andrea Osorio

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The lack of comprehension of the reservoir geomechanics conditions may cause operational problems that cost to the industry billions of dollars per year. The drilling operations at the Ceuta Field, Area 2 South, Maracaibo Lake, have been very expensive due to problems associated with drilling. The principal objective of this investigation is to develop a 3D geomechanical model in this area, in order to optimize the future drillings in the field. For this purpose, a 1D geomechanical model was built at first instance, following the workflow of the MEM (Mechanical Earth Model), this consists of the following steps: 1) Data auditing, 2) Analysis of drilling events and structural model, 3) Mechanical stratigraphy, 4) Overburden stress, 5) Pore pressure, 6) Rock mechanical properties, 7) Horizontal stresses, 8) Direction of the horizontal stresses, 9) Wellbore stability. The 3D MEM was developed through the geostatistic model of the Eocene C-SUP VLG-3676 reservoir and the 1D MEM. With this data the geomechanical grid was embedded. The analysis of the results threw, that the problems occurred in the wells that were examined were mainly due to wellbore stability issues. It was determined that the stress field change as the stratigraphic column deepens, it is normal to strike-slip at the Middle Miocene and Lower Miocene, and strike-slipe to reverse at the Eocene. In agreement to this, at the level of the Eocene, the most advantageous direction to drill is parallel to the maximum horizontal stress (157º). The 3D MEM allowed having a tridimensional visualization of the rock mechanical properties, stresses and operational windows (mud weight and pressures) variations. This will facilitate the optimization of the future drillings in the area, including those zones without any geomechanics information.

Keywords: geomechanics, MEM, drilling, stress

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Authors: Devendra Sillu, Shekhar Agnihotri

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The 'nano-biocatalyst' utilizes an ordered assembling of enzyme on to nanomaterial carriers to catalyze desirable biochemical kinetics and substrate selectivity. The current study describes an inter-disciplinary approach for converting agriculture waste, sugarcane bagasse into D-glucose exploiting halloysite nanotubes (HNTs) decorated cellulase enzyme as nano-biocatalytic system. Cellulase was successfully immobilized on HNTs employing polydopamine as an eco-friendly crosslinker while iron oxide nanoparticles were attached to facilitate magnetic recovery of material. The characterization studies (UV-Vis, TEM, SEM, and XRD) displayed the characteristic features of both cellulase and magnetic HNTs in the resulting nanocomposite. Various factors (i.e., working pH, temp., crosslinker conc., enzyme conc.) which may influence the activity of biocatalytic system were investigated. The experimental design was performed using Response Surface Methodology (RSM) for process optimization. Analyses data demonstrated that the nanobiocatalysts retained 80.30% activity even at elevated temperature (55°C) and excellent storage stabilities after 10 days. The repeated usage of system revealed a remarkable consistent relative activity over several cycles. The immobilized cellulase was employed to decompose agro-waste and the maximum decomposition rate of 67.2 % was achieved. Conclusively, magnetic HNTs can serve as a potential support for enzyme immobilization with long term usage, good efficacy, reusability and easy recovery from solution.

Keywords: halloysite nanotubes, enzyme immobilization, cellulase, response surface methodology, magnetic recovery

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Authors: Anil Bhandari, Imran Khan Pathan, Peeyush K. Sharma, Rakesh K. Patel, Suresh Purohit

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The purpose of this study was to prepare time and pH dependent release tablets of Ayurvedic Churna formulation and evaluate their advantages as colon targeted drug delivery system. The Vidangadi Churna was selected for this study which contains Embelin and Gallic acid. Embelin is used in Helminthiasis as therapeutic agent. Embelin is insoluble in water and unstable in gastric environment so it was formulated in time and pH dependent tablets coated with combination of two polymers Eudragit L100 and ethyl cellulose. The 150mg of core tablet of dried extract and lactose were prepared by wet granulation method. The compression coating was used in the polymer concentration of 150mg for both the layer as upper and lower coating tablet was investigated. The results showed that no release was found in 0.1 N HCl and pH 6.8 phosphate buffers for initial 5 hours and about 98.97% of the drug was released in pH 7.4 phosphate buffer in total 17 hours. The in vitro release profiles of drug from the formulation could be best expressed first order kinetics as highest linearity (r2= 0.9943). The results of the present study have demonstrated that the time and pH dependent tablets system is a promising vehicle for preventing rapid hydrolysis in gastric environment and improving oral bioavailability of Embelin and Gallic acid for treatment of Helminthiasis.

Keywords: embelin, gallic acid, Vidangadi Churna, colon targeted drug delivery

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Authors: S. J. Matlan, M. Mukhlisin, M. R. Taha

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Characterization of the engineering behavior of unsaturated soil is dependent on the soil-water characteristic curve (SWCC), a graphical representation of the relationship between water content or degree of saturation and soil suction. A reasonable description of the SWCC is thus important for the accurate prediction of unsaturated soil parameters. The measurement procedures for determining the SWCC, however, are difficult, expensive, and time-consuming. During the past few decades, researchers have laid a major focus on developing empirical equations for predicting the SWCC, with a large number of empirical models suggested. One of the most crucial questions is how precisely existing equations can represent the SWCC. As different models have different ranges of capability, it is essential to evaluate the precision of the SWCC models used for each particular soil type for better SWCC estimation. It is expected that better estimation of SWCC would be achieved via a thorough statistical analysis of its distribution within a particular soil class. With this in view, a statistical analysis was conducted in order to evaluate the reliability of the SWCC prediction models against laboratory measurement. Optimization techniques were used to obtain the best-fit of the model parameters in four forms of SWCC equation, using laboratory data for relatively coarse-textured (i.e., sandy) soil. The four most prominent SWCCs were evaluated and computed for each sample. The result shows that the Brooks and Corey model is the most consistent in describing the SWCC for sand soil type. The Brooks and Corey model prediction also exhibit compatibility with samples ranging from low to high soil water content in which subjected to the samples that evaluated in this study.

Keywords: soil-water characteristic curve (SWCC), statistical analysis, unsaturated soil, geotechnical engineering

Procedia PDF Downloads 342