Search results for: automated

Authors: Loonibha Manandhar, Rajendra Bhandari, Kumud Raj Kafle

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Nepal’s Terai region is a part of the Ganges river basin which is one of the most disaster-prone areas of the world, with recurrent monsoon flooding causing millions in damage and the death and displacement of hundreds of people and households every year. The vulnerability of human settlements to natural disasters such as floods is increasing, and mapping changes in land use practices and hydro-geological parameters is essential in developing resilient communities and strong disaster management policies. The objective of this study was to develop a flood hazard zonation map of Orai Khola watershed and map the decadal land use/land cover dynamics of the watershed. The watershed area was delineated using SRTM DEM, and LANDSAT images were classified into five land use classes (forest, grassland, sediment and bare land, settlement area and cropland, and water body) using pixel-based semi-automated supervised maximum likelihood classification. Decadal changes in each class were then quantified using spatial modelling. Flood hazard mapping was performed by assigning weights to factors slope, rainfall distribution, distance from the river and land use/land cover on the basis of their estimated influence in causing flood hazard and performing weighed overlay analysis to identify areas that are highly vulnerable. The forest and grassland coverage increased by 11.53 km² (3.8%) and 1.43 km² (0.47%) from 1996 to 2016. The sediment and bare land areas decreased by 12.45 km² (4.12%) from 1996 to 2016 whereas settlement and cropland areas showed a consistent increase to 14.22 km² (4.7%). Waterbody coverage also increased to 0.3 km² (0.09%) from 1996-2016. 1.27% (3.65 km²) of total watershed area was categorized into very low hazard zone, 20.94% (60.31 km²) area into low hazard zone, 37.59% (108.3 km²) area into moderate hazard zone, 29.25% (84.27 km²) area into high hazard zone and 31 villages which comprised 10.95% (31.55 km²) were categorized into high hazard zone area.

Keywords: flood hazard, land use/land cover, Orai river, supervised maximum likelihood classification, weighed overlay analysis

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Authors: Sirajdin Olagoke Adeshina, Haidi Ibrahim, Akeem Salawu

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large lecture theatres cannot be covered by a single camera but rather by a multicamera setup because of their size, shape, and seating arrangements. Although, classroom capture is achievable through a single camera. Therefore, a design and implementation of a multicamera setup for a large lecture hall were considered. Researchers have shown emphasis on the impact of class attendance taken on the academic performance of students. However, the traditional method of carrying out this exercise is below standard, especially for large lecture theatres, because of the student population, the time required, sophistication, exhaustiveness, and manipulative influence. An automated large classroom attendance system is, therefore, imperative. The common approach in this system is face detection and recognition, where known student faces are captured and stored for recognition purposes. This approach will require constant face database updates due to constant changes in the facial features. Alternatively, face counting can be performed by cropping the localized faces on the video or image into a folder and then count them. This research aims to develop a face localization-based approach to detect student faces in classroom images captured using a multicamera setup. A selected Haar-like feature cascade face detector trained with an asymmetric goal to minimize the False Rejection Rate (FRR) relative to the False Acceptance Rate (FAR) was applied on Raspberry Pi 4B. A relationship between the two factors (FRR and FAR) was established using a constant (λ) as a trade-off between the two factors for automatic adjustment during training. An evaluation of the proposed approach and the conventional AdaBoost on classroom datasets shows an improvement of 8% TPR (output result of low FRR) and 7% minimization of the FRR. The average learning speed of the proposed approach was improved with 1.19s execution time per image compared to 2.38s of the improved AdaBoost. Consequently, the proposed approach achieved 97% TPR with an overhead constraint time of 22.9s compared to 46.7s of the improved Adaboost when evaluated on images obtained from a large lecture hall (DK5) USM.

Keywords: automatic attendance, face detection, haar-like cascade, manual attendance

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Authors: Mohammad Abu-Taleb

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The final report of the Royal Commission into Australian financial services misconduct, released in February 2019, has had a significant impact on the financial advice industry. The recommendations released in the Commissioner’s final report include changes to ongoing fee arrangements, a new disciplinary system for financial advisers, and mandatory reporting of compliance concerns. This thesis aims to explore the impact of the Royal Commission’s recommendations on the operating model of financial advice firms in terms of advice products, processes, delivery models, and customer segments. Also, this research seeks to investigate whether the Royal Commission’s outcome has accelerated the use of enhanced technology solutions within the operating model of financial advice firms. And to identify the key challenges confronting financial advice firms whilst implementing the Commissioner’s recommendations across their operating models. In order to achieve the objectives of this thesis, a qualitative research design has been adopted through semi-structured in-depth interviews with 24 financial advisers and managers who are engaged in the operation of financial advice services. The study used the thematic analysis approach to interpret the qualitative data collected from the interviews. The findings of this thesis reveal that customer-centric operating models will become more prominent across the financial advice industry in response to the Commissioner’s final report. And the Royal Commission’s outcome has accelerated the use of advice technology solutions within the operating model of financial advice firms. In addition, financial advice firms have started more than before using simpler and more automated web-based advice services, which enable financial advisers to provide simple advice in a greater scale, and also to accelerate the use of robo-advice models and digital delivery to mass customers in the long term. Furthermore, the study identifies process and technology changes as, long with technical and interpersonal skills development, as the key challenges encountered financial advice firms whilst implementing the Commissioner’s recommendations across their operating models.

Keywords: hayne royal commission, financial planning advice, operating model, advice products, advice processes, delivery models, customer segments, digital advice solutions

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Authors: Mustafa M. Donma, Orkide Donma

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Obesity is defined as a severe chronic disease characterized by a low-grade inflammatory state. Therefore, inflammatory markers gained utmost importance during the evaluation of obesity and metabolic syndrome (MetS), a disease characterized by central obesity, elevated blood pressure, increased fasting blood glucose and elevated triglycerides or reduced high density lipoprotein cholesterol (HDL-C) values. Some inflammatory markers based upon complete blood cell count (CBC) are available. In this study, it was questioned which inflammatory marker was the best to evaluate the differences between various obesity groups. 514 pediatric individuals were recruited. 132 children with MetS, 155 morbid obese (MO), 90 obese (OB), 38 overweight (OW) and 99 children with normal BMI (N-BMI) were included into the scope of this study. Obesity groups were constituted using age- and sex-dependent body mass index (BMI) percentiles tabulated by World Health Organization. MetS components were determined to be able to specify children with MetS. CBC were determined using automated hematology analyzer. HDL-C analysis was performed. Using CBC parameters and HDL-C values, ratio markers of inflammation, which cover neutrophil-to-lymphocyte ratio (NLR), derived neutrophil-to-lymphocyte ratio (dNLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), monocyte-to-HDL-C ratio (MHR) were calculated. Statistical analyses were performed. The statistical significance degree was considered as p < 0.05. There was no statistically significant difference among the groups in terms of platelet count, neutrophil count, lymphocyte count, monocyte count, and NLR. PLR differed significantly between OW and N-BMI as well as MetS. Monocyte-to HDL-C value exhibited statistical significance between MetS and N-BMI, OB, and MO groups. HDL-C value differed between MetS and N-BMI, OW, OB, MO groups. MHR was the ratio, which exhibits the best performance among the other CBC-based inflammatory markers. On the other hand, when MHR was compared to HDL-C only, it was suggested that HDL-C has given much more valuable information. Therefore, this parameter still keeps its value from the diagnostic point of view. Our results suggest that MHR can be an inflammatory marker during the evaluation of pediatric MetS, but the predictive value of this parameter was not superior to HDL-C during the evaluation of obesity.

Keywords: children, complete blood cell count, high density lipoprotein cholesterol, metabolic syndrome, obesity

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Authors: Reena K., Mamatha H. G., Somshekarayya, P. Kumar

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Objectives: The annual proficiency testing of intermediate reference laboratories is conducted by the National Reference Laboratory (NRL) to assess the efficiency of the laboratories to correctly identify Mycobacterium tuberculosis and to determine its drug susceptibility pattern. The proficiency testing results from 2013 to 2017 were analyzed to determine laboratories that were consistent in reporting quality results and those that had difficulty in doing so. Methods: A panel of twenty cultures were sent out to each of these laboratories. The laboratories were expected to grow the cultures in their own laboratories, set up drug susceptibly testing by all the methods they were certified for and report the results within the stipulated time period. The turnaround time for reporting results, specificity, sensitivity positive and negative predictive values and efficiency of the laboratory in identifying the cultures were analyzed. Results: Most of the laboratories had reported their results within the stipulated time period. However, there was enormous delay in reporting results from few of the laboratories. This was mainly due to improper functioning of the biosafety level III laboratory. Only 40% of the laboratories had 100% efficiency in solid culture using Lowenstein Jensen medium. This was expected as a solid culture, and drug susceptibility testing is not used for diagnosing drug resistance. Rapid molecular methods such as Line probe assay and Genexpert are used to determine drug resistance. Automated liquid culture system such as the Mycobacterial growth indicator tube is used to determine prognosis of the patient while on treatment. It was observed that 90% of the laboratories had achieved 100% in the liquid culture method. Almost all laboratories had achieved 100% efficiency in the line probe assay method which is the method of choice for determining drug-resistant tuberculosis. Conclusion: Since the liquid culture and line probe assay technologies are routinely used for the detection of drug-resistant tuberculosis the laboratories exhibited higher level of efficiency as compared to solid culture and drug susceptibility testing which are rarely used. The infrastructure of the laboratory should be maintained properly so that samples can be processed safely and results could be declared on time.

Keywords: annual proficiency testing, drug susceptibility testing, intermediate reference laboratory, national reference laboratory

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Authors: Sandra T. Matarneh, Mark Danso-Amoako, Salam Al-Bizri, Mark Gaterell

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Today’s facilities are ever more sophisticated and the need for available and reliable information for operation and maintenance activities is vital. The key challenge for facilities managers is to have real-time accurate and complete information to perform their day-to-day activities and to provide their senior management with accurate information for decision-making process. Currently, there are various technology platforms, data repositories, or database systems such as Computer-Aided Facility Management (CAFM) that are used for these purposes in different facilities. In most current practices, the data is extracted from paper construction documents and is re-entered manually in one of these computerized information systems. Construction Operations Building information exchange (COBie), is a non-proprietary data format that contains the asset non-geometric data which was captured and collected during the design and construction phases for owners and facility managers use. Recently software vendors developed add-in applications to generate COBie spreadsheet automatically. However, most of these add-in applications are capable of generating a limited amount of COBie data, in which considerable time is still required to enter the remaining data manually to complete the COBie spreadsheet. Some of the data which cannot be generated by these COBie add-ins is essential for facilities manager’s day-to-day activities such as job sheet which includes preventive maintenance schedules. To facilitate a seamless data transfer between BIM models and facilities management systems, we developed a framework that enables automated data generation using the data extracted directly from BIM models to external web database, and then enabling different stakeholders to access to the external web database to enter the required asset data directly to generate a rich COBie spreadsheet that contains most of the required asset data for efficient facilities management operations. The proposed framework is a part of ongoing research and will be demonstrated and validated on a typical university building. Moreover, the proposed framework supplements the existing body of knowledge in facilities management domain by providing a novel framework that facilitates seamless data transfer between BIM models and facilities management systems.

Keywords: building information modeling, BIM, facilities management systems, interoperability, information management

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Authors: Samuel John, S. Sharanya

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Automation has become an important part of our life. It has been used to control home entertainment systems, changing the ambience of rooms for different events etc. One of the main parameters to control in a smart home is the atmospheric comfort. Atmospheric comfort mainly includes temperature and relative humidity. In homes, the desired temperature of different rooms varies from 20 °C to 25 °C and relative humidity is around 50%. However, it varies widely. Hence, automated measurement of these parameters to ensure comfort assumes significance. To achieve this, a fuzzy logic controller using Arduino was developed using MATLAB. Arduino is an open source hardware consisting of a 24 pin ATMEGA chip (atmega328), 14 digital input /output pins and an inbuilt ADC. It runs on 5v and 3.3v power supported by a board voltage regulator. Some of the digital pins in Aruduino provide PWM (pulse width modulation) signals, which can be used in different applications. The Arduino platform provides an integrated development environment, which includes support for c, c++ and java programming languages. In the present work, soft sensor was introduced in this system that can indirectly measure temperature and humidity and can be used for processing several measurements these to ensure comfort. The Sugeno method (output variables are functions or singleton/constant, more suitable for implementing on microcontrollers) was used in the soft sensor in MATLAB and then interfaced to the Arduino, which is again interfaced to the temperature and humidity sensor DHT11. The temperature-humidity sensor DHT11 acts as the sensing element in this system. Further, a capacitive humidity sensor and a thermistor were also used to support the measurement of temperature and relative humidity of the surrounding to provide a digital signal on the data pin. The comfort sensor developed was able to measure temperature and relative humidity correctly. The comfort percentage was calculated and accordingly the temperature in the room was controlled. This system was placed in different rooms of the house to ensure that it modifies the comfort values depending on temperature and relative humidity of the environment. Compared to the existing comfort control sensors, this system was found to provide an accurate comfort percentage. Depending on the comfort percentage, the air conditioners and the coolers in the room were controlled. The main highlight of the project is its cost efficiency.

Keywords: arduino, DHT11, soft sensor, sugeno

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Authors: Nor Azlina A. Rahman, Jamaliah Said, Salwana Hassan

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Prompt changes in global business environment, such as passionate competition, managerial/operational, changing governmental regulation and innovation in technology have significant impacts on the organizations. At present, global business environment demands for more proactive institutions on microfinance to provide an opportunity for the business success. Microfinance providers in Malaysia still accelerate its activities of funding by cash and cheque. These institutions are at high risk as the paper-based system is deemed to be slow and prone to human error, as well as requiring a major annual reconciliation process. The global transformation of financial services, growing involvement of technology, innovation and new business activities had progressively made risk management profile to be more subjective and diversified. The persistent, complex and dynamic nature of risk management activities in the institutions arise due to highly automated advancements of technology. This may thus manifest in a variety of ways throughout the financial services sector. This study seeks out to examine current operational risks management being experienced by microfinance providers in Malaysia; investigate the process of current practices on facilitator control factor mechanisms, and explore how the adoption of technology, innovation and use of management accounting practices would affect the risk management process of operation system in microfinance providers in Malaysia. A case study method was employed in this study. The case study also need to find that the vital past role of management accounting will be used for mitigation of risk management activities towards accountability as an information or guideline to microfinance provider. An empirical element obtainable with qualitative method is needed in this study, where multipart and in-depth information are essential to understand the issues of these institution phenomena. This study is expected to propose a theoretical model for implementation of technology, innovation and management accounting practices into the system of operation to improve internal control and subsequently lead to mitigation of risk management activities among microfinance providers to be more successful.

Keywords: microfinance, accountability, operational risks, management accounting practices

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Authors: C. J. Rossouw, T. I. van Niekerk

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The global manufacturing industry is utilizing the internet and cloud-based services to further explore the anatomy and optimize manufacturing processes in support of the movement into the Fourth Industrial Revolution (4IR). The 4IR from a third world and African perspective is hindered by the fact that many manufacturing systems that were developed in the third industrial revolution are not inherently equipped to utilize the internet and services of the 4IR, hindering the progression of third world manufacturing industries into the 4IR. This research focuses on the development of a non-invasive and cost-effective cyber-physical IoT system that will exploit a machine’s vibration to expose semantic characteristics in the manufacturing process and utilize these results through a real-time cloud-based machine condition monitoring system with the intention to optimize the system. A microcontroller-based IoT sensor was designed to acquire a machine’s mechanical vibration data, process it in real-time, and transmit it to a cloud-based platform via Wi-Fi and the internet. Time-frequency Fourier analysis was applied to the vibration data to form an image representation of the machine’s behaviour. This data was used to train a Convolutional Neural Network (CNN) to learn semantic characteristics in the machine’s behaviour and relate them to a state of operation. The same data was also used to train a Convolutional Autoencoder (CAE) to detect anomalies in the data. Real-time edge-based artificial intelligence was achieved by deploying the CNN and CAE on the sensor to analyse the vibration. A cloud platform was deployed to visualize the vibration data and the results of the CNN and CAE in real-time. The cyber-physical IoT system was deployed on a semi-automated metal granulation machine with a set of trained machine learning models. Using a single sensor, the system was able to accurately visualize three states of the machine’s operation in real-time. The system was also able to detect a variance in the material being granulated. The research demonstrates how non-IoT manufacturing systems can be equipped with edge-based artificial intelligence to establish a remote machine condition monitoring system.

Keywords: IoT, cyber-physical systems, artificial intelligence, manufacturing, vibration analytics, continuous machine condition monitoring

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Authors: Mohanad Al-Behadili, Xiang Song, Djamila Ouelhadj, Alex Fraess-Ehrfeld

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In off-shore wind farms, turbine blade inspection accessibility under various sea states is very challenging and greatly affects the downtime of wind turbines. Maintenance of any offshore system is not an easy task due to the restricted logistics and accessibility. The multirotor unmanned helicopter is of increasing interest in inspection applications due to its manoeuvrability and payload capacity. These advantages increase when many of them are deployed simultaneously in a swarm. Hence this paper proposes a drone swarm framework for inspecting offshore wind turbine blades and nacelles so as to reduce downtime. One of the big challenges of this task is that when operating a drone swarm, an individual drone may not have enough power to fly and communicate during missions and it has no capability of refueling due to its small size. Once the drone power is drained, there are no signals transmitted and the links become intermittent. Vessels equipped with 5G masts and small power units are utilised as platforms for drones to recharge/swap batteries. The research work aims at designing a smart energy management system, which provides automated vessel and drone routing and recharging plans. To achieve this goal, a novel mathematical optimisation model is developed with the main objective of minimising the number of drones and vessels, which carry the charging stations, and the downtime of the wind turbines. There are a number of constraints to be considered, such as each wind turbine must be inspected once and only once by one drone; each drone can inspect at most one wind turbine after recharging, then fly back to the charging station; collision should be avoided during the drone flying; all wind turbines in the wind farm should be inspected within the given time window. We have developed a real-time Ant Colony Optimisation (ACO) algorithm to generate real-time and near-optimal solutions to the drone swarm routing problem. The schedule will generate efficient and real-time solutions to indicate the inspection tasks, time windows, and the optimal routes of the drones to access the turbines. Experiments are conducted to evaluate the quality of the solutions generated by ACO.

Keywords: drone swarm, routing, scheduling, optimisation model, ant colony optimisation

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Authors: Jessica Chorostecki, Aashka Shah, Fen Bao, Ginny Bao, Edwin George, Navid Seraji-Bozorgzad, Veronica Gorden, Christina Caon, Elliot Frohman

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Background: Parkinson’s Disease (PD) is a neuro degenerative disorder associated with the loss of dopaminergic cells and the presence α-synuclein (AS) aggregation in of Lewy bodies. Both dopaminergic cells and AS are found in the retina. Optical coherence tomography (OCT) allows high-resolution in-vivo examination of retinal structure injury in neuro degenerative disorders including PD. Methods: We performed a cross-section OCT study in patients with definite PD and healthy controls (HC) using Spectral Domain SD-OCT platform to measure the peripapillary retinal nerve fiber layer (pRNFL) thickness and total macular volume (TMV). We performed intra-retinal segmentation with fully automated segmentation software to measure the volume of the RNFL, ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), and the outer nuclear layer (ONL). Segmentation was performed blinded to the clinical status of the study participants. Results: 101 eyes from 52 PD patients (mean age 65.8 years) and 46 eyes from 24 HC subjects (mean age 64.1 years) were included in the study. The mean pRNFL thickness was not significantly different (96.95 μm vs 94.42 μm, p=0.07) but the TMV was significantly lower in PD compared to HC (8.33 mm3 vs 8.58 mm3 p=0.0002). Intra-retinal segmentation showed no significant difference in the RNFL volume between the PD and HC groups (0.95 mm3 vs 0.92 mm3 p=0.454). However, GCL, IPL, INL, and ONL volumes were significantly reduced in PD compared to HC. In contrast, the volume of OPL was significantly increased in PD compared to HC. Conclusions: Our finding of the enlarged OPL corresponds with mRNA expression studies showing localization of AS in the OPL across vertebrate species and autopsy studies demonstrating AS aggregation in the deeper layers of retina in PD. We propose that the enlargement of the OPL may represent a potential biomarker of AS aggregation in PD. Longitudinal studies in larger cohorts are warranted to confirm our observations that may have significant implications in disease monitoring and therapeutic development.

Keywords: Optical Coherence Tomography, biomarker, Parkinson's disease, alpha-synuclein, retina

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Authors: Ammaarah Gamieldien

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Depression is a very common and serious mental illness that has a significant impact on both the social and economic aspects of sufferers worldwide. This study aimed to investigate the association between body mass index (BMI), blood pressure, and lipid profiles with mental health symptoms in university students. Secondary objectives included the associations between the variables (BMI, blood pressure, and lipids) with themselves, as they are key factors in cardiometabolic disease. Sixty-three (63) students participated in the study. Thirty-two (32) were assigned to the control group (minimal-mild depressive symptoms), while 31 were assigned to the depressive group (moderate to severe depressive symptoms). Montgomery-Asberg Depression Rating Scale (MADRS) and Beck Depression Inventory (BDI) were used to assess depressive scores. Anthropometric measurements such as weight (kg), height (m), waist circumference (WC), and hip circumference were measured. Body mass index (BMI) and ratios such as waist-to-hip ratio (WHR) and waist-to-height ratio (WtHR) were also calculated. Blood pressure was measured using an automated AfriMedics blood pressure machine, while lipids were measured using a CardioChek plus analyzer machine. Statistics were analyzed via the SPSS statistics program. There were no significant associations between anthropometric measurements and depressive scores (p > 0.05). There were no significant correlations between lipid profiles and depression when running a Spearman’s rho correlation (P > 0.05). However, total cholesterol and LDL-C were negatively associated with depression, and triglycerides were positively associated with depression after running a point-biserial correlation (P < 0.05). Overall, there were no significant associations between blood pressure measurements and depression (P > 0.05). However, there was a significant moderate positive correlation between systolic blood pressure and MADRS scores in males (P < 0.05). Depressive scores positively and strongly correlated to how long it takes participants to fall asleep. There were also significant associations with regard to the secondary objectives. This study indicates the importance of determining the prevalence of depression among university students in South Africa. If the prevalence and factors associated with depression are addressed, depressive symptoms in university students may be improved.

Keywords: depression, blood pressure, body mass index, lipid profiles, mental health symptoms

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Authors: Abisha Isaac Mohanlal

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Amidst all suspicions and cynicism raised by the legal fraternity, Artificial Intelligence has found its way into the legal system and has revolutionized the conventional forms of legal services delivery. Be it legal argumentation and research or resolution of complex legal disputes; artificial intelligence has crept into all legs of modern day legal services. Its impact has been largely felt by way of big data, legal expert systems, prediction tools, e-lawyering, automated mediation, etc., and lawyers around the world are forced to upgrade themselves and their firms to stay in line with the growth of technology in law. Researchers predict that the future of legal services would belong to artificial intelligence and that the age of human lawyers will soon rust. But as far as the Judiciary is concerned, even in the developed countries, the system has not fully drifted away from the orthodoxy of preferring Natural Intelligence over Artificial Intelligence. Since Judicial decision-making involves a lot of unstructured and rather unprecedented situations which have no single correct answer, and looming questions of legal interpretation arise in most of the cases, discretion and Emotional Intelligence play an unavoidable role. Added to that, there are several ethical, moral and policy issues to be confronted before permitting the intrusion of Artificial Intelligence into the judicial system. As of today, the human judge is the unrivalled master of most of the judicial systems around the globe. Yet, scientists of Artificial Intelligence claim that robot judges can replace human judges irrespective of how daunting the complexity of issues is and how sophisticated the cognitive competence required is. They go on to contend that even if the system is too rigid to allow robot judges to substitute human judges in the recent future, Artificial Intelligence may still aid in other judicial tasks such as drafting judicial documents, intelligent document assembly, case retrieval, etc., and also promote overall flexibility, efficiency, and accuracy in the disposal of cases. By deconstructing the major challenges that Artificial Intelligence has to overcome in order to successfully invade the human- dominated judicial sphere, and critically evaluating the potential differences it would make in the system of justice delivery, the author tries to argue that penetration of Artificial Intelligence into the Judiciary could surely be enhancive and reparative, if not fully transformative.

Keywords: artificial intelligence, judicial decision making, judicial systems, legal services delivery

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Authors: A. Assad, T. Zayed

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Water networks (WNs) are responsible of providing adequate amounts of safe, high quality, water to the public. As other critical infrastructure systems, WNs are subjected to deterioration which increases the number of breaks and leaks and lower water quality. In Canada, 35% of water assets require critical attention and there is a significant gap between the needed and the implemented investments. Thus, the need for efficient rehabilitation programs is becoming more urgent given the paradigm of aging infrastructure and tight budget. The first step towards developing such programs is to formulate a Performance Index that reflects the current condition of water assets along with its criticality. While numerous studies in the literature have focused on various aspects of condition assessment and reliability, limited efforts have investigated the criticality of such components. Critical water mains are those whose failure cause significant economic, environmental or social impacts on a community. Inclusion of criticality in computing the performance index will serve as a prioritizing tool for the optimum allocating of the available resources and budget. In this study, several social, economic, and environmental factors that dictate the criticality of a water pipelines have been elicited from analyzing the literature. Expert opinions were sought to provide pairwise comparisons of the importance of such factors. Subsequently, Fuzzy Logic along with Analytical Network Process (ANP) was utilized to calculate the weights of several criteria factors. Multi Attribute Utility Theories (MAUT) was then employed to integrate the aforementioned weights with the attribute values of several pipelines in Montreal WN. The result is a criticality index, 0-1, that quantifies the severity of the consequence of failure of each pipeline. A novel contribution of this approach is that it accounts for both the interdependency between criteria factors as well as the inherited uncertainties in calculating the criticality. The practical value of the current study is represented by the automated tool, Excel-MATLAB, which can be used by the utility managers and decision makers in planning for future maintenance and rehabilitation activities where high-level efficiency in use of materials and time resources is required.

Keywords: water networks, criticality assessment, asset management, fuzzy analytical network process

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Authors: Nihar Bheda

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Protecting digital assets such as networks, systems, and data from advanced cyber threats is the aim of Digital Immunity Systems (DIS), which are a subset of cybersecurity. With features like continuous monitoring, coordinated reactions, and long-term adaptation, DIS seeks to mimic biological immunity. This minimizes downtime by automatically identifying and eliminating threats. Traditional security measures, such as firewalls and antivirus software, are insufficient for enterprises, such as healthcare providers, given the rapid evolution of cyber threats. The number of medical record breaches that have occurred in recent years is proof that attackers are finding healthcare data to be an increasingly valuable target. However, obstacles to enhancing security include outdated systems, financial limitations, and a lack of knowledge. DIS is an advancement in cyber defenses designed specifically for healthcare settings. Protection akin to an "immune system" is produced by core capabilities such as anomaly detection, access controls, and policy enforcement. Coordination of responses across IT infrastructure to contain attacks is made possible by automation and orchestration. Massive amounts of data are analyzed by AI and machine learning to find new threats. After an incident, self-healing enables services to resume quickly. The implementation of DIS is consistent with the healthcare industry's urgent requirement for resilient data security in light of evolving risks and strict guidelines. With resilient systems, it can help organizations lower business risk, minimize the effects of breaches, and preserve patient care continuity. DIS will be essential for protecting a variety of environments, including cloud computing and the Internet of medical devices, as healthcare providers quickly adopt new technologies. DIS lowers traditional security overhead for IT departments and offers automated protection, even though it requires an initial investment. In the near future, DIS may prove to be essential for small clinics, blood banks, imaging centers, large hospitals, and other healthcare organizations. Cyber resilience can become attainable for the whole healthcare ecosystem with customized DIS implementations.

Keywords: digital immunity system, cybersecurity, healthcare data, emerging technology

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Authors: Emre Bayamlıoğlu

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This paper aims to provide an account of the legal and the regulative aspects of the algorithmic reputation systems with a special emphasis on the sharing economy (i.e., Uber, Airbnb, Lyft) business model. The first section starts with an analysis of the legal and commercial nature of the tripartite relationship among the parties, namely, the host platform, individual sharers/service providers and the consumers/users. The section further examines to what extent an algorithmic system of reputational information could serve as an alternative to legal regulation. Shortcomings are explained and analyzed with specific examples from Airbnb Platform which is a pioneering success in the sharing economy. The following section focuses on the issue of governance and control of the reputational information. The section first analyzes the legal consequences of algorithmic filtering systems to detect undesired comments and how a delicate balance could be struck between the competing interests such as freedom of speech, privacy and the integrity of the commercial reputation. The third section deals with the problem of manipulation by users. Indeed many sharing economy businesses employ certain techniques of data mining and natural language processing to verify consistency of the feedback. Software agents referred as "bots" are employed by the users to "produce" fake reputation values. Such automated techniques are deceptive with significant negative effects for undermining the trust upon which the reputational system is built. The third section is devoted to explore the concerns with regard to data mobility, data ownership, and the privacy. Reputational information provided by the consumers in the form of textual comment may be regarded as a writing which is eligible to copyright protection. Algorithmic reputational systems also contain personal data pertaining both the individual entrepreneurs and the consumers. The final section starts with an overview of the notion of reputation as a communitarian and collective form of referential trust and further provides an evaluation of the above legal arguments from the perspective of public interest in the integrity of reputational information. The paper concludes with certain guidelines and design principles for algorithmic reputation systems, to address the above raised legal implications.

Keywords: sharing economy, design principles of algorithmic regulation, reputational systems, personal data protection, privacy

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Authors: Angelis P. Barlampas

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The purpose of this study is to inform the reader, about the various applications of artificial intelligence (AI), in cardiac imaging. AI grows fast and its role is crucial in medical specialties, which use large amounts of digital data, that are very difficult or even impossible to be managed by human beings and especially doctors.Artificial intelligence (AI) refers to the ability of computers to mimic human cognitive function, performing tasks such as learning, problem-solving, and autonomous decision making based on digital data. Whereas AI describes the concept of using computers to mimic human cognitive tasks, machine learning (ML) describes the category of algorithms that enable most current applications described as AI. Some of the current applications of AI in cardiac imaging are the follows: Ultrasound: Automated segmentation of cardiac chambers across five common views and consequently quantify chamber volumes/mass, ascertain ejection fraction and determine longitudinal strain through speckle tracking. Determine the severity of mitral regurgitation (accuracy > 99% for every degree of severity). Identify myocardial infarction. Distinguish between Athlete’s heart and hypertrophic cardiomyopathy, as well as restrictive cardiomyopathy and constrictive pericarditis. Predict all-cause mortality. CT Reduce radiation doses. Calculate the calcium score. Diagnose coronary artery disease (CAD). Predict all-cause 5-year mortality. Predict major cardiovascular events in patients with suspected CAD. MRI Segment of cardiac structures and infarct tissue. Calculate cardiac mass and function parameters. Distinguish between patients with myocardial infarction and control subjects. It could potentially reduce costs since it would preclude the need for gadolinium-enhanced CMR. Predict 4-year survival in patients with pulmonary hypertension. Nuclear Imaging Classify normal and abnormal myocardium in CAD. Detect locations with abnormal myocardium. Predict cardiac death. ML was comparable to or better than two experienced readers in predicting the need for revascularization. AI emerge as a helpful tool in cardiac imaging and for the doctors who can not manage the overall increasing demand, in examinations such as ultrasound, computed tomography, MRI, or nuclear imaging studies.

Keywords: artificial intelligence, cardiac imaging, ultrasound, MRI, CT, nuclear medicine

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Authors: Svenja Pieritz, Jakab Pilaszanovich

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Collaborative Problem Solving (CPS) is recognized as being one of the most important skills of the 21st century with having a potential impact on education, job selection, and collaborative systems design. Therefore, CPS has been adopted in several standardized tests, including the Programme for International Student Assessment (PISA) in 2015. A significant challenge of evaluating CPS is the underlying interplay of cognitive and social skills, which requires a more holistic assessment. However, the majority of the existing tests are using a questionnaire-based assessment, which oversimplifies this interplay and undermines ecological validity. Two major difficulties were identified: Firstly, the creation of a controllable, real-time environment allowing natural behaviors and communication between at least two people. Secondly, the development of an appropriate method to collect and synthesize both cognitive and social metrics of collaboration. This paper proposes a more holistic and automated approach to the assessment of CPS. To address these two difficulties, a multiplayer problem-solving game called Sea of Light was developed: An environment allowing students to deploy a variety of measurable collaborative strategies. This controlled environment enables researchers to monitor behavior through the analysis of game actions and chat. The according solution for the statistical model is a combined approach of Natural Language Processing (NLP) and Bayesian network analysis. Social exchanges via the in-game chat are analyzed through NLP and fed into the Bayesian network along with other game actions. This Bayesian network synthesizes evidence to track and update different subdimensions of CPS. Major findings focus on the correlations between the evidences collected through in- game actions, the participants’ chat features and the CPS self- evaluation metrics. These results give an indication of which game mechanics can best describe CPS evaluation. Overall, Sea of Light gives test administrators control over different problem-solving scenarios and difficulties while keeping the student engaged. It enables a more complete assessment based on complex, socio-cognitive information on actions and communication. This tool permits further investigations of the effects of group constellations and personality in collaborative problem-solving.

Keywords: bayesian network, collaborative problem solving, game-based assessment, natural language processing

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Authors: Alica Höpken, Hergen Pargmann

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The resource-efficient planning of the complex production planning processes in the meat industry and the reduction of food waste is a permanent challenge. The complexity of the production planning process occurs in every part of the supply chain, from agriculture to the end consumer. It arises from long and uncertain planning phases. Uncertainties such as stochastic yields, fluctuations in demand, and resource variability are part of this process. In the meat industry, waste mainly relates to incorrect storage, technical causes in production, or overproduction. The high amount of food waste along the complex supply chain in the meat industry could not be reduced by simple solutions until now. Therefore, resource-efficient production planning by conventional methods is currently only partially feasible. The realization of intelligent, automated production planning is basically possible through the application of machine learning algorithms, such as those of reinforcement learning. By applying the adapted design thinking method, machine learning methods (especially reinforcement learning algorithms) are used for the complex production planning process in the meat industry. This method represents a concretization to the application area. A resource-efficient production planning process is made available by adapting the design thinking method. In addition, the complex processes can be planned efficiently by using this method, since this standardized approach offers new possibilities in order to challenge the complexity and the high time consumption. It represents a tool to support the efficient production planning in the meat industry. This paper shows an elegant adaption of the design thinking method to apply the reinforcement learning method for a resource-efficient production planning process in the meat industry. Following, the steps that are necessary to introduce machine learning algorithms into the production planning of the food industry are determined. This is achieved based on a case study which is part of the research project ”REIF - Resource Efficient, Economic and Intelligent Food Chain” supported by the German Federal Ministry for Economic Affairs and Climate Action of Germany and the German Aerospace Center. Through this structured approach, significantly better planning results are achieved, which would be too complex or very time consuming using conventional methods.

Keywords: change management, design thinking method, machine learning, meat industry, reinforcement learning, resource-efficient production planning

Procedia PDF Downloads 100

Authors: Mariangela Viviani, Daniele Germano, Simone Colace, Agostino Forestiero, Giuseppe Papuzzo, Sara Laurita

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Nowadays, people spend most of their time in closed environments, in offices, or at home. Therefore, secure and highly livable environmental conditions are needed to reduce the probability of aerial viruses spreading. Also, to lower the human impact on the planet, it is important to reduce energy consumption. Heating, Ventilation, and Air Conditioning (HVAC) systems account for the major part of energy consumption in buildings [1]. Devising systems to control and regulate the airflow is, therefore, essential for energy efficiency. Moreover, an optimal setting for thermal comfort and air quality is essential for people’s well-being, at home or in offices, and increases productivity. Thanks to the features of Artificial Intelligence (AI) tools and techniques, it is possible to design innovative systems with: (i) Improved monitoring and prediction accuracy; (ii) Enhanced decision-making and mitigation strategies; (iii) Real-time air quality information; (iv) Increased efficiency in data analysis and processing; (v) Advanced early warning systems for air pollution events; (vi) Automated and cost-effective m onitoring network; and (vii) A better understanding of air quality patterns and trends. We propose AIR SAFE, an IoT-based infrastructure designed to optimize air quality and thermal comfort in indoor environments leveraging AI tools. AIR SAFE employs a network of smart sensors collecting indoor and outdoor data to be analyzed in order to take any corrective measures to ensure the occupants’ wellness. The data are analyzed through AI algorithms able to predict the future levels of temperature, relative humidity, and CO₂ concentration [2]. Based on these predictions, AIR SAFE takes actions, such as opening/closing the window or the air conditioner, to guarantee a high level of thermal comfort and air quality in the environment. In this contribution, we present the results from the AI algorithm we have implemented on the first s et o f d ata c ollected i n a real environment. The results were compared with other models from the literature to validate our approach.

Keywords: air quality, internet of things, artificial intelligence, smart home

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Authors: T. Ewetumo, K. D. Adedayo, Festus Ben

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Knowledge of the thermal properties of foods is of fundamental importance in the food industry to establish the design of processing equipment. However, for tropical fruit juice, there is very little information in literature, seriously hampering processing procedures. This research work describes the development of an instrument for automated thermal conductivity and thermal diffusivity measurement of tropical fruit juice using a transient thermal probe technique based on line heat principle. The system consists of two thermocouple sensors, constant current source, heater, thermocouple amplifier, microcontroller, microSD card shield and intelligent liquid crystal. A fixed distance of 6.50mm was maintained between the two probes. When heat is applied, the temperature rise at the heater probe measured with time at time interval of 4s for 240s. The measuring element conforms as closely as possible to an infinite line source of heat in an infinite fluid. Under these conditions, thermal conductivity and thermal diffusivity are simultaneously measured, with thermal conductivity determined from the slope of a plot of the temperature rise of the heating element against the logarithm of time while thermal diffusivity was determined from the time it took the sample to attain a peak temperature and the time duration over a fixed diffusivity distance. A constant current source was designed to apply a power input of 16.33W/m to the probe throughout the experiment. The thermal probe was interfaced with a digital display and data logger by using an application program written in C++. Calibration of the instrument was done by determining the thermal properties of distilled water. Error due to convection was avoided by adding 1.5% agar to the water. The instrument has been used for measurement of thermal properties of banana, orange and watermelon. Thermal conductivity values of 0.593, 0.598, 0.586 W/m^o C and thermal diffusivity values of 1.053 ×〖10〗^(-7), 1.086 ×〖10〗^(-7), and 0.959 ×〖10〗^(-7) 〖m/s〗^2 were obtained for banana, orange and water melon respectively. Measured values were stored in a microSD card. The instrument performed very well as it measured the thermal conductivity and thermal diffusivity of the tropical fruit juice samples with statistical analysis (ANOVA) showing no significant difference (p>0.05) between the literature standards and estimated averages of each sample investigated with the developed instrument.

Keywords: thermal conductivity, thermal diffusivity, tropical fruit juice, diffusion equation

Procedia PDF Downloads 324

Authors: Armen Grigoryan, Milena Arakelyan

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The idea of smart logistics is still quite a complicated one. It can be used to market products to a large number of customers or to acquire raw materials of the highest quality at the lowest cost in geographically dispersed areas. The use of smart contracts in logistics and supply chain management has the potential to revolutionize the way that goods are tracked, transported, and managed. Smart contracts are simply computer programs written in one of the blockchain programming languages (Solidity, Rust, Vyper), which are capable of self-execution once the predetermined conditions are met. They can be used to automate and streamline many of the traditional manual processes that are currently used in logistics and supply chain management, including the tracking and movement of goods, the management of inventory, and the facilitation of payments and settlements between different parties in the supply chain. Currently, logistics is a core area for companies which is concerned with transporting products between parties. Still, the problem of this sector is that its scale may lead to detainments and defaults in the delivery of goods, as well as other issues. Moreover, large distributors require a large number of workers to meet all the needs of their stores. All this may contribute to big detainments in order processing and increases the potentiality of losing orders. In an attempt to break this problem, companies have automated all their procedures, contributing to a significant augmentation in the number of businesses and distributors in the logistics sector. Hence, blockchain technology and smart contracted legal agreements seem to be suitable concepts to redesign and optimize collaborative business processes and supply chains. The main purpose of this paper is to examine the scope of blockchain technology and smart contracts in the field of logistics and supply chain management. This study discusses the research question of how and to which extent smart contracts and blockchain technology can facilitate and improve the implementation of collaborative business structures for sustainable entrepreneurial activities in smart supply chains. The intention is to provide a comprehensive overview of the existing research on the use of smart contracts in logistics and supply chain management and to identify any gaps or limitations in the current knowledge on this topic. This review aims to provide a summary and evaluation of the key findings and themes that emerge from the research, as well as to suggest potential directions for future research on the use of smart contracts in logistics and supply chain management.

Keywords: smart contracts, smart logistics, smart supply chain management, blockchain and smart contracts in logistics, smart contracts for controlling supply chain management

Procedia PDF Downloads 66

Authors: Mrinal Kanti Bhowmik, Shawli Bardhan Jr., Debotosh Bhattacharjee

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Human skin containing temperature value more than absolute zero, discharges infrared radiation related to the frequency of the body temperature. The difference in infrared radiation from the skin surface reflects the abnormality present in human body. Considering the difference, detection and forecasting the temperature variation of the skin surface is the main objective of using Medical Infrared Thermography(MIT) as a diagnostic tool for pain detection. Medical Infrared Thermography(MIT) is a non-invasive imaging technique that records and monitors the temperature flow in the body by receiving the infrared radiated from the skin and represent it through thermogram. The intensity of the thermogram measures the inflammation from the skin surface related to pain in human body. Analysis of thermograms provides automated anomaly detection associated with suspicious pain regions by following several image processing steps. The paper represents a rigorous study based survey related to the processing and analysis of thermograms based on the previous works published in the area of infrared thermal imaging for detecting inflammatory pain diseases like arthritis, spondylosis, shoulder impingement, etc. The study also explores the performance analysis of thermogram processing accompanied by thermogram acquisition protocols, thermography camera specification and the types of pain detected by thermography in summarized tabular format. The tabular format provides a clear structural vision of the past works. The major contribution of the paper introduces a new thermogram acquisition standard associated with inflammatory pain detection in human body to enhance the performance rate. The FLIR T650sc infrared camera with high sensitivity and resolution is adopted to increase the accuracy of thermogram acquisition and analysis. The survey of previous research work highlights that intensity distribution based comparison of comparable and symmetric region of interest and their statistical analysis assigns adequate result in case of identifying and detecting physiological disorder related to inflammatory diseases.

Keywords: acquisition protocol, inflammatory pain detection, medical infrared thermography (MIT), statistical analysis

Procedia PDF Downloads 315

Authors: Ali Alshumrani, Nathan Clarke, Bogdan Ghite, Stavros Shiaeles

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Digital forensics has become an essential tool in the investigation of cyber and computer-assisted crime. Arguably, given the prevalence of technology and the subsequent digital footprints that exist, it could have a significant role across almost all crimes. However, the variety of technology platforms (such as computers, mobiles, Closed-Circuit Television (CCTV), Internet of Things (IoT), databases, drones, cloud computing services), heterogeneity and volume of data, forensic tool capability, and the investigative cost make investigations both technically challenging and prohibitively expensive. Forensic tools also tend to be siloed into specific technologies, e.g., File System Forensic Analysis Tools (FS-FAT) and Network Forensic Analysis Tools (N-FAT), and a good deal of data sources has little to no specialist forensic tools. Increasingly it also becomes essential to compare and correlate evidence across data sources and to do so in an efficient and effective manner enabling an investigator to answer high-level questions of the data in a timely manner without having to trawl through data and perform the correlation manually. This paper proposes a Unified Forensic Analysis Tool (U-FAT), which aims to establish a common language for electronic information and permit multi-source forensic analysis. Core to this approach is the identification and development of forensic analyses that automate complex data correlations, enabling investigators to investigate cases more efficiently. The paper presents a systematic analysis of major crime categories and identifies what forensic analyses could be used. For example, in a child abduction, an investigation team might have evidence from a range of sources including computing devices (mobile phone, PC), CCTV (potentially a large number), ISP records, and mobile network cell tower data, in addition to third party databases such as the National Sex Offender registry and tax records, with the desire to auto-correlate and across sources and visualize in a cognitively effective manner. U-FAT provides a holistic, flexible, and extensible approach to providing digital forensics in technology, application, and data-agnostic manner, providing powerful and automated forensic analysis.

Keywords: digital forensics, evidence correlation, heterogeneous data, forensics tool

Procedia PDF Downloads 156

Authors: Lyka Selene Magnayi, Marcos Vinas, Roseanne Ramos

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As the real estate industry continually grows in the Philippines, Geographic Information Systems (GIS) provide advantages in generating spatial databases for efficient delivery of information and services. The real estate sector is not only providing qualitative data about real estate properties but also utilizes various spatial aspects of these properties for different applications such as hazard mapping and assessment. In this study, a three-dimensional (3D) model and a spatial database of real estate properties in Fort Bonifacio, Taguig City are developed using GIS and SketchUp. Spatial datasets include political boundaries, buildings, road network, digital terrain model (DTM) derived from Interferometric Synthetic Aperture Radar (IFSAR) image, Google Earth satellite imageries, and hazard maps. Multiple model layers were created based on property listings by a partner real estate company, including existing and future property buildings. Actual building dimensions, building facade, and building floorplans are incorporated in these 3D models for geovisualization. Hazard model layers are determined through spatial overlays, and different scenarios of hazards are also presented in the models. Animated maps and walkthrough videos were created for company presentation and evaluation. Model evaluation is conducted through client surveys requiring scores in terms of the appropriateness, information content, and design of the 3D models. Survey results show very satisfactory ratings, with the highest average evaluation score equivalent to 9.21 out of 10. The output maps and videos obtained passing rates based on the criteria and standards set by the intended users of the partner real estate company. The methodologies presented in this study were found useful and have remarkable advantages in the real estate industry. This work may be extended to automated mapping and creation of online spatial databases for better storage, access of real property listings and interactive platform using web-based GIS.

Keywords: geovisualization, geographic information systems, GIS, real estate, spatial database, three-dimensional model

Procedia PDF Downloads 130

Authors: Paul R Armstrong

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Doubled haploids (DHs) have become an important breeding tool for creating maize inbred lines, although several bottlenecks in the DH production process limit wider development, application, and adoption of the technique. DH kernels are typically sorted manually and represent about 10% of the seeds in a much larger pool where the remaining 90% are hybrid siblings. This introduces time constraints on DH production and manual sorting is often not accurate. Automated sorting based on the chemical composition of the kernel can be effective, but devices, namely NMR, have not achieved the sorting speed to be a cost-effective replacement to manual sorting. This study evaluated a single kernel near-infrared reflectance spectroscopy (skNIR) platform to accurately identify DH kernels based on oil content. The skNIR platform is a higher-throughput device, approximately 3 seeds/s, that uses spectra to predict oil content of each kernel from maize crosses intentionally developed to create larger than normal oil differences, 1.5%-2%, between DH and hybrid kernels. Spectra from the skNIR were used to construct a partial least squares regression (PLS) model for oil and for a categorical reference model of 1 (DH kernel) or 2 (hybrid kernel) and then used to sort several crosses to evaluate performance. Two approaches were used for sorting. The first used a general PLS model developed from all crosses to predict oil content and then used for sorting each induction cross, the second was the development of a specific model from a single induction cross where approximately fifty DH and one hundred hybrid kernels used. This second approach used a categorical reference value of 1 and 2, instead of oil content, for the PLS model and kernels selected for the calibration set were manually referenced based on traditional commercial methods using coloration of the tip cap and germ areas. The generalized PLS oil model statistics were R2 = 0.94 and RMSE = .93% for kernels spanning an oil content of 2.7% to 19.3%. Sorting by this model resulted in extracting 55% to 85% of haploid kernels from the four induction crosses. Using the second method of generating a model for each cross yielded model statistics ranging from R2s = 0.96 to 0.98 and RMSEs from 0.08 to 0.10. Sorting in this case resulted in 100% correct classification but required models that were cross. In summary, the first generalized model oil method could be used to sort a significant number of kernels from a kernel pool but was not close to the accuracy of developing a sorting model from a single cross. The penalty for the second method is that a PLS model would need to be developed for each individual cross. In conclusion both methods could find useful application in the sorting of DH from hybrid kernels.

Keywords: NIR, haploids, maize, sorting

Procedia PDF Downloads 276

Authors: Vishwajit Ghatge, David Looper

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Over the last few decades, oilfield service rolling equipment has significantly increased in weight, primarily because of emissions regulations, which require larger/heavier engines, larger cooling systems, and emissions after-treatment systems, in some cases, etc. Larger engines cause more vibration and shock loads, leading to failure of electronics and control systems. If the vibrating frequency of the engine matches the system frequency, high resonance is observed on structural parts and mounts. One such existing automated control equipment system comprising wire rope mounts used for mounting computers was designed approximately 12 years ago. This includes the use of an industrial- grade computer to control the system operation. The original computer had a smaller, lighter enclosure. After a few years, a newer computer version was introduced, which was 10 lbm heavier. Some failures of internal computer parts have been documented for cases in which the old mounts were used. Because of the added weight, there is a possibility of having the two brackets impact each other under off-road conditions, which causes a high shock input to the computer parts. This added failure mode requires validating the existing mount design to suit the new heavy-weight computer. This paper discusses the modal finite element method (FEM) analysis and experimental modal analysis conducted to study the effects of vibration on the wire rope mounts and the computer. The existing mount was modelled in ANSYS software, and resultant mode shapes and frequencies were obtained. The experimental modal analysis was conducted, and actual frequency responses were observed and recorded. Results clearly revealed that at resonance frequency, the brackets were colliding and potentially causing damage to computer parts. To solve this issue, spring mounts of different stiffness were modeled in ANSYS software, and the resonant frequency was determined. Increasing the stiffness of the system increased the resonant frequency zone away from the frequency window at which the engine showed heavy vibrations or resonance. After multiple iterations in ANSYS software, the stiffness of the spring mount was finalized, which was again experimentally validated.

Keywords: experimental modal analysis, FEM Modal Analysis, frequency, modal analysis, resonance, vibration

Procedia PDF Downloads 299

Authors: Mazoun Nasser Rashid Al Kharusi, Nada Al Siyabi

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Objectives: The main aim of this study is to Find the rate of susceptibility of ESBL E. coli causing UTI to oral antibiotics. Secondary objectives: Prevalence of ESBL E. coli from community urine samples, identify the best empirical oral antibiotics with the least resistance rate for UTI and identify alternative oral antibiotics for testing and utilization. Methods: This study is a retrospective descriptive study of the last three years in five major hospitals in Oman (Khowla Hospital, AN’Nahdha Hospital, Rustaq Hospital, Nizwa Hospital, and Ibri Hospital) equipped with a microbiologist. Inclusion criteria include all eligible outpatient urine culture isolates, excluding isolates from admitted patients with hospital-acquired urinary tract infections. Data was collected through the MOH database. The MOH hospitals are using different types of testing, automated methods like Vitek2 and manual methods. Vitek2 machine uses the principle of the fluorogenic method for organism identification and a turbidimetric method for susceptibility testing. The manual method is done by double disc diffusion for identifying ESBL and the disc diffusion method is for antibiotic susceptibility. All laboratories follow the clinical laboratory science institute (CLSI) guidelines. Analysis was done by SPSS statistical package. Results: Total urine cultures were (23048). E. coli grew in (11637) 49.6% of the urine, whereas (2199) 18.8% of those were confirmed as ESBL. As expected, the resistance rate to amoxicillin and cefuroxime is 100%. Moreover, the susceptibility of those ESBL-producing E. coli to nitrofurantoin, trimethoprim+sulfamethoxazole, ciprofloxacin and amoxicillin-clavulanate is progressing over the years; however, still low. ESBL E. coli was predominating in the female gender and those aged 66-74 years old throughout all the years. Other oral antibiotic options need to be explored and tested so that we add to the pool of oral antibiotics for ESBL E. coli causing UTI in the community. Conclusion: High rate of ESBL E. coli in urine from the community. The high resistance rates to oral antibiotics highlight the need for alternative treatment options for UTIs caused by these bacteria. Further research is needed to identify new and effective treatments for UTIs caused by ESBL-E. Coli.

Keywords: UTI, ESBL, oral antibiotics, E. coli, susceptibility

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Authors: Eliot Britton, Patrick Hart, David Arbez, Kevin McPhillips

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Corporations exploit a collective vulnerability to commercial aesthetics in consumers, using distinct combinations of technology, language, and media. These communication techniques constitute an under-theorized aspect of contemporary music making. Quigital co-opts these techniques both as a tool to engage audience members and a way to critique traditional corporate messaging.Three Quigital works are discussed: 2020’s Quigital: Digital Edition, 2021’s Home Comfort Advisor, and the upcoming Corporate Retreat. In each, the audience interacts with ‘Quigital,’ a fictional company, in ways that closely mimic a typical consumer-corporate interaction: navigating a complex phone tree while wrangling the company AI; using an interactive website to view a smarthome product launch; or using a system of QR codes to interact with other users at an in-person event. However, each work combines these idiomatic interactions with unexpected elements: a choir sings machine learning-generated text trained on smarthome manuals. An audience member receives an automated follow-up text with a recording of their voice, warped and manipulated. The ensuing tension helps commandeer these corporate-inspired processes and technologies, revealing invasive and manipulative behavior in traditional corporate language and consumer interactions.Quigital is powered by a data-driven audiovisual performance framework that harnesses corporate models of iterative design, organization, and development. Quigital’s technical infrastructure, built using this iterative process, comprises a collection of components that can be assembled in a modular fashion to create interactive media. This paper examines how Quigital’s framework and technology establish a credible “corporate fiction” for audience members and how this fiction is used for aesthetic purposes—creating artworks that both leverage and critique consumer culture, corporate language, and surveillance capitalism.

Keywords: performance framework, virtual performance spaces, corporate aesthetics, machine learning in music, networked collaboration, consumer culture, generative composition, critique of corporate language, co-opting corporate aesthetics

Procedia PDF Downloads 33

Authors: Pedro Augusto Da Silva E Souza Miranda, Niloofar Jalali, Shweta Mistry

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BACKGROUND: Software developers utilize automated solutions to scrape users’ reviews to extract meaningful knowledge to identify problems (e.g., bugs, compatibility issues) and possible enhancements (e.g., users’ requests) to their solutions. However, most of these solutions do not consider the health risk aspects to users. Recent works have shed light on the importance of including health risk considerations in the development cycle of mHealth apps to prevent harm to its users. PROBLEM: The COVID-19 Pandemic in Canada (and World) is currently forcing physical distancing upon the general population. This new lifestyle made the usage of mHealth applications more essential than ever, with a projected market forecast of 332 billion dollars by 2025. However, this new insurgency in mHealth usage comes with possible risks to users’ health due to mHealth apps problems (e.g., wrong insulin dosage indication due to a UI error). OBJECTIVE: These works aim to raise awareness amongst mHealth developers of the importance of considering risks to users’ health within their development lifecycle. Moreover, this work also aims to help mHealth developers with a Proof-of-Concept (POC) solution to understand, process, and identify possible health risks to users of mHealth apps based on users’ reviews. METHODS: We conducted a mixed-method study design. We developed a crawler to mine the negative reviews from two samples of mHealth apps (my fitness, medisafe) from the Google Play store users. For each mHealth app, we performed the following steps: • The reviews are divided into two groups, before starting the COVID-19 (reviews’ submission date before 15 Feb 2019) and during the COVID-19 (reviews’ submission date starts from 16 Feb 2019 till Dec 2020). For each period, the Latent Dirichlet Allocation (LDA) topic model was used to identify the different clusters of reviews based on similar topics of review The topics before and during COVID-19 are compared, and the significant difference in frequency and severity of similar topics are identified. RESULTS: We successfully scraped, filtered, processed, and identified health-related topics in both qualitative and quantitative approaches. The results demonstrated the similarity between topics before and during the COVID-19.

Keywords: natural language processing (NLP), topic modeling, mHealth, COVID-19, software engineering, telemedicine, health risks

Procedia PDF Downloads 102