Search results for: e-content producing algorithm

Authors: Mahesh K. Jat, Ankan Jana, Mahender Choudhary

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Rapid population and economic growth resulted in changes in large-scale land use land cover (LULC) changes. Changes in the biophysical properties of the Earth's surface and its impact on climate are of primary concern nowadays. Different approaches, ranging from location-based relationships or modelling earth surface - atmospheric interaction through modelling techniques like surface energy balance (SEB) are used in the recent past to examine the relationship between changes in Earth surface land cover and climatic characteristics like temperature and precipitation. A remote sensing-based model i.e., Surface Energy Balance Algorithm for Land (SEBAL), has been used to estimate the surface heat fluxes over Mahi Bajaj Sagar catchment (India) from 2001 to 2020. Landsat ETM and OLI satellite data are used to model the SEB of the area. Changes in observed precipitation and temperature, obtained from India Meteorological Department (IMD) have been correlated with changes in surface heat fluxes to understand the relative contributions of LULC change in changing these climatic variables. Results indicate a noticeable impact of LULC changes on climatic variables, which are aligned with respective changes in SEB components. Results suggest that precipitation increases at a rate of 20 mm/year. The maximum and minimum temperature decreases and increases at 0.007 ℃ /year and 0.02 ℃ /year, respectively. The average temperature increases at 0.009 ℃ /year. Changes in latent heat flux and sensible heat flux positively correlate with precipitation and temperature, respectively. Variation in surface heat fluxes influences the climate parameters and is an adequate reason for climate change. So, SEB modelling is helpful to understand the LULC change and its impact on climate.

Keywords: LULC, sensible heat flux, latent heat flux, SEBAL, landsat, precipitation, temperature

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Authors: Maryam Zarabian, Hector Guzman, Pedro Pereira-Almao, Abraham Fapojuwo

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Dry reforming of methane (DRM) has sparked significant industrial and scientific interest not only as a viable alternative for addressing the environmental concerns of two main contributors of the greenhouse effect, i.e., carbon dioxide (CO₂) and methane (CH₄), but also produces syngas, i.e., a mixture of hydrogen (H₂) and carbon monoxide (CO) utilized by a wide range of downstream processes as a feedstock for other chemical productions. In this study, we develop an AI-enable syngas production model to tackle the problem of achieving an equivalent H₂/CO ratio [1:1] with respect to the most efficient conversion. Firstly, the unsupervised density-based spatial clustering of applications with noise (DBSAN) algorithm removes outlier data points from the original experimental dataset. Then, random forest (RF) and deep neural network (DNN) models employ the error-free dataset to predict the DRM results. DNN models inherently would not be able to obtain accurate predictions without a huge dataset. To cope with this limitation, we employ reusing pre-trained layers’ approaches such as transfer learning and greedy layer-wise pretraining. Compared to the other deep models (i.e., pure deep model and transferred deep model), the greedy layer-wise pre-trained deep model provides the most accurate prediction as well as similar accuracy to the RF model with R² values 1.00, 0.999, 0.999, 0.999, 0.999, and 0.999 for the total outlet flow, H₂/CO ratio, H₂ yield, CO yield, CH₄ conversion, and CO₂ conversion outputs, respectively.

Keywords: artificial intelligence, dry reforming of methane, artificial neural network, deep learning, machine learning, transfer learning, greedy layer-wise pretraining

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Authors: Theophilus Asante, Comfort Nyarko, Daniel Antwi

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Drug-resistant tuberculosis, together with the associated comorbidities like pulmonary fibrosis and silicosis, has been one of the most serious global public health threats that requires immediate action to curb or mitigate it. This prolongs hospital stays, increases the cost of medication, and increases the death toll recorded annually. Crinum asiaticum bulb (CAE) and betulin (BET) are known for their biological and pharmacological effects. Pharmacological effects reported on CAE include antimicrobial, anti-inflammatory, anti-pyretic, anti-analgesic, and anti-cancer effects. Betulin has exhibited a multitude of powerful pharmacological properties ranging from antitumor, anti-inflammatory, anti-parasitic, anti-microbial, and anti-viral activities. This work sought to investigate the anti-tuberculosis and resistant modulatory effects and also assess their effects on mitigating pulmonary fibrosis and silicosis. In the anti-tuberculosis and resistant modulatory effects, both CAE and BET showed strong antimicrobial activities (31.25 ≤ MIC ≤ 500) µg/ml against the studied microorganisms and also produced significant anti-efflux pump and biofilm inhibitory effects (ρ < 0.0001) as well as exhibiting resistance modulatory and synergistic effects when combined with standard antibiotics. Crinum asiaticum bulbs extract and betulin were shown to possess anti-pulmonary fibrosis effects. There was an increased survival rate in the CAE and BET treatment groups compared to the BLM-induced group. There was a marked decrease in the levels of hydroxyproline and collagen I and III in the CAE and BET treatment groups compared to the BLM-treated group. The treatment groups of CAE and BET significantly downregulated the levels of pro-fibrotic and pro-inflammatory cytokine concentrations such as TGF-β1, MMP9, IL-6, IL-1β and TNF-alpha compared to an increase in the BLM-treated groups. The histological findings of the lungs suggested the curative effects of CAE and BET following BLM-induced pulmonary fibrosis in mice. The study showed improved lung functions with a wide focal area of viable alveolar spaces and few collagen fibers deposition on the lungs of the treatment groups. In the anti-silicosis and pulmonoprotective effects of CAE and BET, the levels of NF-κB, TNF-α, IL-1β, IL-6 and hydroxyproline, collagen types I and III were significantly reduced by CAE and BET (ρ < 0.0001). Both CAE and BET significantly (ρ < 0.0001) inhibited the levels of hydroxyproline, collagen I and III when compared with the negative control group. On BALF biomarkers such as macrophages, lymphocytes, monocytes, and neutrophils, CAE and BET were able to reduce their levels significantly (ρ < 0.0001). The CAE and BET were examined for anti-oxidant activity and shown to raise the levels of catalase (CAT) and superoxide dismutase (SOD) while lowering the level of malondialdehyde (MDA). There was an improvement in lung function when lung tissues were examined histologically. Crinum asiaticum bulbs extract and betulin were discovered to exhibit anti-tubercular and resistance-modulatory properties, as well as the capacity to minimize TB comorbidities such as pulmonary fibrosis and silicosis. In addition, CAE and BET may act as protective mechanisms, facilitating the preservation of the lung's physiological integrity. The outcomes of this study might pave the way for the development of leads for producing single medications for the management of drug-resistant tuberculosis and its accompanying comorbidities.

Keywords: fibrosis, crinum, tuberculosis, antiinflammation, drug resistant

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Authors: An Chengrui, Yin Zi, Wu Bingbing, Ma Yuanzhu, Jin Kaixiu, Chen Xiao, Ouyang Hongwei

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Single cell RNA sequence (scRNA-seq) is one of the effective tools to study transcriptomics of biological processes. Recently, similarity measurement of cells is Euclidian distance or its derivatives. However, the process of scRNA-seq is a multi-variate Bernoulli event model, thus we hypothesize that it would be more efficient when the divergence between cells is valued with relative entropy than Euclidian distance. In this study, we compared the performances of Euclidian distance, Spearman correlation distance and Relative Entropy using scRNA-seq data of the early, medial and late stage of limb development generated in our lab. Relative Entropy is better than other methods according to cluster potential test. Furthermore, we developed KL-SNE, an algorithm modifying t-SNE whose definition of divergence between cells Euclidian distance to Kullback–Leibler divergence. Results showed that KL-SNE was more effective to dissect cell heterogeneity than t-SNE, indicating the better performance of relative entropy than Euclidian distance. Specifically, the chondrocyte expressing Comp was clustered together with KL-SNE but not with t-SNE. Surprisingly, cells in early stage were surrounded by cells in medial stage in the processing of KL-SNE while medial cells neighbored to late stage with the process of t-SNE. This results parallel to Heatmap which showed cells in medial stage were more heterogenic than cells in other stages. In addition, we also found that results of KL-SNE tend to follow Gaussian distribution compared with those of the t-SNE, which could also be verified with the analysis of scRNA-seq data from another study on human embryo development. Therefore, it is also an effective way to convert non-Gaussian distribution to Gaussian distribution and facilitate the subsequent statistic possesses. Thus, relative entropy is potentially a better way to determine the divergence of cells in scRNA-seq data analysis.

Keywords: Single cell RNA sequence, Similarity measurement, Relative Entropy, KL-SNE, t-SNE

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Authors: Maher Ali Rusho, Sudipta Halder

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The purpose of the study is to develop optimal solutions for models of parallel computing systems using the Java language. During the study, programmes were written for the examined models of parallel computing systems. The result of the parallel sorting code is the output of a sorted array of random numbers. When processing data in parallel, the time spent on processing and the first elements of the list of squared numbers are displayed. When processing requests asynchronously, processing completion messages are displayed for each task with a slight delay. The main results include the development of optimisation methods for algorithms and processes, such as the division of tasks into subtasks, the use of non-blocking algorithms, effective memory management, and load balancing, as well as the construction of diagrams and comparison of these methods by characteristics, including descriptions, implementation examples, and advantages. In addition, various specialised libraries were analysed to improve the performance and scalability of the models. The results of the work performed showed a substantial improvement in response time, bandwidth, and resource efficiency in parallel computing systems. Scalability and load analysis assessments were conducted, demonstrating how the system responds to an increase in data volume or the number of threads. Profiling tools were used to analyse performance in detail and identify bottlenecks in models, which improved the architecture and implementation of parallel computing systems. The obtained results emphasise the importance of choosing the right methods and tools for optimising parallel computing systems, which can substantially improve their performance and efficiency.

Keywords: algorithm optimisation, memory management, load balancing, performance profiling, asynchronous programming.

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Authors: Ankita Jadon, Sidi Souvi, Nathalie Girault, Denis Petitprez

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Safety requirements for Generation IV nuclear reactor designs, especially the new generation sodium-cooled fast reactors (SFR) require a risk-informed approach to model severe accidents (SA) and their consequences in case of outside release. In SFRs, aerosols are produced during a core disruptive accident when primary system sodium is ejected into the containment and burn in contact with the air; producing sodium aerosols. One of the key aspects of safety evaluation is the in-containment sodium aerosol behavior and their interaction with fission products. The study of the effects of sodium fires is essential for safety evaluation as the fire can both thermally damage the containment vessel and cause an overpressurization risk. Besides, during the fire, airborne fission product first dissolved in the primary sodium can be aerosolized or, as it can be the case for fission products, released under the gaseous form. The objective of this work is to study the interactions between sodium aerosols and fission products (Iodine, toxic and volatile, being the primary concern). Sodium fires resulting from an SA would produce aerosols consisting of sodium peroxides, hydroxides, carbonates, and bicarbonates. In addition to being toxic (in oxide form), this aerosol will then become radioactive. If such aerosols are leaked into the environment, they can pose a danger to the ecosystem. Depending on the chemical affinity of these chemical forms with fission products, the radiological consequences of an SA leading to containment leak tightness loss will also be affected. This work is split into two phases. Firstly, a method to theoretically understand the kinetics and thermodynamics of the heterogeneous reaction between sodium aerosols and fission products: I2 and HI are proposed. Ab-initio, density functional theory (DFT) calculations using Vienna ab-initio simulation package are carried out to develop an understanding of the surfaces of sodium carbonate (Na2CO3) aerosols and hence provide insight on its affinity towards iodine species. A comprehensive study of I2 and HI adsorption, as well as bicarbonate formation on the calculated lowest energy surface of Na2CO3, was performed which provided adsorption energies and description of the optimized configuration of adsorbate on the stable surface. Secondly, the heterogeneous reaction between (I2)g and Na2CO3 aerosols were investigated experimentally. To study this, (I2)g was generated by heating a permeation tube containing solid I2, and, passing it through a reaction chamber containing Na2CO3 aerosol deposit. The concentration of iodine was then measured at the exit of the reaction chamber. Preliminary observations indicate that there is an effective uptake of (I2)g on Na2CO3 surface, as suggested by our theoretical chemistry calculations. This work is the first step in addressing the gaps in knowledge of in-containment and atmospheric source term which are essential aspects of safety evaluation of SFR SA. In particular, this study is aimed to determine and characterize the radiological and chemical source term. These results will then provide useful insights for the developments of new models to be implemented in integrated computer simulation tool to analyze and evaluate SFR safety designs.

Keywords: iodine adsorption, sodium aerosols, sodium cooled reactor, DFT calculations, sodium carbonate

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Authors: Dario Barsi, Marina Ubaldi, Pietro Zunino, Robert Fink

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A design and optimization procedure is proposed and developed to provide the geometry of a high efficiency compact hydraulic propeller turbine for low head. For the preliminary design of the machine, classic design criteria, based on the use of statistical correlations for the definition of the fundamental geometric parameters and the blade shapes are used. These relationships are based on the fundamental design parameters (i.e., specific speed, flow coefficient, work coefficient) in order to provide a simple yet reliable procedure. Particular attention is paid, since from the initial steps, on the correct conformation of the meridional channel and on the correct arrangement of the blade rows. The preliminary geometry thus obtained is used as a starting point for the hydrodynamic optimization procedure, carried out using a CFD calculation software coupled with a genetic algorithm that generates and updates a large database of turbine geometries. The optimization process is performed using a commercial approach that solves the turbulent Navier Stokes equations (RANS) by exploiting the axial-symmetric geometry of the machine. The geometries generated within the database are therefore calculated in order to determine the corresponding overall performance. In order to speed up the optimization calculation, an artificial neural network (ANN) based on the use of an objective function is employed. The procedure was applied for the specific case of a propeller turbine with an innovative design of a modular type, specific for applications characterized by very low heads. The procedure is tested in order to verify its validity and the ability to automatically obtain the targeted net head and the maximum for the total to total internal efficiency.

Keywords: renewable energy conversion, hydraulic turbines, low head hydraulic energy, optimization design

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Authors: Abolghasem Yousefi-Babadi, Ali Bozorgi-Amiri, Aida Kazempour, Reza Tavakkoli-Moghaddam, Maryam Irani

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All over the world, natural disasters (e.g., earthquakes, floods, volcanoes and hurricanes) causes a lot of deaths. Earthquakes are introduced as catastrophic events, which is accident by unusual phenomena leading to much loss around the world. Such could be replaced by disasters or any other synonyms strongly demand great long-term help and relief, which can be hard to be managed. Supplies and facilities are very important challenges after any earthquake which should be prepared for the disaster regions to satisfy the people's demands who are suffering from earthquake. This paper proposed disaster response facility allocation problem for disaster relief operations as a mathematical programming model. Not only damaged people in the earthquake victims, need the consumable commodities (e.g., food and water), but also they need non-consumable commodities (e.g., clothes) to protect themselves. Therefore, it is concluded that paying attention to disaster points and people's demands are very necessary. To deal with this objective, both commodities including consumable and need non-consumable commodities are considered in the presented model. This paper presented the multi-objective multi-period mathematical programming model regarding the minimizing the average of the weighted response times and minimizing the total operational cost and penalty costs of unmet demand and unused commodities simultaneously. Furthermore, a Chebycheff multi-objective solution procedure as a powerful solution algorithm is applied to solve the proposed model. Finally, to illustrate the model applicability, a case study of the Tehran earthquake is studied, also to show model validation a sensitivity analysis is carried out.

Keywords: facility location, multi-objective model, disaster response, commodity

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Authors: T. Ghatauray, J. Ingram, P. Holborn

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The transition from a ‘carbon rich’ fossil fuel dependent to a ‘sustainable’ and ‘renewable’ hydrogen based society will see the deployment of hydrogen fuel cells (HFC) in transport applications and in the generation of heat and power for buildings, as part of a decentralised power network. Many deployments will be low power HFCs for domestic combined heat and power (CHP) and commercial ‘transportable’ HFCs for environmental situations, such as lighting and telephone towers. For broad commercialisation of small fuel cells to be achieved there needs to be significant confidence in their safety in both domestic and environmental applications. Low power HFCs are housed in protective steel enclosures. Standard enclosures have plain rectangular ventilation openings intended for thermal management of electronics and not the dispersion of a buoyant gas. Degradation of the HFC or supply pipework in use could lead to a low-level leak and a build-up of hydrogen gas in the enclosure. Hydrogen’s wide flammable range (4-75%) is a significant safety concern, with ineffective enclosure ventilation having the potential to cause flammable mixtures to develop with the risk of explosion. Mechanical ventilation is effective at managing enclosure hydrogen concentrations, but drains HFC power and is vulnerable to failure. This is undesirable in low power and remote installations and reliable passive ventilation systems are preferred. Passive ventilation depends upon buoyancy driven flow, with the size, shape and position of ventilation openings critical for producing predictable flows and maintaining low buoyant gas concentrations. With environmentally sited enclosures, ventilation openings with pressed horizontal and angled louvres are preferred to protect the HFC and electronics inside. There is an economic cost to adding louvres, but also a safety concern. A question arises over whether the use of pressed louvre vents impairs enclosure passive ventilation performance, when compared to same opening area plain vents. Comparison small enclosure (0.144m³) tests of same opening area pressed louvre and plain vents were undertaken. A displacement ventilation arrangement was incorporated into the enclosure with opposing upper and lower ventilation openings. A range of vent areas were tested. Helium (used as a safe analogue for hydrogen) was released from a 4mm nozzle at the base of the enclosure to simulate a hydrogen leak at leak rates from 1 to 10 lpm. Helium sensors were used to record concentrations at eight heights in the enclosure. The enclosure was otherwise empty. These tests determined that the use of pressed and angled louvre ventilation openings on the enclosure impaired the passive ventilation flow and increased helium concentrations in the enclosure. High-level stratified buoyant gas layers were also found to be deeper than with plain vent openings and were within the flammable range. The presence of gas within the flammable range is of concern, particularly as the addition of the fuel cell and electronics in the enclosure would further reduce the available volume and increase concentrations. The opening area of louvre vents would need to be greater than equivalent plain vents to achieve comparable ventilation flows or alternative schemes would need to be considered.

Keywords: enclosure, fuel cell, helium, hydrogen safety, louvre vent, passive ventilation

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Authors: Anthony D. Rhodes, Manan Goel

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We provide a high fidelity deep learning algorithm (HyperSeg) for interactive video segmentation tasks using a dense convolutional network with context-aware skip connections and compressed, 'hypercolumn' image features combined with a convolutional tessellation procedure. In order to maintain high output fidelity, our model crucially processes and renders all image features in high resolution, without utilizing downsampling or pooling procedures. We maintain this consistent, high grade fidelity efficiently in our model chiefly through two means: (1) we use a statistically-principled, tensor decomposition procedure to modulate the number of hypercolumn features and (2) we render these features in their native resolution using a convolutional tessellation technique. For improved pixel-level segmentation results, we introduce a boundary loss function; for improved temporal coherence in video data, we include temporal image information in our model. Through experiments, we demonstrate the improved accuracy of our model against baseline models for interactive segmentation tasks using high resolution video data. We also introduce a benchmark video segmentation dataset, the VFX Segmentation Dataset, which contains over 27,046 high resolution video frames, including green screen and various composited scenes with corresponding, hand-crafted, pixel-level segmentations. Our work presents a improves state of the art segmentation fidelity with high resolution data and can be used across a broad range of application domains, including VFX pipelines and medical imaging disciplines.

Keywords: computer vision, object segmentation, interactive segmentation, model compression

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Authors: Andrés Lara Guillén, José M. Soriano Disla, Gemma Castejón Martínez, David Fernández-Gutiérrez

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The worldwide population is exponentially increasing, which causes a rising demand for food, energy and non-renewable resources. These demands must be attended to from a circular economy point of view. Under this approach, the obtention of strategic products from biowaste is crucial for the society to keep the current lifestyle reducing the environmental and social issues linked to the lineal economy. This is the main objective of the VALUEWASTE project. VALUEWASTE is about valorizing urban biowaste into proteins for food and feed and biofertilizers, closing the loop of this waste stream. In order to achieve this objective, the project validates three value chains, which begin with the anaerobic digestion of the biowaste. From the anaerobic digestion, three by-products are obtained: i) methane that is used by microorganisms, which will be transformed into microbial proteins; ii) digestate that is used by black soldier fly, producing insect proteins; and iii) a nutrient-rich effluent, which will be transformed into biofertilizers. VALUEWASTE is an innovative solution, which combines different technologies to valorize entirely the biowaste. However, it is also required to demonstrate that the solution is greener than other traditional technologies (baseline systems). On one hand, the proteins from microorganisms and insects will be compared with other reference protein production systems (gluten, whey and soybean). On the other hand, the biofertilizers will be compared to the production of mineral fertilizers (ammonium sulphate and synthetic struvite). Therefore, the aim of this study is to provide that biowaste valorization can reduce the environmental impacts linked to both traditional proteins manufacturing processes and mineral fertilizers, not only at a pilot-scale but also at an industrial one. In the present study, both baseline system and VALUEWASTE solution are evaluated through the Environmental Life Cycle Assessment (E-LCA). The E-LCA is based on the standards ISO 14040 and 14044. The Environmental Footprint methodology was the one used in this study to evaluate the environmental impacts. The results for the baseline cases show that the food proteins coming from whey have the highest environmental impact on ecosystems compared to the other proteins sources: 7.5 and 15.9 folds higher than soybean and gluten, respectively. Comparing feed soybean and gluten, soybean has an environmental impact on human health 195.1 folds higher. In the case of biofertilizers, synthetic struvite has higher impacts than ammonium sulfate: 15.3 (ecosystems) and 11.8 (human health) fold, respectively. The results shown in the present study will be used as a reference to demonstrate the better environmental performance of the bio-based products obtained through the VALUEWASTE solution. Other originalities that the E-LCA performed in the VALUEWASTE project provides are the diverse direct implications on investment and policies. On one hand, better environmental performance will serve to remove the barriers linked to these kinds of technologies, boosting the investment that is backed by the E-LCA. On the other hand, it will be a germ to design new policies fostering these types of solutions to achieve two of the key targets of the European Community: being self-sustainable and carbon neutral.

Keywords: anaerobic digestion, biofertilizers, circular economy, nutrients recovery

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Authors: Biki Sarmah, Priyanko Raj Mudiar

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In the modern world of remotely interactive virtual reality-based learning and teaching, including professional skill-building training and acquisition practices, as well as data acquisition and robotic systems, the revolutionary application or implementation of field-programmable neurostimulator aids and first-hand interactive sensitisation techniques into 3D holographic audio-visual platforms have been a coveted dream of many scholars, professionals, scientists, and students. Integration of 'kinaesthetic vibrotactile haptic perception' along with an actuated step magnitude contact profiloscopy in augmented reality-based learning platforms and professional training can be implemented by using an extremely calculated and well-coordinated image telemetry including remote data mining and control technique. A real-time, computer-aided (PLC-SCADA) field calibration based algorithm must be designed for the purpose. But most importantly, in order to actually realise, as well as to 'interact' with some 3D holographic models displayed over a remote screen using remote laser image telemetry and control, all spatio-physical parameters like cardinal alignment, gyroscopic compensation, as well as surface profile and thermal compositions, must be implemented using zero-order type 1 actuators (or transducers) because they provide zero hystereses, zero backlashes, low deadtime as well as providing a linear, absolutely controllable, intrinsically observable and smooth performance with the least amount of error compensation while ensuring the best ergonomic comfort ever possible for the users.

Keywords: haptic feedback, kinaesthetic vibrotactile 3D design, medical simulation training, piezo diaphragm based actuator

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Authors: Somya R. Patro, Arnab Banerjee, G. V. Ramana

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A flexural beam carrying elastically mounted concentrated masses, such as engines, motors, oscillators, or vibration absorbers, is often encountered in mechanical, civil, and aeronautical engineering domains. To prevent resonance conditions, the designers must predict the natural frequencies of such a constrained beam system. This paper investigates experimental and analytical studies on vibration suppression in a cantilever beam with a tip mass with the help of spring-mass to achieve local resonance conditions. The system consists of a 3D printed polylactic acid (PLA) beam screwed at the base plate of the shaker system. The top of the free end is connected by an accelerometer which also acts as a tip mass. A spring and a mass are attached at the bottom to replicate the mechanism of the spring-mass resonator. The Fast Fourier Transform (FFT) algorithm converts time acceleration plots into frequency amplitude plots from which transmittance is calculated as a function of the excitation frequency. The mathematical formulation is based on the transfer matrix method, and the governing differential equations are based on Euler Bernoulli's beam theory. The experimental results are successfully validated with the analytical results, providing us essential confidence in our proposed methodology. The beam spring-mass system is then converted to an equivalent two-degree of freedom system, from which frequency response function is obtained. The H2 optimization technique is also used to obtain the closed-form expression of optimum spring stiffness, which shows the influence of spring stiffness on the system's natural frequency and vibration response.

Keywords: euler bernoulli beam theory, fast fourier transform, natural frequencies, polylactic acid, transmittance, vibration absorbers

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Authors: Priyanka Jain, Ashish K. Sharma, Esha Shukla, K. J. Mukherjee

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We propose a paradigm shift in our approach to design improved platforms for recombinant protein production, by addressing system level issues rather than the individual steps associated with recombinant protein synthesis like transcription, translation, etc. We demonstrate that by controlling and modulating the cellular stress response (CSR), which is responsible for feedback control of protein synthesis, we can generate hyper-producing strains. We did transcriptomic profiling of post-induction cultures, expressing different types of protein, to analyze the nature of this cellular stress response. We found significant down-regulation of substrate utilization, translation, and energy metabolism genes due to generation CSR inside the host cell. However, transcription profiling has also shown that many genes are up-regulated post induction and their role in modulating the CSR is unclear. We hypothesized that these up-regulated genes trigger signaling pathways, generating the CSR and concomitantly reduce the recombinant protein yield. To test this hypothesis, we knocked out the up-regulated genes, which did not have any downstream regulatees, and analyzed their impact on cellular health and recombinant protein expression. Two model proteins i.e., GFP and L-Asparaginase were chosen for this analysis. We observed a significant improvement in expression levels, with some knock-outs showing more than 7-fold higher expression compared to control. The 10 best single knock-outs were chosen to make 45 combinations of all possible double knock-outs. A further increase in expression was observed in some of these double knock- outs with GFP levels being highest in a double knock-out ΔyhbC + ΔelaA. However, for L-Asparaginase which is a secretory protein, the best results were obtained using a combination of ΔelaA+ΔcysW knock-outs. We then tested all the knock outs for their ability to enhance the expression of a 'difficult-to-express' protein. The Rubella virus E1 protein was chosen and tagged with sfGFP at the C-terminal using a linker peptide for easy online monitoring of expression of this fusion protein. Interestingly, the highest increase in Rubella-sGFP levels was obtained in the same double knock-out ΔelaA + ΔcysW (5.6 fold increase in expression yield compared to the control) which gave the highest expression for L-Asparaginase. However, for sfGFP alone, the ΔyhbC+ΔmarR knock-out gave the highest level of expression. These results indicate that there is a fair degree of commonality in the nature of the CSR generated by the induction of different proteins. Transcriptomic profiling of the double knock out showed that many genes associated with the translational machinery and energy biosynthesis did not get down-regulated post induction, unlike the control where these genes were significantly down-regulated. This confirmed our hypothesis of these genes playing an important role in the generation of the CSR and allowed us to design a strategy for making better expression hosts by simply knocking out key genes. This strategy is radically superior to the previous approach of individually up-regulating critical genes since it blocks the mounting of the CSR thus preventing the down-regulation of a very large number of genes responsible for sustaining the flux through the recombinant protein production pathway.

Keywords: cellular stress response, GFP, knock-outs, up-regulated genes

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Authors: Levent Basayigit, Sinan Demir, Burhan Kara, Yusuf Ucar

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Cannabis is mostly used for drug production. In some countries, an excessive amount of illegal cannabis is cultivated and sold. Most of the illegal cannabis cultivation occurs on the lands far from settlements. In farmlands, it is cultivated with other crops. In this method, cannabis is surrounded by tall plants like corn and sunflower. It is also cultivated with tall crops as the mixed culture. The common method of the determination of the illegal cultivation areas is to investigate the information obtained from people. This method is not sufficient for the determination of illegal cultivation in remote areas. For this reason, more effective methods are needed for the determination of illegal cultivation. Remote Sensing is one of the most important technologies to monitor the plant growth on the land. The aim of this study is to monitor cannabis cultivation area using satellite imagery. The main purpose of this study was to develop an applicable method for monitoring the cannabis cultivation. For this purpose, cannabis was grown as single or surrounded by the corn and sunflower in plots. The morphological characteristics of cannabis were recorded two times per month during the vegetation period. The spectral signature library was created with the spectroradiometer. The parcels were monitored with high-resolution satellite imagery. With the processing of satellite imagery, the cultivation areas of cannabis were classified. To separate the Cannabis plots from the other plants, the multiresolution segmentation algorithm was found to be the most successful for classification. WorldView Improved Vegetative Index (WV-VI) classification was the most accurate method for monitoring the plant density. As a result, an object-based classification method and vegetation indices were sufficient for monitoring the cannabis cultivation in multi-temporal Earthwiev images.

Keywords: Cannabis, drug, remote sensing, object-based classification

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Authors: G. S. Ayyappan, Srinivas Kota, Jaffer R. C. Sheriff, C. Prakash Chandra Joshua

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In the present scenario of energy crises, energy conservation in the electrical machines is very important in the industries. In order to conserve energy, one needs to monitor the performance of an induction motor on-site and in-situ. The instruments available for this purpose are very meager and very expensive. This paper deals with the design and development of induction motor performance analyser on-line, on-site, and in-situ. The system measures only few electrical input parameters like input voltage, line current, power factor, frequency, powers, and motor shaft speed. These measured data are coupled to name plate details and compute the operating efficiency of induction motor. This system employs the method of computing motor losses with the help of equivalent circuit parameters. The equivalent circuit parameters of the concerned motor are estimated using the developed algorithm at any load conditions and stored in the system memory. The developed instrument is a reliable, accurate, compact, rugged, and cost-effective one. This portable instrument could be used as a handy tool to study the performance of both slip ring and cage induction motors. During the analysis, the data can be stored in SD Memory card and one can perform various analyses like load vs. efficiency, torque vs. speed characteristics, etc. With the help of the developed instrument, one can operate the motor around its Best Operating Point (BOP). Continuous monitoring of the motor efficiency could lead to Life Cycle Assessment (LCA) of motors. LCA helps in taking decisions on motor replacement or retaining or refurbishment.

Keywords: energy conservation, equivalent circuit parameters, induction motor efficiency, life cycle assessment, motor performance analysis

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Authors: Yuekun Chen, Yousef Sardahi, Salam Hajjar, Christopher Greer

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This paper presents a multi-objective optimal design of a cascade control system for an underactuated mechanical system. Cascade control structures usually include two control algorithms (inner and outer). To design such a control system properly, the following conflicting objectives should be considered at the same time: 1) the inner closed-loop control must be faster than the outer one, 2) the inner loop should fast reject any disturbance and prevent it from propagating to the outer loop, 3) the controlled system should be insensitive to measurement noise, and 4) the controlled system should be driven by optimal energy. Such a control problem can be formulated as a multi-objective optimization problem such that the optimal trade-offs among these design goals are found. To authors best knowledge, such a problem has not been studied in multi-objective settings so far. In this work, an underactuated mechanical system consisting of a rotary servo motor and a ball and beam is used for the computer simulations, the setup parameters of the inner and outer control systems are tuned by NSGA-II (Non-dominated Sorting Genetic Algorithm), and the dominancy concept is used to find the optimal design points. The solution of this problem is not a single optimal cascade control, but rather a set of optimal cascade controllers (called Pareto set) which represent the optimal trade-offs among the selected design criteria. The function evaluation of the Pareto set is called the Pareto front. The solution set is introduced to the decision-maker who can choose any point to implement. The simulation results in terms of Pareto front and time responses to external signals show the competing nature among the design objectives. The presented study may become the basis for multi-objective optimal design of multi-loop control systems.

Keywords: cascade control, multi-Loop control systems, multiobjective optimization, optimal control

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Authors: Chen Jun-Hong, He Pu, Tao Wen-Quan

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Proton exchange membrane fuel cell (PEMFC) is the most promising future energy source owing to its low operating temperature, high energy efficiency, high power density, and environmental friendliness. In this paper, a comprehensive PEMFC system control-oriented model is developed in the Matlab/Simulink environment, which includes the hydrogen supply subsystem, air supply subsystem, and thermal management subsystem. Besides, Improved Artificial Bee Colony (IABC) is used in the parameter identification of PEMFC semi-empirical equations, making the maximum relative error between simulation data and the experimental data less than 0.4%. Operation temperature is essential for PEMFC, both high and low temperatures are disadvantageous. In the thermal management subsystem, water pump and fan are both controlled with the PID controller to maintain the appreciate operation temperature of PEMFC for the requirements of safe and efficient operation. To improve the control effect further, fuzzy control is introduced to optimize the PID controller of the pump, and the Radial Basis Function (RBF) neural network is introduced to optimize the PID controller of the fan. The results demonstrate that Fuzzy-PID and RBF-PID can achieve a better control effect with 22.66% decrease in Integral Absolute Error Criterion (IAE) of T_st (Temperature of PEMFC) and 77.56% decrease in IAE of T_in (Temperature of inlet cooling water) compared with traditional PID. In the end, a novel thermal management structure is proposed, which uses the cooling air passing through the main radiator to continue cooling the secondary radiator. In this thermal management structure, the parasitic power dissipation can be reduced by 69.94%, and the control effect can be improved with a 52.88% decrease in IAE of T_in under the same controller.

Keywords: PEMFC system, parameter identification, temperature control, Fuzzy-PID, RBF-PID, parasitic power

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Authors: Fernanda M. Assef, Maria Teresinha A. Steiner

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The aim of this paper is to compare and discuss better classifier algorithm options for credit risk assessment by applying different Machine Learning techniques. Using records from a Brazilian financial institution, this study uses a database of 5,432 companies that are clients of the bank, where 2,600 clients are classified as non-defaulters, 1,551 are classified as defaulters and 1,281 are temporarily defaulters, meaning that the clients are overdue on their payments for up 180 days. For each case, a total of 15 attributes was considered for a one-against-all assessment using four different techniques: Artificial Neural Networks Multilayer Perceptron (ANN-MLP), Artificial Neural Networks Radial Basis Functions (ANN-RBF), Logistic Regression (LR) and finally Support Vector Machines (SVM). For each method, different parameters were analyzed in order to obtain different results when the best of each technique was compared. Initially the data were coded in thermometer code (numerical attributes) or dummy coding (for nominal attributes). The methods were then evaluated for each parameter and the best result of each technique was compared in terms of accuracy, false positives, false negatives, true positives and true negatives. This comparison showed that the best method, in terms of accuracy, was ANN-RBF (79.20% for non-defaulter classification, 97.74% for defaulters and 75.37% for the temporarily defaulter classification). However, the best accuracy does not always represent the best technique. For instance, on the classification of temporarily defaulters, this technique, in terms of false positives, was surpassed by SVM, which had the lowest rate (0.07%) of false positive classifications. All these intrinsic details are discussed considering the results found, and an overview of what was presented is shown in the conclusion of this study.

Keywords: artificial neural networks (ANNs), classifier algorithms, credit risk assessment, logistic regression, machine Learning, support vector machines

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Authors: Nico Rosamilia

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The study evaluates the importance of non-financial disclosure practices for regulators, investors, businesses, and markets. It aims to create a sector-specific set of indicators for environmental, social, and governance (ESG) performances alternative to the ratings of the agencies. The existing literature extensively studies the implementation of ESG rating systems. Conversely, this study has a twofold outcome. Firstly, it should generalize incentive systems and governance policies for ESG and sustainable principles. Therefore, it should contribute to the EU Sustainable Finance Disclosure Regulation. Secondly, it concerns the market and the investors by highlighting successful sustainable investing. Indeed, the study contemplates the effect of ESG adoption practices on corporate value. The research explores the asset pricing angle in order to shed light on the fragmented argument on the finance of ESG. Investors may be misguided about the positive or negative effects of ESG on performances. The paper proposes a different method to evaluate ESG performances. By comparing the results of a traditional econometric approach (Lasso) with a machine learning algorithm (Random Forest), the study establishes a set of indicators for ESG performance. Therefore, the research also empirically contributes to the theoretical strands of literature regarding model selection and variable importance in a finance framework. The algorithms will spit out sector-specific indicators. This set of indicators defines an alternative to the compounded scores of ESG rating agencies and avoids the possible offsetting effect of scores. With this approach, the paper defines a sector-specific set of indicators to standardize ESG disclosure. Additionally, it tries to shed light on the absence of a clear understanding of the direction of the ESG effect on corporate value (the problem of endogeneity).

Keywords: ESG ratings, non-financial information, value of firms, sustainable finance

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Authors: Julian Iliev

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The recent release of The Matrix Resurrections persuaded many film scholars that The Matrix trilogy had lost its appeal and its concepts were largely outdated. This study examines the human pods and growing fields in the trilogy. Their functionality is compared to Michel Foucault’s concept of docile bodies: linking fictional and contemporary worlds. This paradigm is scrutinized through surveillance literature. The analogy brings to light common elements of hidden surveillance practices in technologies. The comparison illustrates the effects of body manipulation portrayed in the movies and their relevance with contemporary surveillance practices. Many scholars have utilized a close reading methodology in film studies (J.Bizzocchi, J.Tanenbaum, P.Larsen, S. Herbrechter, and Deacon et al.). The use of a particular lens through which media text is examined is an indispensable factor that needs to be incorporated into the methodology. The study spotlights both scenes from the trilogy depicting the human pods and growing fields. The functionality of the pods and the fields compare directly with Foucault’s concept of docile bodies. By utilizing Foucault’s study as a lens, the research will unearth hidden components and insights into the films. Foucault recognizes three disciplines that produce docile bodies: 1) manipulation and the interchangeability of individual bodies, 2) elimination of unnecessary movements and management of time, and 3) command system guaranteeing constant supervision and continuity protection. These disciplines can be found in the pods and growing fields. Each body occupies a single pod aiding easier manipulation and fast interchangeability. The movement of the bodies in the pods is reduced to the absolute minimum. Thus, the body is transformed into the ultimate object of control – minimum movement correlates to maximum energy generation. Supervision is exercised by wiring the body with numerous types of cables. This ultimate supervision of body activity reduces the body’s purpose to mere functioning. If a body does not function as an energy source, then it’s unplugged, ejected, and liquefied. The command system secures the constant supervision and continuity of the process. To Foucault, the disciplines are distinctly different from slavery because they stop short of a total takeover of the bodies. This is a clear difference from the slave system implemented in the films. Even though their system might lack sophistication, it makes up for it in the elevation of functionality. Further, surveillance literature illustrates the connection between the generation of body energy in The Matrix trilogy to the generation of individual data in contemporary society. This study found that the three disciplines producing docile bodies were present in the portrayal of the pods and fields in The Matrix trilogy. The above comparison combined with surveillance literature yields insights into analogous processes and contemporary surveillance practices. Thus, the constant generation of energy in The Matrix trilogy can be equated to the consistent data generation in contemporary society. This essay shows the relevance of the body manipulation concept in the Matrix films with contemporary surveillance practices.

Keywords: docile bodies, film trilogies, matrix movies, michel foucault, privacy loss, surveillance

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Authors: C. U. Om Kumar, S. Kishore, A. Geetha

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An Operating system (OS) is software that manages computer hardware and software resources by providing services to computer programs. One of the important user expectations of the operating system is to provide the practice of defending information from unauthorized access, disclosure, modification, inspection, recording or destruction. Operating system is always vulnerable to the attacks of malwares such as computer virus, worm, Trojan horse, backdoors, ransomware, spyware, adware, scareware and more. And so the anti-virus software were created for ensuring security against the prominent computer viruses by applying a dictionary based approach. The anti-virus programs are not always guaranteed to provide security against the new viruses proliferating every day. To clarify this issue and to secure the computer system, our proposed expert system concentrates on authorizing the processes as wanted and unwanted by the administrator for execution. The Expert system maintains a database which consists of hash code of the processes which are to be allowed. These hash codes are generated using MD5 message-digest algorithm which is a widely used cryptographic hash function. The administrator approves the wanted processes that are to be executed in the client in a Local Area Network by implementing Client-Server architecture and only the processes that match with the processes in the database table will be executed by which many malicious processes are restricted from infecting the operating system. The add-on advantage of this proposed Expert system is that it limits CPU usage and minimizes resource utilization. Thus data and information security is ensured by our system along with increased performance of the operating system.

Keywords: virus, worm, Trojan horse, back doors, Ransomware, Spyware, Adware, Scareware, sticky software, process table, MD5, CPU usage and resource utilization

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Authors: Irina Khutsishvili

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The presented study develops a decision support methodology for multi-criteria group decision-making problem. The proposed methodology is based on the TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) approach in the hesitant fuzzy environment. The main idea of decision-making problem is a selection of one best alternative or several ranking alternatives among a set of feasible alternatives. Typically, the process of decision-making is based on an evaluation of certain criteria. In many MCDM problems (such as medical diagnosis, project management, business and financial management, etc.), the process of decision-making involves experts' assessments. These assessments frequently are expressed in fuzzy numbers, confidence intervals, intuitionistic fuzzy values, hesitant fuzzy elements and so on. However, a more realistic approach is using linguistic expert assessments (linguistic variables). In the proposed methodology both the values and weights of the criteria take the form of linguistic variables, given by all decision makers. Then, these assessments are expressed in triangular fuzzy numbers. Consequently, proposed approach is based on triangular hesitant fuzzy TOPSIS decision-making model. Following the TOPSIS algorithm, first, the fuzzy positive ideal solution (FPIS) and the fuzzy negative-ideal solution (FNIS) are defined. Then the ranking of alternatives is performed in accordance with the proximity of their distances to the both FPIS and FNIS. Based on proposed approach the software package has been developed, which was used to rank investment projects in the real investment decision-making problem. The application and testing of the software were carried out based on the data provided by the ‘Bank of Georgia’.

Keywords: fuzzy TOPSIS approach, investment project, linguistic variable, multi-criteria decision making, triangular hesitant fuzzy set

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Authors: Yujiang Wu

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As electrical machine (e-machine) power density re-querulents become more stringent in vehicle electrification, mounting a temperature sensor for e-machine stator windings becomes increasingly difficult. This can lead to higher manufacturing costs, complicated harnesses, and reduced reliability. In this paper, we propose a deep-learning method for predicting electric machine winding temperature, which can either replace the sensor entirely or serve as a backup to the existing sensor. We compare the performance of our method, the stateful long short-term memory networks (LSTM) with truncated backpropagation through time (TBTT), with that of linear regression, as well as stateless LSTM with/without residual connection. Our results demonstrate the strength of combining stateful LSTM and TBTT in tackling nonlinear time series prediction problems with long sequence lengths. Additionally, in industrial applications, high-temperature region prediction accuracy is more important because winding temperature sensing is typically used for derating machine power when the temperature is high. To evaluate the performance of our algorithm, we developed a temperature-stratified MSE. We propose a simple but effective data preprocessing trick to improve the high-temperature region prediction accuracy. Our experimental results demonstrate the effectiveness of our proposed method in accurately predicting winding temperature, particularly in high-temperature regions, while also reducing manufacturing costs and improving reliability.

Keywords: deep learning, electrical machine, functional safety, long short-term memory networks (LSTM), thermal management, time series prediction

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Authors: Rana Mohamed, Ahmed E. Gomaa, Gehan Safwat, Ayman Diab

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Background: Bio-fabrication is a multidisciplinary research field that combines several principles, fabrication techniques, and protocols from different fields. The open-source-software movement is a movement that supports the use of open-source licenses for some or all software as part of the broader notion of open collaboration. Additive manufacturing is the concept of 3D printing, where it is a manufacturing method through adding layer-by-layer using computer-aided designs (CAD). There are several types of AM system used, and they can be categorized by the type of process used. One of these AM technologies is Digital light processing (DLP) which is a 3D printing technology used to rapidly cure a photopolymer resin to create hard scaffolds. DLP uses a projected light source to cure (Harden or crosslinking) the entire layer at once. Current applications of DLP are focused on dental and medical applications. Other developments have been made in this field, leading to the revolutionary field 3D bioprinting. The open-source movement was started to spread the concept of open-source software to provide software or hardware that is cheaper, reliable, and has better quality. Objective: Modification of desktop 3D printer into 3D bio-printer and the integration of DLP technology and bio-fabrication to produce an antibacterial dental model. Method: Modification of a desktop 3D printer into a 3D bioprinter. Gelatin hydrogel and sodium alginate hydrogel were prepared with different concentrations. Rhizome of Zingiber officinale, Flower buds of Syzygium aromaticum, and Bulbs of Allium sativum were extracted, and extractions were selected on different levels (Powder, aqueous extracts, total oils, and Essential oils) prepared for antibacterial bioactivity. Agar well diffusion method along with the E. coli have been used to perform the sensitivity test for the antibacterial activity of the extracts acquired by Zingiber officinale, Syzygium aromaticum, and Allium sativum. Lastly, DLP printing was performed to produce several dental models with the natural extracted combined with hydrogel to represent and simulate the Hard and Soft tissues. Result: The desktop 3D printer was modified into 3D bioprinter using open-source software Marline and modified custom-made 3D printed parts. Sodium alginate hydrogel and gelatin hydrogel were prepared at 5% (w/v), 10% (w/v), and 15%(w/v). Resin integration with the natural extracts of Rhizome of Zingiber officinale, Flower buds of Syzygium aromaticum, and Bulbs of Allium sativum was done following the percentage 1- 3% for each extract. Finally, the Antimicrobial dental model was printed; exhibits the antimicrobial activity, followed by merging with sodium alginate hydrogel. Conclusion: The open-source movement was successful in modifying and producing a low-cost Desktop 3D Bioprinter showing the potential of further enhancement in such scope. Additionally, the potential of integrating the DLP technology with bioprinting is a promising step toward the usage of the antimicrobial activity using natural products.

Keywords: 3D printing, 3D bio-printing, DLP, hydrogel, antibacterial activity, zingiber officinale, syzygium aromaticum, allium sativum, panax ginseng, dental applications

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Authors: Larry Turkish

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Introduction: The NBA is an example of mediocrity and this will be evident in the following paper. The study examines and evaluates the characteristics of the NBA champions. As divisions and playoff teams increase, there is an increase in the probability that the champion originates from the mediocre category. Since it’s inception in 1947, the league has been mediocre and continues to this day. Why does a professional league allow any team with a less than 50% winning percentage into the playoffs? As long as the finances flow into the league, owners will not change the current algorithm. The objective of this paper is to determine if the regular season has meaning in finding an NBA champion. Statistical Analysis: The data originates from the NBA website. The following variables are part of the statistical analysis: Rank, the rank of a team relative to other teams in the league based on the regular season win-loss record; Winning Percentage of a team based on the regular season; Divisions, the number of divisions within the league and Playoff Teams, the number of playoff teams relative to a particular season. The following statistical applications are applied to the data: Pearson Product-Moment Correlation, Analysis of Variance, Factor and Regression analysis. Conclusion: The results indicate that the divisional structure and number of playoff teams results in a negative effect on the winning percentage of playoff teams. It also prevents teams with higher winning percentages from accessing the playoffs. Recommendations: 1. Teams that have a winning percentage greater than 1 standard deviation from the mean from the regular season will have access to playoffs. (Eliminates mediocre teams.) 2. Eliminate Divisions (Eliminates weaker teams from access to playoffs.) 3. Eliminate Conferences (Eliminates weaker teams from access to the playoffs.) 4. Have a balanced regular season schedule, (Reduces the number of regular season games, creates equilibrium, reduces bias) that will reduce the need for load management.

Keywords: alignment, mediocrity, regression, z-score

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Authors: Abdoulaye Maihatchi Ahamed, Zubin Arora, Benjamin Swobada, Jean-yves Lansot, Alexandre Chagnes

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Lithium-ion battery (LiB) is the technology of choice in the development of electric vehicles. But there are still many challenges, including the development of positive electrode materials exhibiting high cycle ability, high energy density, and low environmental impact. For this latter, LiBs must be manufactured in a circular approach by developing the appropriate strategies to reuse and recycle them. Presently, the recycling of LiBs is carried out by the pyrometallurgical route, but more and more processes implement or will implement the hydrometallurgical route or a combination of pyrometallurgical and hydrometallurgical operations. After producing the black mass by mineral processing, the hydrometallurgical process consists in leaching the black mass in order to uptake the metals contained in the cathodic material. Then, these metals are extracted selectively by liquid-liquid extraction, solid-liquid extraction, and/or precipitation stages. However, liquid-liquid extraction combined with precipitation/crystallization steps is the most implemented operation in the LiB recycling process to selectively extract copper, aluminum, cobalt, nickel, manganese, and lithium from the leaching solution and precipitate these metals as high-grade sulfate or carbonate salts. Liquid-liquid extraction consists in contacting an organic solvent and an aqueous feed solution containing several metals, including the targeted metal(s) to extract. The organic phase is non-miscible with the aqueous phase. It is composed of an extractant to extract the target metals and a diluent, which is usually aliphatic kerosene produced from the petroleum industry. Sometimes, a phase modifier is added in the formulation of the extraction solvent to avoid the third phase formation. The extraction properties of the diluent do not depend only on the chemical structure of the extractant, but it may also depend on the nature of the diluent. Indeed, the interactions between the diluent can influence more or less the interactions between extractant molecules besides the extractant-diluent interactions. Only a few studies in the literature addressed the influence of the diluent on the extraction properties, while many studies focused on the effect of the extractants. Recently, new low-carbon footprint aliphatic diluents were produced by catalytic dearomatisation and distillation of bio-based oil. This study aims at investigating the influence of the nature of the diluent on the extraction properties of three extractants towards cobalt, nickel, manganese, copper, aluminum, and lithium: Cyanex®272 for nickel-cobalt separation, DEHPA for manganese extraction, and Acorga M5640 for copper extraction. The diluents used in the formulation of the extraction solvents are (i) low-odor aliphatic kerosene produced from the petroleum industry (ELIXORE 180, ELIXORE 230, ELIXORE 205, and ISANE IP 175) and (ii) bio-sourced aliphatic diluents (DEV 2138, DEV 2139, DEV 1763, DEV 2160, DEV 2161 and DEV 2063). After discussing the effect of the diluents on the extraction properties, this conference will address the development of a low carbon footprint process based on the use of the best bio-sourced diluent for the production of high-grade cobalt sulfate, nickel sulfate, manganese sulfate, and lithium carbonate, as well as metal copper.

Keywords: diluent, hydrometallurgy, lithium-ion battery, recycling

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Authors: Pedro P. Drumond, James E. Ball, Priscilla M. Moura, Márcia M. L. P. Coelho

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In recent decades, On-site Stormwater Detention (OSD) systems have been implemented in many cities around the world. In Brazil, urban drainage source control policies were created in the 1990’s and were mainly based on OSD. The concept of this technique is to promote the detention of additional stormwater runoff caused by impervious areas, in order to maintain pre-urbanization peak flow levels. In Australia OSD, was first adopted in the early 1980’s by the Ku-ring-gai Council in Sydney’s northern suburbs and Wollongong City Council. Many papers on the topic were published at that time. However, source control techniques related to stormwater quality have become to the forefront and OSD has been relegated to the background. In order to evaluate the effectiveness of the current regulations regarding OSD, the existing policies were compared in Australian cities, a country considered experienced in the use of this technique, and in Brazilian cities where OSD adoption has been increasing. The cities selected for analysis were Wollongong and Belo Horizonte, the first municipalities to adopt OSD in their respective countries, and Sydney and Porto Alegre, cities where these policies are local references. The Australian and Brazilian cities are located in Southern Hemisphere of the planet and similar rainfall intensities can be observed, especially in storm bursts greater than 15 minutes. Regarding technical criteria, Brazilian cities have a site-based approach, analyzing only on-site system drainage. This approach is criticized for not evaluating impacts on urban drainage systems and in rare cases may cause the increase of peak flows downstream. The city of Wollongong and most of the Sydney Councils adopted a catchment-based approach, requiring the use of Permissible Site Discharge (PSD) and Site Storage Requirements (SSR) values based on analysis of entire catchments via hydrograph-producing computer models. Based on the premise that OSD should be designed to dampen storms of 100 years Average Recurrence Interval (ARI) storm, the values of PSD and SSR in these four municipalities were compared. In general, Brazilian cities presented low values of PSD and high values of SSR. This can be explained by site-based approach and the low runoff coefficient value adopted for pre-development conditions. The results clearly show the differences between approaches and methodologies adopted in OSD designs among Brazilian and Australian municipalities, especially with regard to PSD values, being on opposite sides of the scale. However, lack of research regarding the real performance of constructed OSD does not allow for determining which is best. It is necessary to investigate OSD performance in a real situation, assessing the damping provided throughout its useful life, maintenance issues, debris blockage problems and the parameters related to rain-flow methods. Acknowledgments: The authors wish to thank CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico (Chamada Universal – MCTI/CNPq Nº 14/2014), FAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerais, and CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior for their financial support.

Keywords: on-site stormwater detention, source control, stormwater, urban drainage

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Authors: Abdul Khalique Baloch, Ali Okatan

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The quality analysis of food and vegetable from image is hot topic now a day, where researchers make them better then pervious findings through different technique and methods. In this research we have review the literature, and find gape from them, and suggest better proposed approach, design the algorithm, developed a software to measure the quality from images, where accuracy of image show better results, and compare the results with Perouse work done so for. The Application we uses an open-source dataset and python language with tensor flow lite framework. In this research we focus to sort food and vegetable from image, in the images, the application can sorts and make them grading after process the images, it could create less errors them human base sorting errors by manual grading. Digital pictures datasets were created. The collected images arranged by classes. The classification accuracy of the system was about 94%. As fruits and vegetables play main role in day-to-day life, the quality of fruits and vegetables is necessary in evaluating agricultural produce, the customer always buy good quality fruits and vegetables. This document is about quality detection of fruit and vegetables using images. Most of customers suffering due to unhealthy foods and vegetables by suppliers, so there is no proper quality measurement level followed by hotel managements. it have developed software to measure the quality of the fruits and vegetables by using images, it will tell you how is your fruits and vegetables are fresh or rotten. Some algorithms reviewed in this thesis including digital images, ResNet, VGG16, CNN and Transfer Learning grading feature extraction. This application used an open source dataset of images and language used python, and designs a framework of system.

Keywords: deep learning, computer vision, image processing, rotten fruit detection, fruits quality criteria, vegetables quality criteria

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Authors: Ramkumar R., Gaurav Shinde, Pratik Shroff, Sachin Kumar Jain, Nagesh Walke

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Advancements in electric vehicles have significantly reduced the powertrain noise and moving components of vehicles. As a result, in-cab noises have become more noticeable to passengers inside the car. To ensure a comfortable ride for drivers and other passengers, it has become crucial to eliminate undesirable component noises during the development phase. Standard practices are followed to identify the severity of noises based on subjective ratings, but it can be a tedious process to identify the severity of each development sample and make changes to reduce it. Additionally, the severity rating can vary from jury to jury, making it challenging to arrive at a definitive conclusion. To address this, an automotive component was identified to evaluate squeak and rattle noise issue. Physical tests were carried out for random and sine excitation profiles. Aim was to subjectively assess the noise using jury rating method and objectively evaluate the same by measuring the noise. Suitable jury evaluation method was selected for the said activity, and recorded sounds were replayed for jury rating. Objective data sound quality metrics viz., loudness, sharpness, roughness, fluctuation strength and overall Sound Pressure Level (SPL) were measured. Based on this, correlation co-efficients was established to identify the most relevant sound quality metrics that are contributing to particular identified noise issue. Regression analysis was then performed to establish the correlation between subjective and objective data. Mathematical model was prepared using artificial intelligence and machine learning algorithm. The developed model was able to predict the subjective rating with good accuracy.

Keywords: BSR, noise, correlation, regression

Procedia PDF Downloads 80