Search results for: thermal processing

Authors: Michel A. Thomet

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A discrete-event simulation model was used to perform a Reliability-Availability-Maintainability (RAM) study of a large petrochemical complex which included sixteen process units, and seven feeds and intermediate streams. All the feeds and intermediate streams have associated storage tanks, so that if a processing unit fails and shuts down, the downstream units can keep producing their outputs. This also helps the upstream units which do not have to reduce their outputs, but can store their excess production until the failed unit restart. Each process unit and each pipe section carrying the feeds and intermediate streams has a probability of failure with an associated distribution and a Mean Time Between Failure (MTBF), as well as a distribution of the time to restore and a Mean Time To Restore (MTTR). The utilities supporting the process units can also fail and have their own distributions with specific MTBF and MTTR. The model runs are for ten years or more and the runs are repeated several times to obtain statistically relevant results. One of the main results is the On-Stream factor (OSF) of each process unit (percent of hours in a year when the unit is running in nominal conditions). One of the objectives of the study was to investigate if the storage capacity of each of the feeds and the intermediate stream was adequate. This was done by increasing the storage capacities in several steps and through running the simulation to see if the OSF were improved and by how much. Other objectives were to see if the failure of the utilities were an important factor in the overall OSF, and what could be done to reduce their failure rates through redundant equipment.

Keywords: business continuity, on-stream factor, petrochemical, RAM study, simulation, MTBF

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Authors: D. Venu, N. V. Koteswara Rao

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Space – time adaptive processing (STAP) is an effective tool for detecting a moving target in spaceborne or airborne radar systems. Since airborne passive radar systems utilize broadcast, navigation and excellent communication signals to perform various surveillance tasks and also has attracted significant interest from the distinct past, therefore the need of the hour is to have cost effective systems as compared to conventional active radar systems. Moreover, requirements of small number of secondary samples for effective clutter suppression in bi-static passive radar offer abundant illuminator resources for passive surveillance radar systems. This paper presents a framework for incorporating knowledge sources directly in the space-time beam former of airborne adaptive radars. STAP algorithm for clutter mitigation for passive bi-static radar has better quantitation of the reduction in sample size thereby amalgamating the earlier data bank with existing radar data sets. Also, we proposed a novel method to estimate the clutter matrix and perform STAP for efficient clutter suppression based on small sample size. Furthermore, the effectiveness of the proposed algorithm is verified using MATLAB simulations in order to validate STAP algorithm for passive bi-static radar. In conclusion, this study highlights the importance for various applications which augments traditional active radars using cost-effective measures.

Keywords: bistatic radar, clutter, covariance matrix passive radar, STAP

Procedia PDF Downloads 273

Authors: Ahmed Aljudaya, Derek Ingham, Lin Ma, Kevin Hughes, Mohammed Pourkashanian

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Researchers, power companies, and state politicians have given concentrated solar power (CSP) much attention due to its capacity to generate large amounts of electricity whereas overcoming the intermittent nature of solar resources. The Linear Fresnel Reflector (LFR) is a well-known CSP technology type for being inexpensive, having a low land use factor, and suffering from low optical efficiency. The LFR was considered a cost-effective alternative option to the Parabolic Trough Collector (PTC) because of its simplistic design, and this often outweighs its lower efficiency. The LFR has been found to be a promising option for directly producing steam to a thermal cycle in order to generate low-cost electricity, but also it has been shown to be promising for indirect steam generation. The purpose of this important analysis is to compare the annual performance of the Direct Steam Generation (DSG) and Indirect Steam Generation (ISG) of LFR power plants using molten salt and other different Heat Transfer Fluids (HTF) to investigate their technical and economic effects. A 50 MWe solar-only system is examined as a case study for both steam production methods in extreme weather conditions. In addition, a parametric analysis is carried out to determine the optimal solar field size that provides the lowest Levelized Cost of Electricity (LCOE) while achieving the highest technical performance. As a result of optimizing the optimum solar field size, the solar multiple (SM) is found to be between 1.2 – 1.5 in order to achieve as low as 9 Cent/KWh for the direct steam generation of the linear Fresnel reflector. In addition, the power plant is capable of producing around 141 GWh annually and up to 36% of the capacity factor, whereas the ISG produces less energy at a higher cost. The optimization results show that the DSG’s performance overcomes the ISG in producing around 3% more annual energy, 2% lower LCOE, and 28% less capital cost.

Keywords: concentrated solar power, levelized cost of electricity, linear Fresnel reflectors, steam generation

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Authors: Todd Zhou, Mikhail Yurochkin

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Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

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Authors: Abhinay Puvvala, Veerendra Kumar Rai

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A bottleneck situation arises when the outflow is lesser than the inflow in a pipe-like setup. A more practical interpretation of bottlenecks emphasizes on the realization of Service Level Objectives (SLOs) at given workloads. Our approach detects two key aspects of bottlenecks – when and where. To identify ‘when’ we continuously poll on certain key metrics such as resource utilization, processing time, request backlog and throughput at a system level. Further, when the slope of the expected sojourn time at a workload is greater than ‘K’ times the slope of expected sojourn time at the previous step of the workload while the workload is being gradually increased in discrete steps, a bottleneck situation arises. ‘K’ defines the threshold condition and is computed based on the system’s service level objectives. The second aspect of our approach is to identify the location of the bottleneck. In multi-tier systems with a complex network of layers, it is a challenging problem to locate bottleneck that affects the overall system performance. We stage the system by varying workload incrementally to draw a correlation between load increase and system performance to the point where Service Level Objectives are violated. During the staging process, multiple metrics are monitored at hardware and application levels. The correlations are drawn between metrics and the overall system performance. These correlations along with the Service Level Objectives are used to arrive at the threshold conditions for each of these metrics. Subsequently, the same method used to identify when a bottleneck occurs is used on metrics data with threshold conditions to locate bottlenecks.

Keywords: bottleneck, workload, service level objectives (SLOs), throughput, system performance

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Authors: Christos Theoharatos, Dimitris Tsourounis, Spiros Oikonomou, Andreas Makedonas

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This paper focuses on the problem of visually inspecting and recognizing the road conditions in front of moving vehicles, targeting automotive scenarios. The goal of road inspection is to identify whether the road is slippery or not, as well as to detect possible anomalies on the road surface like potholes or body bumps/humps. Our work is based on an artificial intelligence methodology for real-time monitoring of road conditions in autonomous driving scenarios, using state-of-the-art deep convolutional neural network (CNN) techniques. Initially, the road and ego lane are segmented within the field of view of the camera that is integrated into the front part of the vehicle. A novel classification CNN is utilized to identify among plain and slippery road textures (e.g., wet, snow, etc.). Simultaneously, a robust detection CNN identifies severe surface anomalies within the ego lane, such as potholes and speed bumps/humps, within a distance of 5 to 25 meters. The overall methodology is illustrated under the scope of an integrated application (or system), which can be integrated into complete Advanced Driver-Assistance Systems (ADAS) systems that provide a full range of functionalities. The outcome of the proposed techniques present state-of-the-art detection and classification results and real-time performance running on AI accelerator devices like Intel’s Myriad 2/X Vision Processing Unit (VPU).

Keywords: deep learning, convolutional neural networks, road condition classification, embedded systems

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Authors: A. Somaya Madkour, B. Abeer sheta, C. Fatma Badr El Dine, D. Yasser Elwakeel, E. Nermine AbdAllah

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Criminal offenders have a fundamental goal not to leave any traces at the crime scene. Some may suppose that items recovered underwater will have no forensic value, therefore, they try to destroy the traces by throwing items in water. These traces are subjected to the destructive environmental effects. This can represent a challenge for Forensic experts investigating finger marks. Accordingly, the present study was conducted to determine the optimal method for latent fingerprints development on non-porous surfaces submerged in aquatic environments at different time interval. The two factors analyzed in this study were the nature of aquatic environment and length of submerged time. In addition, the quality of developed finger marks depending on the used method was also assessed. Therefore, latent fingerprints were deposited on metallic, plastic and glass objects and submerged in fresh or sea water for one, two, and ten days. After recovery, the items were subjected to cyanoacrylate fuming, black powder and small particle reagent processing and the prints were examined. Each print was evaluated according to fingerprint quality assessment scale. The present study demonstrated that the duration of submersion affects the quality of finger marks; the longer the duration, the worse the quality.The best results of visualization were achieved using cyanoacrylate either in fresh or sea water. This study has also revealed that the exposure to sea water had more destructive influence on the quality of detected finger marks.

Keywords: fingerprints, fresh water, sea, non-porous

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Authors: Sonia Sassi, Mostapha Tarfaoui, Hamza Ben Yahia

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In this investigation, an experimental technique in which the dynamic response, damage kinetic and heat dissipation are measured simultaneously during high strain rates on adhesively bonded joints materials. The material used in this study is widely used in the design of structures for military applications. It was composed of a 45° Bi-axial fiber-glass mat of 0.286 mm thickness in a Polyester resin matrix. In adhesive bonding, a NORPOL Polyvinylester of 1 mm thickness was used to assemble the composite substrate. The experimental setup consists of a compression Split Hopkinson Pressure Bar (SHPB), a high-speed infrared camera and a high-speed Fastcam rapid camera. For the dynamic compression tests, 13 mm x 13 mm x 9 mm samples for out-of-plane tests were considered from 372 to 1030 s-1. Specimen surface is controlled and monitored in situ and in real time using the high-speed camera which acquires the damage progressive in specimens and with the infrared camera which provides thermal images in time sequence. Preliminary compressive stress-strain vs. strain rates data obtained show that the dynamic material strength increases with increasing strain rates. Damage investigations have revealed that the failure mainly occurred in the adhesive/adherent interface because of the brittle nature of the polymeric adhesive. Results have shown the dependency of the dynamic parameters on strain rates. Significant temperature rise was observed in dynamic compression tests. Experimental results show that the temperature change depending on the strain rate and the damage mode and their maximum exceed 100 °C. The dependence of these results on strain rate indicates that there exists a strong correlation between damage rate sensitivity and heat dissipation, which might be useful when developing damage models under dynamic loading tacking into account the effect of the energy balance of adhesively bonded joints.

Keywords: adhesive bonded joints, Hopkinson bars, out-of-plane tests, dynamic compression properties, damage mechanisms, heat dissipation

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Authors: Goodness Onwuka, Khaled Abou-El-Hossein

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Borosilicate-crown (BK7) glass has found broad application in the optic and automotive industries and the growing demands for nanometric surface finishes is becoming a necessity in such applications. Thus, it has become paramount to optimize the parameters influencing the surface roughness of this precision lens. The research was carried out on a 4-axes Nanoform 250 precision lathe machine with an ultra-high precision grinding spindle. The experiment varied the machining parameters of feed rate, wheel speed and depth of cut at three levels for different combinations using Box Behnken design of experiment and the resulting surface roughness values were measured using a Taylor Hobson Dimension XL optical profiler. Acoustic emission monitoring technique was applied at a high sampling rate to monitor the machining process while further signal processing and feature extraction methods were implemented to generate the input to a neural network algorithm. This paper highlights the training and development of a back propagation neural network prediction algorithm through careful selection of parameters and the result show a better classification accuracy when compared to a previously developed response surface model with very similar machining parameters. Hence artificial neural network algorithms provide better surface roughness prediction accuracy in the ultra-high precision grinding of BK7 glass.

Keywords: acoustic emission technique, artificial neural network, surface roughness, ultra-high precision grinding

Procedia PDF Downloads 280

Authors: Hamsasew Hankebo Lemago, Dóra Hessz, Tamás Igricz, Zoltán Erdélyi, , Imre Miklós Szilágyi

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Metal oxide inverse opals are a promising class of photocatalysts with a unique hierarchical structure. Atomic layer deposition (ALD) is a versatile technique for the synthesis of high-precision metal oxide thin films, including inverse opals. In this study, we report the synthesis of TiO₂, ZnO, and Al₂O₃ inverse opal and their composites photocatalysts using thermal or plasma-enhanced ALD. The synthesized photocatalysts were characterized using a variety of techniques, including scanning electron microscopy (SEM)-energy dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), Raman spectroscopy, photoluminescence (PL), ellipsometry, and UV-visible spectroscopy. The results showed that the ALD-synthesized metal oxide inverse opals had a highly ordered structure and a tunable pore size. The PL spectroscopy results showed low recombination rates of photogenerated electron-hole pairs, while the ellipsometry and UV-visible spectroscopy results showed tunable optical properties and band gap energies. The photocatalytic activity of the samples was evaluated by the degradation of methylene blue under visible light irradiation. The results showed that the ALD-synthesized metal oxide inverse opals exhibited high photocatalytic activity, even under visible light irradiation. The composites photocatalysts showed even higher activity than the individual metal oxide inverse opals. The enhanced photocatalytic activity of the composites can be attributed to the synergistic effect between the different metal oxides. For example, Al₂O₃ can act as a charge carrier scavenger, which can reduce the recombination of photogenerated electron-hole pairs. The ALD-synthesized metal oxide inverse opals and their composites are promising photocatalysts for a variety of applications, such as wastewater treatment, air purification, and energy production. The ALD-synthesized metal oxide inverse opals and their composites are promising photocatalysts for a variety of applications, such as wastewater treatment, air purification, and energy production.

Keywords: ALD, metal oxide inverse opals, photocatalysis, composites

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Authors: R. Suresh, Pavan Kumar Nimmagadda, Ming Zo Tan, Shane Hart, Sharp Ugwuocha

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Polycrystalline diamond compact (PDC) drill bits are extensively used in the oil and gas industry as well as the mining industry. Industry engineers continually improve upon PDC drill bit designs and hydraulic conditions. Optimized injection nozzles play a key role in improving the drilling performance and efficiency of these ever changing PDC drill bits. In the first part of this study, computational fluid dynamics (CFD) modelling is performed to investigate the hydrodynamic characteristics of drilling fluid flow around the PDC drill bit. An Open-source CFD software – OpenFOAM simulates the flow around the drill bit, based on the field input data. A specifically developed console application integrates the entire CFD process including, domain extraction, meshing, and solving governing equations and post-processing. The results from the OpenFOAM solver are then compared with that of the ANSYS Fluent software. The data from both software programs agree. The second part of the paper describes the parametric study of the PDC drill bit nozzle to determine the effect of parameters such as number of nozzles, nozzle velocity, nozzle radial position and orientations on the flow field characteristics and bit washing patterns. After analyzing a series of nozzle configurations, the best configuration is identified and recommendations are made for modifying the PDC bit design.

Keywords: ANSYS Fluent, computational fluid dynamics, nozzle configuration, OpenFOAM, PDC dill bit

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Authors: A. Ashraff

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The increasing importance of the web as a medium for electronic and business transactions has served as a catalyst or rather a driving force for the introduction and implementation of recommender systems. Recommender Systems play a major role in processing and analyzing thousands of data rows or reviews and help humans make a purchase decision of a product or service. It also has the ability to predict whether a particular user would rate a product or service based on the user’s profile behavioral pattern. At present, Recommender Systems are being used extensively in every domain known to us. They are said to be ubiquitous. However, in the field of recruitment, it’s not being utilized exclusively. Recent statistics show an increase in staff turnover, which has negatively impacted the organization as well as the employee. The reasons being company culture, working flexibility (work from home opportunity), no learning advancements, and pay scale. Further investigations revealed that there are lacking guidance or support, which helps a job seeker find the company that will suit him best, and though there’s information available about companies, job seekers can’t read all the reviews by themselves and get an analytical decision. In this paper, we propose an approach to study the available review data on IT companies (score their reviews based on user review sentiments) and gather information on job seekers, which includes their Psychometric evaluations. Then presents the job seeker with useful information or rather outputs on which company is most suitable for the job seeker. The theoretical approach, Algorithmic approach and the importance of such a system will be discussed in this paper.

Keywords: psychometric tests, recommender systems, sentiment analysis, hybrid recommender systems

Procedia PDF Downloads 82

Authors: S. Shankar Bharathi

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Inspection of surface defects on metallic components has always been challenging due to its specular property. Occurrences of defects such as scratches, rust, pitting are very common in metallic surfaces during the manufacturing process. These defects if unchecked can hamper the performance and reduce the life time of such component. Many of the conventional image processing algorithms in detecting the surface defects generally involve segmentation techniques, based on thresholding, edge detection, watershed segmentation and textural segmentation. They later employ other suitable algorithms based on morphology, region growing, shape analysis, neural networks for classification purpose. In this paper the work has been focused only towards detecting scratches. Global and other thresholding techniques were used to extract the defects, but it proved to be inaccurate in extracting the defects alone. However, this paper does not focus on comparison of different segmentation techniques, but rather describes a novel approach towards segmentation combined with hausdorff dilation distance. The proposed algorithm is based on the distribution of the intensity levels, that is, whether a certain gray level is concentrated or evenly distributed. The algorithm is based on extraction of such concentrated pixels. Defective images showed higher level of concentration of some gray level, whereas in non-defective image, there seemed to be no concentration, but were evenly distributed. This formed the basis in detecting the defects in the proposed algorithm. Hausdorff dilation distance based on mathematical morphology was used to strengthen the segmentation of the defects.

Keywords: metallic surface, scratches, segmentation, hausdorff dilation distance, machine vision

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Authors: Oriyomi M. Okeyinka, David A. Oloke, Jamal M. Khatib

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Large quantities of solid wastes being generated worldwide from sources such as household, domestic, industrial, commercial and construction demolition activities, leads to environmental concerns. Utilization of these wastes in making building construction materials can reduce the magnitude of the associated problems. When these waste products are used in place of other conventional materials, natural resources and energy are preserved and expensive and/or potentially harmful waste disposal is avoided. Recycling which is regarded as the third most preferred waste disposal option, with its numerous environmental benefits, stand as a viable option to offset the environmental impact associated with the construction industry. This paper reviews the results of laboratory tests and important research findings, and the potential of using these wastes in building construction materials with focus on sustainable development. Research gaps, which includes; the need to develop standard mix design for solid waste based building materials; the need to develop energy efficient method of processing solid waste use in concrete; the need to study the actual behavior or performance of such building materials in practical application and the limited real life application of such building materials have also been identified. Therefore a research is being proposed to develop an environmentally friendly, lightweight building block from recycled waste paper, without the use of cement, and with properties suitable for use as walling unit. This proposed research intends to incorporate, laboratory experimentation and modeling to address the identified research gaps.

Keywords: recycling, solid wastes, construction, building materials

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Authors: Mohamed El Morsy, Gabriela Achtenová

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Research on damage of gears and gear pairs using vibration signals remains very attractive, because vibration signals from a gear pair are complex in nature and not easy to interpret. Predicting gear pair defects by analyzing changes in vibration signal of gears pairs in operation is a very reliable method. Therefore, a suitable vibration signal processing technique is necessary to extract defect information generally obscured by the noise from dynamic factors of other gear pairs. This article presents the value of cepstrum analysis in vehicle gearbox fault diagnosis. Cepstrum represents the overall power content of a whole family of harmonics and sidebands when more than one family of sidebands is present at the same time. The concept for the measurement and analysis involved in using the technique are briefly outlined. Cepstrum analysis is used for detection of an artificial pitting defect in a vehicle gearbox loaded with different speeds and torques. The test stand is equipped with three dynamometers; the input dynamometer serves as the internal combustion engine, the output dynamometers introduce the load on the flanges of the output joint shafts. The pitting defect is manufactured on the tooth side of a gear of the fifth speed on the secondary shaft. Also, a method for fault diagnosis of gear faults is presented based on order cepstrum. The procedure is illustrated with the experimental vibration data of the vehicle gearbox. The results show the effectiveness of cepstrum analysis in detection and diagnosis of the gear condition.

Keywords: cepstrum analysis, fault diagnosis, gearbox, vibration signals

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Authors: Abdul Manan

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The necessity for efficient and cost-effective energy storage devices to intelligently store the inconsistent energy output from modern renewable energy sources is peaked today. The scientific community is struggling to identify the appropriate material system for energy storage applications. Countless contributions by researchers worldwide have now helped us identify the possible snags and limitations associated with each material/method. Energy storage has attracted great attention for its use in portable electronic devices military field. Different devices, such as dielectric capacitors, supercapacitors, and batteries, are used for energy storage. Of these, dielectric capacitors have high energy output, a long life cycle, fast charging and discharging capabilities, work at high temperatures, and excellent fatigue resistance. The energy storage characteristics have been studied to be highly affected by various factors, such as grain size, optimized compositions, grain orientation, energy band gap, processing techniques, defect engineering, core-shell formation, interface engineering, electronegativity difference, the addition of additives, density, secondary phases, the difference of Pmax-Pr, sample thickness, area of the electrode, testing frequency, and AC/DC conditions. The data regarding these parameters/factors are scattered in the literature, and the aim of this study is to gather the data into a single paper that will be beneficial for new researchers in the field of interest. Furthermore, control over and optimizing these parameters will lead to enhancing the energy storage properties.

Keywords: strategies, ceramics, energy storage, capacitors

Procedia PDF Downloads 52

Authors: Min Chang Shin, Byung Hun Jeong, Jeong Sik Han, Jung Hoon Park

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In modern times, as flight speeds have increased due to improvements in aircraft and missile engine performance, thermal loads have also increased. Because of the friction heat of air flow with high speed on the surface of the vehicle, it is not easy to cool the superheat of the vehicle by the simple air cooling method. For this reason, a cooling method through endothermic heat is attracting attention by using a fuel that causes an endothermic reaction in a high-speed vehicle. There are two main ways of cooling the fuel through the endothermic reaction. The first is physical heat absorption. When the temperature rises, there is a sensible heat that accompanies it. The second is the heat of reaction corresponding to the chemical heat absorption, which absorbs heat during the fuel decomposes. Generally, since the decomposition reaction of the fuel proceeds at a high temperature, it does not achieve a great efficiency in cooling the high-speed flight body. However, when the catalyst is used, decomposition proceeds at a low temperature thereby increasing the cooling efficiency. However, when the catalyst is used as a powder, the catalyst enters the engine and damages the engine or the catalyst can deteriorate the performance due to the sintering. On the other hand, when used in the form of pellets, catalyst loss can be prevented. However, since the specific surface of pellet is small, the efficiency of the catalyst is low. And it can interfere with the flow of fuel, resulting in pressure loss and problems with fuel injection. In this study, we tried to maximize the performance of the catalyst by preparing a hollow fiber type pellet for zeolite ZSM-5, which has a higher amount of heat absorption, than other conventional pellets. The hollow fiber type pellet was prepared by phase inversion method. The hollow fiber type pellet has a finger-like pore and sponge-like pore. So it has a higher specific surface area than conventional pellets. The crystal structure of the prepared ZSM-5 catalyst was confirmed by XRD, and the characteristics of the catalyst were analyzed by TPD/TPR device. This study was conducted as part of the Basic Research Project (Pure-17-20) of Defense Acquisition Program Administration.

Keywords: catalyst, endothermic reaction, high-speed vehicle cooling, zeolite, ZSM-5

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Authors: Hao Cheng, Yingzhou Ni, Amr M. Bakry, Li Liang

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Functional foods containing bioactive nutrients offer benefits beyond basic nutrition and hence the possibility of delaying and preventing chronic diseases. However, many bioactive nutrients degrade rapidly under food processing and storage conditions. Encapsulation can be used to overcome these limitations. Food proteins have been widely used as carrier materials for the preparation of nano/micro-particles because of their ability to form gels and emulsions and to interact with polysaccharides. The mechanisms of interaction between bioactive nutrients and proteins must be understood in order to develop protein-based lipid-free delivery systems. Beta-lactoglobulin, a small globular protein in milk whey, exhibits an affinity to a wide range of compounds. Alfa-tocopherol, resveratrol and folic acid were respectively bound to the central cavity, the outer surface near Trp19–Arg124 and the hydrophobic pocket in the groove between the alfa-helix and the beta-barrel of the protein. Beta-lactoglobulin could thus bind the three bioactive nutrients simultaneously to form protein-multi-ligand complexes. Beta-casein, an intrinsically unstructured but major milk protein, could also interact with resveratrol and folic acid to form complexes. These results suggest the potential to develop milk-protein-based complex carrier systems for encapsulation of multiple bioactive nutrients for functional food application and also pharmaceutical and medical uses.

Keywords: milk protein, bioactive nutrient, interaction, protection

Procedia PDF Downloads 385

Authors: Sachin Deshmukh

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Mind and body synchronization occurs through memory and sensation production. Sensations are the guiding language of subconscious mind for conscious mind to take a proper action. Mind-mechanism is based upon memories collected so far since intrauterine life. There are three universal triggers for memory creation; they are persons, situations and objects. Memory is created as sensations experienced by special senses. Based upon experiencing comfort or discomfort, the triggers are categorized as safe or unsafe triggers. A memory comprises of ‘safe or unsafe feeling for triggers, and actions taken for that feeling’. Memories for triggers are created slowly, thoughtfully and consciously by the conscious mind, and archived in the subconscious mind for future references. Later on, similar triggers can come in contact with the individual. Subconscious mind uses these stored feelings to decide whether these triggers are safe or unsafe. It produces comfort or discomfort sensations as emotions accordingly and reacts in the same way as has been recorded in memory. Speed of sensing and processing the triggers, and reacting by subconscious mind is that of the speed of bioelectricity. Hence, formula for human emotions has been designed in this paper as follows: Emotion (Stress or Peace) = Trigger (Person or Situation or object) x Mass of feelings (stressful or peaceful) associated with the Trigger x Speed of Light². We also establish modern medical scientific facts about relationship between reflex activity and memory. This research further develops the ‘Rational Memory Therapy’ focusing on therapeutic feelings conversion techniques, for stress prevention and management.

Keywords: memory, sensations, feelings, emotions, rational memory therapy

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Authors: Kristian Bautista, Ruben A. Idoy

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A bio-inspired metaheuristic algorithm inspired by the theory of collective animal behavior (CAB) was integrated to density-based clustering modeled as multimodal optimization problem. The algorithm was tested on synthetic, Iris, Glass, Pima and Thyroid data sets in order to measure its effectiveness relative to CDE-based Clustering algorithm. Upon preliminary testing, it was found out that one of the parameter settings used was ineffective in performing clustering when applied to the algorithm prompting the researcher to do an investigation. It was revealed that fine tuning distance δ3 that determines the extent to which a given data point will be clustered helped improve the quality of cluster output. Even though the modification of distance δ3 significantly improved the solution quality and cluster output of the algorithm, results suggest that there is no difference between the population mean of the solutions obtained using the original and modified parameter setting for all data sets. This implies that using either the original or modified parameter setting will not have any effect towards obtaining the best global and local animal positions. Results also suggest that CDE-based clustering algorithm is better than CAB-density clustering algorithm for all data sets. Nevertheless, CAB-density clustering algorithm is still a good clustering algorithm because it has correctly identified the number of classes of some data sets more frequently in a thirty trial run with a much smaller standard deviation, a potential in clustering high dimensional data sets. Thus, the researcher recommends further investigation in the post-processing stage of the algorithm.

Keywords: clustering, metaheuristics, collective animal behavior algorithm, density-based clustering, multimodal optimization

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Authors: Pitigalage Chamath Chandira Peiris

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A widely researched domain in the field of image processing in recent times has been single image super-resolution, which tries to restore a high-resolution image from a single low-resolution image. Many more single image super-resolution efforts have been completed utilizing equally traditional and deep learning methodologies, as well as a variety of other methodologies. Deep learning-based super-resolution methods, in particular, have received significant interest. As of now, the most advanced image restoration approaches are based on convolutional neural networks; nevertheless, only a few efforts have been performed using Transformers, which have demonstrated excellent performance on high-level vision tasks. The effectiveness of CNN-based algorithms in image super-resolution has been impressive. However, these methods cannot completely capture the non-local features of the data. Enhancer is a simple yet powerful Transformer-based approach for enhancing the resolution of images. A method for single image super-resolution was developed in this study, which utilized an efficient and effective transformer design. This proposed architecture makes use of a locally enhanced window transformer block to alleviate the enormous computational load associated with non-overlapping window-based self-attention. Additionally, it incorporates depth-wise convolution in the feed-forward network to enhance its ability to capture local context. This study is assessed by comparing the results obtained for popular datasets to those obtained by other techniques in the domain.

Keywords: single image super resolution, computer vision, vision transformers, image restoration

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Authors: Surya Chandra Tiwari, Kamal Kishore Pant, Sreedevi Upadhyayula

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CO2 capture using benign cost-effective solvents is an essential unit operation not only in the process industry for CO2 separation and recovery from industrial off-gas streams but also for direct capture from air to clean the environment. Several solvents are identified, by researchers, with high CO2 capture efficiency due to their favorable chemical and physical properties, interaction mechanism with CO2, and low regeneration energy cost. However, N-Methyldiethanolamine (MDEA) is the most frequently used solvent for CO2 capture with promoters such as piperazine (Pz) and monoethanolamine (MEA). These promoters have several issues such as low thermal stability, heat-stable salt formation, and being highly degradable. Therefore, new class promoters need to be used to overcome these issues. Functionalized ionic liquids (FILs) have the potential to overcome these limitations. Hence, in this work, four different new class functionalized ionic liquids (FILs) were used as promoters and determined their effectivity toward enhancement of the CO2 absorption performance. The CO2 absorption is performed at different pressure (2 bar, 4.4 bar, and 7 bar) and different temperature (303, 313, and 323K). The results confirmed that CO2 loading increases around 18 to 22% after 5wt% FILs blended in the MDEA. It was noticed that the CO2 loading increases with increasing pressure and decreases with increasing temperature for all absorbents systems. Further, the absorption kinetics was determined, and results showed that all the FILs provide an excellent absorption rate enhancement. Additionally, for the interaction mechanism study, 13C NMR analysis was performed for the blend aqueous MDEA-CO2 system. The results suggested that the FILs blend MDEA system produced a high amount of carbamates and bicarbonates during CO2 absorption, which further decreases with increasing temperature. Eventually, regeneration energy was calculated, and results confirmed that the energy heat duty penalty was lower in the [TETAH][Im] blend MDEA system. Overall, [TETAH][Pz], [TETAH][Im], [DETAH][Im] and [DETAH][Tz] showed the promising ability as promoters to enhance CO2 capturing performance of MDEA.

Keywords: CO2 capture, interaction mechanism, kinetics, Ionic liquids

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Authors: Yi-Chiang Huang, Hsu-Feng Lee, Yu-Chao Tseng, Wen-Yao Huang

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Proton exchange membranes as a key component in fuel cells have been widely studying over the past few decades. As proton exchange, membranes should have some main characteristics, such as good mechanical properties, low oxidative stability and high proton conductivity. In this work, trifluoromethyl groups had been introduced on polymer backbone and phenyl side chain which can provide densely located sulfonic acid group substitution and also promotes solubility, thermal and oxidative stability. Herein, a series of novel sulfonated aromatic hydrocarbon polyelectrolytes was synthesized by polycondensation of 4,4''''-difluoro-3,3''''- bis(trifluoromethyl)-2'',3''-bis(3-(trifluoromethyl)phenyl)-1,1':4',1'':4'',1''':4''',1''''-quinquephenyl with 2'',3''',5'',6''-tetraphenyl-[1,1':4',1'': 4'',1''':4''',1''''-quinquephenyl]-4,4''''-diol and post-sulfonated was through chlorosulfonic acid to given sulfonated polymers (SFC3-X) possessing ion exchange capacities ranging from 1.93, 1.91 and 2.53 mmol/g. ¹H NMR and FT-IR spectroscopy were applied to confirm the structure and composition of sulfonated polymers. The membranes exhibited considerably dimension stability (10-27.8% in length change; 24-56.5% in thickness change) and excellent oxidative stability (weight remain higher than 97%). The mechanical properties of membranes demonstrated good tensile strength on account of the high rigidity multi-phenylated backbone. Young's modulus were ranged 0.65-0.77GPa which is much larger than that of Nafion 211 (0.10GPa). Proton conductivities of membranes ranged from 130 to 240 mS/cm at 80 °C under fully humidified which were comparable or higher than that of Nafion 211 (150 mS/cm). The morphology of membranes was investigated by transmission electron microscopy which demonstrated a clear hydrophilic/hydrophobic phase separation with spherical ionic clusters in the size range of 5-20 nm. The SFC3-1.97 single fuel cell performance demonstrates the maximum power density at 1.08W/cm², and Nafion 211 was 1.24W/cm² as a reference in this work. The result indicated that SFC3-X are good candidates for proton exchange membranes in fuel cell applications. Fuel cell of other membranes is under testing.

Keywords: fuel cells, polyelectrolyte, proton exchange membrane, sulfonated polymers

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Authors: Tal Remez, Or Litany, Alex Bronstein

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The pursuit of smaller pixel sizes at ever increasing resolution in digital image sensors is mainly driven by the stringent price and form-factor requirements of sensors and optics in the cellular phone market. Recently, Eric Fossum proposed a novel concept of an image sensor with dense sub-diffraction limit one-bit pixels (jots), which can be considered a digital emulation of silver halide photographic film. This idea has been recently embodied as the EPFL Gigavision camera. A major bottleneck in the design of such sensors is the image reconstruction process, producing a continuous high dynamic range image from oversampled binary measurements. The extreme quantization of the Poisson statistics is incompatible with the assumptions of most standard image processing and enhancement frameworks. The recently proposed maximum-likelihood (ML) approach addresses this difficulty, but suffers from image artifacts and has impractically high computational complexity. In this work, we study a variant of a sensor with binary threshold pixels and propose a reconstruction algorithm combining an ML data fitting term with a sparse synthesis prior. We also show an efficient hardware-friendly real-time approximation of this inverse operator. Promising results are shown on synthetic data as well as on HDR data emulated using multiple exposures of a regular CMOS sensor.

Keywords: binary pixels, maximum likelihood, neural networks, sparse coding

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Authors: K. Paradkar, S. N. Mudliar, A. Sharma, A. B. Pandit, R. A. Pandey

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The study explores the hybrid combination of Hydrodynamic Cavitation (HC) and Subcritical Wet Air Oxidation-based pretreatment of complex industrial effluent to enhance the biodegradability selectively (without major COD destruction) to facilitate subsequent enhanced downstream processing via anaerobic or aerobic biological treatment. Advanced oxidation based techniques can be less efficient as standalone options and a hybrid approach by combining Hydrodynamic Cavitation (HC), and Wet Air Oxidation (WAO) can lead to a synergistic effect since both the options are based on common free radical mechanism. The HC can be used for initial turbulence and generation of hotspots which can begin the free radical attack and this agitating mixture then can be subjected to less intense WAO since initial heat (to raise the activation energy) can be taken care by HC alone. Lab-scale venturi-based hydrodynamic cavitation and wet air oxidation reactor with biomethanated distillery wastewater (BMDWW) as a model effluent was examined for establishing the proof-of-concept. The results indicated that for a desirable biodegradability index (BOD: COD - BI) enhancement (up to 0.4), the Cavitation (standalone) pretreatment condition was: 5 bar and 88 min reaction time with a COD reduction of 36 % and BI enhancement of up to 0.27 (initial BI - 0.17). The optimum WAO condition (standalone) was: 150oC, 6 bar and 30 minutes with 31% COD reduction and 0.33 BI. The hybrid pretreatment (combined Cavitation + WAO) worked out to be 23.18 min HC (at 5 bar) followed by 30 min WAO at 150oC, 6 bar, at which around 50% COD was retained yielding a BI of 0.55. FTIR & NMR analysis of pretreated effluent indicated dissociation and/or reorientation of complex organic compounds in untreated effluent to simpler organic compounds post-pretreatment.

Keywords: hybrid, hydrodynamic cavitation, wet air oxidation, biodegradability index

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Authors: Anthony Townsend, Robyn Fasser

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While complete objectivity is a desirable scientific position from which to conduct a care and contact evaluation (CCE), it is precisely the recognition that we are inherently incapable of operating objectively that is the foundation of ethical practice and skilled assessment. Drawing upon recent research from Daniel Kahneman (2021) on the differences between noise and bias, as well as different inherent biases collectively termed “The Elephant in the Brain” by Kevin Simler and Robin Hanson (2019) from Oxford University, this presentation addresses both the various ways in which our judgments, perceptions and even procedures can be distorted and contaminated while conducting a CCE, but also considers the value of second order cybernetics and the psychodynamic concept of ‘ambivalence’ as a conceptual basis to inform our assessment methodologies to limit such errors or at least better identify them. Both a conceptual framework for ambivalence, our higher-order capacity to allow for the convergence and consideration of multiple emotional experiences and cognitive perceptions to inform our reasoning, and a practical methodology for assessment relying on data triangulation, Bayesian inference and hypothesis testing is presented as a means of promoting ethical practice for health care professionals conducting CCEs. An emphasis on widening awareness and perspective, limiting ‘splitting’, is demonstrated both in how this form of emotional processing plays out in alienating dynamics in families as well as the assessment thereof. In addressing this concept, this presentation aims to illuminate the value of ambivalence as foundational to ethical practice for assessors.

Keywords: ambivalence, forensic, psychology, noise, bias, ethics

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Authors: S. Zith Dey Babu, S. Kour, S. Verma, C. Verma, V. Pathania, A. Agrawal, V. Chaudhary, A. Manoj Puthur, R. Goyal, A. Pal, T. Danti Dey, A. Kumar, K. Wadhwa, O. Ved

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Background: Skin cancer is now is the buzzing button in the field of medical science. The cyst's pandemic is drastically calibrating the body and well-being of the global village. Methods: The extracted image of the skin tumor cannot be used in one way for diagnosis. The stored image contains anarchies like the center. This approach will locate the forepart of an extracted appearance of skin. Partitioning image models has been presented to sort out the disturbance in the picture. Results: After completing partitioning, feature extraction has been formed by using genetic algorithm and finally, classification can be performed between the trained and test data to evaluate a large scale of an image that helps the doctors for the right prediction. To bring the improvisation of the existing system, we have set our objectives with an analysis. The efficiency of the natural selection process and the enriching histogram is essential in that respect. To reduce the false-positive rate or output, GA is performed with its accuracy. Conclusions: The objective of this task is to bring improvisation of effectiveness. GA is accomplishing its task with perfection to bring down the invalid-positive rate or outcome. The paper's mergeable portion conflicts with the composition of deep learning and medical image processing, which provides superior accuracy. Proportional types of handling create the reusability without any errors.

Keywords: computer-aided system, detection, image segmentation, morphology

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Authors: Arum Perwitasari

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Learning L2 sounds is influenced by the first language (L1) sound system. This current study seeks to examine how the listeners with a different L1 vowel system perceive L2 sounds. The fact that English has a bigger number of vowel inventory than Javanese and Sundanese L1 might cause problems for Javanese and Sundanese English language learners perceiving English sounds. To reveal the L2 sound perception over time, we measured the mouse trajectories related to the hand movements made by Javanese and Sundanese language learners, two of Indonesian local languages. Do the Javanese and Sundanese listeners show higher velocity than the English listeners when they perceive English vowels which are similar and new to their L1 system? The study aims to map the patterns of real-time processing through compatible hand movements to reveal any uncertainties when making selections. The results showed that the Javanese listeners exhibited significantly slower velocity values than the English listeners for similar vowels /I, ɛ, ʊ/ in the 826-1200ms post stimulus. Unlike the Javanese, the Sundanese listeners showed slow velocity values except for similar vowel /ʊ/. For the perception of new vowels /i:, æ, ɜ:, ʌ, ɑː, u:, ɔ:/, the Javanese listeners showed slower velocity in making the lexical decision. In contrast, the Sundanese listeners showed slow velocity only for vowels /ɜ:, ɔ:, æ, I/ indicating that these vowels are hard to perceive. Our results fit well with the second language model representing how the L1 vowel system influences the L2 sound perception.

Keywords: velocity profiles, EFL learners, speech perception, experimental linguistics

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Authors: Sfiso Radebe

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The design models dealing with flawless composite structures are in abundance, where the mechanical properties of composite structures are assumed to be known a priori. However, if the worst case scenario is assumed, where material defects combined with processing anomalies in composite structures are expected, a different solution is attained. Furthermore, if the system being designed combines in series hybrid elements, individually affected by material constant variations, it implies that a different approach needs to be taken. In the body of literature, there is a compendium of research that investigates different modes of failure affecting hybrid metal-composite structures. It covers areas pertaining to the failure of the hybrid joints, structural deformation, transverse displacement, the suppression of vibration and noise. In the present study a system employing a combination of two or more hybrid power transmitting elements will be explored for the least favourable dynamic loads as well as weight minimization, subject to uncertain material properties. Elastic constants are assumed to be uncertain-but-bounded quantities varying slightly around their nominal values where the solution is determined using convex models of uncertainty. Convex analysis of the problem leads to the computation of the least favourable solution and ultimately to a robust design. This approach contrasts with a deterministic analysis where the average values of elastic constants are employed in the calculations, neglecting the variations in the material properties.

Keywords: convex modelling, hybrid, metal-composite, robust design

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Authors: Nasreen Basoudan, Andrea Tales, Frederic Boy

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Non-clinical anxiety may be comprised of state anxiety - temporarily experienced anxiety related to a specific situation, and trait anxiety - a longer lasting response or a general disposition to anxiety. While temporary and occasional anxiety whether as a mood state or personality dimension is normal, nonclinical anxiety may influence many more components of information processing than previously recognized. In ageing and dementia-related research, disease characterization now involves attempts to understand a much wider range of brain function such as loss of inhibitory control, as against the more common focus on memory and cognition. However, in many studies, the tendency has been to include individuals with clinical anxiety disorders while excluding persons with lower levels of state or trait anxiety. Loss of inhibitory cognitive control can lead to behaviors such as aggression, reduced sensitivity to others, sociopathic thoughts and actions. Anxiety has also been linked to inhibitory control, with research suggesting that people with anxiety are less capable of inhibiting their emotions than the average person. This study investigates the relationship between anxiety and loss of inhibitory control in younger and older adults, using a variety of questionnaires and computers-based tests. Based on the premise that irrespective of classification, anxiety is associated with a wide range of physical, affective, and cognitive responses, this study explores evidence indicative of the potential influence anxiety per se on loss of inhibitory control, in order to contribute to discussion and appropriate consideration of anxiety-related factors in methodological practice.

Keywords: anxiety, ageing, dementia, inhibitory control

Procedia PDF Downloads 217