Search results for: virtual machine

Authors: Ali Moharrek, Hosein Mobli, Ali Jafari, Ahmad Tabataee Far

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Biomass is a material generally produced by plants living on soil or water and their derivatives. The remains of agricultural and forest products contain biomass which is changeable into fuel. Besides, you can obtain biogas and ethanol from the charcoal produced from biomass through specific actions. this technology was designed for as a useful Native Fuel and Technology in Energy disasters Management Due to the sudden interruption of the flow of heat energy One of the problems confronted by mankind in the future is the limitations of fossil energy which necessitates production of new energies such as biomass. In order to produce biomass from the remains of the plants, different methods shall be applied considering factors like cost of production, production technology, area of requirement, speed of work easy utilization, ect. In this article we are focusing on designing a biomass briquetting portable machine. The speed of installation of the machine on a tractor is estimated as 80 MF 258. Screw press is used in designing this machine. The needed power for running this machine which is estimated as 17.4 kW is provided by the power axis of tractor. The pressing speed of the machine is considered to be 375 RPM Finally the physical and mechanical properties of the product were compared with utilized material which resulted in appropriate outcomes. This machine is designed for Gathering Raw materials of the ground by Head Section. During delivering the raw materials to Briquetting section, they Crushed, Milled & Pre Heated in Transmission section. This machine is a Combine Portable Tractor unit machine and can use all type of Agriculture, Forest & Livestock Animals Resides as Raw material to make Bio fuel. The Briquetting Section was manufactured and it successfully made bio fuel of Sawdust. Also this machine made a biofuel with Ethanol of sugarcane Wastes. This Machine is using P.T.O power source for Briquetting and Hydraulic Power Source for Pre Processing of Row Materials.

Keywords: biomass, briquette, screw press, sawdust, animal wastes, portable, tractors

Procedia PDF Downloads 297

Authors: Benjamin Bressolette, S´ebastien Denjean, Vincent Roussarie, Mitsuko Aramaki, Sølvi Ystad, Richard Kronland-Martinet

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New sensors and technologies – such as microphones, touchscreens or infrared sensors – are currently making their appearance in the automotive sector, introducing new kinds of Human-Machine Interfaces (HMIs). The interactions with such tools might be cognitively expensive, thus unsuitable for driving tasks. It could for instance be dangerous to use touchscreens with a visual feedback while driving, as it distracts the driver’s visual attention away from the road. Furthermore, new technologies in car cockpits modify the interactions of the users with the central system. In particular, touchscreens are preferred to arrays of buttons for space improvement and design purposes. However, the buttons’ tactile feedback is no more available to the driver, which makes such interfaces more difficult to manipulate while driving. Gestures combined with an auditory feedback might therefore constitute an interesting alternative to interact with the HMI. Indeed, gestures can be performed without vision, which means that the driver’s visual attention can be totally dedicated to the driving task. In fact, the auditory feedback can both inform the driver with respect to the task performed on the interface and on the performed gesture, which might constitute a possible solution to the lack of tactile information. As audition is a relatively unused sense in automotive contexts, gesture sonification can contribute to reducing the cognitive load thanks to the proposed multisensory exploitation. Our approach consists in using a virtual object (VO) to sonify the consequences of the gesture rather than the gesture itself. This approach is motivated by an ecological point of view: Gestures do not make sound, but their consequences do. In this experiment, the aim was to identify efficient sound strategies, to transmit dynamic information of VOs to users through sound. The swipe gesture was chosen for this purpose, as it is commonly used in current and new interfaces. We chose two VO parameters to sonify, the hand-VO distance and the VO velocity. Two kinds of sound parameters can be chosen to sonify the VO behavior: Spectral or temporal parameters. Pitch and brightness were tested as spectral parameters, and amplitude modulation as a temporal parameter. Performances showed a positive effect of sound compared to a no-sound situation, revealing the usefulness of sounds to accomplish the task.

Keywords: auditory feedback, gesture sonification, sound perception, virtual object

Procedia PDF Downloads 280

Authors: Pierre F. Gerard, Frederic F. Leymarie, William Latham

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Architects have developed a series of objective evaluations, using spatial analysis tools such as Isovist, that show how certain spatial qualities are beneficial to specific human activities hosted in the built environments. In return, they can build more adapted environments by tuning those spatial qualities in their design. In parallel, virtual reality technologies have been developed by engineers with the dream of creating a system that immerses users in a new form of spatial experiences. They already have demonstrated a useful range of benefits not only in simulating critical events to assist people in acquiring new skills, but also to enhance memory retention, to name just a few. This paper investigates the effects of two spatial qualities, openness, and complexity, on cognitive performance within immersive virtual environments. Isovist measure is used to design a series of room settings with different levels of each spatial qualities. In an empirical study, each room was then used by every participant to solve a navigational puzzle game and give a rating of their spatial experience. They were then asked to fill in a questionnaire before solving the visual-spatial memory quiz, which addressed how well they remembered the different rooms. Findings suggest that those spatial qualities have an effect on some of the measures, including navigation performance and memory retention. In particular, there is an order effect for the navigation puzzle game. Participants tended to spend a longer time in the complex room settings. Moreover, there is an interaction effect while with more open settings, participants tended to perform better when in a simple setting; however, with more closed settings, participants tended to perform better in a more complex setting. For the visual-spatial memory quiz, participants performed significantly better within the more open rooms. We believe this is a first step in using virtual environments to enhance participant cognitive performances through better use of specific spatial qualities.

Keywords: architecture, navigation, spatial cognition, virtual reality

Procedia PDF Downloads 114

Authors: Sam Khozama, Ali M. Mayya

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Breast cancer is one of the most common types in women. Early prediction of breast cancer helps physicians detect cancer in its early stages. Big cancer data needs a very powerful tool to analyze and extract predictions. Machine learning and deep learning are two of the most efficient tools for predicting cancer based on textual data. In this study, we developed a fusion model of two machine learning and deep learning models. To obtain the final prediction, Long-Short Term Memory (LSTM) and ensemble learning with hyper parameters optimization are used, and score-level fusion is used. Experiments are done on the Breast Cancer Surveillance Consortium (BCSC) dataset after balancing and grouping the class categories. Five different training scenarios are used, and the tests show that the designed fusion model improved the performance by 3.3% compared to the individual models.

Keywords: machine learning, deep learning, cancer prediction, breast cancer, LSTM, fusion

Procedia PDF Downloads 139

Authors: Jin Uk Han, Hye Won Han, Hyo Lim Kang, Tae An Kim, Seung Ho Han

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Direct-drive generators for large-scale wind turbine, which are divided into AFPM(Axial Flux Permanent Magnet) and RFPM(Radial Flux Permanent Magnet) type machine, have attracted interest because of a higher energy density in comparison with gear train type generators. Each type of the machines provides distinguishable geometrical features such as narrow width with a large diameter for the AFPM-type machine and wide width with a certain diameter for the RFPM-type machine. When the AFPM-type machine is applied, an increase of electric power production through a multi-stage arrangement in axial direction is easily achieved. On the other hand, the RFPM-type machine can be applied by using its geometric feature of wide width. In this study, a hybrid two-stage direct-drive generator for 6.2MW class wind turbine was proposed, in which the two-stage AFPM-type machine for 5 MW was composed of two models arranged in axial direction with a hollow shape topology of the rotor with annular disc, the stator and the main shaft mounted on coupled slew bearings. In addition, the RFPM-type machine for 1.2MW was installed at the empty space of the rotor. Analytic results obtained from an electro-magnetic and structural interaction analysis showed that the structural weight of the proposed hybrid two-stage direct-drive generator can be achieved as 155tonf in a condition satisfying the requirements of structural behaviors such as allowable air-gap clearance and strength. Therefore, it was sure that the 6.2MW hybrid two-stage direct-drive generator is competitive than conventional generators. (NRF grant funded by the Korea government MEST, No. 2017R1A2B4005405).

Keywords: AFPM-type machine, direct-drive generator, electro-magnetic analysis, large-scale wind turbine, RFPM-type machine

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Authors: Joel Weymouth, Ramesh K. Karne, Alexander L. Wijesinha

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Bare Machine Internet is part of the Bare Machine Computing (BMC) paradigm. It is used in programming application ns to run directly on a device. It is software that runs directly against the hardware using CPU, Memory, and I/O. The software application runs without an Operating System and resident mass storage. An important part of the BMC paradigm is the Bare Machine Internet. It utilizes an Application Development model software that interfaces directly with the hardware on a network server and file server. Because it is “bare,” it is a powerful teaching and research tool that can readily display the internals of the network protocols, software, and hardware of the applications running on the Bare Server. It was also demonstrated that the bare server was accessible by laptop and by smartphone/android. The purpose was to show the further practicality of Bare Internet in Computer Engineering and Computer Science Education and Research. It was also to show that an undergraduate student could take advantage of a bare server with any device and any browser at any release version connected to the internet. This paper presents the Bare Web Server as an educational tool. We will discuss possible applications of this paradigm.

Keywords: bare machine computing, online research, network technology, visualizing network internals

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Authors: Pei-Ru Yang, Yu-Chen Lin, Jia-Min Wu

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Background: A 58-year-old man with a history of osteoporosis and diabetes presented with chronic pain in his left knee due to severe knee joint degeneration. The knee replacement surgery was recommended by the doctor. But the patient suffered from low pain tolerance and wondered if virtual reality could relieve acute postoperative wound pain. Methods: We used the PICO (patient, intervention, comparison, and outcome) approach to generate indexed keywords and searched systematic review articles from 2017 to 2021 on the Cochran Library, PubMed, and Clinical Key databases. Results: The initial literature results included 38 articles, including 12 Cochrane library articles and 26 PubMed articles. One article was selected for further analysis after removing duplicates and off-topic articles. The eight trials included in this article were published between 2013 and 2019 and recruited a total of 723 participants. The studies, conducted in India, Lebanon, Iran, South Korea, Spain, and China, included adults who underwent hemorrhoidectomy, dental surgery, craniotomy or spine surgery, episiotomy repair, and knee surgery, with a mean age (24.1 ± 4.1 to 73.3 ± 6.5). Virtual reality is an emerging non-drug postoperative analgesia method. The findings showed that pain control was reduced by a mean of 1.48 points (95% CI: -2.02 to -0.95, p-value < 0.0001) in minor surgery and 0.32 points in major surgery (95% CI: -0.53 to -0.11, p-value < 0.03), and the overall postoperative satisfaction has improved. Discussion: Postoperative pain is a common clinical problem in surgical patients. Research has confirmed that virtual reality can create an immersive interactive environment, communicate with patients, and effectively relieve postoperative pain. However, virtual reality requires the purchase of hardware and software and other related computer equipment, and its high cost is a disadvantage. We selected the best literature based on clinical questions to answer the patient's question and used share decision making (SDM) to help the patient make decisions based on the clinical situation after knee replacement surgery to improve the quality of patient-centered care.

Keywords: knee replacement surgery, postoperative pain, share decision making, virtual reality

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Authors: Muhammad Fanseem E. M., Rama Satya Satish Kumar, Indrajeet Bhausaheb Bhavar, Deepak M.

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Keeping the right balance of electricity between the supply and demand sides of the grid is one of the most important objectives of electrical grid operation. Power generation and demand forecasting are the core of power management and generation scheduling. Large, centralized producing units were used in the construction of conventional power systems in the past. A certain level of balance was possible since the generation kept up with the power demand. However, integrating renewable energy sources into power networks has proven to be a difficult challenge due to its intermittent nature. The power imbalance caused by rising demands and peak loads is negatively affecting power quality and dependability. Demand side management and demand response were one of the solutions, keeping generation the same but altering or rescheduling or shedding completely the load or demand. However, shedding the load or rescheduling is not an efficient way. There comes the significance of virtual power plants. The virtual power plant integrates distributed generation, dispatchable load, and distributed energy storage organically by using complementing control approaches and communication technologies. This would eventually increase the utilization rate and financial advantages of distributed energy resources. Most of the writing on virtual power plant models ignored technical limitations, and modeling was done in favor of a financial or commercial viewpoint. Therefore, this paper aims to address the modeling intricacies of VPPs and their technical limitations, shedding light on a holistic understanding of this innovative power management approach.

Keywords: cost optimization, distributed energy resources, dynamic modeling, model quality tests, power system modeling

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Authors: Yan Yan, Erhong Sun, Lin Peng, Xuchun Ye

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Aims: The present study aimed to explore Intensive Care Unit (ICU) nurses’ mental workload (MWL) and associated factors with it in performing human-machine tasks. Background: A wide range of emerging technologies have penetrated widely in the field of health care, and ICU nurses are facing a dramatic increase in nursing human-machine tasks. However, there is still a paucity of literature reporting on the general MWL of ICU nurses performing human-machine tasks and the associated influencing factors. Methods: A cross-sectional survey was employed. The data was collected from January to February 2021 from 9 tertiary hospitals in 6 provinces (Shanghai, Gansu, Guangdong, Liaoning, Shandong, and Hubei). Two-stage sampling was used to recruit eligible ICU nurses (n=427). The data were collected with an electronic questionnaire comprising sociodemographic characteristics and the measures of MWL, self-efficacy, system usability, and task difficulty. The univariate analysis, two-way analysis of variance (ANOVA), and a linear mixed model were used for data analysis. Results: Overall, the mental workload of ICU nurses in performing human-machine tasks was medium (score 52.04 on a 0-100 scale). Among the typical nursing human-machine tasks selected, the MWL of ICU nurses in completing first aid and life support tasks (‘Using a defibrillator to defibrillate’ and ‘Use of ventilator’) was significantly higher than others (p < .001). And ICU nurses’ MWL in performing human-machine tasks was also associated with age (p = .001), professional title (p = .002), years of working in ICU (p < .001), willingness to study emerging technology actively (p = .006), task difficulty (p < .001), and system usability (p < .001). Conclusion: The MWL of ICU nurses is at a moderate level in the context of a rapid increase in nursing human-machine tasks. However, there are significant differences in MWL when performing different types of human-machine tasks, and MWL can be influenced by a combination of factors. Nursing managers need to develop intervention strategies in multiple ways. Implications for practice: Multidimensional approaches are required to perform human-machine tasks better, including enhancing nurses' willingness to learn emerging technologies actively, developing training strategies that vary with tasks, and identifying obstacles in the process of human-machine system interaction.

Keywords: mental workload, nurse, ICU, human-machine, tasks, cross-sectional study, linear mixed model, China

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Authors: Nima Mahmoudi, Hamzeh Khazaei

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Serving machine learning inference workloads on the cloud is still a challenging task at the production level. The optimal configuration of the inference workload to meet SLA requirements while optimizing the infrastructure costs is highly complicated due to the complex interaction between batch configuration, resource configurations, and variable arrival process. Serverless computing has emerged in recent years to automate most infrastructure management tasks. Workload batching has revealed the potential to improve the response time and cost-effectiveness of machine learning serving workloads. However, it has not yet been supported out of the box by serverless computing platforms. Our experiments have shown that for various machine learning workloads, batching can hugely improve the system’s efficiency by reducing the processing overhead per request. In this work, we present MLProxy, an adaptive reverse proxy to support efficient machine learning serving workloads on serverless computing systems. MLProxy supports adaptive batching to ensure SLA compliance while optimizing serverless costs. We performed rigorous experiments on Knative to demonstrate the effectiveness of MLProxy. We showed that MLProxy could reduce the cost of serverless deployment by up to 92% while reducing SLA violations by up to 99% that can be generalized across state-of-the-art model serving frameworks.

Keywords: serverless computing, machine learning, inference serving, Knative, google cloud run, optimization

Procedia PDF Downloads 154

Authors: S. Apprich, F. Wulle, A. Lechler, A. Pott, A. Verl

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Large-scale machine tools for the manufacturing of large work pieces, e.g. blades, casings or gears for wind turbines, feature pose-dependent dynamic behavior. Small structural damping coefficients lead to long decay times for structural vibrations that have negative impacts on the production process. Typically, these vibrations are handled by increasing the stiffness of the structure by adding mass. That is counterproductive to the needs of sustainable manufacturing as it leads to higher resource consumption both in material and in energy. Recent research activities have led to higher resource efficiency by radical mass reduction that rely on control-integrated active vibration avoidance and damping methods. These control methods depend on information describing the dynamic behavior of the controlled machine tools in order to tune the avoidance or reduction method parameters according to the current state of the machine. The paper presents the appearance, consequences and challenges of the pose-dependent dynamic behavior of lightweight large-scale machine tool structures in production. The paper starts with the theoretical introduction of the challenges of lightweight machine tool structures resulting from reduced stiffness. The statement of the pose-dependent dynamic behavior is corroborated by the results of the experimental modal analysis of a lightweight test structure. Afterwards, the consequences of the pose-dependent dynamic behavior of lightweight machine tool structures for the use of active control and vibration reduction methods are explained. Based on the state of the art on pose-dependent dynamic machine tool models and the modal investigation of an FE-model of the lightweight test structure, the criteria for a pose-dependent model for use in vibration reduction are derived. The description of the approach for a general pose-dependent model of the dynamic behavior of large lightweight machine tools that provides the necessary input to the aforementioned vibration avoidance and reduction methods to properly tackle machine vibrations is the outlook of the paper.

Keywords: dynamic behavior, lightweight, machine tool, pose-dependency

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Authors: Andi Intan Purnamasari, Supriyadi, Sulbadana, Aminuddin Kasim

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Law functions as social control. Providing arrangements not only for legal certainty, but also in the scope of justice and expediency. The three values ​​achieved by law essentially function to bring comfort to each individual in carrying out daily activities. However, it is undeniable that global conditions have changed the orientation of people's lifestyles. Some people want to ensure their existence in the digital world which is popularly known as the metaverse. Some countries even project their city to be a metaverse city. The order of life is no longer limited to the real space, but also to the cyber world. Not infrequently, legal events that occur in the cyber world also force the law to position its position and even prevent crime in cyberspace. Through this research, conceptually it provides a view of the legal position in crime prevention in the Metaverse world. when the law acts to regulate the situation in the virtual world, of course some people will feel disturbed, this is due to the thought that the virtual world is a world in which an avatar can do things that cannot be done in the real world, or can be called a world without boundaries. Therefore, when the law is present to provide boundaries, of course the concept of the virtual world itself becomes no longer a cyber world that is not limited by space and time, it becomes a new order of life. approach, approach, approach, approach, and approach will certainly be the method used in this research.

Keywords: crime, cyber, metaverse, law

Procedia PDF Downloads 127

Authors: Hamidreza Akbari

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In this paper, for detection of inclined eccentricity in an induction motor, time–frequency analysis of the stator startup current is carried out. For this purpose, the discrete wavelet transform is used. Data are obtained from simulations, using winding function approach. The results show the validity of the approach for detecting the fault and discriminating with respect to other faults.

Keywords: induction machine, fault, DWT, electric

Procedia PDF Downloads 331

Authors: Hernández Yasmín, Ochoa Alberto, Hurtado Diego

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The aim of this study is developing an intelligent tutoring system for electrical operators training with virtual reality systems at the laboratory center of partials discharges LAPEM. The electrical domain requires efficient and well trained personnel, due to the danger involved in the partials discharges field, qualified electricians are required. This paper presents an overview of the intelligent tutor adaptive learning design and user interface with VR. We propose the develop of constructing a model domain of a subset of partial discharges enables adaptive training through a trainee model which represents the affective and knowledge states of trainees. According to the success of the intelligent tutor system with VR, it is also hypothesized that the trainees will able to learn the electrical domain installations of partial discharges and gain knowledge more efficient and well trained than trainees using traditional methods of teaching without running any risk of being in danger, traditional methods makes training lengthily, costly and dangerously.

Keywords: intelligent tutoring system, artificial intelligence, virtual reality, partials discharges, adaptive learning

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Authors: Sannikumar Patel, Brian Nolan, Markus Hofmann, Philip Owende, Kunjan Patel

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Sentiment analysis and opinion mining have become emerging topics of research in recent years but most of the work is focused on data in the English language. A comprehensive research and analysis are essential which considers multiple languages, machine translation techniques, and different classifiers. This paper presents, a comparative analysis of different approaches for multilingual sentiment analysis. These approaches are divided into two parts: one using classification of text without language translation and second using the translation of testing data to a target language, such as English, before classification. The presented research and results are useful for understanding whether machine translation should be used for multilingual sentiment analysis or building language specific sentiment classification systems is a better approach. The effects of language translation techniques, features, and accuracy of various classifiers for multilingual sentiment analysis is also discussed in this study.

Keywords: cross-language analysis, machine learning, machine translation, sentiment analysis

Procedia PDF Downloads 692

Authors: Jeelka Ray

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Survey of the papers revealed that there is no practical design available for packaging machine based on Embedded system, so the need arose for the development of the prototype model. In this paper, author has worked on the development of an ARM7 based Embedded Controller for controlling the sequence of packaging machine. The unit is made user friendly with TFT and Touch Screen implementing human machine interface (HMI). The different system components are briefly discussed, followed by a description of the overall design. The major functions which involve bag forming, sealing temperature control, fault detection, alarm, animated view on the home screen when the machine is working as per different parameters set makes the machine performance more successful. LPC2478 ARM 7 Embedded Microcontroller controls the coordination of individual control function modules. In back gone days, these machines were manufactured with mechanical fittings. Later on, the electronic system replaced them. With the help of ongoing technologies, these mechanical systems were controlled electronically using Microprocessors. These became the backbone of the system which became a cause for the updating technologies in which the control was handed over to the Microcontrollers with Servo drives for accurate positioning of the material. This helped to maintain the quality of the products. Including all, RS 485 MODBUS Communication technology is used for synchronizing AC Drive & Servo Drive. These all concepts are operated either manually or through a Graphical User Interface. Automatic tuning of heaters, sealers and their temperature is controlled using Proportional, Integral and Derivation loops. In the upcoming latest technological world, the practical implementation of the above mentioned concepts is really important to be in the user friendly environment. Real time model is implemented and tested on the actual machine and received fruitful results.

Keywords: packaging machine, embedded system, ARM 7, micro controller, HMI, TFT, touch screen, PID

Procedia PDF Downloads 257

Authors: Muhtasim Shafi Kader, Fizar Ahmed, Annesha Acharjee

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Machine learning and data mining are crucial in health care, as well as medical information and detection. Machine learning approaches are now being utilized to improve awareness of a variety of critical health issues, including diabetes detection, neuron cell tumor diagnosis, COVID 19 identification, and so on. Parkinson’s disease is basically a disease for our senior citizens in Bangladesh. Parkinson's Disease indications often seem progressive and get worst with time. People got affected trouble walking and communicating with the condition advances. Patients can also have psychological and social vagaries, nap problems, hopelessness, reminiscence loss, and weariness. Parkinson's disease can happen in both men and women. Though men are affected by the illness at a proportion that is around partial of them are women. In this research, we have to get out the accurate ML algorithm to find out the disease with a predictable dataset and the model of the following machine learning classifiers. Therefore, nine ML classifiers are secondhand to portion study to use machine learning approaches like as follows, Naive Bayes, Adaptive Boosting, Bagging Classifier, Decision Tree Classifier, Random Forest classifier, XBG Classifier, K Nearest Neighbor Classifier, Support Vector Machine Classifier, and Gradient Boosting Classifier are used.

Keywords: naive bayes, adaptive boosting, bagging classifier, decision tree classifier, random forest classifier, XBG classifier, k nearest neighbor classifier, support vector classifier, gradient boosting classifier

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Authors: O. Punjarat, S. Chucheepsakul, T. Phanyasahachart

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This paper focuses on a variational formulation of large amplitude free vibration behavior of a very sag marine cable. In the static equilibrium state, the marine cable has a very large sag configuration. In the motion state, the marine cable is assumed to vibrate in in-plane motion with large amplitude from the static equilibrium position. The total virtual work-energy of the marine cable at the dynamic state is formulated which involves the virtual strain energy due to axial deformation, the virtual work done by effective weight, and the inertia forces. The equations of motion for the large amplitude free vibration of marine cable are obtained by taking into account the difference between the Euler’s equation in the static state and the displaced state. Based on the Galerkin finite element procedure, the linear and nonlinear stiffness matrices, and mass matrices of the marine cable are obtained and the eigenvalue problem is solved. The natural frequency spectrum and the large amplitude free vibration behavior of marine cable are presented.

Keywords: axial deformation, free vibration, Galerkin finite element method, large amplitude, variational method

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Authors: Katie Dolaghan, Christina Tran, Kim Hamilton, Amanda Beresford, Vicky Adams, Jamie Toole, John Marley

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During the Covid 19 pandemic routine outpatient appointments were not possible face to face. That resulted in many branches of healthcare starting virtual clinics. These clinics have continued following the return to face to face patient appointments. With these new types of clinic it is important to ensure that a high standard of patient care is maintained. In order to improve patient and clinician experience of the telephone clinics a quality improvement project was carried out to ensure the patient and clinician experience of these clinics was enhanced whilst remaining a safe, effective and an efficient use of resources. The project began by developing a process map for the consultation process and agreed on the design of a driver diagram and tests of change. In plan do study act (PDSA) cycle1 a single consultant completed an online survey after every patient encounter over a 5 week period. Baseline patient responses were collected using a follow-up telephone survey for each patient. Piloting led to several iterations of both survey designs. Salient results of PDSA1 included; patients not receiving appointment letters, patients feeling more anxious about a virtual appointment and many would prefer a face to face appointment. The initial clinician data showed a positive response with a provisional diagnosis being reached in 96.4% of encounters. PDSA cycle 2 included provision of a patient information sheet and information leaflets relevant to the patients’ conditions were developed and sent following new patient telephone clinics with follow-up survey analysis as before to monitor for signals of change. We also introduced the ability for patients to send an images of their lesion prior to the consultation. Following the changes implemented we noted an improvement in patient satisfaction and, in fact, many patients preferring virtual clinics as it lead to less disruption of their working lives. The extra reading material both before and after the appointments eased patients’ anxiety around virtual clinics and helped them to prepare for their appointment. Following the patient feedback virtual clinics are now used for review patients as well, with all four consultants within the department continuing to utilise virtual clinics. During this presentation the progression of these clinics and the reasons that these clinics are still operating following the return to face to face appointments will be explored. The lessons that have been gained using a QI approach have helped to deliver an optimal service that is valid and reliable as well as being safe, effective and efficient for the patient along with helping reduce the pressures from ever increasing waiting lists. In summary our work in improving the quality of virtual clinics has resulted in improved patient satisfaction along with reduced pressures on the facilities of the health trust.

Keywords: clinic, satisfaction, telephone, virtual

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Authors: Michael McTighe

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Background: The COVID-19 pandemic caused the cancellation of many large-scale in-person sporting events, which led to an increase in the availability of virtual ultra-marathons. This study intended to assess how participation in virtual long distances races relates to levels of physical activity for an extended period of time. Moreover, traditional ultra-marathons are known for being not only physically demanding, but also mentally and emotionally challenging. A second component of this study was to assess how psychological contructs related to emotion regulation and mental toughness predict overall performance in the sport. Method: 83 virtual runners participating in a four-month 1000-kilometer race with the option to exceed 1000 kilometers completed a questionnaire exploring demographics, their performance, and experience in the virtual race. Participants also completed the Difficulties in Emotions Regulation Scale (DERS) and the Sports Mental Toughness Questionnaire (SMTQ). Logistics regressions assessed these constructs’ utility in predicting completion of the 1000-kilometer distance in the time allotted. Multiple regression was employed to predict the total distance traversed during the fourmonth race beyond 1000-kilometers. Result: Neither mental toughness nor emotional regulation was a significant predictor of completing the virtual race’s basic 1000-kilometer finish. However, both variables included together were marginally significant predictors of total miles traversed over the entire event beyond 1000 K (p = .051). Additionally, participation in the event promoted an increase in healthy activity with participants running and walking significantly more in the four months during the event than the four months leading up to it. Discussion: This research intended to explore how psychological constructs relate to performance in a virtual type of endurance event, and how involvement in these types of events related to levels of activity. Higher levels of mental toughness and lower levels in difficulties in emotion regulation were associated with greater performance, and participation in the event promoted an increase in athletic involvement. Future psychological skill training aimed at improving emotion regulation and mental toughness may be used to enhance athletic performance in these sports, and future investigations into these events could explore how general participation may influence these constructs over time. Finally, these results suggest that participation in this logistically accessible, and affordable type of sport can promote greater involvement in healthy activities related to running and walking.

Keywords: virtual races, emotion regulation, mental toughness, ultra-marathon, predictors in performance

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Authors: Tugba Saricaoglu

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This study argues about the potential representation and interpretation of Basilica of Smyrna through gamification. Representation can be defined as a key which plays a role as a converter in order to provide interpretation of something according to the person who perceives. Representation of cultural heritage is a hypothetical and factual approach in terms of its sustainable conservation. Today, both site interpreters and public of cultural heritage have varying perspectives due to their different demographic, social, and even cultural backgrounds. Additionally, gamification application offers diversion of methods suchlike video games to improve user perspective of non-game platforms, contexts, and issues. Hence, cultural heritage and video game decided to be analyzed. Moreover, there are basically different ways of representation of cultural heritage such as digital, physical, and virtual methods in terms of conservation. Virtual reality (VR) and augmented reality (AR) technologies are two of the contemporary digital methods of heritage conservation. In this study, 3D documented ruins of the Basilica will be presented in the virtual and augmented reality based technology as a theoretical gamification sample. Also, this paper will focus on two sub-topics: First, evaluation of the video-game platforms applied to cultural heritage sites, and second, potentials of cultural heritage to be represented in video game platforms. The former will cover the analysis of some case(s) with regard to the concepts and representational aspects of cultural heritage. The latter will include the investigation of cultural heritage sites which carry such a potential and their sustainable conversation. Consequently, after mutual collection of information from cultural heritage and video game platforms, a perspective will be provided in terms of interpretation of representation of cultural heritage by sampling that on Basilica of Smyrna by using VR and AR based technologies.

Keywords: Basilica of Smyrna, cultural heritage, digital heritage, gamification

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Authors: Tolulope A. Olukunle

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Plastic waste management has emerged as one of the greatest challenges facing developing countries. This paper describes the design of various components of a plastic shredder. This machine is widely used in industries and recycling plants. The introduction of plastic shredder machine will promote reduction of post-consumer plastic waste accumulation and serves as a system for wealth creation and empowerment through conversion of waste into economically viable products. In this design research, a 10 kW electric motor with a rotational speed of 500 rpm was chosen to drive the shredder. A pulley size of 400 mm is mounted on the electric motor at a distance of 1000 mm away from the shredder pulley. The shredder rotational speed is 300 rpm.

Keywords: design, machine, plastic waste, recycling

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Authors: Saleh Elawgali

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Current spectrums of a four pole-pair, 400 kW induction machine were calculated for the cases of full symmetry and static eccentricity. The calculations involve integration of 93 electrical plus four mechanical ordinary differential equations. Electrical equations account for variable inductances affected by slotting and eccentricities. The calculations were followed by Fourier analysis of the stator currents in steady state operation. Zooms of the current spectrums, around the 50 Hz fundamental harmonic as well as of the main slot harmonic zone, were included. The spectrums included refer to both calculated and measured currents.

Keywords: diagnostic, harmonic, induction machine, spectrum

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Authors: G. Götz, P. Stich, J. Backhaus, G. Reinhart

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The rising demand for format-flexible packaging machines is caused by current market changes. Increasing the formatflexibility is a new goal for the packaging machine manufacturers’ product development process. There are no methodical or designorientated tools for a comprehensive consideration of this target. This paper defines the term format-flexibility in the context of packaging machines and shows the state-of-the-art for improving the changeover of production machines. The requirements for a new approach and the concept itself will be introduced, and the method elements will be explained. Finally, the use of the concept and the result of the development of a format-flexible packaging machine will be shown.

Keywords: packaging machine, format-flexibility, changeover, design method

Procedia PDF Downloads 417

Authors: Parsa Mahdavi, M. Amin Hariri-Ardebili

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The development of accurate machine learning and deep learning models traditionally demands hands-on expertise and a solid background to fine-tune hyperparameters. With the continuous expansion of datasets in various scientific and engineering domains, researchers increasingly turn to machine learning methods to unveil hidden insights that may elude classic regression techniques. This surge in adoption raises concerns about the adequacy of the resultant meta-models and, consequently, the interpretation of the findings. In response to these challenges, automated machine learning (AutoML) emerges as a promising solution, aiming to construct machine learning models with minimal intervention or guidance from human experts. AutoML encompasses crucial stages such as data preparation, feature engineering, hyperparameter optimization, and neural architecture search. This paper provides a comprehensive overview of the principles underpinning AutoML, surveying several widely-used AutoML platforms. Additionally, the paper offers a glimpse into the application of AutoML on various engineering datasets. By comparing these results with those obtained through classical machine learning methods, the paper quantifies the uncertainties inherent in the application of a single ML model versus the holistic approach provided by AutoML. These examples showcase the efficacy of AutoML in extracting meaningful patterns and insights, emphasizing its potential to revolutionize the way we approach and analyze complex datasets.

Keywords: automated machine learning, uncertainty, engineering dataset, regression

Procedia PDF Downloads 40

Authors: Krishang Surapaneni

Abstract:

The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42%

Keywords: finance, linear regression model, machine learning model, neural network, stock price

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Authors: Dulanjali Ranasinghe, Isuru Supasan, Kaushalya Premachandra, Ranjan Dissanayake, Ajith Rajapaksha, Eustace Fernando

Abstract:

Proteomics studies of organisms are considered to be significantly information-rich compared to their genomic counterparts because proteomes of organisms represent the expressed state of all proteins of an organism at a given time. In modern top-down and bottom-up proteomics workflows, the primary analysis methods employed are gel–based methods such as two-dimensional (2D) electrophoresis and mass spectrometry based methods. Machine learning (ML) and artificial intelligence (AI) have been used increasingly in modern biological data analyses. In particular, the fields of genomics, DNA sequencing, and bioinformatics have seen an incremental trend in the usage of ML and AI techniques in recent years. The use of aforesaid techniques in the field of proteomics studies is only beginning to be materialised now. Although there is a wealth of information available in the scientific literature pertaining to proteomics workflows, no comprehensive review addresses various aspects of the combined use of proteomics and machine learning. The objective of this review is to provide a comprehensive outlook on the application of machine learning into the known proteomics workflows in order to extract more meaningful information that could be useful in a plethora of applications such as medicine, agriculture, and biotechnology.

Keywords: proteomics, machine learning, gel-based proteomics, mass spectrometry

Procedia PDF Downloads 132

Authors: Isabella Schwartz, Ori Safran, Naama Karniel, Michal Abel, Adina Berko, Martin Seyres, Tamir Tsoar, Sigal Portnoy

Abstract:

Virtual Reality allows to manipulate the patient’s perception, thereby providing a motivational addition to real-time biofeedback exercises. We aimed to test the effect of manipulated virtual kinematic intervention on measures of active and passive Range of Motion (ROM), pain, and disability level in individuals with traumatic stiff shoulder. In a double-blinded study, patients with stiff shoulder following proximal humerus fracture and non-operative treatment were randomly divided into a non-manipulated feedback group (NM-group; N=6) and a manipulated feedback group (M-group; N=7). The shoulder ROM, pain, and the Disabilities of the Arm, Shoulder and Hand (DASH) scores were tested at baseline and after the 6 sessions, during which the subjects performed shoulder flexion and abduction in front of a graphic visualization of the shoulder angle. The biofeedback provided to the NM-group was the actual shoulder angle and the feedback provided to the M-group was manipulated so that 10° were constantly subtracted from the actual angle detected by the motion capture system. The M-group showed greater improvement in the active flexion ROM, with median and interquartile range of 197.1 (140.5-425.0) compared to 142.5 (139.1-151.3) for the NM-group (p=.046). Also, the M-group showed greater improvement in the DASH scores, with median and interquartile range of 67.7 (52.8-86.2) compared to 89.7 (83.8-98.3) for the NM-group (p=.022). Manipulated intervention is beneficial in individuals with traumatic stiff shoulder and should be further tested for other populations with orthopedic injuries.

Keywords: virtual reality, biofeedback, shoulder pain, range of motion

Procedia PDF Downloads 104

Authors: Chen-Jung Chen, Yi-Chang Chen

Abstract:

While traditional virtual reality and augmented reality only allow for small movement learning and cannot provide a truly immersive teaching experience to give it the illusion of movement, the new technology of both content creation and immersive interactive simulation of the metaverse can just reach infinite close to the natural teaching situation. However, the mixed reality virtual classroom of metaverse has not yet explored its theory, and it is rarely implemented in the situational simulation teaching of nursing education. Therefore, in the first year, the study will intend to use grounded theory and case study methods and in-depth interviews with nursing education and information experts. Analyze the interview data to investigate the uniqueness of metaverse development. The proposed analysis will lead to alternative theories and methods for the development of nursing education. In the second year, it will plan to integrate the metaverse virtual situation simulation technology into the alternate teaching strategy in the pediatric nursing technology course and explore the nursing students' use of this teaching method as the construction of personal technology and experience. By leveraging the unique features of distinct teaching platforms and developing processes to deliver alternative teaching strategies in a nursing technology teaching environment. The aim is to increase learning achievements without compromising teaching quality and teacher-student relationships in the post-pandemic era. A descriptive and convergent mixed methods design will be employed. Sixty third-grade nursing students will be recruited to participate in the research and complete the pre-test. The students in the experimental group (N=30) agreed to participate in 4 real-time mixed virtual situation simulation courses in self-practice after class and conducted qualitative interviews after each 2 virtual situation courses; the control group (N=30) adopted traditional practice methods of self-learning after class. Both groups of students took a post-test after the course. Data analysis will adopt descriptive statistics, paired t-tests, one-way analysis of variance, and qualitative content analysis. This study addresses key issues in the virtual reality environment for teaching and learning within the metaverse, providing valuable lessons and insights for enhancing the quality of education. The findings of this study are expected to contribute useful information for the future development of digital teaching and learning in nursing and other practice-based disciplines.

Keywords: metaverse, post-pandemic era, online virtual classroom, immersive teaching

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Authors: Hailyie Tekleselase

Abstract:

Deep learning is increasingly used as a building block of security systems. However, neural networks are hard to interpret and typically solid to the practitioner. This paper presents a detail survey of computing methods in cyber security, and analyzes the prospects of enhancing the cyber security capabilities by suggests that of accelerating the intelligence of the security systems. There are many AI-based applications used in industrial scenarios such as Internet of Things (IoT), smart grids, and edge computing. Machine learning technologies require a training process which introduces the protection problems in the training data and algorithms. We present machine learning techniques currently applied to the detection of intrusion, malware, and spam. Our conclusions are based on an extensive review of the literature as well as on experiments performed on real enterprise systems and network traffic. We conclude that problems can be solved successfully only when methods of artificial intelligence are being used besides human experts or operators.

Keywords: artificial intelligence, machine learning, deep learning, cyber security, big data

Procedia PDF Downloads 106