Search results for: traffic noise

Authors: Pratap Chandra Pati, Prabina Rajib, Parama Barai

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The traditional finance theory neglects the role of sentiment factor in asset pricing. However, the behavioral approach to asset-pricing based on noise trader model and limit to arbitrage includes investor sentiment as a priced risk factor in the assist pricing model. Investor sentiment affects stock more that are vulnerable to speculation, hard to value and risky to arbitrage. It includes small stocks, high volatility stocks, growth stocks, distressed stocks, young stocks and non-dividend-paying stocks. Since the introduction of Chicago Board Options Exchange (CBOE) volatility index (VIX) in 1993, it is used as a measure of future volatility in the stock market and also as a measure of investor sentiment. CBOE VIX index, in particular, is often referred to as the ‘investors’ fear gauge’ by public media and prior literature. The upward spikes in the volatility index are associated with bouts of market turmoil and uncertainty. High levels of the volatility index indicate fear, anxiety and pessimistic expectations of investors about the stock market. On the contrary, low levels of the volatility index reflect confident and optimistic attitude of investors. Based on the above discussions, we investigate whether market-wide fear levels measured volatility index is priced factor in the standard asset pricing model for the Indian stock market. First, we investigate the performance and validity of Fama and French three-factor model and Carhart four-factor model in the Indian stock market. Second, we explore whether India volatility index as a proxy for fearful market-based sentiment indicators affect the cross section of stock returns after controlling for well-established risk factors such as market excess return, size, book-to-market, and momentum. Asset pricing tests are performed using monthly data on CNX 500 index constituent stocks listed on the National stock exchange of India Limited (NSE) over the sample period that extends from January 2008 to March 2017. To examine whether India volatility index, as an indicator of fear sentiment, is a priced risk factor, changes in India VIX is included as an explanatory variable in the Fama-French three-factor model as well as Carhart four-factor model. For the empirical testing, we use three different sets of test portfolios used as the dependent variable in the in asset pricing regressions. The first portfolio set is the 4x4 sorts on the size and B/M ratio. The second portfolio set is the 4x4 sort on the size and sensitivity beta of change in IVIX. The third portfolio set is the 2x3x2 independent triple-sorting on size, B/M and sensitivity beta of change in IVIX. We find evidence that size, value and momentum factors continue to exist in Indian stock market. However, VIX index does not constitute a priced risk factor in the cross-section of returns. The inseparability of volatility and jump risk in the VIX is a possible explanation of the current findings in the study.

Keywords: India VIX, Fama-French model, Carhart four-factor model, asset pricing

Procedia PDF Downloads 241

Authors: Chuanqiao Zheng, Wei Zeng, Haozhen Lin

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As of March 2020, the number of Chinese netizens has reached 904 million. The proportion of Internet users accessing the Internet through mobile phones is as high as 99.3%. Under the background of 'Internet +', tourists have a stronger sense of independence in the choice of tourism destinations and tourism products. Tourists are more inclined to learn about the relevant information on tourism destinations and other tourists' evaluations of tourist products through the Internet. The search engine, as an integrated platform that contains a wealth of information, is highly valuable to the analysis of the characteristics of the Internet attention given to various tourism destinations, through big data mining and analysis. This article uses the Baidu Index as the data source, which is one of the products of Baidu Search. The Baidu Index is based on big data, which collects and shares the search results of a large number of Internet users on the Baidu search engine. The big data used in this article includes search index, demand map, population profile, etc. The main research methods used are: (1) based on the search index, analyzing the Internet attention given to the tourism in five cities in Northeast Sichuan at different times, so as to obtain the overall trend and individual characteristics of tourism development in the region; (2) based on the demand map and the population profile, analyzing the demographic characteristics and market positioning of the tourist groups in these cities to understand the characteristics and needs of the target groups; (3) correlating the Internet attention data with the permanent population of each province in China in the corresponding to construct the Boston matrix of the Internet attention rate of the Northeast Sichuan tourism, obtain the tourism target markets, and then propose development strategies for different markets. The study has found that: a) the Internet attention given to the tourism in the region can be categorized into tourist off-season and peak season; the Internet attention given to tourism in different cities is quite different. b) tourists look for information including tour guide information, ticket information, traffic information, weather information, and information on the competing tourism cities; with regard to the population profile, the main group of potential tourists searching for the keywords of tourism in the five prefecture-level cities in Northeast Sichuan are youth. The male to female ratio is about 6 to 4, with males being predominant. c) through the construction of the Boston matrix, it is concluded that the star market for tourism in the Northeast Sichuan Economic Zone includes Sichuan and Shaanxi; the cash cows market includes Hainan and Ningxia; the question market includes Jiangsu and Shanghai; the dog market includes Hubei and Jiangxi. The study concludes with the following planning strategies and recommendations: i) creating a diversified business format that integrates cultural and tourism; ii) creating a brand image of niche tourism; iii) focusing on the development of tourism products; iv) innovating composite three-dimensional marketing channels.

Keywords: Baidu Index, big data, internet attention, tourism

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Authors: Vikrant Gupta, Amrit Goswami

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The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

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Authors: Anna Lower, Michal Lower, Robert Masztalski, Agnieszka Szumilas

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Contemporary cities are facing serious congestion and parking problems. In urban transport policy the introduction of the park and ride system (P&R) is an increasingly popular way of limiting vehicular traffic. The determining of P&R facilities location is a key aspect of the system. Criteria for assessing the quality of the selected location are formulated generally and descriptively. The research outsourced to specialists are expensive and time consuming. The most focus is on the examination of a few selected places. The practice has shown that the choice of the location of these sites in a intuitive way without a detailed analysis of all the circumstances, often gives negative results. Then the existing facilities are not used as expected. Methods of location as a research topic are also widely taken in the scientific literature. Built mathematical models often do not bring the problem comprehensively, e.g. assuming that the city is linear, developed along one important communications corridor. The paper presents a new method where the expert knowledge is applied to fuzzy inference model. With such a built system even a less experienced person could benefit from it, e.g. urban planners, officials. The analysis result is obtained in a very short time, so a large number of the proposed location can also be verified in a short time. The proposed method is intended for testing of car parks location in a city. The paper will show selected examples of locations of the P&R facilities in cities planning to introduce the P&R. The analysis of existing objects will also be shown in the paper and they will be confronted with the opinions of the system users, with particular emphasis on unpopular locations. The research are executed using the fuzzy inference model which was built and described in more detail in the earlier paper of the authors. The results of analyzes are compared to documents of P&R facilities location outsourced by the city and opinions of existing facilities users expressed on social networking sites. The research of existing facilities were conducted by means of the fuzzy model. The results are consistent with actual users feedback. The proposed method proves to be good, but does not require the involvement of a large experts team and large financial contributions for complicated research. The method also provides an opportunity to show the alternative location of P&R facilities. The performed studies show that the method has been confirmed. The method can be applied in urban planning of the P&R facilities location in relation to the accompanying functions. Although the results of the method are approximate, they are not worse than results of analysis of employed experts. The advantage of this method is ease of use, which simplifies the professional expert analysis. The ability of analyzing a large number of alternative locations gives a broader view on the problem. It is valuable that the arduous analysis of the team of people can be replaced by the model's calculation. According to the authors, the proposed method is also suitable for implementation on a GIS platform.

Keywords: fuzzy logic inference, park and ride system, P&R facilities, P&R location

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Authors: Brayden Mi

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Reverse time migration has been widely used in the Petroleum exploration industry to reveal subsurface images and to detect rock and fluid properties since the early 1980s. The seismic technology involves the construction of a velocity model through interpretive model construction, seismic tomography, or full waveform inversion, and the application of the reverse-time propagation of acquired seismic data and the original wavelet used in the acquisition. The methodology has matured from 2D, simple media to present-day to handle full 3D imaging challenges in extremely complex geological conditions. Conventional Ultrasound computed tomography (USCT) utilize travel-time-inversion to reconstruct the velocity structure of an organ. With the velocity structure, USCT data can be migrated with the “bend-ray” method, also known as migration. Its seismic application counterpart is called Kirchhoff depth migration, in which the source of reflective energy is traced by ray-tracing and summed to produce a subsurface image. It is well known that ray-tracing-based migration has severe limitations in strongly heterogeneous media and irregular acquisition geometries. Reverse time migration (RTM), on the other hand, fully accounts for the wave phenomena, including multiple arrives and turning rays due to complex velocity structure. It has the capability to fully reconstruct the image detectable in its acquisition aperture. The RTM algorithms typically require a rather accurate velocity model and demand high computing powers, and may not be applicable to real-time imaging as normally required in day-to-day medical operations. However, with the improvement of computing technology, such a computational bottleneck may not present a challenge in the near future. The present-day (RTM) algorithms are typically implemented from a flat datum for the seismic industry. It can be modified to accommodate any acquisition geometry and aperture, as long as sufficient illumination is provided. Such flexibility of RTM can be conveniently implemented for the application in USCT imaging if the spatial coordinates of the transmitters and receivers are known and enough data is collected to provide full illumination. This paper proposes an implementation of a full 3D RTM algorithm for USCT imaging to produce an accurate 3D acoustic image based on the Phase-shift-plus-interpolation (PSPI) method for wavefield extrapolation. In this method, each acquired data set (shot) is propagated back in time, and a known ultrasound wavelet is propagated forward in time, with PSPI wavefield extrapolation and a piece-wise constant velocity model of the organ (breast). The imaging condition is then applied to produce a partial image. Although each image is subject to the limitation of its own illumination aperture, the stack of multiple partial images will produce a full image of the organ, with a much-reduced noise level if compared with individual partial images.

Keywords: illumination, reverse time migration (RTM), ultrasound computed tomography (USCT), wavefield extrapolation

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Authors: Stanislavs Gendelis, Maris Sinka, Andris Jakovics

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Insulation materials made from natural sources have become more popular for the ecologisation of buildings, meaning wide use of such renewable materials. Such natural materials replace synthetic products which consume a large quantity of energy. The most common and the cheapest natural materials in Latvia are cellulose-based (wood and agricultural plants). The ecological aspects of such materials are well known, but experimental data about physical properties remains lacking. In this study, six different samples of wood wool panels and a mixture of hemp shives and lime (hempcrete) are analysed. Thermal conductivity and heat capacity measurements were carried out for wood wool and cement panels using the calibrated hot plate device. Water vapor permeability was tested for hempcrete material by using the gravimetric dry cup method. Studied wood wool panels are eco-friendly and harmless material, which is widely used in the interior design of public and residential buildings, where noise absorption and sound insulation is of importance. They are also suitable for high humidity facilities (e.g., swimming pools). The difference in panels was the width of used wood wool, which is linked to their density. The results of measured thermal conductivity are in a wide range, showing the worsening of properties with the increasing of the wool width (for the least dense 0.066, for the densest 0.091 W/(m·K)). Comparison with mineral insulation materials shows that thermal conductivity for such materials are 2-3 times higher and are comparable to plywood and fibreboard. Measured heat capacity was in a narrower range; here, the dependence on the wool width was not so strong due to the fact that heat capacity value is related to mass, not volume. The resulting heat capacity is a combination of two main components. A comparison of results for different panels allows to select the most suitable sample for a specific application because the dependencies of the thermal insulation and heat capacity properties on the wool width are not the same. Hempcrete is a much denser material compared to conventional thermal insulating materials. Therefore, its use helps to reinforce the structural capacity of the constructional framework, at the same time, it is lightweight. By altering the proportions of the ingredients, hempcrete can be produced as a structural, thermal, or moisture absorbent component. The water absorption and water vapor permeability are the most important properties of these materials. Information about absorption can be found in the literature, but there are no data about water vapor transmission properties. Water vapor permeability was tested for a sample of locally made hempcrete using different air humidity values to evaluate the possible difference. The results show only the slight influence of the air humidity on the water vapor permeability value. The absolute ‘sd value’ measured is similar to mineral wool and wood fiberboard, meaning that due to very low resistance, water vapor passes easily through the material. At the same time, other properties – structural and thermal of the hempcrete is totally different. As a result, an experimentally-based knowledge of thermal and water vapor transmission properties for cellulose-based materials was significantly improved.

Keywords: heat capacity, hemp concrete, thermal conductivity, water vapor transmission, wood wool

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Authors: Juan C. Pérez-Mesa, Emilio Galdeano-Gómez, Jerónimo De Burgos-Jiménez, José F. Bienvenido-Bárcena, José F. Jiménez-Guerrero

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Environmental cost and transport congestion on roads resulting from product distribution in Europe have to lead to the creation of various programs and studies seeking to reduce these negative impacts. In this regard, apart from other institutions, the European Commission (EC) has designed plans in recent years promoting a more sustainable transportation model in an attempt to ultimately shift traffic from the road to the sea by using intermodality to achieve a model rebalancing. This issue proves especially relevant in supply chains from peripheral areas of the continent, where the supply of certain agrifood products is high. In such cases, the most difficult challenge is managing perishable goods. This study focuses on new approaches that strengthen the modal shift, as well as the reduction of externalities. This problem is analyzed by attempting to promote intermodal system (truck and short sea shipping) for transport, taking as point of reference highly perishable products (vegetables) exported from southeast Spain, which is the leading supplier to Europe. Methodologically, this paper seeks to contribute to the literature by proposing a different and complementary approach to establish a comparison between intermodal and the “only road” alternative. For this purpose, the multicriteria decision is utilized in a p-median model (P-M) adapted to the transport of perishables and to a means of shipping selection problem, which must consider different variables: transit cost, including externalities, time, and frequency (including agile response time). This scheme avoids bias in decision-making processes. By observing the results, it can be seen that the influence of the externalities as drivers of the modal shift is reduced when transit time is introduced as a decision variable. These findings confirm that the general strategies, those of the EC, based on environmental benefits lose their capacity for implementation when they are applied to complex circumstances. In general, the different estimations reveal that, in the case of perishables, intermodality would be a secondary and viable option only for very specific destinations (for example, Hamburg and nearby locations, the area of influence of London, Paris, and the Netherlands). Based on this framework, the general outlook on this subject should be modified. Perhaps the government should promote specific business strategies based on new trends in the supply chain, not only on the reduction of externalities, and find new approaches that strengthen the modal shift. A possible option is to redefine ports, conceptualizing them as digitalized redistribution and coordination centers and not only as areas of cargo exchange.

Keywords: environmental externalities, intermodal transport, perishable food, transit time

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Authors: Panagiotis Lemonakis, Nikolaos Moisiadis, Andromachi Gkoutzini, George Kaliabetsos, Nikos Eliou

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The proper surface water drainage not only affects vehicle movement dynamics but also increases the likelihood of an accident due to the fact that inadequate drainage is associated with potential hydroplaning and splash and spray driving conditions. Nine solutions have been proposed to address hydroplaning in sections with inadequate drainage, e.g., augmented superelevation and longitudinal rates, reduction of runoff length, and skew superelevation. The latter has been extensively implemented in highways recently, enhancing the safety level in the applied road segments in regards to the effective drainage of the rainwater. However, the concept of the skew superelevation has raised concerns regarding the driver’s comfort when traveling over skew superelevation sections, particularly at high speeds. These concerns alleviated through the concept of the round-up skew superelevation, which reduces both the lateral and the vertical acceleration imposed to the drivers and hence, improves comfort and traffic safety. Various research studies aimed at investigating driving comfort by evaluating the lateral and vertical accelerations sustained by the road users and vehicles. These studies focused on the influence of the skew superelevation to passenger cars, buses and trucks, and the drivers themselves, traveling at a certain range of speeds either below or above the design speed. The outcome of these investigations which based on the use of simulations, revealed that the imposed accelerations did not exceed the statutory thresholds even when the travelling speed was significantly greater than the design speed. Nevertheless, the effect of the skew superelevation to other vehicle types for instance, motorcycles, has not been investigated so far. The present research study aims to bridge this gap by investigating the impact of skew superelevation on the motorcycle rider’s comfort. Power two-wheeler riders are susceptible to any changes on the pavement surface and therefore a comparison between the traditional superelevation practice and the skew superelevation concept is of paramount importance. The methodology based on the utilization of sophisticated software in order to design the model of the road for several values of the longitudinal slope. Based on the values of the slopes and the use of a mathematical equation, the accelerations imposed on the wheel of the motorcycle were calculated. Due to the fact that the final aim of the study is the influence of the skew superelevation to the rider, it was deemed necessary to convey the calculated accelerations from the wheel to the rider. That was accomplished by implementing the quarter car suspension model adjusted to the features of two-wheeler vehicles. Finally, the accelerations derived from this process evaluated according to specific thresholds originated from the International Organization for Standardization, which correspond to certain levels of comfort. The most important conclusion drawn is that the comfort of the riders is not dependent on the form of road gradient to a great extent due to the fact that the vertical acceleration imposed to the riders took similar values regardless of the value of the longitudinal slope.

Keywords: acceleration, comfort, motorcycle, safety, skew superelevation

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Authors: M. Sirhan, S. Bekhor, A. Sidess

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In-service pavements deteriorate with time due to traffic wheel loads, environment, and climate conditions. Pavement deterioration leads to a reduction in their serviceability and structural behavior. Consequently, proper maintenance and rehabilitation (M&R) are necessary actions to keep the in-service pavement network at the desired level of serviceability. Due to resource and financial constraints, the pavement management system (PMS) prioritizes roads most in need of maintenance and rehabilitation action. It recommends a suitable action for each pavement based on the performance and surface condition of each road in the network. The pavement performance and condition are usually quantified and evaluated by different types of roughness-based and stress-based indices. Examples of such indices are Pavement Serviceability Index (PSI), Pavement Serviceability Ratio (PSR), Mean Panel Rating (MPR), Pavement Condition Rating (PCR), Ride Number (RN), Profile Index (PI), International Roughness Index (IRI), and Pavement Condition Index (PCI). PCI is commonly used in PMS as an indicator of the extent of the distresses on the pavement surface. PCI values range between 0 and 100; where 0 and 100 represent a highly deteriorated pavement and a newly constructed pavement, respectively. The PCI value is a function of distress type, severity, and density (measured as a percentage of the total pavement area). PCI is usually calculated iteratively using the 'Paver' program developed by the US Army Corps. The use of soft computing techniques, especially Artificial Neural Network (ANN), has become increasingly popular in the modeling of engineering problems. ANN techniques have successfully modeled the performance of the in-service pavements, due to its efficiency in predicting and solving non-linear relationships and dealing with an uncertain large amount of data. Typical regression models, which require a pre-defined relationship, can be replaced by ANN, which was found to be an appropriate tool for predicting the different pavement performance indices versus different factors as well. Subsequently, the objective of the presented study is to develop and train an ANN model that predicts the PCI values. The model’s input consists of percentage areas of 11 different damage types; alligator cracking, swelling, rutting, block cracking, longitudinal/transverse cracking, edge cracking, shoving, raveling, potholes, patching, and lane drop off, at three severity levels (low, medium, high) for each. The developed model was trained using 536,000 samples and tested on 134,000 samples. The samples were collected and prepared by The National Transport Infrastructure Company. The predicted results yielded satisfactory compliance with field measurements. The proposed model predicted PCI values with relatively low standard deviations, suggesting that it could be incorporated into the PMS for PCI determination. It is worth mentioning that the most influencing variables for PCI prediction are damages related to alligator cracking, swelling, rutting, and potholes.

Keywords: artificial neural networks, computer programming, pavement condition index, pavement management, performance prediction

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Authors: Vitor M. Vilas Boas, Cleison D. Silva, Gustavo S. Mafra, Alexandre Trofino Neto

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Motor imagery (MI) based brain-computer interfaces (BCI) uses event-related (de)synchronization (ERS/ ERD), typically recorded using electroencephalography (EEG), to translate brain electrical activity into control commands. To mitigate undesirable artifacts and noise measurements on EEG signals, methods based on band-pass filters defined by a specific frequency band (i.e., 8 – 30Hz), such as the Infinity Impulse Response (IIR) filters, are typically used. Spatial techniques, such as Common Spatial Patterns (CSP), are also used to estimate the variations of the filtered signal and extract features that define the imagined motion. The CSP effectiveness depends on the subject's discriminative frequency, and approaches based on the decomposition of the band of interest into sub-bands with smaller frequency ranges (SBCSP) have been suggested to EEG signals classification. However, despite providing good results, the SBCSP approach generally increases the computational cost of the filtering step in IM-based BCI systems. This paper proposes the use of the Fast Fourier Transform (FFT) algorithm in the IM-based BCI filtering stage that implements SBCSP. The goal is to apply the FFT algorithm to reduce the computational cost of the processing step of these systems and to make them more efficient without compromising classification accuracy. The proposal is based on the representation of EEG signals in a matrix of coefficients resulting from the frequency decomposition performed by the FFT, which is then submitted to the SBCSP process. The structure of the SBCSP contemplates dividing the band of interest, initially defined between 0 and 40Hz, into a set of 33 sub-bands spanning specific frequency bands which are processed in parallel each by a CSP filter and an LDA classifier. A Bayesian meta-classifier is then used to represent the LDA outputs of each sub-band as scores and organize them into a single vector, and then used as a training vector of an SVM global classifier. Initially, the public EEG data set IIa of the BCI Competition IV is used to validate the approach. The first contribution of the proposed method is that, in addition to being more compact, because it has a 68% smaller dimension than the original signal, the resulting FFT matrix maintains the signal information relevant to class discrimination. In addition, the results showed an average reduction of 31.6% in the computational cost in relation to the application of filtering methods based on IIR filters, suggesting FFT efficiency when applied in the filtering step. Finally, the frequency decomposition approach improves the overall system classification rate significantly compared to the commonly used filtering, going from 73.7% using IIR to 84.2% using FFT. The accuracy improvement above 10% and the computational cost reduction denote the potential of FFT in EEG signal filtering applied to the context of IM-based BCI implementing SBCSP. Tests with other data sets are currently being performed to reinforce such conclusions.

Keywords: brain-computer interfaces, fast Fourier transform algorithm, motor imagery, sub-band common spatial patterns

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Authors: Tanya Kaur Bedi, Shankha Pratim Bhattacharya

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The US Environmental Protection Agency defines indoor air quality as “The air quality within and around buildings, especially as it relates to the health and comfort of building occupants”. According to the 2021 report by the Energy Policy Institute at Chicago, Indian residents, a country which is home to the highest levels of air pollution in the world, lose about 5.9 years from life expectancy due to poor air quality and yet has numerous dwellings dependent on natural ventilation. Currently the urban population spends 90% of the time indoors, this scenario raises a concern for occupant health and well-being. The built environment can affect health directly and indirectly through immediate or long-term exposure to indoor air pollutants. Health effects associated with indoor air pollutants include eye/nose/throat irritation, respiratory diseases, heart disease, and even cancer. This study attempts to demonstrate the causal relationship between the indoor air quality and its determining aspects. Detailed indoor air quality audits were conducted in residential buildings located in Kolkata, India in the months of December and January 2021. According to the air pollution knowledge assessment city program in India, Kolkata is also the second most polluted mega-city after Delhi. Although the air pollution levels are alarming year-long, the winter months are most crucial due to the unfavorable environmental conditions. While emissions remain typically constant throughout the year, cold air is denser and moves slower than warm air, trapping the pollution in place for much longer and consequently is breathed in at a higher rate than the summers. The air pollution monitoring period was selected considering environmental factors and major pollution contributors like traffic and road dust. This study focuses on the relationship between the built environment and the spatial-temporal distribution of air pollutants in and around it. The measured parameters include, temperature, relative humidity, air velocity, particulate matter, volatile organic compounds, formaldehyde, and benzene. A total of 56 rooms were audited, selectively targeting the most dominant middle-income group. The data-collection was conducted using a set of instruments positioned in the human breathing-zone. The study assesses indoor air quality based on factors determining natural ventilation and air pollution dispersion such as surrounding environment, dominant wind, openable window to floor area ratio, windward or leeward side openings, and natural ventilation type in the room: single side or cross-ventilation, floor height, residents cleaning habits, etc.

Keywords: indoor air quality, occupant health, urban housing, air pollution, natural ventilation, architecture, urban issues

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Authors: Osungwu Emeka

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Until recently, Port Harcourt was known as the “Garden City of Nigeria” because of its neatness and the overwhelming presence of vegetation all over the metropolis. But today, the presence of piles of refuse dotting the entire city may have turned Port Harcourt into a “Garbage City”. Indiscriminate dumping of industrial, commercial and household wastes such as food waste, paper, polythene, textiles, scrap metals, glasses, wood, plastic, etc. at street corners and gutters, is still very common. The waste management problem in the state affects the citizens both directly and indirectly. The dumping of waste along the roadside obstructs traffic and, after mixing with rain water may sip underground with the possibility of the leachate contaminating the groundwater. The basic solid waste management processes of collection, transportation, segregation and final disposal appear to be very inefficient. This study was undertaken to assess waste utilization using metal waste scavengers. Highlighting their activities as a part of the informal sector of the solid waste management system with a view to identifying their challenges, prospects and possible contributions to the solid waste management system in the Port Harcourt metropolis. Therefore, the aim was to understand and assess scavenging as a system of solid waste management in Port Harcourt and to identify the main bottlenecks to its efficiency and the way forward. This study targeted people who engage in scavenging metal scraps across 5 major waste dump sites across Port Harcourt. To achieve this, a mixed method study was conducted to provide both experiential evidence on this waste utilization method using a qualitative study and a survey to collect numeric evidence on this subject. The findings from the qualitative string of this study provided insight on scavenging as a waste utilization activity and how their activities can reduce the gross waste generated and collected from the subject areas. It further showed the nature and characteristics of scavengers in the waste recycling system as a means of achieving the millennium development goals towards poverty alleviation, job creation and the development of a sustainable, cleaner environment. The study showed that in Port Harcourt, the waste management practice involves the collection, transportation and disposal of waste by refuse contractors using cart pushers and disposal vehicles at designated dumpsites where the scavengers salvage metal scraps for recycling and reuse. This study further indicates that there is a great demand for metal waste materials/products that are clearly identified as genuinely sustainable, even though they may be perceived as waste. The market for these waste materials shall promote entrepreneurship as a profitable venture for waste recovery and recycling in Port Harcourt. Therefore, the benefit of resource recovery and recycling as a means of the solid waste management system will enhance waste to wealth that will reduce pollution, create job opportunities thereby alleviate poverty.

Keywords: scavengers, metal waste, waste-to-wealth, recycle, Port Harcourt, Nigeria, waste reduction, garden city, waste

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Authors: Tamar Trop

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Bike sharing systems can be important elements of smart cities as they have the potential for impact on multiple levels. These systems can add a significant alternative to other modes of mass transit in cities that are continuously looking for measures to become more livable and maintain their attractiveness for citizens, businesses and tourism. Bike-sharing began in Europe in 1965, and a viable format emerged in the mid-2000s thanks to the introduction of information technology. The rate of growth in bike-sharing schemes and fleets has been very rapid since 2008 and has probably outstripped growth in every other form of urban transport. Today, public bike-sharing systems are available on five continents, including over 700 cities, operating more than 800,000 bicycles at approximately 40,000 docking stations. Since modern bike sharing systems have become prevalent only in the last decade, the existing literature analyzing these systems and their sustainability is relatively new. The purpose of the presented study is to assess the sustainability of these newly emerging transportation systems, by using the Envision™ rating system as a methodological framework and the Israeli 'Tel -O-Fun' – bike sharing project as a case study. The assessment was conducted by project team members. Envision™ is a new guidance and rating system used to assess and improve the sustainability of all types and sizes of infrastructure projects. This tool provides a holistic framework for evaluating and rating the community, environmental, and economic benefits of infrastructure projects over the course of their life cycle. This evaluation method has 60 sustainability criteria divided into five categories: Quality of life, leadership, resource allocation, natural world, and climate and risk. 'Tel -O-Fun' project was launched in Tel Aviv-Yafo on 2011 and today provides about 1,800 bikes for rent, at 180 rental stations across the city. The system is based on a complex computer terminal that is located in the docking stations. The highest-rated sustainable features that the project scored include: (a) Improving quality of life by: offering a low cost and efficient form of public transit, improving community mobility and access, enabling the flexibility of travel within a multimodal transportation system, saving commuters time and money, enhancing public health and reducing air and noise pollution; (b) improving resource allocation by: offering inexpensive and flexible last-mile connectivity, reducing space, materials and energy consumption, reducing wear and tear on public roads, and maximizing the utility of existing infrastructure, and (c) reducing of greenhouse gas emissions from transportation. Overall, 'Tel -O-Fun' project was highly scored as an environmentally sustainable and socially equitable infrastructure. The use of this practical framework for evaluation also yielded various interesting insights on the shortcoming of the system and the characteristics of good solutions. This can contribute to the improvement of the project and may assist planners and operators of bike sharing systems to develop a sustainable, efficient and reliable transportation infrastructure within smart cities.

Keywords: bike sharing, Envision™, sustainability rating system, sustainable infrastructure

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Authors: Stephen Kyakulumbye, Vivian Kobusingye

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Generally, World over, COVID-19 has had adverse effects on all sectors but with more debilitating effects on the education sector. After reactive lockdowns, education institutions that could continue teaching and learning had to go a distance mediated by digital technological tools. In Uganda, the Ministry of Education thereby issued COVID-19 Online Distance E-learning (ODeL) emergent guidelines. Despite such guidelines, academic institutions in Uganda and similar developing contexts with academically constrained resource environments were caught off-guard and ill-prepared to transform from face-to-face learning to online distance learning mode. Most academic institutions that migrated spontaneously did so with no deliberate tools, systems, strategies, or software to cause active, meaningful, and engaging learning for students. By experience, most of these academic institutions shifted to Zoom and WhatsApp and instead conducted online teaching in real-time than blended synchronous and asynchronous tools. This paper provides students’ experiences while blending synchronous and asynchronous content-creating and learning tools within a technological resource-constrained environment to navigate in such a challenging Uganda context. These conceptual case-based findings, using experience from Uganda Christian University (UCU), point at the design of learning activities with two certain characteristics, the enhancement of synchronous learning technologies with asynchronous ones to mitigate the challenge of system breakdown, passive learning to active learning, and enhances the types of presence (social, cognitive and facilitatory). The paper, both empirical and experiential in nature, uses online experiences from third-year students in Bachelor of Business Administration student lectured using asynchronous text, audio, and video created with Open Broadcaster Studio software and compressed with Handbrake, all open-source software to mitigate disk space and bandwidth usage challenges. The synchronous online engagements with students were a blend of zoom or BigBlueButton, to ensure that students had an alternative just in case one failed due to excessive real-time traffic. Generally, students report that compared to their previous face-to-face lectures, the pre-recorded lectures via Youtube provided them an opportunity to reflect on content in a self-paced manner, which later on enabled them to engage actively during the live zoom and/or BigBlueButton real-time discussions and presentations. The major recommendation is that lecturers and teachers in a resource-constrained environment with limited digital resources like the internet and digital devices should harness this approach to offer students access to learning content in a self-paced manner and thereby enabling reflective active learning through reflective and high-order thinking.

Keywords: synchronous learning, asynchronous learning, active learning, reflective learning, resource-constrained environment

Procedia PDF Downloads 120

Authors: Sachil Kumar, Anoop Kumar Verma, Wahid Ali, Uma Shankar Singh

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Globally approximately 55.3 million people die each year. In the India there were 95 lakh annual deaths in 2013. The number of deaths resulted from homicides, suicides and unintentional injuries in the same period was about 5.7 lakh. The ever-increasing crime rate necessitated the development of methods for determining time since death. An erroneous time of death window can lead investigators down the wrong path or possibly focus a case on an innocent suspect. In this regard a research was carried out by analyzing the temperature dependent degradation of a Cardiac Troponin-T protein (cTnT) in the myocardium postmortem as a marker for time since death. Cardiac tissue samples were collected from (n=6) medico-legal autopsies, (in the Department of Forensic Medicine and Toxicology, King George’s Medical University, Lucknow India) after informed consent from the relatives and studied post-mortem degradation by incubation of the cardiac tissue at room temperature (20±2 OC), 12 0C, 25 0C and 37 0C for different time periods ((~5, 26, 50, 84, 132, 157, 180, 205, and 230 hours). The cases included were the subjects of road traffic accidents (RTA) without any prior history of disease who died in the hospital and their exact time of death was known. The analysis involved extraction of the protein, separation by denaturing gel electrophoresis (SDS-PAGE) and visualization by Western blot using cTnT specific monoclonal antibodies. The area of the bands within a lane was quantified by scanning and digitizing the image using Gel Doc. The data shows a distinct temporal profile corresponding to the degradation of cTnT by proteases found in cardiac muscle. The disappearance of intact cTnT and the appearance of lower molecular weight bands are easily observed. Western blot data clearly showed the intact protein at 42 kDa, two major (27 kDa, 10kDa) fragments, two additional minor fragments (32 kDa) and formation of low molecular weight fragments as time increases. At 12 0C the intensity of band (intact cTnT) decreased steadily as compared to RT, 25 0C and 37 0C. Overall, both PMI and temperature had a statistically significant effect where the greatest amount of protein breakdown was observed within the first 38 h and at the highest temperature, 37 0C. The combination of high temperature (37 0C) and long Postmortem interval (105.15 hrs) had the most drastic effect on the breakdown of cTnT. If the percent intact cTnT is calculated from the total area integrated within a Western blot lane, then the percent intact cTnT shows a pseudo-first order relationship when plotted against the log of the time postmortem. These plots show a good coefficient of correlation of r = 0.95 (p=0.003) for the regression of the human heart at different temperature conditions. The data presented demonstrates that this technique can provide an extended time range during which Postmortem interval can be more accurately estimated.

Keywords: degradation, postmortem interval, proteolysis, temperature, troponin

Procedia PDF Downloads 378

Authors: Ramalan Musa Yerima

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The paper titled “loading by number” explained a strategy developed recently by Zonal Commanding Officer of the Federal Road Safety Corps of Nigeria, covering Sokoto, Kebbi and Zamfara States of Northern Nigeria. The strategy is aimed at reducing competition, which will invariably leads to the reduction in speed, reduction in dangerous driving, reduction in crash rate, reduction in injuries, reduction in property damages and reduction in death through road traffic crashes (RTC). This research paper presents a study focused on enhancing the safety of commercial vehicles. The background of this study highlights the alarming statistics related to commercial vehicle crashes in Nigeria with focus on Sokoto, Kebbi and Zamfara States, which often result in significant damage to property, loss of lives, and economic costs. The significance and aims is to investigate and propose effective strategy to enhance the safety of commercial vehicles. The study recognizes the pressing need for heightened safety measures in commercial transportation, as it impacts not only the well-being of drivers and passengers but also the overall public safety. To achieve the objectives, an examination of accident data, including causes and contributing factors, was performed to identify critical areas for improvement. The major finding of the study reveals that when competition comes into play within the realm of commercial driving, it has detrimental effects on road safety and resource management. Commercial drivers are pushed to complete their routes quickly, deliver goods on time or they pushed themselves to arrive quickly for more passengers and new contracts. This competitive environment, fuelled by internal and external pressures such as tight deadlines, poverty and greed, often leads to sad endings. The study recommend that if a strategy called loading by number is integrated with other multiple safety measures such as driver training programs, regulatory enforcement, and infrastructure improvements, commercial vehicle safety can be significantly enhanced. "Loading by Number” approach is design to ensure that the sequence of departure of drivers from motor park ‘A’ would be communicated to motor park officials of park ‘B’, which would be considered sequentially when giving them returning passengers, regardless of the first to arrive. In conclusion, this paper underscores the significance of improving the safety measures of commercial vehicles, as they are often larger and heavier than other vehicles on the road. Whenever they are involved in accidents, the consequences can be more severe. Commercial vehicles are also frequently involved in long-haul or interstate transportation, which means they cover longer distances and spend more time on the road. This increased exposure to driving conditions increases the probability of accidents occurring. By implementing the suggested measures, policymakers, transportation authorities, and industry stakeholders can work collectively towards ensuring a safer commercial transportation system.

Keywords: commercial, safety, strategy, transportation

Procedia PDF Downloads 50

Authors: Ramalan Musa Yerima

Abstract:

The paper titled “Loading by number” explained a strategy developed recently by the Zonal Commanding Officer of the Federal Road Safety Corps of Nigeria, covering Sokoto, Kebbi and Zamfara States of Northern Nigeria. The strategy is aimed at reducing competition, which will invariably lead to a reduction in speed, reduction in dangerous driving, reduction in crash rate, reduction in injuries, reduction in property damages and reduction in death through road traffic crashes (RTC). This research paper presents a study focused on enhancing the safety of commercial vehicles. The background of this study highlights the alarming statistics related to commercial vehicle crashes in Nigeria with a focus on Sokoto, Kebbi and Zamfara States, which often result in significant damage to property, loss of lives, and economic costs. The significance and aims is to investigate and propose an effective strategy to enhance the safety of commercial vehicles. The study recognizes the pressing need for heightened safety measures in commercial transportation, as it impacts not only the well-being of drivers and passengers but also the overall public safety. To achieve the objectives, an examination of accident data, including causes and contributing factors, was performed to identify critical areas for improvement. The major finding of the study reveals that when competition comes into play within the realm of commercial driving, it has detrimental effects on road safety and resource management. Commercial drivers are pushed to complete their routes quickly and deliver goods on time, or they push themselves to arrive quickly for more passengers and new contracts. This competitive environment, fuelled by internal and external pressures such as tight deadlines, poverty and greed, often leads to sad endings. The study recommends that if a strategy called loading by number is integrated with other multiple safety measures, such as driver training programs, regulatory enforcement, and infrastructure improvements, commercial vehicle safety can be significantly enhanced. "Loading by Number” approach is designed to ensure that the sequence of departure of drivers from the motor park ‘A’ would be communicated to motor park officials of park ‘B’, which would be considered sequentially when giving them returning passengers, regardless of the first to arrive. In conclusion, this paper underscores the significance of improving the safety measures of commercial vehicles, as they are often larger and heavier than other vehicles on the road. Whenever they are involved in accidents, the consequences can be more severe. Commercial vehicles are also frequently involved in long-haul or interstate transportation, which means they cover longer distances and spend more time on the road. This increased exposure to driving conditions increases the probability of accidents occurring. By implementing the suggested measures, policymakers, transportation authorities, and industry stakeholders can work collectively toward ensuring a safer commercial transportation system.

Keywords: commercial, safety, strategy, transport

Procedia PDF Downloads 51

Authors: Zhuang Ma, Zihan Zhang, Yu Li

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Environmental concerns from stakeholders (e.g., governments & customers) have pushed firms to integrate environmental management systems into business processes such as R&D, manufacturing, and marketing. Environmental systems include managing environmental risks and pollution control (e.g., air pollution control, waste-water treatment, noise control, energy recycling & solid waste treatment) through raw material management, the elimination and reduction of contaminants, recycling, and reuse in firms' operational processes. Despite increasing studies on firms' proactive adoption of environmental management, their focus is primarily on large corporations operating in developed economies. Investigations in the environmental management efforts of small and medium-sized enterprises (SMEs) are scarce. This is problematic for SMEs because, unlike large corporations, SMEs have limited awareness, resources, capabilities to adapt their operational routines to address environmental impacts. The purpose of this study is to explore how SMEs develop organizational capabilities through interactions with business partners (e.g., environmental management specialists & customers). Drawing on the resource-based view (RBV) and an organizational capabilities perspective, this study investigates the interactively developed capabilities that allow SMEs to adopt environmental management systems. Using an exploratory approach, the study includes 12 semi-structured interviews with senior managers from four SMEs, two environmental management specialists, and two customers in the pharmaceutical sector in Chongqing, China. Findings of this study include four key organizational capabilities: 1) ‘dynamic marketing’ capability, which allows SMEs to recoup the investments in environmental management systems by developing environmentally friendly products to address customers' ever-changing needs; 2) ‘process improvement’ capability, which allows SMEs to select and adopt the latest technologies from biology, chemistry, new material, and new energy sectors into the production system for improved environmental performance and cost-reductions; and 3) ‘relationship management’ capability which allows SMEs to improve corporate image among the public, social media, government agencies, and customers, who in turn help SMEs to overcome their competitive disadvantages. These interactively developed capabilities help SMEs to address larger competitors' foothold in the local market, reduce market constraints, and exploit competitive advantages in other regions (e.g., Guangdong & Jiangsu) of China. These findings extend the RBV and organizational capabilities perspective; that is, SMEs can develop the essential resources and capabilities required for environmental management through interactions with upstream and downstream business partners. While a limited number of studies did highlight the importance of interactions among SMEs, customers, suppliers, NGOs, industrial associations, and consulting firms, they failed to explore the specific capabilities developed through these interactions. Additionally, the findings can explain how a proactive adoption of environmental management systems could help some SMEs to overcome the institutional and market restraints on their products, thereby springboarding into larger, more environmentally demanding, yet more profitable markets compared with their existing market.

Keywords: capabilities, environmental management systems, interactions, SMEs

Procedia PDF Downloads 168

Authors: Nikitha Francis, Anjana Hoode, Vinitha George, Jayashree S. Bhat

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The interaction between cognition and language, referred as cognitive-communication, is very intricate, involving several mental processes such as perception, memory, attention, lexical retrieval, decision making, motor planning, self-monitoring and knowledge. Cognitive-communication disorders are difficulties in communicative competencies that result from underlying cognitive impairments of attention, memory, organization, information processing, problem solving, and executive functions. Traumatic brain injury (TBI) is an acquired, non - progressive condition, resulting in distinct deficits of cognitive communication abilities such as naming, word-finding, self-monitoring, auditory recognition, attention, perception and memory. Cognitive-communication intervention in TBI is individualized, in order to enhance the person’s ability to process and interpret information for better functioning in their family and community life. The present case report illustrates the cognitive-communicative behaviors and the intervention outcomes of an adult with TBI, who was brought to the Department of Audiology and Speech Language Pathology, with cognitive and communicative disturbances, consequent to road traffic accident. On a detailed assessment, she showed naming deficits along with perseverations and had severe difficulty in recalling the details of the accident, her house address, places she had visited earlier, names of people known to her, as well as the activities she did each day, leading to severe breakdowns in her communicative abilities. She had difficulty in initiating, maintaining and following a conversation. She also lacked orientation to time and place. On administration of the Manipal Manual of Cognitive Linguistic Abilities (MMCLA), she exhibited poor performance on tasks related to visual and auditory perception, short term memory, working memory and executive functions. She attended 20 sessions of cognitive-communication intervention which followed a domain-general, adaptive training paradigm, with tasks relevant to everyday cognitive-communication skills. Compensatory strategies such as maintaining a dairy with reminders of her daily routine, names of people, date, time and place was also recommended. MMCLA was re-administered and her performance in the tasks showed significant improvements. Occurrence of perseverations and word retrieval difficulties reduced. She developed interests to initiate her day-to-day activities at home independently, as well as involve herself in conversations with her family members. Though she lacked awareness about her deficits, she actively involved herself in all the therapy activities. Rehabilitation of moderate to severe head injury patients can be done effectively through a holistic cognitive retraining with a focus on different cognitive-linguistic domains. Selection of goals and activities should have relevance to the functional needs of each individual with TBI, as highlighted in the present case report.

Keywords: cognitive-communication, executive functions, memory, traumatic brain injury

Procedia PDF Downloads 336

Authors: N. Uaphongsathorn, C. Worawong, S. Thaewpia

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Background and significance: The population is aging in Thai society, the elderly dependent is at great risk of various functional, psychological, and socio-economic problems as well as less access to health care. They may require long term care at home to maximize their functional abilities and activities of daily living and to improve their quality of life during their own age. Therefore, there is a need to develop a home-based long term care to meet the long term care needs of elders dependent. Methods: The research purpose was to develop long term care model among the elderly dependent in Chaiyaphum province in Northeast region of Thailand. Action Research which is composing of planning, action, observation, and reflection phases was used. Research was carried out for 12 months in all sub-districts of 6 districts in Chaiyaphum province. Participants (N = 1,010) participating in the processes of model development were comprised of 3 groups: a) a total of 110 health care professionals, b) a total of 600 health volunteers and family caregivers and c) a total of 300 the elderly dependent with chronically medical illnesses or disabilities. Descriptive statistics and content analysis were used to analyze data. Findings: Results have shown that the most common health problems among elders dependent with physical disabilities to function independently were cardiovascular disease, dementia, and traffic injuries. The development of home-based long term care model among elders dependent in Chaiyaphum province was composed of six key steps. They are: a) initiating policies supporting formal and informal caregivers for the elder dependent in all sub-districts, b) building network and multidisciplinary team, c) developing 3-day care manager training program and 3-day care provider training program d) training case managers and care providers for the elderly dependent through team and action learning, e) assessing, planning and providing care based on care individual’s needs of the elderly dependent, and f) sharing experiences for good practice and innovation for long term care at homes in district urban and rural areas. Among all care managers and care providers, the satisfaction level for training programs was high with a mean score of 3.98 out of 5. The elders dependent and family caregivers addressed that long term care at home could contribute to improving life’s daily activities, family relationship, health status, and quality of life. Family caregivers and volunteers have feeling a sense of personal satisfaction and experiencing providing meaningful care and support for elders dependent. Conclusion: In conclusion, a home-based long term care is important to Thai elders dependent. Care managers and care providers play a large role and responsibility to provide appropriate care to meet the elders’ needs in both urban and rural areas in Thai society. Further research could be rigorously studied with a larger group of populations in similar socio-economic and cultural contexts.

Keywords: elderly people, care manager, care provider, long term care

Procedia PDF Downloads 296

Authors: Desmond Appiah, Fan Zhang, Shouqi Yuan, Wei Xueyuan, Stephen N. Asomani

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The side channel pump has distinctive hydraulic performance characteristics over other vane pumps because of its generation of high pressure heads in only one impeller revolution. Hence, there is soaring utilization and application in the fields of petrochemical, food processing fields, automotive and aerospace fuel pumping where high heads are required at low flows. The side channel pump is characterized by unstable flow because after fluid flows into the impeller passage, it moves into the side channel and comes back to the impeller again and then moves to the next circulation. Consequently, the flow leaves the side channel pump following a helical path. However, the pressure fluctuation exhibited in the flow greatly contributes to the unwanted noise and vibration which is associated with the flow. In this paper, a side channel pump prototype was examined thoroughly through numerical calculations based on SST k-ω turbulence model to ascertain the pressure fluctuation behavior. The pressure fluctuation intensity of the 3D unstable flow dynamics were carefully investigated under different working conditions 0.8QBEP, 1.0 QBEP and 1.2QBEP. The results showed that the pressure fluctuation distribution around the pressure side of the blade is greater than the suction side at the impeller and side channel interface (z=0) for all three operating conditions. Part-load condition 0.8QBEP recorded the highest pressure fluctuation distribution because of the high circulation velocity thus causing an intense exchanged flow between the impeller and side channel. Time and frequency domains spectra of the pressure fluctuation patterns in the impeller and the side channel were also analyzed under the best efficiency point value, QBEP using the solution from the numerical calculations. It was observed from the time-domain analysis that the pressure fluctuation characteristics in the impeller flow passage increased steadily until the flow reached the interrupter which separates low-pressure at the inflow from high pressure at the outflow. The pressure fluctuation amplitudes in the frequency domain spectrum at the different monitoring points depicted a gentle decreasing trend of the pressure amplitudes which was common among the operating conditions. The frequency domain also revealed that the main excitation frequencies occurred at 600Hz, 1200Hz, and 1800Hz and continued in the integers of the rotating shaft frequency. Also, the mass flow exchange plots indicated that the side channel pump is characterized with many vortex flows. Operating conditions 0.8QBEP, 1.0 QBEP depicted less and similar vortex flow while 1.2Q recorded many vortex flows around the inflow, middle and outflow regions. The results of the numerical calculations were finally verified experimentally. The performance characteristics curves from the simulated results showed that 0.8QBEP working condition recorded a head increase of 43.03% and efficiency decrease of 6.73% compared to 1.0QBEP. It can be concluded that for industrial applications where the high heads are mostly required, the side channel pump can be designed to operate at part-load conditions. This paper can serve as a source of information in order to optimize a reliable performance and widen the applications of the side channel pumps.

Keywords: exchanged flow, pressure fluctuation, numerical simulation, side channel pump

Procedia PDF Downloads 127

Authors: Zhao Gao, Eran Edirisinghe

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The production of an accurate sketch of a suspect based on a verbal description obtained from a witness is an essential task for most criminal investigations. The criminal investigation system employs specifically trained professional artists to manually draw a facial image of the suspect according to the descriptions of an eyewitness for subsequent identification. Within the advancement of Deep Learning, Recurrent Neural Networks (RNN) have shown great promise in Natural Language Processing (NLP) tasks. Additionally, Generative Adversarial Networks (GAN) have also proven to be very effective in image generation. In this study, a trained GAN conditioned on textual features such as keywords automatically encoded from a verbal description of a human face using an RNN is used to generate photo-realistic facial images for criminal investigations. The intention of the proposed system is to map corresponding features into text generated from verbal descriptions. With this, it becomes possible to generate many reasonably accurate alternatives to which the witness can use to hopefully identify a suspect from. This reduces subjectivity in decision making both by the eyewitness and the artist while giving an opportunity for the witness to evaluate and reconsider decisions. Furthermore, the proposed approach benefits law enforcement agencies by reducing the time taken to physically draw each potential sketch, thus increasing response times and mitigating potentially malicious human intervention. With publically available 'CelebFaces Attributes Dataset' (CelebA) and additionally providing verbal description as training data, the proposed architecture is able to effectively produce facial structures from given text. Word Embeddings are learnt by applying the RNN architecture in order to perform semantic parsing, the output of which is fed into the GAN for synthesizing photo-realistic images. Rather than the grid search method, a metaheuristic search based on genetic algorithms is applied to evolve the network with the intent of achieving optimal hyperparameters in a fraction the time of a typical brute force approach. With the exception of the ‘CelebA’ training database, further novel test cases are supplied to the network for evaluation. Witness reports detailing criminals from Interpol or other law enforcement agencies are sampled on the network. Using the descriptions provided, samples are generated and compared with the ground truth images of a criminal in order to calculate the similarities. Two factors are used for performance evaluation: The Structural Similarity Index (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). A high percentile output from this performance matrix should attribute to demonstrating the accuracy, in hope of proving that the proposed approach can be an effective tool for law enforcement agencies. The proposed approach to criminal facial image generation has potential to increase the ratio of criminal cases that can be ultimately resolved using eyewitness information gathering.

Keywords: RNN, GAN, NLP, facial composition, criminal investigation

Procedia PDF Downloads 149

Authors: Ja-Keoung Koo, Kensuke Nakamura, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Byung-Woo Hong

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The machine learning techniques based on a convolutional neural network (CNN) have been actively developed and successfully applied to a variety of image analysis tasks including reconstruction, noise reduction, resolution enhancement, segmentation, motion estimation, object recognition. The classical visual information processing that ranges from low level tasks to high level ones has been widely developed in the deep learning framework. It is generally considered as a challenging problem to derive visual interpretation from high dimensional imagery data. A CNN is a class of feed-forward artificial neural network that usually consists of deep layers the connections of which are established by a series of non-linear operations. The CNN architecture is known to be shift invariant due to its shared weights and translation invariance characteristics. However, it is often computationally intractable to optimize the network in particular with a large number of convolution layers due to a large number of unknowns to be optimized with respect to the training set that is generally required to be large enough to effectively generalize the model under consideration. It is also necessary to limit the size of convolution kernels due to the computational expense despite of the recent development of effective parallel processing machinery, which leads to the use of the constantly small size of the convolution kernels throughout the deep CNN architecture. However, it is often desired to consider different scales in the analysis of visual features at different layers in the network. Thus, we propose a CNN model where different sizes of the convolution kernels are applied at each layer based on the random projection. We apply random filters with varying sizes and associate the filter responses with scalar weights that correspond to the standard deviation of the random filters. We are allowed to use large number of random filters with the cost of one scalar unknown for each filter. The computational cost in the back-propagation procedure does not increase with the larger size of the filters even though the additional computational cost is required in the computation of convolution in the feed-forward procedure. The use of random kernels with varying sizes allows to effectively analyze image features at multiple scales leading to a better generalization. The robustness and effectiveness of the proposed CNN based on random kernels are demonstrated by numerical experiments where the quantitative comparison of the well-known CNN architectures and our models that simply replace the convolution kernels with the random filters is performed. The experimental results indicate that our model achieves better performance with less number of unknown weights. The proposed algorithm has a high potential in the application of a variety of visual tasks based on the CNN framework. Acknowledgement—This work was supported by the MISP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by IITP, and NRF-2014R1A2A1A11051941, NRF2017R1A2B4006023.

Keywords: deep learning, convolutional neural network, random kernel, random projection, dimensionality reduction, object recognition

Procedia PDF Downloads 279

Authors: Sunil Chandra, Triparna Barman, Vikas Gusain, Himanshu Rawat

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Forest fires are a recurrent phenomenon in the Himalayan region owing to the presence of vulnerable forest types, topographical gradients, climatic weather conditions, and anthropogenic pressure. The present study focuses on the identification of forest fire-affected areas in a small part of the West Himalayan region using a differential normalized burnt ratio method and spectral unmixing methods. The study area has a rugged terrain with the presence of sub-tropical pine forest, montane temperate forest, and sub-alpine forest and scrub. The major reason for fires in this region is anthropogenic in nature, with the practice of human-induced fires for getting fresh leaves, scaring wild animals to protect agricultural crops, grazing practices within the reserved forest, and igniting fires for cooking and other reasons. The fires caused by the above reasons affect a large area on the ground, necessitating its precise estimation for further management and policy making. In the present study, two approaches have been used for carrying out a burnt area analysis. The first approach followed for burnt area analysis uses a differential burnt normalized ratio index (dNBR) approach that uses the burnt ratio values generated using Short Wave Infra Red (SWIR) band and Near Infra Red (NIR) bands of the Sentinel-2A image. The results of the dNBR have been compared with the outputs of the spectral mixing methods. It has been found that the dNBR is able to create good results in fire-affected areas having homogenous forest stratum and with slope degree <5 degrees. However, in a rugged terrain where the landscape is largely influenced by the topographical variations, vegetation types, tree density, the results may be largely influenced by the effects of topography, complexity in tree composition, fuel load composition, and soil moisture. Hence, such variations in the factors influencing burnt area assessment may not be effectively carried out using a dNBR approach which is commonly followed for burnt area assessment over a large area. Hence, another approach that has been attempted in the present study utilizes a spectral mixing method where the individual pixel is tested before assigning an information class to it. The method uses a neural network approach utilizing Sentinel 2A bands. The training and testing data are generated from the sentinel-2A data and the national field inventory, which is further used for generating outputs using ML tools. The analysis of the results indicates that the fire-affected regions and their severity can be better estimated in rugged terrain using spectral unmixing methods which have the capability to resolve the noise in the data and can classify the individual pixel to the precise burnt/unburnt class.

Keywords: dNBR, spectral unmixing, neural network, forest stratum

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Authors: Matej Komanec, Tomas Nemecek, Dmytro Suslov, Petr Chvojka, Stanislav Zvanovec

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Nonlinear phenomena in the near- and mid-infrared region are attracting scientific attention mainly due to the supercontinuum generation possibilities and subsequent utilizations for ultra-wideband applications like e.g. absorption spectroscopy or optical coherence tomography. Thulium-based fiber lasers provide access to high-power ultrashort pump pulses in the vicinity of 2000 nm, which can be easily exploited for various nonlinear applications. The paper presents a simulation and experimental study of a pulsed thulium laser based for near-infrared (NIR) and mid-infrared (MIR) nonlinear applications in specialty optical fibers. In the first part of the paper the thulium laser is discussed. The thulium laser is based on a gain-switched seed-laser and a series of amplification stages for obtaining output peak powers in the order of kilowatts for pulses shorter than 200 ps in full-width at half-maximum. The pulsed thulium laser is first studied in a simulation software, focusing on seed-laser properties. Afterward, a pre-amplification thulium-based stage is discussed, with the focus of low-noise signal amplification, high signal gain and eliminating pulse distortions during pulse propagation in the gain medium. Following the pre-amplification stage a second gain stage is evaluated with incorporating a thulium-fiber of shorter length with increased rare-earth dopant ratio. Last a power-booster stage is analyzed, where the peak power of kilowatts should be achieved. Examples of analytical study are further validated by the experimental campaign. The simulation model is further corrected based on real components – parameters such as real insertion-losses, cross-talks, polarization dependencies, etc. are included. The second part of the paper evaluates the utilization of nonlinear phenomena, their specific features at the vicinity of 2000 nm, compared to e.g. 1550 nm, and presents supercontinuum modelling, based on the thulium laser pulsed output. Supercontinuum generation simulation is performed and provides reasonably accurate results, once fiber dispersion profile is precisely defined and fiber nonlinearity is known, furthermore input pulse shape and peak power must be known, which is assured thanks to the experimental measurement of the studied thulium pulsed laser. The supercontinuum simulation model is put in relation to designed and characterized specialty optical fibers, which are discussed in the third part of the paper. The focus is placed on silica and mainly on non-silica fibers (fluoride, chalcogenide, lead-silicate) in their conventional, microstructured or tapered variants. Parameters such as dispersion profile and nonlinearity of exploited fibers were characterized either with an accurate model, developed in COMSOL software or by direct experimental measurement to achieve even higher precision. The paper then combines all three studied topics and presents a possible application of such a thulium pulsed laser system working with specialty optical fibers.

Keywords: nonlinear phenomena, specialty optical fibers, supercontinuum generation, thulium laser

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Authors: Muhammad Ahnaf Zahin, Yaw Adu-Gyamfi

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Bus transit is a crucial component of transportation networks, especially in urban areas. Any intelligent transportation system must have accurate real-time information on bus travel times since it minimizes waiting times for passengers at different stations along a route, improves service reliability, and significantly optimizes travel patterns. Bus agencies must enhance the quality of their information service to serve their passengers better and draw in more travelers since people waiting at bus stops are frequently anxious about when the bus will arrive at their starting point and when it will reach their destination. For solving this issue, different models have been developed for predicting bus travel times recently, but most of them are focused on smaller road networks due to their relatively subpar performance in high-density urban areas on a vast network. This paper develops a deep learning-based architecture using a single-step multi-station forecasting approach to predict average bus travel times for numerous routes, stops, and trips on a large-scale network using heterogeneous bus transit data collected from the GTFS database. Over one week, data was gathered from multiple bus routes in Saint Louis, Missouri. In this study, Gated Recurrent Unit (GRU) neural network was followed to predict the mean vehicle travel times for different hours of the day for multiple stations along multiple routes. Historical time steps and prediction horizon were set up to 5 and 1, respectively, which means that five hours of historical average travel time data were used to predict average travel time for the following hour. The spatial and temporal information and the historical average travel times were captured from the dataset for model input parameters. As adjacency matrices for the spatial input parameters, the station distances and sequence numbers were used, and the time of day (hour) was considered for the temporal inputs. Other inputs, including volatility information such as standard deviation and variance of journey durations, were also included in the model to make it more robust. The model's performance was evaluated based on a metric called mean absolute percentage error (MAPE). The observed prediction errors for various routes, trips, and stations remained consistent throughout the day. The results showed that the developed model could predict travel times more accurately during peak traffic hours, having a MAPE of around 14%, and performed less accurately during the latter part of the day. In the context of a complicated transportation network in high-density urban areas, the model showed its applicability for real-time travel time prediction of public transportation and ensured the high quality of the predictions generated by the model.

Keywords: gated recurrent unit, mean absolute percentage error, single-step forecasting, travel time prediction.

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Authors: Shilpi Bora, Abhirup Chatterjee, Debkumar Chakrabarti

Abstract:

Organizations of all the sectors around the world are increasingly revisiting their workplace strategies with due concern for women working therein. Limited office space and rigid work arrangements contribute to lesser job satisfaction and greater work impoundments for any organization. Flexible workspace strategies are indispensable to accommodate the progressive rise of modular workstations and involvement of women. Today’s generation of employees deserves malleable office environments with employee-friendly job conditions and strategies. The workplace nowadays stands on rapid organizational changes in progressive and flexible work culture. Occupational well-being practices need to keep pace with the rapid changes in office-based work. Working at the office (workspace) with awkward postures or for long periods can cause pain, discomfort, and injury. The world is stirring towards the era of globalization and progress. The 4000 women police personnel constitute less than one per cent of the total police strength of India. Lots of innovative fields are growing fast, and it is important that we should accommodate women in those arenas. The timeworn trends should be set apart to set out for fresh opportunities and possibilities of development and success through more involvement of women in the workplace. The notion of women policing is gaining position throughout the world, and various countries are putting solemn efforts to mainstream women in policing. As the role of women policing in a society is budding, and thus it is also notable that the accessibility of women at general police stations should be considered. Accordingly, the impact of workspace at police station on the employee productivity has been widely deliberated as a crucial contributor to employee satisfaction leading to better functional motivation. Thus the present research aimed to look into the office workstation design of police station with reference to womanhood specific issues to uplift occupational wellbeing of the policewomen. Personal interview and individual responses collected through administering to a subjective assessment questionnaire on thirty women police as well as to have their views on these issues by purposive non-probability sampling of women police personnel of different ranks posted in Guwahati, Assam, India. Scrutiny of the collected data revealed that office design has a substantial impact on the policewomen job satisfaction in the police station. In this study, the workspace was designed in such a way that the set of factors would impact on the individual to ensure increased productivity. Office design such as furniture, noise, temperature, lighting and spatial arrangement were considered. The primary feature which affected the productivity of policewomen was the furniture used in the workspace, which was found to disturb the everyday and overall productivity of policewomen. Therefore, it was recommended to have proper and adequate ergonomics design intervention to improve the office design for better performance. This type of study is today’s need-of-the-hour to empower women and facilitate their inner talent to come up in service of the nation. The office workspace design also finds critical importance at several other occupations also – where office workstation needs further improvement.

Keywords: office workspace design, policewomen, womanhood concerns at workspace, occupational wellbeing

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Authors: Ammar Alali, Mahmoud Abughaban

Abstract:

Cost reduction and drilling optimization is the goal of many drilling operators. Historically, stuck pipe incidents were a major segment of non-productive time (NPT) associated costs. Traditionally, stuck pipe problems are part of the operations and solved post-sticking. However, the real key to savings and success is in predicting the stuck pipe incidents and avoiding the conditions leading to its occurrences. Previous attempts in stuck-pipe predictions have neglected the local geology of the problem. The proposed predictive tool utilizes geophysical data processing techniques and Machine Learning (ML) algorithms to predict drilling activities events in real-time using surface drilling data with minimum computational power. The method combines two types of analysis: (1) real-time prediction, and (2) cause analysis. Real-time prediction aggregates the input data, including historical drilling surface data, geological formation tops, and petrophysical data, from wells within the same field. The input data are then flattened per the geological formation and stacked per stuck-pipe incidents. The algorithm uses two physical methods (stacking and flattening) to filter any noise in the signature and create a robust pre-determined pilot that adheres to the local geology. Once the drilling operation starts, the Wellsite Information Transfer Standard Markup Language (WITSML) live surface data are fed into a matrix and aggregated in a similar frequency as the pre-determined signature. Then, the matrix is correlated with the pre-determined stuck-pipe signature for this field, in real-time. The correlation used is a machine learning Correlation-based Feature Selection (CFS) algorithm, which selects relevant features from the class and identifying redundant features. The correlation output is interpreted as a probability curve of stuck pipe incidents prediction in real-time. Once this probability passes a fixed-threshold defined by the user, the other component, cause analysis, alerts the user of the expected incident based on set pre-determined signatures. A set of recommendations will be provided to reduce the associated risk. The validation process involved feeding of historical drilling data as live-stream, mimicking actual drilling conditions, of an onshore oil field. Pre-determined signatures were created for three problematic geological formations in this field prior. Three wells were processed as case studies, and the stuck-pipe incidents were predicted successfully, with an accuracy of 76%. This accuracy of detection could have resulted in around 50% reduction in NPT, equivalent to 9% cost saving in comparison with offset wells. The prediction of stuck pipe problem requires a method to capture geological, geophysical and drilling data, and recognize the indicators of this issue at a field and geological formation level. This paper illustrates the efficiency and the robustness of the proposed cross-disciplinary approach in its ability to produce such signatures and predicting this NPT event.

Keywords: drilling optimization, hazard prediction, machine learning, stuck pipe

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Authors: Sanguk Nam, Van Ha Nguyen, Hanjung Song

Abstract:

For low-power applications such as adapters for portable devices and USB chargers, the primary side regulation (PSR) fly-back converter is widely used in lieu of the conventional fly-back converter using opto-coupler because of its simpler structure and lower cost. In the literature, there has been studies focusing on the design of PSR circuit; however, the conventional sensing method in PSR circuit using RC delay has a lower accuracy as compared to the conventional fly-back converter using opto-coupler. In this paper, we propose a novel PWM/PFM controller using new sensing technique for the PSR fly-back converter which can control an accurate output voltage. The conventional PSR circuit can sense the output voltage information from the auxiliary winding to regulate the duty cycle of the clock that control the output voltage. In the sensing signal waveform, there has two transient points at time the voltage equals to Vout+VD and Vout, respectively. In other to sense the output voltage, the PSR circuit must detect the time at which the current of the diode at the output equals to zero. In the conventional PSR flyback-converter, the sensing signal at this time has a non-sharp-negative slope that might cause a difficulty in detecting the output voltage information since a delay of sensing signal or switching clock may exist which brings out an unstable operation of PSR fly-back converter. In this paper instead of detecting output voltage at a non-sharp-negative slope, a sharp-positive slope is used to sense the proper information of the output voltage. The proposed PRS circuit consists of a saw-tooth generator, a summing circuit, a sample and hold circuit and a peak detector. Besides, there is also the start-up circuit which protects the chip from high surge current when the converter is turned on. Additionally, to reduce the standby power loss, a second mode which operates in a low frequency is designed beside the main mode at high frequency. In general, the operation of the proposed PSR circuit can be summarized as following: At the time the output information is sensed from the auxiliary winding, a saw-tooth signal from the saw-tooth generator is generated. Then, both of these signals are summed using a summing circuit. After this process, the slope of the peak of the sensing signal at the time diode current is zero becomes positive and sharp that make the peak easy to detect. The output of the summing circuit then is fed into a peak detector and the sample and hold circuit; hence, the output voltage can be properly sensed. By this way, we can sense more accurate output voltage information and extend margin even circuit is delayed or even there is the existence of noise by using only a simple circuit structure as compared with conventional circuits while the performance can be sufficiently enhanced. Circuit verification was carried out using 0.35μm 700V Magnachip process. The simulation result of sensing signal shows a maximum error of 5mV under various load and line conditions which means the operation of the converter is stable. As compared to the conventional circuit, we achieved very small error only used analog circuits compare with conventional circuits. In this paper, a PWM/PFM controller using a simple and effective sensing method for PSR fly-back converter has been presented in this paper. The circuit structure is simple as compared with the conventional designs. The gained results from simulation confirmed the idea of the design

Keywords: primary side regulation, PSR, sensing technique, peak detector, PWM/PFM control, fly-back converter

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Authors: Prajakta Patil, Yash Huzurbazar, Abhijeet Shinde

Abstract:

Aim: To develop a treatment protocol for the management of tinnitus through multi-modality brain stimulation. Methodology: Present study included 33 adults with unilateral (31 subjects) and bilateral (2 subjects) chronic tinnitus with and/or without hearing loss independent of their etiology. The Treatment protocol included 5 consecutive sessions with follow-up of 6 months. Each session was divided into 3 parts: • Pre-treatment: a) Informed consent b) Pitch and loudness matching. • Treatment: Bimanual paper pen task with tinnitus masking for 30 minutes. • Post-treatment: a) Pitch and loudness matching b) Directive counseling and obtaining feedback. Paper-pen task is to be performed bimanually that included carrying out two different writing activities in different context. The level of difficulty of the activities was increased in successive sessions. Narrowband noise of a frequency same as that of tinnitus was presented at 10 dBSL of tinnitus for 30 minutes simultaneously in the ear with tinnitus. Result: The perception of tinnitus was no longer present in 4 subjects while in remaining subjects it reduced to an intensity that its perception no longer troubled them without causing residual facilitation. In all subjects, the intensity of tinnitus decreased by an extent of 45 dB at an average. However, in few subjects, the intensity of tinnitus also decreased by more than 45 dB. The approach resulted in statistically significant reductions in Tinnitus Functional Index and Tinnitus Handicap Inventory scores. The results correlate with pre and post treatment score of Tinnitus Handicap Inventory that dropped from 90% to 0%. Discussion: Brain mapping(qEEG) Studies report that there is multiple parallel overlapping of neural subnetworks in the non-auditory areas of the brain which exhibits abnormal, constant and spontaneous neural activity involved in the perception of tinnitus with each subnetwork and area reflecting a specific aspect of tinnitus percept. The paper pen task and directive counseling are designed and delivered respectively in a way that is assumed to induce normal, rhythmically constant and premeditated neural activity and mask the abnormal, constant and spontaneous neural activity in the above-mentioned subnetworks and the specific non-auditory area. Counseling was focused on breaking the vicious cycle causing and maintaining the presence of tinnitus. Diverting auditory attention alone is insufficient to reduce the perception of tinnitus. Conscious awareness of tinnitus can be suppressed when individuals engage in cognitively demanding tasks of non-auditory nature as the paper pen task used in the present study. To carry out this task selective, divided, sustained, simultaneous and split attention act cumulatively. Bimanual paper pen task represents a top-down activity which underlies brain’s ability to selectively attend to the bimanual written activity as a relevant stimulus and to ignore tinnitus that is the irrelevant stimuli in the present study. Conclusion: The study suggests that this novel treatment approach is cost effective, time saving and efficient to vanish the tinnitus or to reduce the intensity of tinnitus to a negligible level and thereby eliminating the negative reactions towards tinnitus.

Keywords: multi-modality brain stimulation, neural subnetworks, non-auditory areas, paper-pen task, top-down activity

Procedia PDF Downloads 138