Search results for: look ahead vs. look back

Authors: Rei-Heng Cheng, Wen-Pinn Fang

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A wireless sensor network (WSN) is composed by a large number of sensors and one or a few base stations, where the sensor is responsible for detecting specific event information, which is sent back to the base station(s). However, how to save electricity consumption to extend the network lifetime is a problem that cannot be ignored in the wireless sensor networks. Since the sensor network is used to monitor a region or specific events, how the information can be reliably sent back to the base station is surly important. Network coding technique is often used to enhance the reliability of the network transmission. When a node needs to send out M data packets, it encodes these data with redundant data and sends out totally M + R packets. If the receiver can get any M packets out from these M + R packets, it can decode and get the original M data packets. To transmit redundant packets will certainly result in the excess energy consumption. This paper will explore relationship between the quality of wireless transmission and the number of redundant packets. Hopefully, each sensor can overhear the nearby transmissions, learn the wireless transmission quality around it, and dynamically determine the number of redundant packets used in network coding.

Keywords: energy consumption, network coding, transmission reliability, wireless sensor networks

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Authors: Shadi Fathi, Moura Mehravar, Mujib Rahman

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The railwaytransportation network in the UK is over 100 years old and is known as one of the oldest mass transit systems in the world. This aged track network requires frequent closure for maintenance. One of the main reasons for closure is inadequate drainage due to the leakage in the buried drainage pipes. The leaking water can cause localised subgrade weakness, which subsequently can lead to major ground/substructure failure.Different condition assessment methods are available to assess the railway substructure. However, the existing condition assessment methods are not able to detect any local ground weakness/damageand provide details of the damage (e.g. size and location). To tackle this issue, a hybrid back-analysis technique based on artificial neural network (ANN) and genetic algorithm (GA) has been developed to predict the substructurelayers’ moduli and identify any soil weaknesses. At first, afinite element (FE) model of a railway track section under Falling Weight Deflection (FWD) testing was developed and validated against field trial. Then a drainage pipe and various scenarios of the local defect/ soil weakness around the buried pipe with various geometriesand physical properties were modelled. The impact of the soil local weaknesson the track surface deflection wasalso studied. The FE simulations results were used to generate a database for ANN training, and then a GA wasemployed as an optimisation tool to optimise and back-calculate layers’ moduli and soil weakness moduli (ANN’s input). The hybrid ANN-GA back-analysis technique is a computationally efficient method with no dependency on seed modulus values. The modelcan estimate substructures’ layer moduli and the presence of any localised foundation weakness.

Keywords: finite element (FE) model, drainage defect, falling weight deflectometer (FWD), hybrid ANN-GA

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Authors: Wali Ullah, Muhammad Nishat

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The study investigates the extent to which the so called Nelson-Siegel model (DNS) and its extended version that accounts for time varying volatility (DNS-EGARCH) can optimally fit the yield curve and predict its future path in the context of an emerging economy. For the in-sample fit, both models fit the curve remarkably well even in the emerging markets. However, the DNS-EGARCH model fits the curve slightly better than the DNS. Moreover, both specifications of yield curve that are based on the Nelson-Siegel functional form outperform the benchmark VAR forecasts at all forecast horizons. The DNS-EGARCH comes with more precise forecasts than the DNS for the 6- and 12-month ahead forecasts, while the two have almost similar performance in terms of RMSE for the very short forecast horizons.

Keywords: yield curve, forecasting, emerging markets, Kalman filter, EGARCH

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Authors: Goodness C. Aye

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This paper examines the effect of the volatility of oil prices on food price in South Africa using monthly data covering the period 2002:01 to 2014:09. Food price is measured by the South African consumer price index for food while oil price is proxied by the Brent crude oil. The study employs the GARCH-in-mean VAR model, which allows the investigation of the effect of a negative and positive shock in oil price volatility on food price. The model also allows the oil price uncertainty to be measured as the conditional standard deviation of a one-step-ahead forecast error of the change in oil price. The results show that oil price uncertainty has a positive and significant effect on food price in South Africa. The responses of food price to a positive and negative oil price shocks is asymmetric.

Keywords: oil price volatility, food price, bivariate, GARCH-in-mean VAR, asymmetric

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Authors: Peng Liu, Chyng Wen Tee, Xiaofei Xu

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This paper integrates machine learning forecasting techniques into the multi-period portfolio optimization framework, enabling dynamic asset allocation based on multiple future periods. We explore both theoretical foundations and practical applications, employing diverse machine learning models for return forecasting. This comprehensive guide demonstrates the superiority of multi-period optimization over single-period approaches, particularly in risk mitigation through strategic rebalancing and enhanced market trend forecasting. Our goal is to promote wider adoption of multi-period optimization, providing insights that can significantly enhance the decision-making capabilities of practitioners and researchers alike.

Keywords: multi-period portfolio optimization, look-ahead constrained optimization, machine learning, sequential decision making

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Authors: Ritesh Kumar, Gitanjali Chhetri, Mandira Bhatia, Mohit Mishra, Abhijith Bailur, Abhinav

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The prevalence of the internet and technology in our day to day lives is creating more security issues than ever. The need for protecting and providing a secure access to private and business data has led to the development of many security systems. One of the potential solutions is to employ the bio-metric authentication technique. In this paper we present an innovative biometric authentication method that utilizes a person’s EEG signal, which is acquired in response to an auditory stimulus,and transferred wirelessly to a computer that has the necessary ANN algorithm-Multi layer perceptrol neural network because of is its ability to differentiate between information which is not linearly separable.In order to determine the weights of the hidden layer we use Gaussian random weight initialization. MLP utilizes a supervised learning technique called Back propagation for training the network. The complex algorithm used for EEG classification reduces the chances of intrusion into the protected public or private data.

Keywords: EEG signal, auditory evoked potential, biometrics, multilayer perceptron neural network, back propagation rule, Gaussian random weight initialization

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Authors: Haziyah Hussin, Syazwan Abdul Samad, Rosnani Jusoh

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This study is aimed towards the automisation of basic patternmaking for traditional clothes for the purpose of mass production using AutoCAD to apply AutoLISP feature under software Hazi Attire. A standard dress form (industrial form) with the size of small (S), medium (M) and large (L) size is measured using full body scanning machine. Later, the pattern for the clothes is designed parametrically based on the measured dress form. Hazi Attire program is used within the framework of AutoCAD to generate the basic pattern of front bodice, back bodice, front skirt, back skirt and sleeve block (sloper). The generation of pattern is based on the parameters inputted by user, whereby in this study, the parameters were determined based on the measured size of dress form. The finalized pattern parameter shows that the pattern fit perfectly on the dress form. Since the pattern is generated almost instantly, these proved that using the AutoLISP programming, the manufacturing lead time for the mass production of the traditional clothes can be decreased.

Keywords: apparel, AutoLISP, Malay traditional clothes, pattern ganeration

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Authors: Maad Kamal Al-Anni

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This study is about an algorithmic dependence of Artificial Neural Network on Multilayer Perceptron (MPL) pertaining to the classification and clustering presentations for Mobile Adhoc Network vulnerabilities. Moreover, mobile ad hoc network (MANET) is ubiquitous intelligent internetworking devices in which it has the ability to detect their environment using an autonomous system of mobile nodes that are connected via wireless links. Security affairs are the most important subject in MANET due to the easy penetrative scenarios occurred in such an auto configuration network. One of the powerful techniques used for inspecting the network packets is Intrusion Detection System (IDS); in this article, we are going to show the effectiveness of artificial neural networks used as a machine learning along with stochastic approach (information gain) to classify the malicious behaviors in simulated network with respect to different IDS techniques. The monitoring agent is responsible for detection inference engine, the audit data is collected from collecting agent by simulating the node attack and contrasted outputs with normal behaviors of the framework, whenever. In the event that there is any deviation from the ordinary behaviors then the monitoring agent is considered this event as an attack , in this article we are going to demonstrate the  signature-based IDS approach in a MANET by implementing the back propagation algorithm over ensemble-based Traffic Table (TT), thus the signature of malicious behaviors or undesirable activities are often significantly prognosticated and efficiently figured out, by increasing the parametric set-up of Back propagation algorithm during the experimental results which empirically shown its effectiveness  for the ratio of detection index up to 98.6 percentage. Consequently it is proved in empirical results in this article, the performance matrices are also being included in this article with Xgraph screen show by different through puts like Packet Delivery Ratio (PDR), Through Put(TP), and Average Delay(AD).

Keywords: Intrusion Detection System (IDS), Mobile Adhoc Networks (MANET), Back Propagation Algorithm (BPA), Neural Networks (NN)

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Authors: Ashwani Kumar Aggarwal

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NIR Light is non-ionizing and can pass easily through living tissues such as breast without any harmful effects. Therefore, use of NIR light for imaging the biological tissue and to quantify its optical properties is a good choice over other invasive methods. Optical tomography involves two steps. One is the forward problem and the other is the reconstruction problem. The forward problem consists of finding the measurements of transmitted light through the tissue from source to detector, given the spatial distribution of absorption and scattering properties. The second step is the reconstruction problem. In X-ray tomography, there is standard method for reconstruction called filtered back projection method or the algebraic reconstruction methods. But this method cannot be applied as such, in optical tomography due to highly scattering nature of biological tissue. A hybrid algorithm for reconstruction has been implemented in this work which takes into account the highly scattered path taken by photons while back projecting the forward data obtained during Monte Carlo simulation. The reconstructed image suffers from blurring due to point spread function. This blurred reconstructed image has been enhanced using a digital filter which is optimal in mean square sense.

Keywords: least-squares optimization, filtering, tomography, laser interaction, light scattering

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Authors: Doaa El-Shereef

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This paper will focus on proving a theory that it is an Egyptian knife with Canaanite relief and will discuss the nature of the Gebel el-Arak Knife and the civilization to which it belongs and the relationship of the Canaanite deity with Mount Abydos in Egypt, and the influence between the ancient Egyptian religion and the Canaanite religion and their effect on each other. Finally, the paper will discuss in full detail the engraving of the two faces of the knife handle and analyze the register on the front face, which is the scene of the two lions engraved, and between them, an old man symbolized as the deity in the middle and compared it with other drawings similar in the Egyptian civilization. The paper will also discuss the registers on the back face, which are the engravings of a battle, soldiers, uniforms, and boats, and how the back face describes a water battle between three Egyptian boats and two foreign ships. In addition, it will prove that those foreign ships were not Mesopotamian ships because, in the period to which the knife of Gebel Al-Arak belongs, between 3300-3100 BC, there were no battles or trade exchanges between Egypt and Mesopotamia on sea routes. However, there was already a strong land and sea trade between Canaan and Egypt during Chalcolithic Age (4500-3500 BC), as described in many primary sources.

Keywords: Canaan, Egypt, Gebel el-Arak Knife, Louvre

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Authors: Shayla He

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Background and Purpose: According to Chamie (2017), it’s estimated that no less than 150 million people, or about 2 percent of the world’s population, are homeless. The homeless population in the United States has grown rapidly in the past four decades. In New York City, the sheltered homeless population has increased from 12,830 in 1983 to 62,679 in 2020. Knowing the trend on the homeless population is crucial at helping the states and the cities make affordable housing plans, and other community service plans ahead of time to better prepare for the situation. This study utilized the data from New York City, examined the key factors associated with the homelessness, and developed systematic modeling to predict homeless populations of the future. Using the best model developed, named HP-RNN, an analysis on the homeless population change during the months of 2020 and 2021, which were impacted by the COVID-19 pandemic, was conducted. Moreover, HP-RNN was tested on the data from Seattle. Methods: The methodology involves four phases in developing robust prediction methods. Phase 1 gathered and analyzed raw data of homeless population and demographic conditions from five urban centers. Phase 2 identified the key factors that contribute to the rate of homelessness. In Phase 3, three models were built using Linear Regression, Random Forest, and Recurrent Neural Network (RNN), respectively, to predict the future trend of society's homeless population. Each model was trained and tuned based on the dataset from New York City for its accuracy measured by Mean Squared Error (MSE). In Phase 4, the final phase, the best model from Phase 3 was evaluated using the data from Seattle that was not part of the model training and tuning process in Phase 3. Results: Compared to the Linear Regression based model used by HUD et al (2019), HP-RNN significantly improved the prediction metrics of Coefficient of Determination (R2) from -11.73 to 0.88 and MSE by 99%. HP-RNN was then validated on the data from Seattle, WA, which showed a peak %error of 14.5% between the actual and the predicted count. Finally, the modeling results were collected to predict the trend during the COVID-19 pandemic. It shows a good correlation between the actual and the predicted homeless population, with the peak %error less than 8.6%. Conclusions and Implications: This work is the first work to apply RNN to model the time series of the homeless related data. The Model shows a close correlation between the actual and the predicted homeless population. There are two major implications of this result. First, the model can be used to predict the homeless population for the next several years, and the prediction can help the states and the cities plan ahead on affordable housing allocation and other community service to better prepare for the future. Moreover, this prediction can serve as a reference to policy makers and legislators as they seek to make changes that may impact the factors closely associated with the future homeless population trend.

Keywords: homeless, prediction, model, RNN

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Authors: Farnoosh Davarian

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The present research focuses on organizing the cooperation of units constrained by demand response (DR) programs, considering ancillary services in the P-Q power market. Moreover, it provides a comprehensive exploration of the effects of demand reduction and redistribution across several predefined scenarios (in three pre-designed demand response programs, for example, ranging from 5% to 20%) on system voltage and losses in a smart distribution system (in the studied network, distributed energy resources (DERs) such as synchronous distributed generators and wind turbines offer their active and reactive power for the proposed market).GAMS, a specialized software for high-powered modeling, is used for optimizing linear, nonlinear, and integer programming challenges. GAMS modeling is separate from its solution method, which is a notable feature. Thus, by providing changes in the solver, it is possible to solve the model using various methods (linear, nonlinear, integer, etc.). Finally, the combined active and reactive market challenge in smart distribution systems, considering renewable distributed sources and demand response programs in GAMS, will be evaluated. The active and reactive power trading by the distribution company is carried out in the wholesale market. What is demanded is active power. By using the buy-back/payment program, it is possible for responsive loads or aggregators to participate in the market. The objective function of the proposed market is to minimize the price of active and reactive power for DERs and distribution companies and the penalty cost for CO2 emissions and the cost of the buy-back/payment program. In this research, the objective function is to minimize the cost of active and reactive power from distributed generation sources and distribution companies, the cost of carbon dioxide emissions, and the cost of the buy-back/payment program. The effectiveness of the proposed method has been evaluated in a case study.

Keywords: consumer behavior, demand response, pollution cost, combined active and reactive market

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Authors: Da-Eun Kim, Heon-Seock Cynn, Sil-Ah Choi, A-Reum Shin

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Bridging exercise in supine position with the hips and knees flexed have been commonly performed as one of the therapeutic exercises and is a comfortable and pain-free position to most individuals with chronic low back pain (CLBP). Many previous studies have investigated the beneficial way of performing bridging exercises to improve activation of abdominal and gluteal muscle and reduce muscle activity of hamstrings (HAM) and erector spinae (ES) and compensatory lumbopelvic motion. The purpose of this study was to compare the effects of three different bridging exercises on the HAM, ES, gluteus maximus (Gmax), gluteus medius (Gmed), and transverse abdominis/internal abdominis oblique (TrA/IO) activities and anterior pelvic tilt angle in subjects with CLBP. Seventeen subjects with CLBP participated in this study. They performed bridging under three different conditions (with 30° hip abduction, isometric hip abduction, and isometric hip adduction). Surface electromyography was used to measure muscle activity, and the ImageJ software was used to calculate anterior pelvic tilt angle. One-way repeated-measures analysis of variance was used to assess the statistical significance of the measured variables. HAM activity was significantly lower in bridging with 30° hip abduction and isometric hip abduction than in bridging with isometric hip adduction. Gmax and Gmed activities were significantly greater in bridging with isometric hip abduction than in bridging with 30° hip abduction and isometric hip adduction. TrA/IO muscle activity was significantly greater and anterior pelvic tilt angle was significantly lower in bridging with isometric hip adduction than in bridging with 30° hip abduction and isometric hip abduction. Bridging with isometric hip abduction using Thera-Band can effectively reduce HAM activity, and increase Gmax and Gmed activities in subjects with CLBP. Bridging with isometric hip adduction using a pressure biofeedback unit can be a beneficial exercise to improve TrA/IO activity and minimize anterior pelvic tilt in subjects with CLBP.

Keywords: bridging exercise, electromyography, low back pain, lower limb exercise

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Authors: Vu Hoang Thu Huong

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Background: Surface electrical stimulation (SES) is a popular non-invasive approach that offers a wide range of treatments for many diseases of physical therapy. It involves applying electrical stimulation to the skin via surface electrodes to stimulate nerve fibers. SES was regularly used to treat the back and upper or lower extremities, but it was rarely used to treat the chest and abdomen. SES on the thorax and abdomen should be administered with more attention because crucial organs are under those areas (i.e., heart, lungs, liver, etc.). In these areas, safety precautions are suggested, and some SES applications might even be a contraindication. The fact that physical therapists have less experience with SES in these situations can also be attributed to these. Although a few earlier studies applied it to these settings and discovered hopeful results, none of them highlight the relationship between the intensity of SES and its depth of impact for safety considerations. Objective: To assure feasibility when using SES in these areas, the purpose of this study is to summarize the common location and intensity of those areas that have been conducted in previous studies. Method: A thorough systematic review was conducted to determine the common surface electrode position for the thorax and abdomen areas. The studies with the randomized controlled design were systematically searched using inclusion and exclusion criteria through nine electronic databases, including Pubmed, Scopus, etc., between 1975 and Dec 2021. Results: Thirty-three studies with over 1800 participants and 4 types of SES (TENS, IFC, NMES, and FES) with various categories of department hospitals were found. Following an anterior, lateral, and posterior observation, the particular SES positions found that it concentrated on 6 regions (the thoracic, abdomen, upper lateral, lower lateral, upper back, and lower back regions), and its intensity for each region was also summarized. Conclusion: This systematic review figured out the popular locations of SES in the thorax and abdominal areas as well as a summarized maximum of intensity that was found in previous studies with outstanding outcomes.

Keywords: surface electrical stimulation, electrical stimulation, thoracic, abdomen, abdominal.

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Authors: Zubier Arfan, Paul Johnson

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The electronification of financial markets and the rise of algorithmic trading has sparked a lot of interest from the mathematical community, for the market making-problem in particular. The research presented in this short paper solves the classic stochastic control problem in order to derive the strategy for a market-maker. It also shows how to calibrate and simulate the strategy with real limit order book data for back-testing. The ambiguity of limit-order priority in back-testing is dealt with by considering optimistic and pessimistic priority scenarios. The model, although it does outperform a naive strategy, assumes constant volatility, therefore, is not best suited to the LOB data. The Heston model is introduced to describe the price and variance process of the asset. The Trader's constant absolute risk aversion utility function is optimised by numerically solving a 3-dimensional Hamilton-Jacobi-Bellman partial differential equation to find the optimal limit order quotes. The results show that the stochastic volatility market-making model is more suitable for a risk-averse trader and is also less sensitive to calibration error than the constant volatility model.

Keywords: market-making, market-microsctrucure, stochastic volatility, quantitative trading

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Authors: Zhuo Job Chen, Kevin Nute

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The work reported examines potential linkages between spatial and temporal prospects, and more specifically, between variations in the spatial depth and foreground obstruction of window views, and observers’ sense of connection to the future. It was found that external views from indoor spaces were strongly associated with a sense of the future, that partially obstructing such a view with foreground objects significantly reduced its association with the future, and replacing it with a pictorial representation of the same scene (with no real actual depth) removed most of its temporal association. A lesser change in the spatial depth of the view, however, had no apparent effect on association with the future. While the role of spatial depth has still to be confirmed, the results suggest that spatial prospects directly affect temporal ones. The word “prospect” typifies the overlapping of the spatial and temporal in most human languages. It originated in classical times as a purely spatial term, but in the 16th century took on the additional temporal implication of an imagined view ahead, of the future. The psychological notion of prospection, then, has its distant origins in a spatial analogue. While it is not yet proven that space directly structures our processing of time at a physiological level, it is generally agreed that it commonly does so conceptually. The mental representation of possible futures has been a central part of human survival as a species (Boyer, 2008; Suddendorf & Corballis, 2007). A sense of the future seems critical not only practically, but also psychologically. It has been suggested, for example, that lack of a positive image of the future may be an important contributing cause of depression (Beck, 1974; Seligman, 2016). Most people in the developed world now spend more than 90% of their lives indoors. So any direct link between external views and temporal prospects could have important implications for both human well-being and building design. We found that the ability to see what lies in front of us spatially was strongly associated with a sense of what lies ahead temporally. Partial obstruction of a view was found to significantly reduce that sense connection to the future. Replacing a view with a flat pictorial representation of the same scene removed almost all of its connection with the future, but changing the spatial depth of a real view appeared to have no significant effect. While foreground obstructions were found to reduce subjects’ sense of connection to the future, they increased their sense of refuge and security. Consistent with Prospect and Refuge theory, an ideal environment, then, would seem to be one in which we can “see without being seen” (Lorenz, 1952), specifically one that conceals us frontally from others, without restricting our own view. It is suggested that these optimal conditions might be translated architecturally as screens, the apertures of which are large enough for a building occupant to see through unobstructed from close by, but small enough to conceal them from the view of someone looking from a distance outside.

Keywords: foreground obstructions, prospection, spatial depth, window views

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Authors: Ioannis P. Panapakidis, Marios N. Moschakis

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The Greek Energy Market is structured as a mandatory pool where the producers make their bid offers in day-ahead basis. The System Operator solves an optimization routine aiming at the minimization of the cost of produced electricity. The solution of the optimization problem leads to the calculation of the System Marginal Price (SMP). Accurate forecasts of the SMP can lead to increased profits and more efficient portfolio management from the producer`s perspective. Aim of this study is to provide a comparative analysis of various machine learning models such as artificial neural networks and neuro-fuzzy models for the prediction of the SMP of the Greek market. Machine learning algorithms are favored in predictions problems since they can capture and simulate the volatilities of complex time series.

Keywords: deregulated energy market, forecasting, machine learning, system marginal price

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Authors: Triem T. Hoang

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Loop heat pipes (LHPs) are two-phase capillary-pumped heat transports. An appropriate working fluid is selected for the intended application temperature range. A closed-loop is evacuated to a high vacuum, back-filled partially with the working fluid, and then hermetically sealed under the fluid own pressure. Heat from a heat source conducts through the evaporator casing to vaporize liquid on the outer surface of the wick structure inside the evaporator. The generated vapor is compelled to vent out of the evaporator and into the vapor line for transport to the condenser assembly. There, heat is removed and rejected to a heat sink to condensed vapor back to liquid. The liquid exits the condenser and travels in the liquid line to return to the evaporator to complete the cycle. The circulation of fluid, and thus the heat transport in the LHP, is accomplished entirely by capillary action. The LHP contains no mechanical moving part to wear out or break down and, therefore possesses, reliability and a long life even without maintenance. In this paper, the author not only attempts to introduce the LHP technology in simplistic terms to those who are not familiar with it but also provides necessary technical information to potential users for the proper design and analysis of the LHP system.

Keywords: two-phase heat transfer, loop heat pipe, capillary pumped technology, thermal-fluid modeling

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Authors: Amal A. Hassan, Faten H. Fahmy, Abd El-Shafy A. Nafeh, Hosam K. M. Youssef

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Due to the growing penetration of wind energy into the power grid, it is very important to study its interactions with the power system and to provide good control technique in order to deliver high quality power. In this paper, an intelligent control methodology is proposed for optimizing the controllers’ parameters of doubly fed induction generator (DFIG) based wind turbine generation system (WTGS). The genetic algorithm (GA) and particle swarm optimization (PSO) are employed and compared for the parameters adaptive tuning of the proposed proportional integral (PI) multiple controllers of the back to back converters of the DFIG based WTGS. For this purpose, the dynamic model of WTGS with DFIG and its associated controllers is presented. Furthermore, the simulation of the system is performed using MATLAB/SIMULINK and SIMPOWERSYSTEM toolbox to illustrate the performance of the optimized controllers. Finally, this work is validated to 33-bus test radial system to show the interaction between wind distributed generation (DG) systems and the distribution network.

Keywords: DFIG wind turine, intelligent control, distributed generation, particle swarm optimization, genetic algorithm

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Authors: Thamo Sutharssan, Diogo Montalvao, Wen-Chung Wang, Yong Chen, Claudia Pisac

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A combined heat and power (CHP) system is an efficient and clean way to generate power (electricity). Heat produced by the CHP system can be used for water and space heating. The CHP system which uses hydrogen as fuel produces zero carbon emission. Its’ efficiency can reach more than 80% whereas that of a traditional power station can only reach up to 50% because much of the thermal energy is wasted. The other advantages of CHP systems include that they can decentralize energy generation, improve energy security and sustainability, and significantly reduce the energy cost to the users. This paper presents the economic benefits of using a CHP system in the domestic environment. For this analysis, natural gas is considered as potential fuel as the hydrogen fuel cell based CHP systems are rarely used. UK government incentives for CHP systems are also considered as the added benefit. Results show that CHP requires a significant initial investment in return it can reduce the annual energy bill significantly. Results show that an investment may be paid back in 7 years. After the back period, CHP can run for about 3 years as most of the CHP manufacturers provide 10-year warranty.

Keywords: combined heat and power, clean energy, hydrogen fuel cell, economic analysis of CHP, zero emission

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Authors: Omid Giahi, Ebrahim Darvishi, Mahdi Akbarzadeh

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Introduction: Occupational low back pain (LBP) is one of the most prevalent work-related musculoskeletal disorders in which a lot of risk factors are involved that. The present study focuses on the relation between personal, psycho-social and occupational risk factors and LBP severity in industrial workers. Materials and Methods: This research was a case-control study which was conducted in Kurdistan province. 100 workers (Mean Age ± SD of 39.9 ± 10.45) with LBP were selected as the case group, and 100 workers (Mean Age ± SD of 37.2 ± 8.5) without LBP were assigned into the control group. All participants were selected from various industrial units, and they had similar occupational conditions. The required data including demographic information (BMI, smoking, alcohol, and family history), occupational (posture, mental workload (MWL), force, vibration and repetition), and psychosocial factors (stress, occupational satisfaction and security) of the participants were collected via consultation with occupational medicine specialists, interview, and the related questionnaires and also the NASA-TLX software and REBA worksheet. Chi-square test, logistic regression and structural equation modeling (SEM) were used to analyze the data. For analysis of data, IBM Statistics SPSS 24 and Mplus6 software have been used. Results: 114 (77%) of the individuals were male and 86 were (23%) female. Mean Career length of the Case Group and Control Group were 10.90 ± 5.92, 9.22 ± 4.24, respectively. The statistical analysis of the data revealed that there was a significant correlation between the Posture, Smoking, Stress, Satisfaction, and MWL with occupational LBP. The odds ratios (95% confidence intervals) derived from a logistic regression model were 2.7 (1.27-2.24) and 2.5 (2.26-5.17) and 3.22 (2.47-3.24) for Stress, MWL, and Posture, respectively. Also, the SEM analysis of the personal, psycho-social and occupational factors with LBP revealed that there was a significant correlation. Conclusion: All three broad categories of risk factors simultaneously increase the risk of occupational LBP in the workplace. But, the risks of Posture, Stress, and MWL have a major role in LBP severity. Therefore, prevention strategies for persons in jobs with high risks for LBP are required to decrease the risk of occupational LBP.

Keywords: industrial workers occupational, low back pain, occupational risk factors, psychosocial factors

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Authors: B. Gopal, Pannala Krishna Murthy, G. N. Sreenivas

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The increasing demand of electric power is giving an emphasis on the need for the maximum utilization of renewable energy sources. On the other hand maintaining power quality to satisfaction of utility is an essential requirement. In this paper the design aspects of a Unified Power Quality Conditioner integrated with photovoltaic system in a distributed generation is presented. The proposed system consist of series inverter, shunt inverter are connected back to back on the dc side and share a common dc-link capacitor with Distributed Generation through a boost converter. The primary task of UPQC is to minimize grid voltage and load current disturbances along with reactive and harmonic power compensation. In addition to primary tasks of UPQC, other functionalities such as compensation of voltage interruption and active power transfer to the load and grid in both islanding and interconnected mode have been addressed. The simulation model is design in MATLAB/ Simulation environment and the results are in good agreement with the published work.

Keywords: distributed generation (DG), interconnected mode, islanding mode, maximum power point tracking (mppt), power quality (PQ), unified power quality conditioner (UPQC), photovoltaic array (PV)

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Authors: Paulomi (Polly) Burey, Zoe Lynch

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In STEM education, students often have difficulty linking static images and words from textbooks or online resources, to the underlying mechanisms of the topic of study. This can often dissuade some students from pursuing study in the physical and chemical sciences. A growing movement in current day students demonstrates that the YouTube generation feel they learn best from video or dynamic, interactive learning tools, and will seek these out as alternatives to their textbooks and the classroom learning environment. Chemistry, and in particular visualization of molecular structures in everyday materials, can prove difficult to comprehend without significant interaction with the teacher of the content and concepts, beyond the timeframe of a typical class. This can cause a learning hurdle for distance education students, and so it is necessary to provide strong electronic tools and resources to aid their learning. As one of the electronic resources, an animation design approach to link everyday materials to their underlying chemistry would be beneficial for student learning, with the focus here being on food. These animations were designed and storyboarded with a scaling approach and commence with a focus on the food material itself and its component parts. This is followed by animated transitions to its underlying microstructure and identifying features, and finally showing the molecules responsible for these microstructural features. The animation ends with a reverse transition back through the molecular structure, microstructure, all the way back to the original food material, and also animates some reactions that may occur during food processing to demonstrate the purpose of the underlying chemistry and how it affects the food we eat. Using this cyclical approach of linking students’ existing knowledge of food to help guide them to understanding more complex knowledge, and then reinforcing their learning by linking back to their prior knowledge again, enhances student understanding. Food is also an ideal material system for students to interact with, in a hands-on manner to further reinforce their learning. These animations were launched this year in a 2nd year University Food Chemistry course with improved learning outcomes for the cohort.

Keywords: chemistry, food science, future pedagogy, STEM Education

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Authors: Kevin D. Manalo, Jumelyn L. Torres, Noel B. Linsangan

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This paper focuses on a wireless myoelectric prosthesis of the upper-limb that uses a Multilayer Perceptron Neural network with back propagation. The algorithm is widely used in pattern recognition. The network can be used to train signals and be able to use it in performing a function on their own based on sample inputs. The paper makes use of the Neural Network in classifying the electromyography signal that is produced by the muscle in the amputee’s skin surface. The gathered data will be passed on through the Classification Stage wirelessly through Zigbee Technology. The signal will be classified and trained to be used in performing the arm positions in the prosthesis. Through programming using Verilog and using a Field Programmable Gate Array (FPGA) with Zigbee, the EMG signals will be acquired and will be used for classification. The classified signal is used to produce the corresponding Hand Movements (Open, Pick, Hold, and Grip) through the Zigbee controller. The data will then be processed through the MLP Neural Network using MATLAB which then be used for the surface myoelectric prosthesis. Z-test will be used to display the output acquired from using the neural network.

Keywords: field programmable gate array, multilayer perceptron neural network, verilog, zigbee

Procedia PDF Downloads 395

Authors: B. K. Umesh Kumar, Waleed Al Shukaili

Abstract:

Air travel is one of the safest modes of travel. Inflight injuries occur due to various factors such as air turbulence, spillage of hot liquids, and fall of improperly stowed overhead baggage. Injuries occur not only to passengers but also to the flight attendants who are handling the passengers throughout the flight. A retrospective study of all records of crew safety report by the captain of the aircraft for all the flights from 01 Mar 2015 to 31 Mar 2019 in a National Carrier of Middle Eastern country, were analyzed. There was one injury to Flight attendant every 1200 flights. Commonest aircraft involved was Boeing. Inflight phase had 82% of all injuries. 63% of accidents involved female Attendants. Commonest age group involved was from 25-30 years. Cart and container injuries were the commonest and accounted for nearly 62% of the total injuries followed by turbulence. Back injuries were the commonest injuries followed by ankle, shoulder, and burns. Mean days of absence from work seen in shoulder injuries 40 days followed by injuries to back, which accounted for 38 Days. Reduction in injuries to flight attendants can be brought about by proper selection of crew, reduction in cart load. Proper maintenance of cart and container plays a major role in prevention of occupational accidents.

Keywords: flight attendants, in-flight injuries, types of injuries, work related injury prevention

Procedia PDF Downloads 131

Authors: Gary Peach, Furqan Hameed

Abstract:

Musaimeer outfall tunnel is one of the longest storm water tunnels in the world, with a total length of 10.15 km. The tunnel will accommodate surface and rain water received from the drainage networks from 270 km of urban areas in southern Doha with a pumping capacity of 19.7m³/sec. The tunnel is excavated by Tunnel Boring Machine (TBM) through Rus Formation, Midra Shales, and Simsima Limestone. Water inflows at high pressure, complex mixed ground, and weaker ground strata prone to karstification with the presence of vertical and lateral fractures connected to the sea bed were also encountered during mining. In addition to pre-tender geotechnical investigations, the Contractor carried out a supplementary offshore geophysical investigation in order to fine-tune the existing results of geophysical and geotechnical investigations. Electric resistivity tomography (ERT) and Seismic Reflection survey was carried out. Offshore geophysical survey was performed, and interpretations of rock mass conditions were made to provide an overall picture of underground conditions along the tunnel alignment. This allowed the critical tunnelling area and cutter head intervention to be planned accordingly. Karstification was monitored with a non-intrusive radar system facility installed on the TBM. The Boring Electric Ahead Monitoring(BEAM) was installed at the cutter head and was able to predict the rock mass up to 3 tunnel diameters ahead of the cutter head. BEAM system was provided with an online system for real time monitoring of rock mass condition and then correlated with the rock mass conditions predicted during the interpretation phase of offshore geophysical surveys. The further correlation was carried by Samples of the rock mass taken from tunnel face inspections and excavated material produced by the TBM. The BEAM data was continuously monitored to check the variations in resistivity and percentage frequency effect (PFE) of the ground. This system provided information about rock mass condition, potential karst risk, and potential of water inflow. BEAM system was found to be more than 50% accurate in picking up the difficult ground conditions and faults as predicted in the geotechnical interpretative report before the start of tunnelling operations. Upon completion of the project, it was concluded that the combined use of different geophysical investigation results can make the execution stage be carried out in a more confident way with the less geotechnical risk involved. The approach used for the prediction of rock mass condition in Geotechnical Interpretative Report (GIR) and Geophysical Reflection and electric resistivity tomography survey (ERT) Geophysical Reflection surveys were concluded to be reliable as the same rock mass conditions were encountered during tunnelling operations.

Keywords: tunnel boring machine (TBM), subsea, karstification, seismic reflection survey

Procedia PDF Downloads 257

Authors: Bandar Alahmadi, Lethia Jackson

Abstract:

Machine Learning model is used today in many real-life applications. The safety and security of such model is important, so the results of the model are as accurate as possible. One challenge of machine learning model security is the adversarial examples attack. Adversarial examples are designed by the attacker to cause the machine learning model to misclassify the input. We propose a method to generate adversarial examples to attack image classifiers. We are modifying the successfully classified images, so a classifier misclassifies them after the modification. In our method, we do not update the whole image, but instead we detect the important region, modify it, place it back to the original image, and then run it through a classifier. The algorithm modifies the detected region using two methods. First, it will add abstract image matrix on back of the detected image matrix. Then, it will perform a rotation attack to rotate the detected region around its axes, and embed the trace of image in image background. Finally, the attacked region is placed in its original position, from where it was removed, and a smoothing filter is applied to smooth the background with foreground. We test our method in cascade classifier, and the algorithm is efficient, the classifier confident has dropped to almost zero. We also try it in CNN (Convolutional neural network) with higher setting and the algorithm was successfully worked.

Keywords: adversarial examples, attack, computer vision, image processing

Procedia PDF Downloads 342

Authors: Jessica BrodeFrank, Beka Bryer, Lacey Wilson, Sierra Van Ryck deGroot

Abstract:

Museums are enjoying quite the moment in pop culture. From discussions of labor in Bob’s Burger to introducing cultural repatriation in The Black Panther, discussions of various museum issues are making their way to popular media. “Talking Back to Hollywood” analyzes the impact museums have on movies and television. The paper will highlight a series of cultural cameos and discuss what each reveals about critical themes in museums: repatriation, labor, obfuscated histories, institutional legacies, artificial intelligence, and holograms. Using a mixed methods approach to include surveys, descriptive research, thematic analysis, and context analysis, the authors of this paper will explore how we, as the museum staff, might begin to cite museums and movies together as texts. Drawing from their experience working in museums and public history, this contingent of mid-career professionals will highlight the impact museums have had on movies and television and the didactic lessons these portrayals can provide back to cultural heritage professionals. From tackling critical themes in museums such as repatriation, labor conditions/inequities, obfuscated histories, curatorial choice and control, institutional legacies, and more, this paper is grounded in the cultural zeitgeist of the 2000s and the message these media portrayals send to the public and the cultural heritage sector. In particular, the paper will examine how portrayals of AI, holograms, and more technology can be used as entry points for necessary discussions with the public on mistrust, misinformation, and emerging technologies. This paper will not only expose the legacy and cultural understanding of the museum field within popular culture but also will discuss actionable ways that public historians can use these portrayals as an entry point for discussions with the public, citing literature reviews and quantitative and qualitative analysis of survey results. As Hollywood is talking about museums, museums can use that to better connect to the audiences who feel comfortable at the cinema but are excluded from the museum.

Keywords: museums, public memory, representation, popular culture

Procedia PDF Downloads 89

Authors: S. AliReza Mirghasemi, Amin Mohamadi, Zameer Hussain, Narges Rahimi Gabaran, Mir Mostafa Sadat, Shervin Rashidinia

Abstract:

In progressive cases of Ankylosing Spondylitis, patients will have high degrees of kyphosis leading to severe disabilities. Several operative techniques have been used in this stage, but little knowledge exists on the indications for and outcome of these methods. In this study, we examined the efficacy of monosegmental transverse open wedge osteotomy of L3 in 11 patients with progressive spinal kyphosis. The average correction was 36̊ (20 to 42) with no loss of correction after operation. The average operating time was 120 minutes (100 to 130) and the mean blood loss was 1500 ml (1100 to 2000). Osteotomy corrected all patients sufficiently to allow them to see ahead and their posture was improved. There were no fatal complications but one patient had paraplegia after the operation.

Keywords: ankylosing spondylitis, thoracolumbar kyphosis, open wedge osteotomy, L3 transverse open wedge osteotomy

Procedia PDF Downloads 397

Authors: Shan-Jen Cheng, Te-Jen Chang, Kuang-Hsiung Tan, Shou-Ling Kuo

Abstract:

Polymer Electrolyte Membrane Fuel Cell (PEMFC) is such a time-vary nonlinear dynamic system. The traditional linear modeling approach is hard to estimate structure correctly of PEMFC system. From this reason, this paper presents a nonlinear modeling of the PEMFC using Neural Network Auto-regressive model with eXogenous inputs (NNARX) approach. The multilayer perception (MLP) network is applied to evaluate the structure of the NNARX model of PEMFC. The validity and accuracy of NNARX model are tested by one step ahead relating output voltage to input current from measured experimental of PEMFC. The results show that the obtained nonlinear NNARX model can efficiently approximate the dynamic mode of the PEMFC and model output and system measured output consistently.

Keywords: PEMFC, neural network, nonlinear modeling, NNARX

Procedia PDF Downloads 384