Search results for: automated guided vehicle

Authors: Zahow Muoftah

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Traditional manual visual grading of fruits has been one of the agricultural industry’s major challenges due to its laborious nature as well as inconsistency in the inspection and classification process. The main requirements for computer vision and visual processing are some effective techniques for identifying defects and estimating defect areas. Automated defect detection using computer vision and machine learning has emerged as a promising area of research with a high and direct impact on the visual inspection domain. Grading, sorting, and disease detection are important factors in determining the quality of fruits after harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have been conducted to identify diseases and pests that affect the fruits of agricultural crops. However, most previous studies concentrated solely on the diagnosis of a lesion or disease. This study focused on a comprehensive study to identify pests and diseases of apple and banana fruits using detection and classification defects on Banana and Apples by Computer Vision. As a result, the current article includes research from these domains as well. Finally, various pattern recognition techniques for detecting apple and banana defects are discussed.

Keywords: computer vision, banana, apple, detection, classification

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Authors: Ayush Shrivastava, Arpit Chaudhary, Devang Kulshreshtha, Vibhav Prakash Singh, Rajeev Srivastava

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Early diagnosis of breast cancer can improve the survival rate by detecting cancer at an early stage. Breast region segmentation is an essential step in the analysis of digital mammograms. Accurate image segmentation leads to better detection of cancer. It aims at separating out Region of Interest (ROI) from rest of the image. The procedure begins with removal of labels, annotations and tags from the mammographic image using morphological opening method. Pectoral Muscle Sliding Window Algorithm (PMSWA) is used for removal of pectoral muscle from mammograms which is necessary as the intensity values of pectoral muscles are similar to that of ROI which makes it difficult to separate out. After removing the pectoral muscle, Dispersed Region Growing Algorithm (DRGA) is used for segmentation of mammogram which disperses seeds in different regions instead of a single bright region. To demonstrate the validity of our segmentation method, 322 mammographic images from Mammographic Image Analysis Society (MIAS) database are used. The dataset contains medio-lateral oblique (MLO) view of mammograms. Experimental results on MIAS dataset show the effectiveness of our proposed method.

Keywords: CAD, dispersed region growing algorithm (DRGA), image segmentation, mammography, pectoral muscle sliding window algorithm (PMSWA)

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Authors: Itamir de Morais Barroca Filho, Cephas A. S. Barreto, Ramon Malaquias, Cezar Miranda Paula de Souza, Arthur Costa Gorgônio, João C. Xavier-Júnior, Mateus Firmino, Fellipe Matheus Costa Barbosa

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Information and Communication Technologies (ICT) in healthcare are crucial for efficiently delivering medical healthcare services to patients. These ICTs are also known as e-health and comprise technologies such as electronic record systems, telemedicine systems, and personalized devices for diagnosis. The focus of e-health is to improve the quality of health information, strengthen national health systems, and ensure accessible, high-quality health care for all. All the data gathered by these technologies make it possible to help clinical staff with automated decisions using machine learning. In this context, we collected patient data, such as heart rate, oxygen saturation (SpO2), blood pressure, respiration, and others. With this data, we were able to develop machine learning models for patients’ risk of death and estimate the length of stay in ICU beds. Thus, this paper presents the methodology for applying machine learning techniques to develop these models. As a result, although we implemented these models on an IoT healthcare platform, helping clinical staff in healthcare in an ICU, it is essential to create a robust clinical validation process and monitoring of the proposed models.

Keywords: ICT, e-health, machine learning, ICU, healthcare

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Authors: Osama Shibani

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Corporate governance effective is critical to the proper functioning of the banking sector and the economy as a whole, the Basel Committee have issued principles of corporate governance inspired from Organisation for Economic Co-operation and Development (OECD), but there is no single model of corporate governance that can work well in every country; each country, or even each organization should develop its own model that can cater for its specific needs and objectives, the corporate governance in Islamic Institutions is unique and offers a particular structure and guided by a control body which is Shariah supervisory Board (SSB), for this reason Islamic Financial Services Board in Malaysia (IFSB) has amended BCBS corporate governance principles commensurate with Islamic financial Institutions to suit the nature of the work of Islamic institutions, this paper highlight these amended by using comparative analysis method in context of the differences of corporate governance structure of Islamic banks and conventional banks. We find few different between principles (Principle 1: The Board's overall responsibilities, Principles 3: Board’s own structure and practices, Principles 9: Compliance, Principle 10: Internal audit, Principle 12: Disclosure and transparency) and there are similarities between principles (Principle 2: Board qualifications and composition, Principles 4: Senior Management (composition and tasks), Principle 6: Risk Management and Principle 8: Risk communication). Finally, we found that corporate governance principles issued by Islamic Financial Services Board (IFSB) are complemented to CG principles of Basel Committee on Banking Supervision (BCBS) with some modifications to suit the composition of Islamic banks, there are deficiencies in the interest of the Basel Committee to Islamic banks.

Keywords: basel committee (BCBS), corporate governance principles, Islamic financial services board (IFSB), agency theory

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Authors: Roberto Pili, Alessandro Romagnoli, Hartmut Spliethoff, Christoph Wieland

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Internal combustion engines (ICE) are today the most common energy system to drive vehicles and transportation systems. Numerous studies state that 50-60% of the fuel energy content is lost to the ambient as sensible heat. ORC offers a valuable alternative to recover such waste heat from ICE, leading to fuel energy savings and reduced emissions. In contrast, the additional weight of the ORC affects the net energy balance of the overall system and the ORC occupies additional volume that competes with vehicle transportation capacity. Consequently, a lower income from delivered freight or passenger tickets can be achieved. The economic feasibility of integrating an ORC into an ICE and the resulting economic impact of weight and volume have not been analyzed in open literature yet. This work intends to define such a benchmark for ORC applications in the transportation sector and investigates the current situation on the market. The applied methodology refers to the freight market, but it can be extended to passenger transportation as well. The economic parameter X is defined as the ratio between the variation of the freight revenues and the variation of fuel costs when an ORC is installed as a bottoming cycle for an ICE with respect to a reference case without ORC. A good economic situation is obtained when the reduction in fuel costs is higher than the reduction of revenues for the delivered freight, i.e. X<1. Through this constraint, a maximum allowable change of transport capacity for a given relative reduction in fuel consumption is determined. The specific fuel consumption is influenced by the ORC in two ways. Firstly because the transportable freight is reduced and secondly because the total weight of the vehicle is increased. Note, that the generated electricity of the ORC influences the size of the ICE and the fuel consumption as well. Taking the above dependencies into account, the limiting condition X = 1 results in a second order equation for the relative change in transported cargo. The described procedure is carried out for a typical city bus, a truck of 24-40 t of payload capacity, a middle-size freight train (1000 t), an inland water vessel (Va RoRo, 2500 t) and handysize-like vessel (25000 t). The maximum allowable mass and volume of the ORC are calculated in dependence of its efficiency in order to satisfy X < 1. Subsequently, these values are compared with weight and volume of commercial ORC products. For ships of any size, the situation appears already highly favorable. A different result is obtained for road and rail vehicles. For trains, the mass and the volume of common ORC products have to be reduced at least by 50%. For trucks and buses, the situation looks even worse. The findings of the present study show a theoretical and practical approach for the economic application of ORC in the transportation sector. In future works, the potential for volume and mass reduction of the ORC will be addressed, together with the integration of an economic assessment for the ORC.

Keywords: ORC, transportation, volume, weight

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Authors: Kavita Bodke

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Structural health monitoring (SHM) is experiencing growth, necessitating the development of distinct methodologies to address its expanding scope effectively. In this study, we developed automatic structure damage identification, which incorporates three unique types of a building’s structural integrity. The first pertains to the presence of fractures within the structure, the second relates to the issue of dampness within the structure, and the third involves corrosion inside the structure. This study employs image classification techniques to discern between intact and impaired structures within structural data. The aim of this research is to find automatic damage detection with the probability of each damage class being present in one image. Based on this probability, we know which class has a higher probability or is more affected than the other classes. Utilizing photographs captured by a mobile camera serves as the input for an image classification system. Image classification was employed in our study to perform multi-class and multi-label classification. The objective was to categorize structural data based on the presence of cracks, moisture, and corrosion. In the context of multi-class image classification, our study employed three distinct methodologies: Random Forest, Multilayer Perceptron, and CNN. For the task of multi-label image classification, the models employed were Rasnet, Xceptionet, and Inception.

Keywords: SHM, CNN, deep learning, multi-class classification, multi-label classification

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Authors: Alvaro Javier Ortega

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A large number of companies face the challenge of matching highly-skilled professionals to high-end positions by human resource deployment professionals. However, when the professional list and tasks to be matched are larger than a few dozens, this process result is far from optimal and takes a long time to be made. Therefore, an automated assignment algorithm for this workforce management problem is needed. The majority of companies are divided into several sectors or departments, where trained employees with different experience levels deal with a large number of tasks daily. Also, the execution order of all tasks is of mater consequence, due to some of these tasks just can be run it if the result of another task is provided. Thus, a wrong execution order leads to large waiting times between consecutive tasks. The desired goal is, therefore, creating accurate matches and a near-optimal execution order that maximizes the number of tasks performed and minimizes the idle time of the expensive skilled employees. The problem described before can be model as a mixed-integer non-linear programming (MINLP) as it will be shown in detail through this paper. A large number of MINLP algorithms have been proposed in the literature. Here, genetic algorithm solutions are considered and a comparison between two different mutation approaches is presented. The simulated results considering different complexity levels of assignment decisions show the appropriateness of the proposed model.

Keywords: employees, genetic algorithm, industry management, workforce

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Authors: Do Chi Thanh, Dang Ngoc Huy

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Nowadays, there are more and more variable speed drive systems in small-scale and large-scale applications such as the electric vehicle industry, household appliances, medical equipment, and other industrial fields led to the development of BLDC (Brushless DC) motors. BLDC drive has many advantages, such as higher efficiency, better speed torque characteristics, high power density, and low maintenance cost compared to other conventional motors. Most BLDC motors use a proportional-integral (PI) controller and a pulse width modulation (PWM) scheme for speed control. This article describes the simulation model of BLDC motor drive control with the help of MATLAB - SIMULINK simulation software. The built simulation model includes a BLDC motor dynamic block, Hall sensor signal generation block, inverter converter block, and PI controller.

Keywords: brushless DC motor, BLDC, six-step inverter, PI speed

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Authors: Sagri Sharma

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Analysis of diseases integrating multi-factors increases the complexity of the problem and therefore, development of frameworks for the analysis of diseases is an issue that is currently a topic of intense research. Due to the inter-dependence of the various parameters, the use of traditional methodologies has not been very effective. Consequently, newer methodologies are being sought to deal with the problem. Supervised Learning Algorithms are commonly used for performing the prediction on previously unseen data. These algorithms are commonly used for applications in fields ranging from image analysis to protein structure and function prediction and they get trained using a known dataset to come up with a predictor model that generates reasonable predictions for the response to new data. Gene expression profiles generated by DNA analysis experiments can be quite complex since these experiments can involve hypotheses involving entire genomes. The application of well-known machine learning algorithm - Support Vector Machine - to analyze the expression levels of thousands of genes simultaneously in a timely, automated and cost effective way is thus used. The objectives to undertake the presented work are development of a methodology to identify genes relevant to Hepatocellular Carcinoma (HCC) from gene expression dataset utilizing supervised learning algorithms and statistical evaluations along with development of a predictive framework that can perform classification tasks on new, unseen data.

Keywords: artificial intelligence, biomarker, gene expression datasets, hepatocellular carcinoma, machine learning, supervised learning algorithms, support vector machine

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Authors: Gaylord K. Booto, Marinelli Giuseppe, Helge Brattebø, Rolf A. Bohne

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Emissions reduction has emerged among the principal targets in the process of planning and designing road alignments today. Intelligent road design methods that can result in optimized alignment constitute concrete and innovative responses towards better alternatives and more sustainable road infrastructures. As the largest amount of emissions of road infrastructures occur in the operation stage, it becomes very important to consider traffic weight and distribution in alignment design process. This study analyzes the effects of four traffic factors (i.e. operating speed, vehicle category, technology and fuel type) on adjusting the vertical alignment of a given road, using optimization techniques. Further, factors’ effects are assessed qualitatively and quantitatively, and the emission profiles of resulting alignment alternatives are compared.

Keywords: alignment adjustment, emissions reduction, optimization, traffic-oriented

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Authors: Garima Joshi, Pratap Sharan, V. Sreenivas, Nand Kumar, Kameshwar Prasad, Ashima N. Wadhawan

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Schizophrenia is a debilitating disorder and is marked by cognitive impairment, which deleteriously impacts the social and professional functioning along with the quality of life of the patients and the caregivers. Often the cognitive symptoms are in their prodromal state and worsen as the illness progresses; they have proven to have a good predictive value for the prognosis of the illness. It has been shown that intensive cognitive rehabilitation (CR) leads to improvements in the healthy as well as cognitively-impaired subjects. As the majority of population in India falls in the lower to middle socio-economic status and have low education levels, using the existing packages, a majority of which are developed in the West, for cognitive rehabilitation becomes difficult. The use of technology is also restricted due to the high costs involved and the limited availability and familiarity with computers and other devices, which pose as an impedance for continued therapy. Cognitive rehabilitation in India uses a plethora of retraining methods for the patients with schizophrenia targeting the functions of attention, information processing, executive functions, learning and memory, and comprehension along with Social Cognition. Psychologists often have to follow an integrative therapy approach involving social skills training, family therapy and psychoeducation in order to maintain the gains from the cognitive rehabilitation in the long run. This paper reviews the methodologies and cognitive retaining programs used in India. It attempts to elucidate the evolution and development of methodologies used, from traditional paper-pencil based retraining to more sophisticated neuroscience-informed techniques in cognitive rehabilitation of deficits in schizophrenia as home-based or supervised and guided programs for cognitive rehabilitation.

Keywords: schizophrenia, cognitive rehabilitation, neuropsychological interventions, integrated approached to rehabilitation

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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: Sarah Eldefrawi, Maike Didero

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In Egypt, residents’ urban interventions (colloquially named A’hali’s interventions) are always tackled by government, scholars, and media as an encroachment (taeadiyat), chaotic (a’shwa’i) or informal (gheir mokanan) practices. This paper argues that those interventions cannot be simply described as an encroachment on public space or chaotic behaviour. We claim here that they are relevant to traditional governing methods (‘Urf) that were governing Arab cities for many decades. Through an in-depth field study conducted in a real estate public housing project in the city of Giza called 'Masaken El-Barageel', we traced the urban transformations demonstrated in private and public spaces. To understand those transformations, we used wide-range of qualitative research methods such as semi-guided and informal interviews, observations and mapping of the built environment and the newly added interventions. This study was as well strengthened through the contributions of the author in studying nine sectors emerging by Ahali in six districts in Great Cairo. The results of this study indicate that a culturally and socially sensitive framework has to be related to the individual actions toward the spatial and social structures as well as to culturally transmitted views and meanings connected with 'Urf'. The study could trace three crucial principals in ‘urf that influenced these interventions; the eliminating of harm (Al-Marafiq wa Man’ al-Darar), the appropriation of space (Haqq el-Intefa’) and public interest (maslaha a’ma). Our findings open the discussion for the (il) legitimate of a’hali governing methods in contemporary cities.

Keywords: Urf, urban governance, public space, public housing, encroachments, chaotic, Egyptian cities

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Authors: Farid Eltom A. E., Mustafa Abdul-Halim, Abdalla Markaz, Sami Atta, Mohamed Azhari, Ahmed Rashed

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Smart environment sensors integrated with ‘Internet of Things’ (IoT) technology can provide a new concept in tracking, sensing, and monitoring objects in the environment. The aim of the study is to evaluate the farm environmental monitoring system based on (IoT) and to realize the automated management of agriculture and the implementation of precision production. Until now, irrigation monitoring operations in Sudan have been carried out using traditional methods, which is a very costly and unreliable mechanism. However, by utilizing soil moisture sensors, irrigation can be conducted only when needed without fear of plant water stress. The result showed that software application allows farmers to display current and historical data on soil moisture and nutrients in the form of line charts. Design measurements of the soil factors: moisture, electrical, humidity, conductivity, temperature, pH, phosphorus, and potassium; these factors, together with a timestamp, are sent to the data server using the Lora WAN interface. It is considered scientifically agreed upon in the modern era that artificial intelligence works to arrange the necessary procedures to take care of the terrain, predict the quality and quantity of production through deep analysis of the various operations in agricultural fields, and also support monitoring of weather conditions.

Keywords: smart environment, monitoring systems, IoT, LoRa Gateway, center pivot

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Authors: Bisola Olumegbon

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Globally, there were about 229 million cases of malaria in 2022. The sub-Saharan African region accounted for 92% of the reported cases and 94% of deaths. Nigeria had the highest number of malaria cases and deaths, representing 27% of global cases. This scholarly project was a case study guided by the political determinants of health. Triangulation of data using thematic analysis was used to identify the political determinants of malaria in Nigeria and to understand how the concept of interaction contributes to the persistence of the disease. The analysis involved a deductive and inductive approach based on the literature review and the evidence of political determinants gathered in the data. Participants’ in-depth interviews were used to collect data from frontline personnel. Data triangulation was done using thematic analysis, a method used to identify patterns and themes in qualitative data. The study findings revealed a correlation between political determinants of health and malaria management efforts in Nigeria. Some influencing factors included voting challenges, inadequate funding, lack of health priority from the government, noncompliance among patients, and hurdles to effective communication. The findings suggest a need to deliberately increase dedication to the political agenda, provide sufficient financial resources, enhance communication, and active community involvement to address the persistent malaria endemic effectively. Further study is recommended to identify interventions to address identified factors of political determinants of health to reduce malaria in Nigeria. Such intervention must involve collaboration with diverse stakeholders such as policymakers, healthcare professionals, community leaders, and researchers.

Keywords: malaria, malaria management, health worker, stakeholders, political determinant of health

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Authors: Oscar Nieto-Garzón, Angélica Lozano

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In urban areas, on-road freight transportation creates several social and environmental externalities. Then, it is crucial that freight transport considers not only economic aspects, like retailer distribution cost reduction and service improvement, but also environmental effects such as global CO2 and local emissions (e.g. Particulate Matter, NOX, CO) and noise. Inadequate infrastructure development, high rate of urbanization, the increase of motorization, and the lack of transportation planning are characteristics that urban areas from developing countries share. The Metropolitan Area of Mexico City (MAMC), the Metropolitan Area of São Paulo (MASP), and Bogota are three of the largest urban areas in Latin America where air pollution is often a problem associated with emissions from mobile sources. The effect of the lockdown due to COVID-19 was analyzedfor these urban areas, comparing the same period (January to August) of years 2016 – 2019 with 2020. A strong reduction in the concentration of primary criteria pollutants emitted by road traffic were observed at the beginning of 2020 and after the lockdown measures.Daily mean concentration of NOx decreased 40% in the MAMC, 34% in the MASP, and 62% in Bogota. Daily mean ozone levels increased after the lockdown measures in the three urban areas, 25% in MAMC, 30% in the MASP and 60% in Bogota. These changes in emission patterns from mobile sources drastically changed the ambient atmospheric concentrations of CO and NOX. The CO/NOX ratioat the morning hours is often used as an indicator of mobile sources emissions. In 2020, traffic from cars and light vehicles was significantly reduced due to the first lockdown, but buses and trucks had not restrictions. In theory, it implies a decrease in CO and NOX from cars or light vehicles, maintaining the levels of NOX by trucks(or lower levels due to the congestion reduction). At rush hours, traffic was reduced between 50% and 75%, so trucks could get higher speeds, which would reduce their emissions. By means an emission model, it was found that an increase in the average speed (75%) would reduce the emissions (CO, NOX, and PM) from diesel trucks by up to 30%. It was expected that the value of CO/NOXratio could change due to thelockdownrestrictions. However, although there was asignificant reduction of traffic, CO/NOX kept its trend, decreasing to 8-9 in 2020. Hence, traffic restrictions had no impact on the CO/NOX ratio, although they did reduce vehicle emissions of CO and NOX. Therefore, these emissions may not adequately represent the change in the vehicle emission patterns, or this ratio may not be a good indicator of emissions generated by vehicles. From the comparison of the theoretical data and those observed during the lockdown, results that the real NOX reduction was lower than the theoretical reduction. The reasons could be that there are other sources of NOX emissions, so there would be an over-representation of NOX emissions generated by diesel vehicles, or there is an underestimation of CO emissions. Further analysis needs to consider this ratioto evaluate the emission inventories and then to extend these results forthe determination of emission control policies to non-mobile sources.

Keywords: COVID-19, emissions, freight transport, latin American metropolis

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Authors: Esther O. Adebitan, Florence Oyelade

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The Millennium Development Goals (MDGs) was initiated by the UN member nations’ aspiration for the betterment of human life. It is expressed in a set of numerical ‎and time-bound targets. In more recent time, the aspiration is shifting away from just the achievement to the sustainability of achieved MDGs beyond the 2015 target. The main objective of this study was assessing how much the hotel industry within the Nigerian Federal Capital Territory (FCT) as a member of the global community is involved in the achievement of sustainable MDGs within the FCT. The study had two population groups consisting of 160 hotels and the communities where these are located. Stratified random sampling technique was adopted in selecting 60 hotels based on large, medium ‎and small hotels categorisation, while simple random sampling technique was used to elicit information from 30 residents of three of the hotels host communities. The study was guided by tree research questions and two hypotheses aimed to ascertain if hotels see the need to be involved in, and have policies in pursuit of achieving sustained MDGs, and to determine public opinion regarding hotels contribution towards the achievement of the MDGs in their communities. A 22 item questionnaire was designed ‎and administered to hotel managers while 11 item questionnaire was designed ‎and administered to hotels’ host communities. Frequency distribution and percentage as well as Chi-square were used to analyse data. Results showed no significant involvement of the hotel industry in achieving sustained MDGs in the FCT and that there was disconnect between the hotels and their immediate communities. The study recommended that hotels should, as part of their Corporate Social Responsibility pick at least one of the goals to work on in order to be involved in the attainment of enduring Millennium Development Goals.

Keywords: MDGs, hotels, FCT, host communities, corporate social responsibility

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Authors: S. Mantzoukis, M. Gerasimou, P. Christodoulou, N. Varsamis, G. Kolliopoulou, N. Zotos

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Purpose: Patients in Intensive Care Units (ICU) are exposed to a different spectrum of microorganisms relative to the hospital. Due to the fact that the majority of these patients are intubated, bronchial secretions should be examined. Material and Method: Bronchial secretions should be taken with care so as not to be mixed with sputum or saliva. The bronchial secretions are placed in a sterile container and then inoculated into blood, Mac Conkey No2, Chocolate, Mueller Hinton, Chapman and Saboureaud agar. After this period, if any number of microbial colonies are detected, gram staining is performed and then the isolated organisms are identified by biochemical techniques in the automated Microscan system (Siemens) followed by a sensitivity test in the same system using the minimum inhibitory concentration MIC technique. The sensitivity test is verified by a Kirby Bauer test. Results: In 2017 the Laboratory of Microbiology received 365 samples of bronchial secretions from the Intensive Care Unit. 237 were found positive. S. epidermidis was identified in 1 specimen, A. baumannii in 60, K. pneumoniae in 42, P. aeruginosa in 50, C. albicans in 40, P. mirabilis in 4, E. coli in 4, S. maltophilia in 6, S. marcescens in 6, S. aureus in 12, S. pneumoniae in 1, S. haemolyticus in 4, P. fluorescens in 1, E. aerogenes in 1, E. cloacae in 5. Conclusions: The majority of ICU patients appear to be a fertile ground for the development of infections. The nature of the findings suggests that a significant part of the bacteria found comes from the unit (nosocomial infection).

Keywords: bronchial secretions, cultures, infections, intensive care units

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Authors: Varathagowry Vasudevan

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This paper attempts to demonstrate the importance of capacity building of the para-professionals in community based corrections for enhancing family and child welfare as a crucial factor in providing in-service training as a responsive methodology in community based corrections to enhance the best practices. The Diploma programme in community-based corrections initiated by the National Institute of Social Development has been engaged in this noble task of training quality personnel knowledgeable in the best practices and fieldwork skills on community-based correction and its best practice. To protect the families and children and enhance best practices, National Institute of Social Development with support from the department of community-based corrections initiated a Diploma programme in community-based corrections to enhance and update the knowledge, skills, attitudes with the right mindset of the work supervisors employed at the department of community-based corrections. This study based on reflective practice illustrated the effectiveness of curriculum of in-service training programme as a tool to enhance the capacities of the relevant officers in Sri Lanka. The data for the study was obtained from participants and coordinator through classroom discussions and key informant interviews. This study showed that use of appropriate tailor-made curriculum and field practice manual by the officers during the training was very much dependent on the provision of appropriate administrative facilities, passion, teaching methodology that promote capacity to involve best practices. It also demonstrated further the fact that professional social work response, strengthening families within legal framework was very much grounded in the adoption of proper skills imbibed through training in appropriate methodology practiced in the field under guided supervision.

Keywords: capacity building, community-based corrections, in-service training, paraprofessionals

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Authors: D. Muley, H. S. Al-Mandhari

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This paper evaluates the performance of a multi-lane four-legged modern roundabout operating in Muscat using SIDRA model. The performance measures include Degree of Saturation (DOS), average delay, and queue lengths. The geometric and traffic data were used for model preparation. Gap acceptance parameters, critical gap, and follow-up headway were used for calibration of SIDRA model. The results from the analysis showed that currently the roundabout is experiencing delays up to 610 seconds with DOS 1.67 during peak hour. Further, sensitivity analysis for general and roundabout parameters was performed, amongst lane width, cruise speed, inscribed diameter, entry radius, and entry angle showed that inscribed diameter is the most crucial factor affecting delay and DOS. Upgradation of the roundabout to the fully signalized junction was found as the suitable solution which will serve for future years with LOS C for design year having DOS of 0.9 with average control delay of 51.9 seconds per vehicle.

Keywords: performance analysis, roundabout, sensitivity analysis, SIDRA

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Authors: Walter Bernhofer, Peter Haber, Tobias Mayer, Manfred Mayr, Markus Ziegler

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Current preventive maintenance measures are cost intensive and not efficient. With the available sensor data of state of the art internet of things devices new possibilities of automated data processing emerge. Current advances in data science and in machine learning enable new, so called predictive maintenance technologies, which empower data scientists to forecast possible system failures. The goal of this approach is to cut expenses in preventive maintenance by automating the detection of possible failures and to improve efficiency and quality of maintenance measures. Additionally, a centralization of the sensor data monitoring can be achieved by using this approach. This paper describes the approach of three students to define a reference architecture for a predictive maintenance solution in the internet of things domain with a connected smartphone app for service technicians. The reference architecture is validated by a case study. The case study is implemented with current Microsoft Azure cloud technologies. The results of the case study show that the reference architecture is valid and can be used to achieve a system for predictive maintenance execution with the cloud components of Microsoft Azure. The used concepts are technology platform agnostic and can be reused in many different cloud platforms. The reference architecture is valid and can be used in many use cases, like gas station maintenance, elevator maintenance and many more.

Keywords: case study, internet of things, predictive maintenance, reference architecture

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Authors: Alden Boby, Dane Brown, James Connan

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Character recognition is a well-researched topic across disciplines. Regardless, creating a solution that can cater to multiple situations is still challenging. Vehicle licence plates lack an international standard, meaning that different countries and regions have their own licence plate format. A problem that arises from this is that the typefaces and designs from different regions make it difficult to create a solution that can cater to a wide range of licence plates. The main issue concerning detection is the character recognition stage. This paper aims to create an object detection-based character recognition model trained on a custom dataset that consists of typefaces of licence plates from various regions. Given that characters have featured consistently maintained across an array of fonts, YOLO can be trained to recognise characters based on these features, which may provide better performance than OCR methods such as Tesseract OCR.

Keywords: computer vision, character recognition, licence plate recognition, object detection

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Authors: C. Manjula, Lilly Florence

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Software technology is developing rapidly which leads to the growth of various industries. Now-a-days, software-based applications have been adopted widely for business purposes. For any software industry, development of reliable software is becoming a challenging task because a faulty software module may be harmful for the growth of industry and business. Hence there is a need to develop techniques which can be used for early prediction of software defects. Due to complexities in manual prediction, automated software defect prediction techniques have been introduced. These techniques are based on the pattern learning from the previous software versions and finding the defects in the current version. These techniques have attracted researchers due to their significant impact on industrial growth by identifying the bugs in software. Based on this, several researches have been carried out but achieving desirable defect prediction performance is still a challenging task. To address this issue, here we present a machine learning based hybrid technique for software defect prediction. First of all, Genetic Algorithm (GA) is presented where an improved fitness function is used for better optimization of features in data sets. Later, these features are processed through Decision Tree (DT) classification model. Finally, an experimental study is presented where results from the proposed GA-DT based hybrid approach is compared with those from the DT classification technique. The results show that the proposed hybrid approach achieves better classification accuracy.

Keywords: decision tree, genetic algorithm, machine learning, software defect prediction

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Authors: Gurcan A. B., Baker T. N.

Abstract:

Due to economic and environmental requirements, it is becoming increasingly important to reduce vehicle weight. The first approach consisted in using light materials with high thermal conductivity, such as aluminium alloys. This choice allowed significant mass reduction and lower temperature but required recourse to ventilated discs. Among aluminium alloys, Al 6xxx series alloys enjoy the highest strength-to-weight ratio and, therefore, have found wide applications in the automobile and aerospace industries. However, these alloys lose their high strength rapidly when they are exposed to elevated temperatures. This rapid decline in the strength is directly related to the coarsening of very fine precipitates which are then not as effective in obstructing the dislocations. The incorporation of micro-scale and nano-scale particulates in aluminium systems can greatly enhance their mechanical characteristics.

Keywords: mechanical and tribological behaviour, scanning electron microscope, optical test, mechanical properties test, experimental test

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Authors: Hameed Ullah, Karim Elakabawi, Han KE, Najeeb Ullah, Habib Ullah, Sardar Ali Shah, Hamad Haider Khan, Muhammad Asad Khan, Ning Guo, Zuyi Yuan

Abstract:

Background: Predictors of decreased fractional flow reserve at left circumflex coronary artery after left main (LM) crossover stenting are still lacking. The objectives of the present study were to provide the predictors for low Fractional flow reserve (FFR) at coronary artery (LCx) and the possible treatment strategies for the compromised LCx-together with their long term outcomes. Methods: A total of 563 included patients out of 1974 patients admitted to our hospital from February 2015 to November 2020 with significant distal LM-bifurcation lesions. The enrolled patients underwent single-stent cross-over PCI under IVUS guidance with further LCx intervention as indicated by measured FFR. Results: The included patients showed angiographic significant LCx ostial affection after LM-stenting, but only 116 (20.6%) patients had FFR <0.8. The 3-year composite MACE rates were comparable between the high and low FFR groups (16.8% vs. 15.5%, respectively; P=0.744). In a multivariable analysis, a low FFR in the LCx was associated with post-stenting MLA of the LCx (OR: 0.032, P <0.001), post-stenting LCx-plaque burden (OR: 1.166, P <0.001), post-stenting LM-MLA (OR: 0.821, P =0.038) and pre-stenting LCx-MLA (OR: 0.371, P =0.044). In patients with low FFR, management of compromised LCx with DEB had the lowest 3-year MACE rate (8.1%) as compared to either KBI (17.5%) or stenting group (20.5%), P =0.299. Conclusion: FFR-guided LCx intervention can avoid unnecessary LCx intervention. The post-stenting predictors of low FFR include post-stenting MLA and plaque burden of the LCx and MV stent length. The 3-year MACE rates were comparable between high FFR patients and patients who had low FFR and were adequately managed.

Keywords: fractional flow reserve, left main stem, percutaneous coronary interventions, intravascular ultrasound

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Authors: Aubry Martin, Nehed Amara, Jeff Nyalosaso, Audrey Potdevin, FrançOis ReVeret, Michel Langlet, Genevieve Chadeyron

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Energy-saving lighting devices based on LightEmitting Diodes (LEDs) combine a semiconductor chip emitting in the ultraviolet or blue wavelength region to one or more phosphor(s) deposited in the form of coatings. The most common ones combine a blue LED with the yellow phosphor Y₃Al₅O₁₂:Ce³⁺ (YAG:Ce) and a red phosphor. Even if these devices are characterized by satisfying photometric parameters (Color Rendering Index, Color Temperature) and good luminous efficiencies, further improvements can be carried out to enhance light extraction efficiency (increase in phosphor forward emission). One of the possible strategies is to pattern the phosphor coatings. Here, we have worked on different ways to nanostructure the coating surface. On the one hand, we used the colloidal lithography combined with the Langmuir-Blodgett technique to directly pattern the surface of YAG:Tb³⁺ sol-gel derived coatings, YAG:Tb³⁺ being used as phosphor model. On the other hand, we achieved composite architectures combining YAG:Ce coatings and ZnO nanowires. Structural, morphological and optical properties of both systems have been studied and compared to flat YAG coatings. In both cases, nanostructuring brought a significative enhancement of photoluminescence properties under UV or blue radiations. In particular, angle-resolved photoluminescence measurements have shown that nanostructuring modifies photons path within the coatings, with a better extraction of the guided modes. These two strategies have the advantage of being versatile and applicable to any phosphor synthesizable by sol-gel technique. They then appear as promising ways to enhancement luminescence efficiencies of both phosphor coatings and the optical devices into which they are incorporated, such as LED-based lighting or safety devices.

Keywords: phosphor coatings, nanostructuring, light extraction, ZnO nanowires, colloidal lithography, LED devices

Procedia PDF Downloads 172

Authors: Nalini C. Iyer, Shraddha H., Anagha B. Varahamurthy, Dikshith C. S., Ishwar G. Kubasad, Vinayak I. Karalatti, Pavan B. Mulimani

Abstract:

The current scenario of the pandemic due to COVID-19 has led to the awareness among the people to avoid unneces-sary contact in public places. There is a need to avoid contact with physical objects to stop the spreading of infection. The contactless feature has to be included in the systems in public places wherever possible. For example, attendance monitoring systems with fingerprint biometric can be replaced with a contactless feature. One more important protocol followed in the current situation is temperature monitoring and screening. The paper describes an attendance system with a contactless feature and temperature screening for the university. The system displays a QR code to scan, which redirects to the student login web page only if the location is valid (the location where the student scans the QR code should be the location of the display of the QR code). Once the student logs in, the temperature of the student is scanned by the contactless temperature sensor (mlx90614) with an error of 0.5°C. If the temperature falls in the range of the desired value (range of normal body temperature), then the attendance of the student is marked as present, stored in the database, and the door opens automatically. The attendance is marked as absent in the other case, alerted with the display of temperature, and the door remains closed. The door is automated with the help of a servomotor. To avoid the proxy, IR sensors are used to count the number of students in the classroom. The hardware system consisting of a contactless temperature sensor and IR sensor is implemented on the microcontroller, NodeMCU.

Keywords: NodeMCU, IR sensor, attendance monitoring, contactless, temperature

Procedia PDF Downloads 182

Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

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Interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis applications. Segmentation of liver, spleen and kidneys is regarded as a major primary step in the computer-aided diagnosis of abdominal organ diseases. In this paper, a semi-automated method for medical image data is presented for the abdominal organ segmentation data using mathematical morphology. Our proposed method is based on hierarchical segmentation and watershed algorithm. In our approach, a powerful technique has been designed to suppress over-segmentation based on mosaic image and on the computation of the watershed transform. Our algorithm is currency in two parts. In the first, we seek to improve the quality of the gradient-mosaic image. In this step, we propose a method for improving the gradient-mosaic image by applying the anisotropic diffusion filter followed by the morphological filters. Thereafter, we proceed to the hierarchical segmentation of the liver, spleen and kidney. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work.

Keywords: anisotropic diffusion filter, CT images, morphological filter, mosaic image, simultaneous organ segmentation, the watershed algorithm

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Authors: Auwal Abubakar, Ahmed Ahidjo, Shazril Imran Shaukat, Noor Khairiah A. Karim, Gokula Kumar Appalanaido, Hafiz Mohd Zin

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Background: The use of optical surface imaging systems (OSISs) is increasingly becoming popular in radiotherapy practice, especially during breast cancer treatment. This study reviews the accuracy of the available commercial OSISs for breast radiotherapy. Method: A literature search was conducted and identified the available commercial OSISs from different manufacturers that are integrated into radiotherapy practice for setup verification during breast radiotherapy. Studies that evaluated the accuracy of the OSISs during breast radiotherapy using cone beam computed tomography (CBCT) as a reference were retrieved and analyzed. The physics and working principles of the systems from each manufacturer were discussed together with their respective strength and limitations. Results: A total of five (5) different commercially available OSISs from four (4) manufacturers were identified, each with a different working principle. Six (6) studies were found to evaluate the accuracy of the systems during breast radiotherapy in conjunction with CBCT as a goal standard. The studies revealed that the accuracy of the system in terms of mean difference ranges from 0.1 to 2.1 mm. The correlation between CBCT and OSIS ranges between 0.4 and 0.9. The limit of agreements obtained using bland Altman analysis in the studies was also within an acceptable range. Conclusion: The OSISs have an acceptable level of accuracy and could be used safely during breast radiotherapy. The systems are non-invasive, ionizing radiation-free, and provide real-time imaging of the target surface at no extra concomitant imaging dose. However, the system should only be used to complement rather than replace x-ray-based image guidance techniques such as CBCT.

Keywords: optical surface imaging system, Cone beam computed tomography (CBCT), surface guided radiotherapy, Breast radiotherapy

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Authors: Shirka Kassam Jwasshaka

Abstract:

Graduates from higher educational institutions in Nigeria need to leave with the necessary skills to be independent in the emergence work environment. The goal of this study is to develop a framework of digital skills that Nigerian graduates in building construction need to be proficient in various digital skills to comfortably fit into the global advances in a technological labour market. The descriptive survey design was used in this investigation. The study's population consisted of building construction experts selected from different sites within the North Central geographical zones of Nigeria. Using random sampling approaches, 120 seasoned experts were chosen. Three research questions raised by the researchers guided the study. The data was gathered using a 60-item, structured questionnaire. The questions were formulated around three key skill areas such as digital skills related to ICT, digital skills related to general workforce, and basic digital literacy skills that students should have. A building construction specialist validated the questionnaire. Winstep in conjunction with SPSS was used to determine the Cronbach Alpha reliability of the items' internal consistency and person separation,item measure, item fit based on PTMEA CORR, polarity items, misfit items, unidimensionality, and a person-item map. The Cronbach Coefficient reliability of items for the three sub constructs was 0.70. The results showed nearly every sub component within the three areas of digital skills was regarded as significant to be learn by experts. The researchers recommended among other things, that all parties involved in the education sector should work together to develop a curriculum that covers digital skills which can meet employer’s' needs.

Keywords: lifelong learning, digital skill, framework, building technology

Procedia PDF Downloads 55