Search results for: machine translation (MT)

Authors: Eriko Ochiai, Fumiko Satoh, Keiko Miyashita, Yu Kakimoto, Motoki Osawa

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Sudden and unexpected deaths from unknown causes occur in infants and youths. Recently, molecular links between a part of these deaths and several genetic diseases are examined in the postmortem. For instance, hereditary long QT syndrome and Burgada syndrome are occasionally fatal through critical ventricular tachyarrhythmia. There are a large number of target genes responsible for such diseases, the conventional analysis using the Sanger’s method has been laborious. In this report, we attempted to analyze sudden deaths comprehensively using the next generation sequencing (NGS) technique. Multiplex PCR to subject’s DNA was performed using Ion AmpliSeq Library Kits 2.0 and Ion AmpliSeq Inherited Disease Panel (Life Technologies). After the library was constructed by emulsion PCR, the amplicons were sequenced 500 flows on Ion Personal Genome Machine System (Life Technologies) according to the manufacture instruction. SNPs and indels were analyzed to the sequence reads that were mapped on hg19 of reference sequences. This project has been approved by the ethical committee of Tokai University School of Medicine. As a representative case, the molecular analysis to a 40 years old male who received a diagnosis of Brugada syndrome demonstrated a total of 584 SNPs or indels. Non-synonymous and frameshift nucleotide substitutions were selected in the coding region of heart disease related genes of ANK2, AKAP9, CACNA1C, DSC2, KCNQ1, MYLK, SCN1B, and STARD3. In particular, c.629T-C transition in exon 3 of the SCN1B gene, resulting in a leu210-to-pro (L210P) substitution is predicted “damaging” by the SIFT program. Because the mutation has not been reported, it was unclear if the substitution was pathogenic. Sudden death that failed in determining the cause of death constitutes one of the most important unsolved subjects in forensic pathology. The Ion AmpliSeq Inherited Disease Panel can amplify the exons of 328 genes at one time. We realized the difficulty in selection of the true source from a number of candidates, but postmortem genetic testing using NGS analysis deserves of a diagnostic to date. We now extend this analysis to SIDS suspected subjects and young sudden death victims.

Keywords: postmortem genetic testing, sudden death, SIDS, next generation sequencing

Procedia PDF Downloads 328

Authors: Ahmed Saeed Mohamed, Sadok Sassi, Mohammad Roshun Paurobally

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Gears are important machine components that are widely used to transmit power and change speed in many rotating machines. Any breakdown of these vital components may cause severe disturbance to production and incur heavy financial losses. One of the most common causes of gear failure is the tooth fatigue crack. Early detection of teeth cracks is still a challenging task for engineers and maintenance personnel. So far, to analyze the vibration behavior of gears, different approaches have been tried based on theoretical developments, numerical simulations, or experimental investigations. The objective of this study was to develop a numerical model that could be used to simulate the effect of teeth cracks on the resulting vibrations and hence to permit early fault detection for gear transmission systems. Unlike the majority of published papers, where only one single crack has been considered, this work is more realistic, since it incorporates the possibility of multiple simultaneous cracks with different lengths. As cracks significantly alter the gear mesh stiffness, we performed a finite element analysis using SolidWorks software to determine the stiffness variation with respect to the angular position for different combinations of crack lengths. A simplified six degrees of freedom non-linear lumped parameter model of a one-stage gear system is proposed to study the vibration of a pair of spur gears, with and without tooth cracks. The model takes several physical properties into account, including variable gear mesh stiffness and the effect of friction, but ignores the lubrication effect. The vibration simulation results of the gearbox were obtained via Matlab and Simulink. The results were found to be consistent with the results from previously published works. The effect of one crack with different levels was studied and very similar changes in the total mesh stiffness and the vibration response, both were observed and compared to what has been found in previous studies. The effect of the crack length on various statistical time domain parameters was considered and the results show that these parameters were not equally sensitive to the crack percentage. Multiple cracks are introduced at different locations and the vibration response and the statistical parameters were obtained.

Keywords: dynamic simulation, gear mesh stiffness, simultaneous tooth cracks, spur gear, vibration-based fault detection

Procedia PDF Downloads 185

Authors: Torsten Hermanns, Thoufik Al Khawli, Wolfgang Schulz

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In laser cutting as well as in long pulsed laser drilling of metals, it can be demonstrated that the ablation shape (the shape of cut faces respectively the hole shape) that is formed approaches a so-called asymptotic shape such that it changes only slightly or not at all with further irradiation. These findings are already known from the ultrashort pulse (USP) ablation of dielectric and semiconducting materials. The explanation for the occurrence of an asymptotic shape in laser cutting and long pulse drilling of metals is identified, its underlying mechanism numerically implemented, tested and clearly confirmed by comparison with experimental data. In detail, there now is a model that allows the simulation of the temporal (pulse-resolved) evolution of the hole shape in laser drilling as well as the final (asymptotic) shape of the cut faces in laser cutting. This simulation especially requires much less in the way of resources, such that it can even run on common desktop PCs or laptops. Individual parameters can be adjusted using sliders – the simulation result appears in an adjacent window and changes in real time. This is made possible by an application-specific reduction of the underlying ablation model. Because this reduction dramatically decreases the complexity of calculation, it produces a result much more quickly. This means that the simulation can be carried out directly at the laser machine. Time-intensive experiments can be reduced and set-up processes can be completed much faster. The high speed of simulation also opens up a range of entirely different options, such as metamodeling. Suitable for complex applications with many parameters, metamodeling involves generating high-dimensional data sets with the parameters and several evaluation criteria for process and product quality. These sets can then be used to create individual process maps that show the dependency of individual parameter pairs. This advanced simulation makes it possible to find global and local extreme values through mathematical manipulation. Such simultaneous optimization of multiple parameters is scarcely possible by experimental means. This means that new methods in manufacturing such as self-optimization can be executed much faster. However, the software’s potential does not stop there; time-intensive calculations exist in many areas of industry. In laser welding or laser additive manufacturing, for example, the simulation of thermal induced residual stresses still uses up considerable computing capacity or is even not possible. Transferring the principle of reduced models promises substantial savings there, too.

Keywords: asymptotic ablation shape, interactive process simulation, laser drilling, laser cutting, metamodeling, reduced modeling

Procedia PDF Downloads 193

Authors: Rohit Vashistha, Amit Kumar Gupta, G. P. Ravishankar, Mahesh P. Padwale

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Accurate thrust measurement is required for aircraft during takeoff and after ski-jump. In a developmental aircraft, takeoff from ship is extremely critical and thrust produced by the engine should be known to the pilot before takeoff so that if thrust produced is not sufficient then take-off can be aborted and accident can be avoided. After ski-jump, thrust produced by engine is required because the horizontal speed of aircraft is less than the normal takeoff speed. Engine should be able to produce enough thrust to provide nominal horizontal takeoff speed to the airframe within prescribed time limit. The contemporary low bypass gas turbine engines generally have three mounts where the two side mounts transfer the engine thrust to the airframe. The third mount only takes the weight component. It does not take any thrust component. In the present method of thrust estimation, the strain gauging of the two side mounts is carried out. The strain produced at various power settings is used to estimate the thrust produced by the engine. The quarter Wheatstone bridge is used to acquire the strain data. The engine mount assembly is subjected to Universal Test Machine for determination of equivalent elasticity of assembly. This elasticity value is used in the analytical approach for estimation of engine thrust. The estimated thrust is compared with the test bed load cell thrust data. The experimental strain data is also compared with strain data obtained from FEM analysis. Experimental setup: The strain gauge is mounted on the tapered portion of the engine mount sleeve. Two strain gauges are mounted on diametrically opposite locations. Both of the strain gauges on the sleeve were in the horizontal plane. In this way, these strain gauges were not taking any strain due to the weight of the engine (except negligible strain due to material's poison's ratio) or the hoop's stress. Only the third mount strain gauge will show strain when engine is not running i.e. strain due to weight of engine. When engine starts running, all the load will be taken by the side mounts. The strain gauge on the forward side of the sleeve was showing a compressive strain and the strain gauge on the rear side of the sleeve shows a tensile strain. Results and conclusion: the analytical calculation shows that the hoop stresses dominate the bending stress. The estimated thrust by strain gauge shows good accuracy at higher power setting as compared to lower power setting. The accuracy of estimated thrust at max power setting is 99.7% whereas at lower power setting is 78%.

Keywords: engine mounts, finite elements analysis, strain gauge, stress

Procedia PDF Downloads 447

Authors: Vishwanath Mali, Hemant Warhatkar, Raju Pawade

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Drilling of bone has been always challenging for surgeons due to the adverse effect it may impart to bone tissues. Force has to be applied manually by the surgeon while performing conventional bone drilling which may lead to permanent death of bone tissues and nerves. During bone drilling the temperature of the bone tissues increases to higher values above 47 ⁰C that causes thermal osteonecrosis resulting into screw loosening and subsequent implant failures. An attempt has been made here to study the input drilling parameters and surgical drill bit geometry affecting bone health during bone drilling. A One Factor At a Time (OFAT) method is used to plan the experiments. Input drilling parameters studied include spindle speed and feed rate. The drill bit geometry parameter studied include point angle and helix angle. The output variables are drilling thrust force and bone temperature. The experiments were conducted on goat femur bone at room temperature 30 ⁰C. For measurement of thrust forces KISTLER cutting force dynamometer Type 9257BA was used. For continuous data acquisition of temperature NI LabVIEW software was used. Fixture was made on RPT machine for holding the bone specimen while performing drilling operation. Bone specimen were preserved in deep freezer (LABTOP make) under -40 ⁰C. In case of drilling parameters, it is observed that at constant feed rate when spindle speed increases, thrust force as well as temperature decreases and at constant spindle speed when feed rate increases thrust force as well as temperature increases. The effect of drill bit geometry shows that at constant helix angle when point angle increases thrust force as well as temperature increases and at constant point angle when helix angle increase thrust force as well as temperature decreases. Hence it is concluded that as the thrust force increases temperature increases. In case of drilling parameter, the lowest thrust force and temperature i.e. 35.55 N and 36.04 ⁰C respectively were recorded at spindle speed 2000 rpm and feed rate 0.04 mm/rev. In case of drill bit geometry parameter, the lowest thrust force and temperature i.e. 40.81 N and 34 ⁰C respectively were recorded at point angle 70⁰ and helix angle 25⁰ Hence to avoid thermal necrosis of bone it is recommended to use higher spindle speed, lower feed rate, low point angle and high helix angle. The hard nature of cortical bone contributes to a greater rise in temperature whereas a considerable drop in temperature is observed during cancellous bone drilling.

Keywords: bone drilling, helix angle, point angle, thrust force, temperature, thermal necrosis

Procedia PDF Downloads 285

Authors: Muhammad Arif Jalil, Xaythavay Luangvilay, Montree Bunruangses, Somchat Sonasang, Preecha Yupapin

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Black and white holes form the entangled pair⟨BH│WH⟩, where a white hole occurs when the particle moves at the same speed as light. The entangled black-white hole pair is at the center with the radian between the gap. When the speed of particle motion is slower than light, the black hole is gravitational (positive gravity), where the white hole is smaller than the black hole. On the downstream side, the entangled pair appears to have a black hole outside the gap increases until the white holes disappear, which is the emptiness paradox. On the upstream side, when moving faster than light, white holes form times tunnels, with black holes becoming smaller. It will continue to move faster and further when the black hole disappears and becomes a wormhole (Singularity) that is only a white hole in emptiness (Emptiness). This research studies use of black and white holes generated by a two-level circuit for communication transmission carriers, in which high ability and capacity of data transmission can be obtained. The black and white hole pair can be generated by the two-level system circuit when the speech of a particle on the circuit is equal to the speed of light. The black hole forms when the particle speed has increased from slower to equal to the light speed, while the white hole is established when the particle comes down faster than light. They are bound by the entangled pair, signal and idler, ⟨Signal│Idler⟩, and the virtual ones for the white hole, which has an angular displacement of half of π radian. A two-level system is made from an electronic circuit to create black and white holes bound by the entangled bits that are immune or cloning-free from thieves. Start by creating a wave-particle behavior when its speed is equal to light black hole is in the middle of the entangled pair, which is the two bit gate. The required information can be input into the system and wrapped by the black hole carrier. A timeline (Tunnel) occurs when the wave-particle speed is faster than light, from which the entangle pair is collapsed. The transmitted information is safely in the time tunnel. The required time and space can be modulated via the input for the downlink operation. The downlink is established when the particle speed is given by a frequency(energy) form is down and entered into the entangled gap, where this time the white hole is established. The information with the required destination is wrapped by the white hole and retrieved by the clients at the destination. The black and white holes are disappeared, and the information can be recovered and used.

Keywords: cloning free, time machine, teleportation, two-level system

Procedia PDF Downloads 39

Authors: Mohammad Ghahramani, Fahimeh Saei Manesh

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Winning a soccer game is based on thorough and deep analysis of the ongoing match. On the other hand, giant gambling companies are in vital need of such analysis to reduce their loss against their customers. In this research work, we perform deep, real-time analysis on every soccer match around the world that distinguishes our work from others by focusing on particular seasons, teams and partial analytics. Our contributions are presented in the platform called “Analyst Masters.” First, we introduce various sources of information available for soccer analysis for teams around the world that helped us record live statistical data and information from more than 50,000 soccer matches a year. Our second and main contribution is to introduce our proposed in-play performance evaluation. The third contribution is developing new features from stable soccer matches. The statistics of soccer matches and their odds before and in-play are considered in the image format versus time including the halftime. Local Binary patterns, (LBP) is then employed to extract features from the image. Our analyses reveal incredibly interesting features and rules if a soccer match has reached enough stability. For example, our “8-minute rule” implies if 'Team A' scores a goal and can maintain the result for at least 8 minutes then the match would end in their favor in a stable match. We could also make accurate predictions before the match of scoring less/more than 2.5 goals. We benefit from the Gradient Boosting Trees, GBT, to extract highly related features. Once the features are selected from this pool of data, the Decision trees decide if the match is stable. A stable match is then passed to a post-processing stage to check its properties such as betters’ and punters’ behavior and its statistical data to issue the prediction. The proposed method was trained using 140,000 soccer matches and tested on more than 100,000 samples achieving 98% accuracy to select stable matches. Our database from 240,000 matches shows that one can get over 20% betting profit per month using Analyst Masters. Such consistent profit outperforms human experts and shows the inefficiency of the betting market. Top soccer tipsters achieve 50% accuracy and 8% monthly profit in average only on regional matches. Both our collected database of more than 240,000 soccer matches from 2012 and our algorithm would greatly benefit coaches and punters to get accurate analysis.

Keywords: soccer, analytics, machine learning, database

Procedia PDF Downloads 213

Authors: In-Lee Choi, Min Jae Jeong, Jun Pill Baek, Ho-Min Kang

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As the consumption trends of vegetables become different from the past, it is increased using vegetable more convenience such as fresh-cut vegetables, sprouts, baby vegetables rather than an existing hole piece of vegetables. Selected baby vegetables have various functional materials but they have short shelf life. This study was conducted to improve storability by using suitable laser ablation OTR (oxygen transmission rate) films. Baby vegetable of endive (Cichorium endivia L.) and pak choi (Brassica rapa chinensis) for this research, around 10 cm height, cultivated in glass greenhouse during 3 weeks. Harvested endive and pak choi were stored at 8 ℃ for 5 days and were packed by PP (Polypropylene) container and covered different types of laser ablation OTR film (DaeRyung Co., Ltd.) such as 1,300 cc, 10,000 cc, 20,000 cc, 40,000 cc /m2•day•atm, and control (perforated film) with heat sealing machine (SC200-IP, Kumkang, Korea). All the samples conducted 5 times replication. Statistical analysis was carried out using a Microsoft Excel 2010 program and results were expressed as standard deviations. The fresh weight loss rate of both baby vegetables were less than 0.3 % in treated films as maximum weight loss rate. On the other hands, control in the final storage day had around 3.0 % weight loss rate and it followed decreasing quantity. Endive had less 2.0 % carbon dioxide contents as maximum contents in 20,000 cc and 40,000 cc. Oxygen contents was maintained between 17 and 20 % in endive, 19 and 20 % in pak choi. Ethylene concentration of both vegetables maintained little lower contents in 20,000 cc treatments than others at final storage day without statistical significance. In the case of hardness, 40,000 cc film was shown little higher value at both baby vegetables without statistical significance. Visual quality was good at 10,000 cc and 20,000 cc in endive and pak choi, and off-flavor was not appeard any off-flavor in both vegetables. Chlorophyll (SPAD-502, Minolta, Japan) value of endive was shown as similar result with initial in all treatments except 20,000 cc as little lower. And chlorophyll value of pak choi decreased in all treatments compared with initial value but was not shown significantly difference each other. Color of leaves (CR-400, Minolta, Japan) changed significantly in 40,000 cc at endive. In an event of pak choi, all the treatments started yellowing by increasing hunter b value, among them control increased substantially. As above the result, 10,000 cc film was most reasonable packaging film for storing at endive and 20,000 cc at pak choi with good quality.

Keywords: carbon dioxide, shelf-life, visual quality, pak choi

Procedia PDF Downloads 764

Authors: Bylhah Mugotitsa, Jim Todd, Agnes Kiragga, Jay Greenfield, Evans Omondi, Lukoye Atwoli, Reinpeter Momanyi

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The INSPIRE network aims to build an open, ethical, sustainable, and FAIR (Findable, Accessible, Interoperable, Reusable) data science platform, particularly for longitudinal mental health (MH) data. While studies have been done at the clinical and population level, there still exists limitations in data and research in LMICs, which pose a risk of underrepresentation of mental disorders. It is vital to examine the existing longitudinal MH data, focusing on how FAIR datasets are. This landscape analysis aimed to provide both overall level of evidence of availability of longitudinal datasets and degree of consistency in longitudinal studies conducted. Utilizing prompters proved instrumental in streamlining the analysis process, facilitating access, crafting code snippets, categorization, and analysis of extensive data repositories related to depression, anxiety, and psychosis in Africa. While leveraging artificial intelligence (AI), we filtered through over 18,000 scientific papers spanning from 1970 to 2023. This AI-driven approach enabled the identification of 228 longitudinal research papers meeting inclusion criteria. Quality assurance revealed 10% incorrectly identified articles and 2 duplicates, underscoring the prevalence of longitudinal MH research in South Africa, focusing on depression. From the analysis, evaluating data and metadata adherence to FAIR principles remains crucial for enhancing accessibility and quality of MH research in Africa. While AI has the potential to enhance research processes, challenges such as privacy concerns and data security risks must be addressed. Ethical and equity considerations in data sharing and reuse are also vital. There’s need for collaborative efforts across disciplinary and national boundaries to improve the Findability and Accessibility of data. Current efforts should also focus on creating integrated data resources and tools to improve Interoperability and Reusability of MH data. Practical steps for researchers include careful study planning, data preservation, machine-actionable metadata, and promoting data reuse to advance science and improve equity. Metrics and recognition should be established to incentivize adherence to FAIR principles in MH research

Keywords: longitudinal mental health research, data sharing, fair data principles, Africa, landscape analysis

Procedia PDF Downloads 29

Authors: Marcio Freire Cruz, Naoaki Ono, Shigehiko Kanaya, Carlos Arthur Mattos Teixeira Cavalcante

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The systems that record patient care information, known as Electronic Medical Record (EMR) and those that monitor vital signs of patients, such as heart rate, body temperature, and blood pressure have been extremely valuable for the effectiveness of the patient’s treatment. Several kinds of research have been using data from EMRs and vital signs of patients to predict illnesses. Among them, we highlight those that intend to predict, classify, or, at least identify patterns, of sepsis illness in patients under vital signs monitoring. Sepsis is an organic dysfunction caused by a dysregulated patient's response to an infection that affects millions of people worldwide. Early detection of sepsis is expected to provide a significant improvement in its treatment. Preceding works usually combined medical, statistical, mathematical and computational models to develop detection methods for early prediction, getting higher accuracies, and using the smallest number of variables. Among other techniques, we could find researches using survival analysis, specialist systems, machine learning and deep learning that reached great results. In our research, patients are modeled as points moving each hour in an n-dimensional space where n is the number of vital signs (variables). These points can reach a sepsis target point after some time. For now, the sepsis target point was calculated using the median of all patients’ variables on the sepsis onset. From these points, we calculate for each hour the position vector, the first derivative (velocity vector) and the second derivative (acceleration vector) of the variables to evaluate their behavior. And we construct a prediction model based on a Long Short-Term Memory (LSTM) Network, including these derivatives as explanatory variables. The accuracy of the prediction 6 hours before the time of sepsis, considering only the vital signs reached 83.24% and by including the vectors position, speed, and acceleration, we obtained 94.96%. The data are being collected from Medical Information Mart for Intensive Care (MIMIC) Database, a public database that contains vital signs, laboratory test results, observations, notes, and so on, from more than 60.000 patients.

Keywords: dynamic analysis, long short-term memory, prediction, sepsis

Procedia PDF Downloads 98

Authors: M.Sivaramakrishnaiah, D. S .Nasan, P. V. Subhanjeneyulu, J. A. Sandeep Kumar, N. Sreenivasulu, B. V. Amarnath Reddy, B. Veeralingam

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Design, numerical simulation, fabrication, and physical experimentation of the Tesla’s Bladeless centripetal turbine for generating electrical power are presented in this research paper. 29 Pressurized air combined with water via a nozzle system is made to pass tangentially through a set of parallel smooth discs surfaces, which impart rotational motion to the discs fastened common shaft for the power generation. The power generated depends upon the fluid speed parameter leaving the nozzle inlet. Physically due to laminar boundary layer phenomena at smooth disc surface, the high speed fluid layers away from the plate moving against the low speed fluid layers nearer to the plate develop a tangential drag from the viscous shear forces. This compels the nearer layers to drag along with the high layers causing the disc to spin. Solid Works design software and fluid mechanics and machine elements design theories was used to compute mechanical design specifications of turbine parts like 48 mm diameter discs, common shaft, central exhaust, plenum chamber, swappable nozzle inlets, etc. Also, ANSYS CFX 2018 was used for the numerical 2 simulation of the physical phenomena encountered in the turbine working. When various numerical simulation and physical experimental results were verified, there is good agreement between them 6, both quantitatively and qualitatively. The sources of input and size of the blades may affect the power generated and turbine efficiency, respectively. The results may change if there is a change in the fluid flowing between the discs. The inlet fluid pressure versus turbine efficiency and the number of discs versus turbine power studies based on both results were carried out to develop the 8 relationships between the inlet and outlet parameters of the turbine. The present research work obtained the turbine efficiency in the range of 7-10%, and for this range; the electrical power output generated was 50-60 W.

Keywords: tesla turbine, cohesion type bladeless turbine, boundary layer theory, cohesion type bladeless turbine, tangential fluid flow, viscous and adhesive forces, plenum chamber, pico hydro systems

Procedia PDF Downloads 59

Authors: V. A. Dubovsky, V. V. Savchenko, A. A. Baryskevich

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The current trend in the automotive industry towards automatic vehicles is creating new challenges related to human factors. This occurs due to the fact that the driver is increasingly relieved of the need to be constantly involved in driving the vehicle, which can negatively impact his/her situation awareness when manual control is required, and decrease driving skills and abilities. These new problems need to be studied in order to provide road safety during the transition towards self-driving vehicles. For this purpose, it is important to develop an appropriate conceptual model of the interaction between the driver and the automated vehicle, which could serve as a theoretical basis for the development of mathematical and simulation models to explore different aspects of driver behaviour in different road situations. Well-known driver behaviour models describe the impact of different stages of the driver's cognitive process on driving performance but do not describe how the driver controls and adjusts his actions. A more complete description of the driver's cognitive process, including the evaluation of the results of his/her actions, will make it possible to more accurately model various aspects of the human factor in different road situations. This paper presents a conceptual model of the 'driver – highly automated vehicle' system based on the P.K. Anokhin's theory of functional systems, which is a theoretical framework for describing internal processes in purposeful living systems based on such notions as goal, desired and actual results of the purposeful activity. A central feature of the proposed model is a dynamic coupling mechanism between the decision-making of a driver to perform a particular action and changes of road conditions due to driver’s actions. This mechanism is based on the stage by stage evaluation of the deviations of the actual values of the driver’s action results parameters from the expected values. The overall functional structure of the highly automated vehicle in the proposed model includes a driver/vehicle/environment state analyzer to coordinate the interaction between driver and vehicle. The proposed conceptual model can be used as a framework to investigate different aspects of human factors in transitions between automated and manual driving for future improvements in driving safety, and for understanding how driver-vehicle interface must be designed for comfort and safety. A major finding of this study is the demonstration that the theory of functional systems is promising and has the potential to describe the interaction of the driver with the vehicle and the environment.

Keywords: automated vehicle, driver behavior, human factors, human-machine system

Procedia PDF Downloads 117

Authors: J. Satya Eswari, J. Sekhar Babub, Meena Murmu, Govardhan Bhat

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Electrical Discharge Machining (EDM) is an unconventional manufacturing process based on removal of material from a part by means of a series of repeated electrical sparks created by electric pulse generators at short intervals between a electrode tool and the part to be machined emmersed in dielectric fluid. In this paper, a study will be performed on the influence of the factors of peak current, pulse on time, interval time and power supply voltage. The output responses measured were material removal rate (MRR) and surface roughness. Finally, the parameters were optimized for maximum MRR with the desired surface roughness. RSM involves establishing mathematical relations between the design variables and the resulting responses and optimizing the process conditions. RSM is not free from problems when it is applied to multi-factor and multi-response situations. Design of experiments (DOE) technique to select the optimum machining conditions for machining AISI 4140 using EDM. The purpose of this paper is to determine the optimal factors of the electro-discharge machining (EDM) process investigate feasibility of design of experiment techniques. The work pieces used were rectangular plates of AISI 4140 grade steel alloy. The study of optimized settings of key machining factors like pulse on time, gap voltage, flushing pressure, input current and duty cycle on the material removal, surface roughness is been carried out using central composite design. The objective is to maximize the Material removal rate (MRR). Central composite design data is used to develop second order polynomial models with interaction terms. The insignificant coefficients’ are eliminated with these models by using student t test and F test for the goodness of fit. CCD is first used to establish the determine the optimal factors of the electro-discharge machining (EDM) for maximizing the MRR. The responses are further treated through a objective function to establish the same set of key machining factors to satisfy the optimization problem of the electro-discharge machining (EDM) process. The results demonstrate the better performance of CCD data based RSM for optimizing the electro-discharge machining (EDM) process.

Keywords: electric discharge machining (EDM), modeling, optimization, CCRD

Procedia PDF Downloads 315

Authors: Cecile Meier, Drago Diaz Aleman, Itahisa Perez Conesa, Jose Luis Saorin Perez, Jorge De La Torre Cantero

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In this work, two ways of creating small-sized metal sculptures are proposed: the first by means of microcasting and the second by electroforming from models printed in 3D using an FDM (Fused Deposition Modeling‎) printer or using a DLP (Digital Light Processing) printer. It is viable to replace the wax in the processes of the artistic foundry with 3D printed objects. In this technique, the digital models are manufactured with resin using a low-cost 3D FDM printer in polylactic acid (PLA). This material is used, because its properties make it a viable substitute to wax, within the processes of artistic casting with the technique of lost wax through Ceramic Shell casting. This technique consists of covering a sculpture of wax or in this case PLA with several layers of thermoresistant material. This material is heated to melt the PLA, obtaining an empty mold that is later filled with the molten metal. It is verified that the PLA models reduce the cost and time compared with the hand modeling of the wax. In addition, one can manufacture parts with 3D printing that are not possible to create with manual techniques. However, the sculptures created with this technique have a size limit. The problem is that when printed pieces with PLA are very small, they lose detail, and the laminar texture hides the shape of the piece. DLP type printer allows obtaining more detailed and smaller pieces than the FDM. Such small models are quite difficult and complex to melt using the lost wax technique of Ceramic Shell casting. But, as an alternative, there are microcasting and electroforming, which are specialized in creating small metal pieces such as jewelry ones. The microcasting is a variant of the lost wax that consists of introducing the model in a cylinder in which the refractory material is also poured. The molds are heated in an oven to melt the model and cook them. Finally, the metal is poured into the still hot cylinders that rotate in a machine at high speed to properly distribute all the metal. Because microcasting requires expensive material and machinery to melt a piece of metal, electroforming is an alternative for this process. The electroforming uses models in different materials; for this study, micro-sculptures printed in 3D are used. These are subjected to an electroforming bath that covers the pieces with a very thin layer of metal. This work will investigate the recommended size to use 3D printers, both with PLA and resin and first tests are being done to validate use the electroforming process of microsculptures, which are printed in resin using a DLP printer.

Keywords: sculptures, DLP 3D printer, microcasting, electroforming, fused deposition modeling

Procedia PDF Downloads 110

Authors: Sulemana Ibrahim

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Food security remains a paramount global challenge, with vulnerable regions grappling with issues of hunger and malnutrition. This study embarks on a comprehensive exploration of data-driven strategies aimed at ameliorating food security in such regions. Our research employs a multifaceted approach, integrating data analytics to predict crop yields, optimizing supply chains, and enhancing food distribution networks. The study unfolds as a multi-dimensional analysis, commencing with the development of robust machine learning models harnessing remote sensing data, historical crop yield records, and meteorological data to foresee crop yields. These predictive models, underpinned by convolutional and recurrent neural networks, furnish critical insights into anticipated harvests, empowering proactive measures to confront food insecurity. Subsequently, the research scrutinizes supply chain optimization to address food security challenges, capitalizing on linear programming and network optimization techniques. These strategies intend to mitigate loss and wastage while streamlining the distribution of agricultural produce from field to fork. In conjunction, the study investigates food distribution networks with a particular focus on network efficiency, accessibility, and equitable food resource allocation. Network analysis tools, complemented by data-driven simulation methodologies, unveil opportunities for augmenting the efficacy of these critical lifelines. This study also considers the ethical implications and privacy concerns associated with the extensive use of data in the realm of food security. The proposed methodology outlines guidelines for responsible data acquisition, storage, and usage. The ultimate aspiration of this research is to forge a nexus between data science and food security policy, bestowing actionable insights to mitigate the ordeal of food insecurity. The holistic approach converging data-driven crop yield forecasts, optimized supply chains, and improved distribution networks aspire to revitalize food security in the most vulnerable regions, elevating the quality of life for millions worldwide.

Keywords: data-driven strategies, crop yield prediction, supply chain optimization, food distribution networks

Procedia PDF Downloads 35

Authors: Malihe Aliasgari, Yousef Nejatbakhsh

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The era of Big Data and the immensity of real-life datasets compels computation tasks to be performed in a distributed fashion, where the data is dispersed among many servers that operate in parallel. However, massive parallelization leads to computational bottlenecks due to faulty servers and stragglers. Stragglers refer to a few slow or delay-prone processors that can bottleneck the entire computation because one has to wait for all the parallel nodes to finish. The problem of straggling processors, has been well studied in the context of distributed computing. Recently, it has been pointed out that, for the important case of linear functions, it is possible to improve over repetition strategies in terms of the tradeoff between performance and latency by carrying out linear precoding of the data prior to processing. The key idea is that, by employing suitable linear codes operating over fractions of the original data, a function may be completed as soon as enough number of processors, depending on the minimum distance of the code, have completed their operations. The problem of matrix-matrix multiplication in the presence of practically big sized of data sets faced with computational and memory related difficulties, which makes such operations are carried out using distributed computing platforms. In this work, we study the problem of distributed matrix-matrix multiplication W = XY under storage constraints, i.e., when each server is allowed to store a fixed fraction of each of the matrices X and Y, which is a fundamental building of many science and engineering fields such as machine learning, image and signal processing, wireless communication, optimization. Non-secure and secure matrix multiplication are studied. We want to study the setup, in which the identity of the matrix of interest should be kept private from the workers and then obtain the recovery threshold of the colluding model, that is, the number of workers that need to complete their task before the master server can recover the product W. The problem of secure and private distributed matrix multiplication W = XY which the matrix X is confidential, while matrix Y is selected in a private manner from a library of public matrices. We present the best currently known trade-off between communication load and recovery threshold. On the other words, we design an achievable PSGPD scheme for any arbitrary privacy level by trivially concatenating a robust PIR scheme for arbitrary colluding workers and private databases and the proposed SGPD code that provides a smaller computational complexity at the workers.

Keywords: coded distributed computation, private information retrieval, secret sharing, stragglers

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Authors: Nahush Modak, Meena Pangarkar, Anand Pathak, Ankita Tamhane

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Background: Breast cancer is the prominent cause of cancer and mortality among women. This study was done to show the statistical analysis of a cohort of over 250 patients detected with breast cancer diagnosed by oncologists using Immunohistochemistry (IHC). IHC was performed by using ER; PR; HER2; Ki-67 antibodies. Materials and methods: Formalin fixed Paraffin embedded tissue samples were obtained by surgical manner and standard protocol was followed for fixation, grossing, tissue processing, embedding, cutting and IHC. The Ventana Benchmark XT machine was used for automated IHC of the samples. Antibodies used were supplied by F. Hoffmann-La Roche Ltd. Statistical analysis was performed by using SPSS for windows. Statistical tests performed were chi-squared test and Correlation tests with p<.01. The raw data was collected and provided by National Cancer Insitute, Jamtha, India. Result: Luminal B was the most prevailing molecular subtype of Breast cancer at our institute. Chi squared test of homogeneity was performed to find equality in distribution and Luminal B was the most prevalent molecular subtype. The worse prognostic indicator for breast cancer depends upon expression of Ki-67 and her2 protein in cancerous cells. Our study was done at p <.01 and significant dependence was observed. There exists no dependence of age on molecular subtype of breast cancer. Similarly, age is an independent variable while considering Ki-67 expression. Chi square test performed on Human epidermal growth factor receptor 2 (HER2) statuses of patients and strong dependence was observed in percentage of Ki-67 expression and Her2 (+/-) character which shows that, value of Ki depends upon Her2 expression in cancerous cells (p<.01). Surprisingly, dependence was observed in case of Ki-67 and Pr, at p <.01. This shows that Progesterone receptor proteins (PR) are over-expressed when there is an elevation in expression of Ki-67 protein. Conclusion: We conclude from that Luminal B is the most prevalent molecular subtype at National Cancer Institute, Jamtha, India. There was found no significant correlation between age and Ki-67 expression in any molecular subtype. And no dependence or correlation exists between patients’ age and molecular subtype. We also found that, when the diagnosis is Luminal A, out of the cohort of 257 patients, no patient shows >14% Ki-67 value. Statistically, extremely significant values were observed for dependence of PR+Her2- and PR-Her2+ scores on Ki-67 expression. (p<.01). Her2 is an important prognostic factor in breast cancer. Chi squared test for Her2 and Ki-67 shows that the expression of Ki depends upon Her2 statuses. Moreover, Ki-67 cannot be used as a standalone prognostic factor for determining breast cancer.

Keywords: breast cancer molecular subtypes , correlation, immunohistochemistry, Ki-67 and HR, statistical analysis

Procedia PDF Downloads 102

Authors: Chrysoula Bousiouta

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Throughout the history of theatre, technology has played an important role both in influencing the relationship between performance and audience and offering different kinds of experiences. The use of technology dates back in ancient times, when the introduction of artifacts, such as “Deus ex machine” in ancient Greek theatre, started. Taking into account the key techniques and experiences used throughout history, this paper investigates how technology, through new media, influences contemporary theatre. In the context of this research, technology is defined as projections, audio environments, video-projections, sensors, tele-connections, all alongside with the performance, challenging audience’s participation. The theoretical framework of the research covers, except for the history of theatre, the theory of “experience economy” that took over the service and goods economy. The research is based on the qualitative and comparative analysis of two case studies, Contact Theatre in Manchester (United Kingdom) and Bios in Athens (Greece). The data selection includes desk research and is complemented with semi structured interviews. Building on the results of the research one could claim that the intended experience of modern/contemporary theatre is that of engagement. In this context, technology -as defined above- plays a leading role in creating it. This experience passes through and exists in the middle of the realms of entertainment, education, estheticism and escapism. Furthermore, it is observed that nowadays, theatre is not only about acting but also about performing; it is that one where the performances are unfinished without the participation of the audience. Both case studies try to achieve the experience of engagement through practices that promote the attraction of attention, the increase of imagination, the interaction, the intimacy and the true activity. These practices are achieved through the script, the scenery, the language and the environment of a performance. Contact and Bios consider technology as an intimate tool in order to accomplish the above, and they make an extended use of it. The research completes a notable record of technological techniques that modern theatres use. The use of technology, inside or outside the limits of film technique’s, helps to rivet the attention of the audience, to make performances enjoyable, to give the sense of the “unfinished” or to be used for things that take place around the spectators and force them to take action, being spect-actors. The advantage of technology is that it can be used as a hook for interaction in all stages of a performance. Further research on the field could involve exploring alternative ways of binding technology and theatre or analyzing how the performance is perceived through the use of technological artifacts.

Keywords: experience of engagement, interactive theatre, modern theatre, performance, technology

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Authors: Narendra Akhadkar, Silvestre Cano, Christophe Gourru

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Injection-molded parts are widely used in power system protection products. One of the biggest challenges in an injection molding process is shrinkage and warpage of the molded parts. All these geometrical variations may have an adverse effect on the quality of the product, functionality, cost, and time-to-market. The situation becomes more challenging in the case of intricate shapes and in mass production using multi-cavity tools. To control the effects of shrinkage and warpage, it is very important to correctly find out the input parameters that could affect the product performance. With the advances in the computer-aided engineering (CAE), different tools are available to simulate the injection molding process. For our case study, we used the MoldFlow insight tool. Our aim is to predict the spread of the functional dimensions and geometrical variations on the part due to variations in the input parameters such as material viscosity, packing pressure, mold temperature, melt temperature, and injection speed. The input parameters may vary during batch production or due to variations in the machine process settings. To perform the accurate product assembly variation simulation, the first step is to perform an individual part variation simulation to render realistic tolerance ranges. In this article, we present a method to simulate part variations coming from the input parameters variation during batch production. The method is based on computer simulations and experimental validation using the full factorial design of experiments (DoE). The robustness of the simulation model is verified through input parameter wise sensitivity analysis study performed using simulations and experiments; all the results show a very good correlation in the material flow direction. There exists a non-linear interaction between material and the input process variables. It is observed that the parameters such as packing pressure, material, and mold temperature play an important role in spread on functional dimensions and geometrical variations. This method will allow us in the future to develop accurate/realistic virtual prototypes based on trusted simulated process variation and, therefore, increase the product quality and potentially decrease the time to market.

Keywords: correlation, molding process, tolerance, sensitivity analysis, variation simulation

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Authors: Gulfam Haider, sana danish

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Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, and the choice of optimizers profoundly affects their performance. The study of optimizers and their adaptations remains a topic of significant importance in machine learning research. While numerous studies have explored and advocated for various optimizers, the efficacy of these optimization techniques is still subject to scrutiny. This work aims to address the challenges surrounding the effectiveness of optimizers by conducting a comprehensive analysis and evaluation. The primary focus of this investigation lies in examining the performance of different optimizers when employed in conjunction with the popular activation function, Rectified Linear Unit (ReLU). By incorporating ReLU, known for its favorable properties in prior research, the aim is to bolster the effectiveness of the optimizers under scrutiny. Specifically, we evaluate the adjustment of these optimizers with both the original Softmax activation function and the modified ReLU activation function, carefully assessing their impact on overall performance. To achieve this, a series of experiments are conducted using a well-established benchmark dataset for image classification tasks, namely the Canadian Institute for Advanced Research dataset (CIFAR-10). The selected optimizers for investigation encompass a range of prominent algorithms, including Adam, Root Mean Squared Propagation (RMSprop), Adaptive Learning Rate Method (Adadelta), Adaptive Gradient Algorithm (Adagrad), and Stochastic Gradient Descent (SGD). The performance analysis encompasses a comprehensive evaluation of the classification accuracy, convergence speed, and robustness of the CNN models trained with each optimizer. Through rigorous experimentation and meticulous assessment, we discern the strengths and weaknesses of the different optimization techniques, providing valuable insights into their suitability for image classification tasks. By conducting this in-depth study, we contribute to the existing body of knowledge surrounding optimizers in CNNs, shedding light on their performance characteristics for image classification. The findings gleaned from this research serve to guide researchers and practitioners in making informed decisions when selecting optimizers and activation functions, thus advancing the state-of-the-art in the field of image classification with convolutional neural networks.

Keywords: deep neural network, optimizers, RMsprop, ReLU, stochastic gradient descent

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Authors: Henry Memczak, Marc Hovestaedt, Bernhard Ay, Sandra Saenger, Thorsten Wolff, Frank F. Bier

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The influenza viruses cause flu epidemics every year and serious pandemics in larger time intervals. The only cost-effective protection against influenza is vaccination. Due to rapid mutation continuously new subtypes appear, what requires annual reimmunization. For a correct vaccination recommendation, the circulating influenza strains had to be detected promptly and exactly and characterized due to their antigenic properties. During the flu season 2016/17, a wrong vaccination recommendation has been given because of the great time interval between identification of the relevant influenza vaccine strains and outbreak of the flu epidemic during the following winter. Due to such recurring incidents of vaccine mismatches, there is a great need to speed up the process chain from identifying the right vaccine strains to their administration. The monitoring of subtypes as part of this process chain is carried out by national reference laboratories within the WHO Global Influenza Surveillance and Response System (GISRS). To this end, thousands of viruses from patient samples (e.g., throat smears) are isolated and analyzed each year. Currently, this analysis involves complex and time-intensive (several weeks) animal experiments to produce specific hyperimmune sera in ferrets, which are necessary for the determination of the antigen profiles of circulating virus strains. These tests also bear difficulties in standardization and reproducibility, which restricts the significance of the results. To replace this test a peptide-based assay for influenza virus subtyping from corresponding virus samples was developed. The differentiation of the viruses takes place by a set of specifically designed peptidic recognition molecules which interact differently with the different influenza virus subtypes. The differentiation of influenza subtypes is performed by pattern recognition guided by machine learning algorithms, without any animal experiments. Synthetic peptides are immobilized in multiplex format on various platforms (e.g., 96-well microtiter plate, microarray). Afterwards, the viruses are incubated and analyzed comparing different signaling mechanisms and a variety of assay conditions. Differentiation of a range of influenza subtypes, including H1N1, H3N2, H5N1, as well as fine differentiation of single strains within these subtypes is possible using the peptide-based subtyping platform. Thereby, the platform could be capable of replacing the current antigenic characterization of influenza strains using ferret hyperimmune sera.

Keywords: antigenic characterization, influenza-binding peptides, influenza subtyping, influenza surveillance

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Authors: Oluwaseun Adeleke, Samuel O. Ikani, Simeon Christian Chukwu, Fidelis Edet, Anthony Nwala, Mopelola Raji, Simeon Christian Chukwu

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Background: Health service providers are offered in-service training periodically to strengthen their ability to deliver services that are ethical, quality, timely and safe. Not all capacity-building courses have successfully resulted in intended service delivery outcomes because of poor training content, design, approach, and ambiance. The Delivering Innovations in Selfcare (DISC) project developed a Moment of Truth innovation, which is a proven training model focused on improving consumer/provider interaction that leads to an increase in the voluntary uptake of subcutaneous depot medroxyprogesterone acetate (DMPA-SC) self-injection among women who opt for injectable contraception. Methodology: Six months after training on a moment of truth (MoT) training manual, the project conducted two intensive rounds of qualitative data collection and triangulation that included provider, client, and community mobilizer interviews, facility observations, and routine program data collection. Respondents were sampled according to a convenience sampling approach, and data collected was analyzed using a codebook and Atlas-TI. Providers and clients were interviewed to understand their experience, perspective, attitude, and awareness about the DMPA-SC self-inject. Data were collected from 12 health facilities in three states – eight directly trained and four cascades trained. The research team members came together for a participatory analysis workshop to explore and interpret emergent themes. Findings: Quality-of-service delivery and performance outcomes were observed to be significantly better in facilities whose providers were trained directly trained by the DISC project than in sites that received indirect training through master trainers. Facilities that were directly trained recorded SI proportions that were twice more than in cascade-trained sites. Direct training comprised of full-day and standalone didactic and interactive sessions constructed to evoke commitment, passion and conviction as well as eliminate provider bias and misconceptions in providers by utilizing human interest stories and values clarification exercises. Sessions also created compelling arguments using evidence and national guidelines. The training also prioritized demonstration sessions, utilized job aids, particularly videos, strengthened empathetic counseling – allaying client fears and concerns about SI, trained on positioning self-inject first and side effects management. Role plays and practicum was particularly useful to enable providers to retain and internalize new knowledge. These sessions provided experiential learning and the opportunity to apply one's expertise in a supervised environment where supportive feedback is provided in real-time. Cascade Training was often a shorter and abridged form of MoT training that leveraged existing training already planned by master trainers. This training was held over a four-hour period and was less emotive, focusing more on foundational DMPA-SC knowledge such as a reorientation to DMPA-SC, comparison of DMPA-SC variants, counseling framework and skills, data reporting and commodity tracking/requisition – no facility practicums. Training on self-injection was not as robust, presumably because they were not directed at methods in the contraceptive mix that align with state/organizational sponsored objectives – in this instance, fostering LARC services. Conclusion: To achieve better performance outcomes, consideration should be given to providing training that prioritizes practice-based and emotive content. Furthermore, a firm understanding and conviction about the value training offers improve motivation and commitment to accomplish and surpass service-related performance outcomes.

Keywords: training, performance outcomes, innovation, family planning, contraception, DMPA-SC, self-care, self-injection.

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Authors: Shinji Kajiwara

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The basic ability of a vehicle is the ability to “run”, “turn” and “stop”. The safeness and comfort during a drive on various road surfaces and speed depends on the performance of these basic abilities of the vehicle. Stability and maneuverability of a vehicle is vital in automotive engineering. Stability of a vehicle is the ability of the vehicle to revert back to a stable state during a drive when faced with crosswind and irregular road conditions. Maneuverability of a vehicle is the ability of the vehicle to change direction during a drive swiftly based on the steering of the driver. The stability and maneuverability of a vehicle can also be defined as the driving stability of the vehicle. Since fossil fueled vehicle is the main type of transportation today, the environmental factor in automotive engineering is also vital. By improving the fuel efficiency of the vehicle, the overall carbon emission will be reduced thus reducing the effect of global warming and greenhouse gas on the Earth. Another main focus of the automotive engineering is the safety performance of the vehicle especially with the worrying increase of vehicle collision every day. With better safety performance on a vehicle, every driver will be more confidence driving every day. Next, let us focus on the “turn” ability of a vehicle. By improving this particular ability of the vehicle, the cornering limit of the vehicle can be improved thus increasing the stability and maneuverability factor. In order to improve the cornering limit of the vehicle, a study to find the balance between the steering systems, the stability of the vehicle, higher lateral acceleration and the cornering limit detection must be conducted. The aim of this research is to study and develop a new suspension system that that will boost the lateral acceleration of the vehicle and ultimately improving the cornering limit of the vehicle. This research will also study environmental factor and the stability factor of the new suspension system. The double wishbone suspension system is widely used in four-wheel vehicle especially for high cornering performance sports car and racing car. The double wishbone designs allow the engineer to carefully control the motion of the wheel by controlling such parameters as camber angle, caster angle, toe pattern, roll center height, scrub radius, scuff and more. The development of the new suspension system will focus on the ability of the new suspension system to optimize the camber control and to improve the camber limit during a cornering motion. The research will be carried out using the CAE analysis tool. Using this analysis tool we will develop a JSAE Formula Machine equipped with the double wishbone system and also the new suspension system and conduct simulation and conduct studies on performance of both suspension systems.

Keywords: automobile, camber thrust, cornering force, suspension

Procedia PDF Downloads 294

Authors: Kanika Gupta, Ashok Kumar

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Biofilms are dense, highly hydrated cell clusters that are irreversibly attached to a substratum, to an interface or to each other, and are embedded in a self-produced gelatinous matrix composed of extracellular polymeric substances. Research in biofilm field has become very significant, as biofilm has shown high mechanical resilience and resistance to antibiotic treatment and constituted as a significant problem in both healthcare and other industry related to microorganisms. The massive information both stated and hidden in the biofilm literature are growing exponentially therefore it is not possible for researchers and practitioners to automatically extract and relate information from different written resources. So, the current work proposes and discusses the use of text mining techniques for the extraction of information from biofilm literature corpora containing 34306 documents. It is very difficult and expensive to obtain annotated material for biomedical literature as the literature is unstructured i.e. free-text. Therefore, we considered unsupervised approach, where no annotated training is necessary and using this approach we developed a system that will classify the text on the basis of growth and development, drug effects, radiation effects, classification and physiology of biofilms. For this, a two-step structure was used where the first step is to extract keywords from the biofilm literature using a metathesaurus and standard natural language processing tools like Rapid Miner_v5.3 and the second step is to discover relations between the genes extracted from the whole set of biofilm literature using pubmed.mineR_v1.0.11. We used unsupervised approach, which is the machine learning task of inferring a function to describe hidden structure from 'unlabeled' data, in the above-extracted datasets to develop classifiers using WinPython-64 bit_v3.5.4.0Qt5 and R studio_v0.99.467 packages which will automatically classify the text by using the mentioned sets. The developed classifiers were tested on a large data set of biofilm literature which showed that the unsupervised approach proposed is promising as well as suited for a semi-automatic labeling of the extracted relations. The entire information was stored in the relational database which was hosted locally on the server. The generated biofilm vocabulary and genes relations will be significant for researchers dealing with biofilm research, making their search easy and efficient as the keywords and genes could be directly mapped with the documents used for database development.

Keywords: biofilms literature, classifiers development, text mining, unsupervised learning approach, unstructured data, relational database

Procedia PDF Downloads 143

Authors: Bruno Cossu, Elio Carlo

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The invention (patent pending) relates to the field of devices aimed to harness wave energy (WEC) especially for artificial upwelling, forced downwelling, production of compressed air. In its basic form, the pump consists of a hydro-pneumatic machine, driven by wave energy, characterised by the fact that it has no moving mechanical parts, and is made up of only two structural components: an hollow body, which is open at the bottom to the sea and partially immersed in sea water, and a tube, both joined together to form a single body. The shape of the hollow body is like a mushroom whose cap and stem are hollow; the stem is open at both ends and the lower part of its surface is crossed by holes; the tube is external and coaxial to the stem and is joined to it so as to form a single body. This shape of the hollow body and the type of connection to the tube allows the pump to operate simultaneously as an air compressor (OWC) on the cap side, and as an airlift on the stem side. The pump can be implemented in four versions, each of which provides different variants and methods of implementation: 1) firstly, for the artificial upwelling of cold, deep ocean water; 2) secondly, for the lifting and transfer of these waters to the place of use (above all, fish farming plants), even if kilometres away; 3) thirdly, for the forced downwelling of surface sea water; 4) fourthly, for the forced downwelling of surface water, its oxygenation, and the simultaneous production of compressed air. The transfer of the deep water or the downwelling of the raised surface water (as for pump versions indicated in points 2 and 3 above), is obtained by making the water raised by the airlift flow into the upper inlet of another pipe, internal or adjoined to the airlift; the downwelling of raised surface water, oxygenation, and the simultaneous production of compressed air (as for the pump version indicated in point 4), is obtained by installing a venturi tube on the upper end of the pipe, whose restricted section is connected to the external atmosphere, so that it also operates like a hydraulic air compressor (trompe). Furthermore, by combining one or more pumps for the upwelling of cold, deep water, with one or more pumps for the downwelling of the warm surface water, the system can be used in an Ocean Thermal Energy Conversion plant to supply the cold and the warm water required for the operation of the same, thus allowing to use, without increased costs, in addition to the mechanical energy of the waves, for the purposes indicated in points 1 to 4, the thermal one of the marine water treated in the process.

Keywords: air lifted upwelling, fish farming plant, hydraulic air compressor, wave energy converter

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Authors: Cherry Yieng Siang Ling, Joong Hee Lee, Myung Hwan Yun

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The quality of a product to be usable has become the basic requirement in consumer’s perspective while failing the requirement ends up the customer from not using the product. Identifying usability issues from analyzing quantitative and qualitative data collected from usability testing and evaluation activities aids in the process of product design, yet the lack of studies and researches regarding analysis methodologies in qualitative text data of usability field inhibits the potential of these data for more useful applications. While the possibility of analyzing qualitative text data found with the rapid development of data analysis studies such as natural language processing field in understanding human language in computer, and machine learning field in providing predictive model and clustering tool. Therefore, this research aims to study the application capability of text processing algorithm in analysis of qualitative text data collected from usability activities. This research utilized datasets collected from LG neckband headset usability experiment in which the datasets consist of headset survey text data, subject’s data and product physical data. In the analysis procedure, which integrated with the text-processing algorithm, the process includes training of comments onto vector space, labeling them with the subject and product physical feature data, and clustering to validate the result of comment vector clustering. The result shows 'volume and music control button' as the usability feature that matches best with the cluster of comment vectors where centroid comments of a cluster emphasized more on button positions, while centroid comments of the other cluster emphasized more on button interface issues. When volume and music control buttons are designed separately, the participant experienced less confusion, and thus, the comments mentioned only about the buttons' positions. While in the situation where the volume and music control buttons are designed as a single button, the participants experienced interface issues regarding the buttons such as operating methods of functions and confusion of functions' buttons. The relevance of the cluster centroid comments with the extracted feature explained the capability of text processing algorithms in analyzing qualitative text data from usability testing and evaluations.

Keywords: usability, qualitative data, text-processing algorithm, natural language processing

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Authors: Noureddine Henka, Sami Tazi, Mohamad Assaad

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The electrical grid is a highly intricate system designed to transfer electricity from production areas to consumption areas. The Transmission System Operator (TSO) is responsible for ensuring the efficient distribution of electricity and maintaining the grid's safety and quality. However, due to the increasing integration of intermittent renewable energy sources, there is a growing level of uncertainty, which requires a faster responsive approach. A potential solution involves the use of electrical segmentation, which involves creating coherence zones where electrical disturbances mainly remain within the zone. Indeed, by means of coherent electrical zones, it becomes possible to focus solely on the sub-zone, reducing the range of possibilities and aiding in managing uncertainty. It allows faster execution of operational processes and easier learning for supervised machine learning algorithms. Electrical segmentation can be applied to various applications, such as electrical control, minimizing electrical loss, and ensuring voltage stability. Since the electrical grid can be modeled as a graph, where the vertices represent electrical buses and the edges represent electrical lines, identifying coherent electrical zones can be seen as a clustering task on graphs, generally called community detection. Nevertheless, a critical criterion for the zones is their ability to remain resilient to the electrical evolution of the grid over time. This evolution is due to the constant changes in electricity generation and consumption, which are reflected in graph structure variations as well as line flow changes. One approach to creating a resilient segmentation is to design robust zones under various circumstances. This issue can be represented through a multiplex graph, where each layer represents a specific situation that may arise on the grid. Consequently, resilient segmentation can be achieved by conducting community detection on this multiplex graph. The multiplex graph is composed of multiple graphs, and all the layers share the same set of vertices. Our proposal involves a model that utilizes a unified representation to compute a flattening of all layers. This unified situation can be penalized to obtain (K) connected components representing the robust electrical segmentation clusters. We compare our robust segmentation to the segmentation based on a single reference situation. The robust segmentation proves its relevance by producing clusters with high intra-electrical perturbation and low variance of electrical perturbation. We saw through the experiences when robust electrical segmentation has a benefit and in which context.

Keywords: community detection, electrical segmentation, multiplex graph, power grid

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Authors: Arjun Prakash, Vinutha H., Karthik N.

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BACKGROUND: Carpal tunnel syndrome (CTS) is a common entrapment neuropathy with an estimated prevalence of 0.6 - 5.8% in the general adult population. It is caused by compression of the Median Nerve (MN) at the wrist as it passes through a narrow osteofibrous canal. Presently, the diagnosis is established by the clinical symptoms and physical examination and Nerve conduction study (NCS) is used to assess its severity. However, it is considered to be painful, time consuming and expensive, with a false-negative rate between 16 - 34%. Ultrasonography (USG) is now increasingly used as a diagnostic tool in CTS due to its non-invasive nature, increased accessibility and relatively low cost. Elastography is a newer modality in USG which helps to assess stiffness of tissues. However, there is limited available literature about its applications in peripheral nerves. OBJECTIVES: Our objectives were to measure the Cross-Sectional Area (CSA) and elasticity of MN at the carpal tunnel using Grey scale Ultrasonography (USG), Strain Elastography (SE) and Shear Wave Elastography (SWE). We also made an attempt to independently evaluate the role of Gray scale USG, SE and SWE in grading the severity of CTS, keeping NCS as the gold standard. MATERIALS AND METHODS: After approval from the Institutional Ethics Review Board, we conducted a comparative cross sectional study for a period of 18 months. The participants were divided into two groups. Group A consisted of 54 patients with clinically diagnosed CTS who underwent NCS, and Group B consisted of 50 controls without any clinical symptoms of CTS. All Ultrasound examinations were performed on SAMSUNG RS 80 EVO Ultrasound machine with 2 - 9 Mega Hertz linear probe. In both groups, CSA of the MN was measured on Grey scale USG, and its elasticity was measured at the carpal tunnel (in terms of Strain ratio and Shear Modulus). The variables were compared between both groups by using ‘Independent t test’, and subgroup analyses were performed using one-way analysis of variance. Receiver operating characteristic curves were used to evaluate the diagnostic performance of each variable. RESULTS: The mean CSA of the MN was 13.60 + 3.201 mm2 and 9.17 + 1.665 mm2 in Group A and Group B, respectively (p < 0.001). The mean SWE was 30.65 + 12.996 kPa and 17.33 + 2.919 kPa in Group A and Group B, respectively (p < 0.001), and the mean Strain ratio was 7.545 + 2.017 and 5.802 + 1.153 in Group A and Group B respectively (p < 0.001). CONCLUSION: The combined use of Gray scale USG, SE and SWE is extremely useful in grading the severity of CTS and can be used as a painless and cost-effective alternative to NCS. Early diagnosis and grading of CTS and effective treatment is essential to avoid permanent nerve damage and functional disability.

Keywords: carpal tunnel, ultrasound, elastography, nerve conduction study

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Authors: Yonwaba Atyosi, Moses Cho, Abel Ramoelo, Nobuhle Majozi, Cecilia Masemola, Yoliswa Mkhize

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Spectroscopy has been widely used to understand the hyperspectral remote sensing of soils. Accurate and efficient measurement of soil moisture is essential for precision agriculture. The aim of this study was to understand the spectral behavior of soil at different soil water content levels and identify the significant spectral bands for soil moisture content retrieval at field-scale. The study consisted of 60 soil samples from a maize farm, divided into four different treatments representing different moisture levels. Spectral signatures were measured for each sample in laboratory under artificial light using an Analytical Spectral Device (ASD) spectrometer, covering a wavelength range from 350 nm to 2500 nm, with a spectral resolution of 1 nm. The results showed that the absorption features at 1450 nm, 1900 nm, and 2200 nm were particularly sensitive to soil moisture content and exhibited strong correlations with the water content levels. Continuum removal was developed in the R programming language to enhance the absorption features of soil moisture and to precisely understand its spectral behavior at different water content levels. Statistical analysis using partial least squares regression (PLSR) models were performed to quantify the correlation between the spectral bands and soil moisture content. This study provides insights into the spectral behavior of soil at different water content levels and identifies the significant spectral bands for soil moisture content retrieval. The findings highlight the potential of spectroscopy for non-destructive and rapid soil moisture measurement, which can be applied to various fields such as precision agriculture, hydrology, and environmental monitoring. However, it is important to note that the spectral behavior of soil can be influenced by various factors such as soil type, texture, and organic matter content, and caution should be taken when applying the results to other soil systems. The results of this study showed a good agreement between measured and predicted values of Soil Moisture Content with high R2 and low root mean square error (RMSE) values. Model validation using independent data was satisfactory for all the studied soil samples. The results has significant implications for developing high-resolution and precise field-scale soil moisture retrieval models. These models can be used to understand the spatial and temporal variation of soil moisture content in agricultural fields, which is essential for managing irrigation and optimizing crop yield.

Keywords: soil moisture content retrieval, precision agriculture, continuum removal, remote sensing, machine learning, spectroscopy

Procedia PDF Downloads 61

Authors: Taylan Kabbani, Ekrem Duman

Abstract:

The design of adaptive systems that take advantage of financial markets while reducing the risk can bring more stagnant wealth into the global market. However, most efforts made to generate successful deals in trading financial assets rely on Supervised Learning (SL), which suffered from various limitations. Deep Reinforcement Learning (DRL) offers to solve these drawbacks of SL approaches by combining the financial assets price "prediction" step and the "allocation" step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with its environment to make optimal decisions through trial and error. In this paper, a continuous action space approach is adopted to give the trading agent the ability to gradually adjust the portfolio's positions with each time step (dynamically re-allocate investments), resulting in better agent-environment interaction and faster convergence of the learning process. In addition, the approach supports the managing of a portfolio with several assets instead of a single one. This work represents a novel DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem, or what is referred to as The Agent Environment as Partially observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. More specifically, we design an environment that simulates the real-world trading process by augmenting the state representation with ten different technical indicators and sentiment analysis of news articles for each stock. We then solve the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which can learn policies in high-dimensional and continuous action spaces like those typically found in the stock market environment. From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of deep reinforcement learning in financial markets over other types of machine learning such as supervised learning and proves its credibility and advantages of strategic decision-making.

Keywords: the stock market, deep reinforcement learning, MDP, twin delayed deep deterministic policy gradient, sentiment analysis, technical indicators, autonomous agent

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