Search results for: clusterization and classification algorithms

Authors: Libor Zachoval, Daire O Broin, Oisin Cawley

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E-learning platforms, such as Blackboard have two major shortcomings: limited data capture as a result of the limitations of SCORM (Shareable Content Object Reference Model), and lack of incorporation of Artificial Intelligence (AI) and machine learning algorithms which could lead to better course adaptations. With the recent development of Experience Application Programming Interface (xAPI), a large amount of additional types of data can be captured and that opens a window of possibilities from which online education can benefit. In a corporate setting, where companies invest billions on the learning and development of their employees, some learner behaviours can be troublesome for they can hinder the knowledge development of a learner. Behaviours that hinder the knowledge development also raise ambiguity about learner’s knowledge mastery, specifically those related to gaming the system. Furthermore, a company receives little benefit from their investment if employees are passing courses without possessing the required knowledge and potential compliance risks may arise. Using xAPI and rules derived from a state-of-the-art review, we identified three learner behaviours, primarily related to guessing, in a corporate compliance course. The identified behaviours are: trying each option for a question, specifically for multiple-choice questions; selecting a single option for all the questions on the test; and continuously repeating tests upon failing as opposed to going over the learning material. These behaviours were detected on learners who repeated the test at least 4 times before passing the course. These findings suggest that gauging the mastery of a learner from multiple-choice questions test scores alone is a naive approach. Thus, next steps will consider the incorporation of additional data points, knowledge estimation models to model knowledge mastery of a learner more accurately, and analysis of the data for correlations between knowledge development and identified learner behaviours. Additional work could explore how learner behaviours could be utilised to make changes to a course. For example, course content may require modifications (certain sections of learning material may be shown to not be helpful to many learners to master the learning outcomes aimed at) or course design (such as the type and duration of feedback).

Keywords: artificial intelligence, corporate e-learning environment, knowledge maintenance, xAPI

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Authors: Mahnaz Khalafehnilsaz, Rozina Rahnama

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Introduction: The elderly population is growing rapidly, and with this growth comes an increased demand for healthcare services. Artificial intelligence (AI) has the potential to revolutionize the delivery of healthcare services to the elderly population. In this study, the various ways in which AI is used to create health content for elderly people and its transformative impact on the healthcare industry will be explored. Method: A systematic review of the literature was conducted to identify studies that have investigated the role of AI in creating health content specifically for elderly people. Several databases, including PubMed, Scopus, and Web of Science, were searched for relevant articles published between 2000 and 2022. The search strategy employed a combination of keywords related to AI, personalized health content, and the elderly. Studies that utilized AI to create health content for elderly individuals were included, while those that did not meet the inclusion criteria were excluded. A total of 20 articles that met the inclusion criteria were identified. Finding: The findings of this review highlight the diverse applications of AI in creating health content for elderly people. One significant application is the use of natural language processing (NLP), which involves the creation of chatbots and virtual assistants capable of providing personalized health information and advice to elderly patients. AI is also utilized in the field of medical imaging, where algorithms analyze medical images such as X-rays, CT scans, and MRIs to detect diseases and abnormalities. Additionally, AI enables the development of personalized health content for elderly patients by analyzing large amounts of patient data to identify patterns and trends that can inform healthcare providers in developing tailored treatment plans. Conclusion: AI is transforming the healthcare industry by providing a wide range of applications that can improve patient outcomes and reduce healthcare costs. From creating chatbots and virtual assistants to analyzing medical images and developing personalized treatment plans, AI is revolutionizing the way healthcare is delivered to elderly patients. Continued investment in this field is essential to ensure that elderly patients receive the best possible care.

Keywords: artificial intelligence, health content, older adult, healthcare

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Authors: Lou-Hon Sun, Yu-Ming Chiu, Chen-Wei Tao, Chia-Yun Tsai

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It is common practice for many companies to ask employees to provide heart-touching service for customers and to emphasize the attitude of 'customer first'. However, services may not necessarily gain praise, and may actually be considered excessive, if customers do not appreciate such behaviors. In reality, many restaurant businesses try to provide as much service as possible without taking into account whether over-provision may lead to negative customer reception. A survey of 894 people in Britain revealed that 49 percent of respondents consider over-attentive waiters the most annoying aspect of dining out. It can be seen that merely aiming to exceed customers’ expectations without actually addressing their needs, only further distances and dissociates the standard of services from the goals of customer satisfaction itself. Over-service is defined, as 'service provided that exceeds customer expectations, or simply that customers deemed redundant, resulting in negative perception'. It was found that customers’ reactions and complaints concerning over-service are not as intense as those against service failures caused by the inability to meet expectations; consequently, it is more difficult for managers to become aware of the existence of over-service. Thus the ability to manage over-service behaviors is a significant topic for consideration. The Kano model classifies customer preferences into five categories: attractive quality attribute, one-dimensional quality attribute, must-be quality attribute, indifferent quality attribute and reverse quality attributes. The model is still very popular for researchers to explore the quality aspects and customer satisfaction. Nevertheless, several studies indicated that Kano’s model could not fully capture the nature of service quality. The concept of over-service can be used to restructure the model and provide a better understanding of the service quality construct. In this research, the structure of Kano's two-dimensional questionnaire will be used to classify the factors into different dimensions. The same questions will be used in the second questionnaire for identifying the over-service experienced of the respondents. The finding of these two questionnaires will be used to analyze the relevance between service quality classification and over-service behaviors. The subjects of this research are customers of fine dining chain restaurants. Three hundred questionnaires will be issued based on the stratified random sampling method. Items for measurement will be derived from DINESERV scale. The tangible dimension of the questionnaire will be eliminated due to this research is focused on the employee behaviors. Quality attributes of the Kano model are often regarded as an instrument for improving customer satisfaction. The concept of over-service can be used to restructure the model and provide a better understanding of service quality construct. The extension of the Kano model will not only develop a better understanding of customer needs and expectations but also enhance the management of service quality.

Keywords: consumer satisfaction, DINESERV, kano model, over-service

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Authors: Marion G. Ben-Jacob, David Wang

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There is a mathematics requirement for entry level college and university students, especially those who plan to study STEM (Science, Technology, Engineering and Mathematics). Most of them take College Algebra, and to continue their studies, they need to succeed in this course. Different pedagogical strategies are employed to promote the success of our students. There is, of course, the Traditional Method of teaching- lecture, examples, problems for students to solve. The Emporium Model, another pedagogical approach, replaces traditional lectures with a learning resource center model featuring interactive software and on-demand personalized assistance. This presentation will compare these two methods of pedagogy and the study done with its results on this comparison. Math is the foundation for science, technology, and engineering. Its work is generally used in STEM to find patterns in data. These patterns can be used to test relationships, draw general conclusions about data, and model the real world. In STEM, solutions to problems are analyzed, reasoned, and interpreted using math abilities in a assortment of real-world scenarios. This presentation will examine specific examples of how math is used in the different STEM disciplines. Math becomes practical in science when it is used to model natural and artificial experiments to identify a problem and develop a solution for it. As we analyze data, we are using math to find the statistical correlation between the cause of an effect. Scientists who use math include the following: data scientists, scientists, biologists and geologists. Without math, most technology would not be possible. Math is the basis of binary, and without programming, you just have the hardware. Addition, subtraction, multiplication, and division is also used in almost every program written. Mathematical algorithms are inherent in software as well. Mechanical engineers analyze scientific data to design robots by applying math and using the software. Electrical engineers use math to help design and test electrical equipment. They also use math when creating computer simulations and designing new products. Chemical engineers often use mathematics in the lab. Advanced computer software is used to aid in their research and production processes to model theoretical synthesis techniques and properties of chemical compounds. Mathematics mastery is crucial for success in the STEM disciplines. Pedagogical research on formative strategies and necessary topics to be covered are essential.

Keywords: emporium model, mathematics, pedagogy, STEM

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Authors: Pavana Basavakumar, Devadas Bhat

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Diabetes is a disease which often goes undiagnosed until its secondary effects are noticed. Early detection of the disease is necessary to avoid serious consequences which could lead to the death of the patient. Conventional invasive tests for screening of diabetes are mostly painful, time consuming and expensive. There’s also a risk of infection involved, therefore it is very essential to develop non-invasive methods to screen and estimate the level of blood glucose. Extensive research is going on with this perspective, involving various techniques that explore optical, electrical, chemical and thermal properties of the human body that directly or indirectly depend on the blood glucose concentration. Thus, non-invasive blood glucose monitoring has grown into a vast field of research. In this project, an attempt was made to device a prototype for screening of diabetes by measuring electrical impedance of the skin and building a model to predict a patient’s condition based on the measured impedance. The prototype developed, passes a negligible amount of constant current (0.5mA) across a subject’s index finger through tetra polar silver electrodes and measures output voltage across a wide range of frequencies (10 KHz – 4 MHz). The measured voltage is proportional to the impedance of the skin. The impedance was acquired in real-time for further analysis. Study was conducted on over 75 subjects with permission from the institutional ethics committee, along with impedance, subject’s blood glucose values were also noted, using conventional method. Nonlinear regression analysis was performed on the features extracted from the impedance data to obtain a model that predicts blood glucose values for a given set of features. When the predicted data was depicted on Clarke’s Error Grid, only 58% of the values predicted were clinically acceptable. Since the objective of the project was to screen diabetes and not actual estimation of blood glucose, the data was classified into three classes ‘NORMAL FASTING’,’NORMAL POSTPRANDIAL’ and ‘HIGH’ using linear Support Vector Machine (SVM). Classification accuracy obtained was 91.4%. The developed prototype was economical, fast and pain free. Thus, it can be used for mass screening of diabetes.

Keywords: Clarke’s error grid, electrical impedance of skin, linear SVM, nonlinear regression, non-invasive blood glucose monitoring, screening device for diabetes

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Authors: Maciej Zasuwa, Grzegorz Ptasinski, Antoni Kopyt

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Nowadays flight simulators offer tremendous possibilities for safe and cost-effective pilot training, by utilization of powerful, computational tools. Due to technology outpacing methodology, vast majority of training related work is done by human instructors. It makes assessment not efficient, and vulnerable to instructors’ subjectivity. The research presents an Objective Assessment Tool (gOAT) developed at the Warsaw University of Technology, and tested on SW-4 helicopter flight simulator. The tool uses database of the predefined manoeuvres, defined and integrated to the virtual environment. These were implemented, basing on Aeronautical Design Standard Performance Specification Handling Qualities Requirements for Military Rotorcraft (ADS-33), with predefined Mission-Task-Elements (MTEs). The core element of the gOAT enhanced algorithm that provides instructor a new set of information. In details, a set of objective flight parameters fused with report about psychophysical state of the pilot. While the pilot performs the task, the gOAT system automatically calculates performance using the embedded algorithms, data registered by the simulator software (position, orientation, velocity, etc.), as well as measurements of physiological changes of pilot’s psychophysiological state (temperature, sweating, heart rate). Complete set of measurements is presented on-line to instructor’s station and shown in dedicated graphical interface. The presented tool is based on open source solutions, and flexible for editing. Additional manoeuvres can be easily added using guide developed by authors, and MTEs can be changed by instructor even during an exercise. Algorithm and measurements used allow not only to implement basic stress level measurements, but also to reduce instructor’s workload significantly. Tool developed can be used for training purpose, as well as periodical checks of the aircrew. Flexibility and ease of modifications allow the further development to be wide ranged, and the tool to be customized. Depending on simulation purpose, gOAT can be adjusted to support simulator of aircraft, helicopter, or unmanned aerial vehicle (UAV).

Keywords: automated assessment, flight simulator, human factors, pilot training

Procedia PDF Downloads 126

Authors: Petr Daniel Edward Kovarik, Malcolm Jackson, Charles Kelly, Rahul Patil, Shahid Iqbal

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Introduction: Recognition of the presence of extra capsular spread (ECS) has been a major change in the TNM 8th edition published by the American Joint Committee on Cancer in 2018. Irrespective of the size or number of lymph nodes, the presence of ECS makes N3b disease a stage IV disease. The objective of this retrospective observational study was to conduct a comparative analysis of survival outcomes in patients with lymph node-positive cutaneous head and neck squamous cell carcinoma (CHNSCC) based on their TNM 7th and TNM 8th editions classification. Materials and Methods: From January 2010 to December 2020, 71 patients with CHNSCC were identified from our centre’s database who were treated with radical surgery and adjuvant radiotherapy. All histopathological reports were reviewed, and comprehensive nodal mapping was performed. The data were collected retrospectively and survival outcomes were compared using TNM 7th and 8th editions. Results: The median age of the whole group of 71 patients was 78 years, range 54 – 94 years, 63 were male and 8 female. In total, 2246 lymph nodes were analysed; 195 were positive for cancer. ECS was present in 130 lymph nodes, which led to a change in TNM staging. The details on N-stage as per TNM 7th edition was as follows; pN1 = 23, pN2a = 14, pN2b = 32, pN2c = 0, pN3 = 2. After incorporating the TNM 8th edition criterion (presence of ECS), the details on N-stage were as follows; pN1 = 6, pN2a = 5, pN2b = 3, pN2c = 0, pN3a = 0, pN3b = 57. This showed an increase in overall stage. According to TNM 7th edition, there were 23 patients were with stage III and remaining 48 patients, stage IV. As per TNM 8th edition, there were only 6 patients with stage III as compared to 65 patients with stage IV. For all patients, 2-year disease specific survival (DSS) and overall survival (OS) were 70% and 46%. 5-year DSS and OS rates were 66% and 20% respectively. Comparing the survival between stage III and stage IV of the two cohorts using both TNM 7th and 8th editions, there is an obvious greater survival difference between the stages if TNM 8th staging is used. However, meaningful statistics were not possible as the majority of patients (n = 65) were with stage IV and only 6 patients were stage III in the TNM 8th cohort. Conclusion: Our study provides a comprehensive analysis on lymph node data mapping in this specific patient population. It shows a better differentiation between stage III and stage IV in the TNM 8th edition as compared to TNM 7th however meaningful statistics were not possible due to the imbalance of patients in the sub-cohorts of the groups.

Keywords: cutaneous head and neck squamous cell carcinoma, extra capsular spread, neck lymphadenopathy, TNM 7th and 8th editions

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Authors: Amal G. Kurian

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The use of mathematical modeling has seen a considerable boost in recent times with the development of many advanced algorithms and mathematical modeling capabilities. The advantages this method has over other methods are that they are much closer to standard physics theories and thus represent a better theoretical model. They take lesser solving time and have the ability to change various parameters for optimization, which is a big advantage, especially in automotive industry. This thesis work focuses on a thorough investigation of the effects of vehicle speed and road roughness on a heavy commercial vehicle ride and structural dynamic responses. Since commercial vehicles are kept in operation continuously for longer periods of time, it is important to study effects of various physical conditions on the vehicle and its user. For this purpose, various experimental as well as simulation methodologies, are adopted ranging from experimental transfer path analysis to various road scenario simulations. To effectively investigate and eliminate several causes of unwanted responses, an efficient and robust technique is needed. Carrying forward this motivation, the present work focuses on the development of a mathematical model of a 4-axle configuration heavy commercial vehicle (HCV) capable of calculating responses of the vehicle on different road PSD inputs and vehicle speeds. Outputs from the model will include response transfer functions and PSDs and wheel forces experienced. A MATLAB code will be developed to implement the objectives in a robust and flexible manner which can be exploited further in a study of responses due to various suspension parameters, loading conditions as well as vehicle dimensions. The thesis work resulted in quantifying the effect of various physical conditions on ride comfort of the vehicle. An increase in discomfort is seen with velocity increase; also the effect of road profiles has a considerable effect on comfort of the driver. Details of dominant modes at each frequency are analysed and mentioned in work. The reduction in ride height or deflection of tire and suspension with loading along with load on each axle is analysed and it is seen that the front axle supports a greater portion of vehicle weight while more of payload weight comes on fourth and third axles. The deflection of the vehicle is seen to be well inside acceptable limits.

Keywords: mathematical modeling, HCV, suspension, ride analysis

Procedia PDF Downloads 221

Authors: Yena Lee, Vassilis M. Charitopoulos, Karthik Thyagarajan, Ian Morris, Jose M. Pinto, Lazaros G. Papageorgiou

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With globalisation, the need to have better coordination of production and distribution decisions has become increasingly important for industrial gas companies in order to remain competitive in the marketplace. In this work, we investigate a problem that integrates production, inventory, and routing decisions in a liquid oxygen supply chain. The oxygen supply chain consists of production facilities, external third-party suppliers, and multiple customers, including hospitals and industrial customers. The product produced by the plants or sourced from the competitors, i.e., third-party suppliers, is distributed by a fleet of heterogenous vehicles to satisfy customer demands. The objective is to minimise the total operating cost involving production, third-party, and transportation costs. The key decisions for production include production and inventory levels and product amount from third-party suppliers. In contrast, the distribution decisions involve customer allocation, delivery timing, delivery amount, and vehicle routing. The optimisation of the coordinated production, inventory, and routing decisions is a challenging problem, especially when dealing with large-size problems. Thus, we present a two-stage procedure to solve the integrated problem efficiently. First, the problem is formulated as a mixed-integer linear programming (MILP) model by simplifying the routing component. The solution from the first-stage MILP model yields the optimal customer allocation, production and inventory levels, and delivery timing and amount. Then, we fix the previous decisions and solve a detailed routing. In the second stage, we propose a column generation scheme to address the computational complexity of the resulting detailed routing problem. A case study considering a real-life oxygen supply chain in the UK is presented to illustrate the capability of the proposed models and solution method. Furthermore, a comparison of the solutions from the proposed approach with the corresponding solutions provided by existing metaheuristic techniques (e.g., guided local search and tabu search algorithms) is presented to evaluate the efficiency.

Keywords: production planning, inventory routing, column generation, mixed-integer linear programming

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Authors: Gulnara Galeeva, Yulia Kasperskaya

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The study deals with the relevant problem related to the formation of the efficient investment portfolio of an enterprise. The structure of the investment portfolio is connected to the degree of influence of intangible assets on the enterprise’s income. This determines the importance of research on the content of intangible assets. However, intangible assets studies do not take into consideration how the enterprise state can affect the content and the importance of intangible assets for the enterprise`s income. This affects accurateness of the calculations. In order to study this problem, the research was divided into several stages. In the first stage, intangible assets were classified based on their synergies as the underlying intangibles and the additional intangibles. In the second stage, this classification was applied. It showed that the lifecycle model and the theory of abrupt development of the enterprise, that are taken into account while designing investment projects, constitute limit cases of a more general theory of bifurcations. The research identified that the qualitative content of intangible assets significant depends on how close the enterprise is to being in crisis. In the third stage, the author developed and applied the Wide Pairwise Comparison Matrix method. This allowed to establish that using the ratio of the standard deviation to the mean value of the elements of the vector of priority of intangible assets makes it possible to estimate the probability of a full-blown crisis of the enterprise. The author has identified a criterion, which allows making fundamental decisions on investment feasibility. The study also developed an additional rapid method of assessing the enterprise overall status based on using the questionnaire survey with its Director. The questionnaire consists only of two questions. The research specifically focused on the fundamental role of stochastic resonance in the emergence of bifurcation (crisis) in the economic development of the enterprise. The synergetic approach made it possible to describe the mechanism of the crisis start in details and also to identify a range of universal ways of overcoming the crisis. It was outlined that the structure of intangible assets transforms into a more organized state with the strengthened synchronization of all processes as a result of the impact of the sporadic (white) noise. Obtained results offer managers and business owners a simple and an affordable method of investment portfolio optimization, which takes into account how close the enterprise is to a state of a full-blown crisis.

Keywords: analytic hierarchy process, bifurcation, investment portfolio, intangible assets, wide matrix

Procedia PDF Downloads 186

Authors: Jayeola A. O., Ayodele O. S., Olususi J. I.

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From field studies, six (6) lithological units were identified to be common around the study area, which includes quartzites, granites, granite gneiss, porphyritic granites, amphibolite and pegmatites. Petrographical analysis was done to establish the major mineral assemblages and accessory minerals present in selected rock samples, which represents the major rock types in the area. For the purpose of this study, twenty (20) pulverized rock samples were taken to the laboratory for geochemical analysis with their results used in the classification, as well as suggest the geochemical attributes of the rocks. Results from petrographical studies of the rocks under both plane and cross polarized lights revealed the major minerals identified under thin sections to include quartz, feldspar, biotite, hornblende, plagioclase and muscovite with opaque other accessory minerals, which include actinolite, spinel and myrmekite. Geochemical results obtained and interpreted using various geochemical plots or discrimination plots all classified the rocks in the area as belonging to both the peralkaline metaluminous and peraluminous types. Results for the major oxides ratios produced for Na₂O/K₂O, Al₂O₃/Na₂O + CaO + K₂O and Na₂O + CaO + K₂O/Al₂O₃ show the excess of alumina, Al₂O₃ over the alkaline Na₂O +CaO +K₂O thus suggesting peraluminous rocks. While the excess of the alkali over the alumina suggests the peralkaline metaluminous rock type. The results of correlation coefficient show a perfect strong positive correlation, which shows that they are of same geogenic sources, while negative correlation coefficient values indicate a perfect weak negative correlation, suggesting that they are of heterogeneous geogenic sources. From factor analysis, five component groups were identified as Group 1 consists of Ag-Cr-Ni elemental associations suggesting Ag, Cr, and Ni mineralization, predicting the possibility of sulphide mineralization. in the study area. Group ll and lll consist of As-Ni-Hg-Fe-Sn-Co-Pb-Hg element association, which are pathfinder elements to the mineralization of gold. Group 1V and V consist of Cd-Cu-Ag-Co-Zn, which concentrations are significant to elemental associations and mineralization. In conclusion, from the potassium radiometric anomaly map produced, the eastern section (northeastern and southeastern) is observed to be the hot spot and mineralization zone for the study area.

Keywords: petrography, Ila Orangun, petrochemistry, pegmatites, peraluminous

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Authors: Castillo-Acevedo J, Mena-Rivero R, Silva-Poot H

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In Mexico, as largely of the developing countries, the rural housing is a study object, since the diversity of constructive idiosyncrasies for locality, involves various factors that make it vulnerable; an important aspect of study is the progressive deterioration that is seen in the rural housing. Various social programs, contribute financial resources in the field of housing to provide support for families living in rural areas, however, they do not provide a coordination with the self-construction that is usually the way in which is built in these areas. The present study, exposes the physical situation and an economic assessment that presents the rural self-constructed housing in three rural communities in the south of the state of Quintana Roo, Mexico, which were built with funding from federal social programs. The information compilation was carried out in a period of seven months in which there was used the intentional sampling of typical cases, where the object study was the housing constructed with supports of the program “Rural Housing” between the year 2009 and 2014. Instruments were used as the interview, ballot papers of observation, ballot papers of technical verification and various measuring equipment laboratory for the classification of pathologies; for the determination of some pathologies constructive Mexican standards were applied how NMX-C-192-ONNCCE, NMX-C-111-ONNCCE, NMX-C-404-ONNCCE and finally used the software of Opus CMS ® with the help of tables of the National Consumer Price Index (CPI) for update of costs and wages according to the line of being applied in Mexico, were used for an economic valuation. The results show 11 different constructive pathologies and exposes greater presence with the 22.50% to the segregation of the concrete; the economic assessment shows that 80% of self-constructed housing, exceed the cost of construction it would have compared to a similar dwelling built by a construction company; It is also exposed to the 46.10% of the universe of study represent economic losses in materials to the social activities by houses not built. The system of self-construction used by the social programs, affect to some extent the program objectives applied in underserved areas, as implicit and additional costs affect the economic capacity of beneficiaries who invest time and effort in an activity that are not specialists, which this research provides foundations for sustainable alternatives or possibly eliminate the practice of self-construction of implemented social programs in marginalized rural communities in the south of state of Quintana Roo, Mexico.

Keywords: economic valuation, pathologies constructive, rural housing, social programs

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Authors: Bechir Hbibi, Lamine Mili

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Understanding the condition of comatose patients can be difficult, but it is crucial to their optimal treatment. Consequently, numerous scoring systems have been developed around the world to categorize patient states based on physiological assessments. Although validated and widely adopted by medical communities, these scores still present numerous limitations and obstacles. Even with the addition of additional tests and extensions, these scoring systems have not been able to overcome certain limitations, and it appears unlikely that they will be able to do so in the future. On the other hand, physiological tests are not the only way to extract ideas about comatose patients. EEG signal analysis has helped extensively to understand the human brain and human consciousness and has been used by researchers in the classification of different levels of disease. The use of EEG in the ICU has become an urgent matter in several cases and has been recommended by medical organizations. In this field, the EEG is used to investigate epilepsy, dementia, brain injuries, and many other neurological disorders. It has recently also been used to detect pain activity in some regions of the brain, for the detection of stress levels, and to evaluate sleep quality. In our recent findings, our aim was to use multifractal analysis, a very successful method of handling multifractal signals and feature extraction, to establish a state of awareness scale for comatose patients based on their electrical brain activity. The results show that this score could be instantaneous and could overcome many limitations with which the physiological scales stock. On the contrary, multifractal analysis stands out as a highly effective tool for characterizing non-stationary and self-similar signals. It demonstrates strong performance in extracting the properties of fractal and multifractal data, including signals and images. As such, we leverage this method, along with other features derived from EEG signal recordings from comatose patients, to develop a scale. This scale aims to accurately depict the vigilance state of patients in intensive care units and to address many of the limitations inherent in physiological scales such as the Glasgow Coma Scale (GCS) and the FOUR score. The results of applying version V0 of this approach to 30 patients with known GCS showed that the EEG-based score similarly describes the states of vigilance but distinguishes between the states of 8 sedated patients where the GCS could not be applied. Therefore, our approach could show promising results with patients with disabilities, injected with painkillers, and other categories where physiological scores could not be applied.

Keywords: coma, vigilance state, EEG, multifractal analysis, feature extraction

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Authors: Nadia E. Christiana, Azzahra A. N. Ginting, Ardhito Nurcahya, Havisa P. Novira

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One of the quickest ways to do a short trip in urban areas is by walking, either individually, in couple or groups. Walkability nowadays becomes one of the parameters to measure the quality of an urban neighborhood. As a Central Business District and public transport transit hub, Dukuh Atas area becomes one of the highest numbers of commuters that pass by the area and interchange between transportation modes daily. Thus, as a public transport hub, a lot of investment should be focused to speed up the development of the area that would support urban transit activity between transportation modes, one of them is revitalizing pedestrian walkways. The purpose of this research is to formulate the visual design concept of 'Walkable City' based on the results of the observation and a series of rankings. To achieve this objective, it is necessary to accomplish several stages of the research that consists of (1) Identifying the system of pedestrian paths in Dukuh Atas area using descriptive qualitative method (2) Analyzing the sidewalk walkability rate according to the perception and the walkability satisfaction rate using the characteristics of pedestrians and non-pedestrians in Dukuh Atas area by using Global Walkability Index analysis and Multicriteria Satisfaction Analysis (3) Analyzing the factors that determine the integration of pedestrian walkways in Dukuh Atas area using descriptive qualitative method. The results achieved in this study is that the walkability level of Dukuh Atas corridor area is 44.45 where the value is included in the classification of 25-49, which is a bit of facility that can be reached by foot. Furthermore, based on the questionnaire, satisfaction rate of pedestrian walkway in Dukuh Atas area reached a number of 64%. It is concluded that commuters have not been fully satisfied with the condition of the sidewalk. Besides, the factors that influence the integration in Dukuh Atas area have been reasonable as it is supported by the utilization of land and modes such as KRL, Busway, and MRT. From the results of all analyzes conducted, the visual design and the application of the concept of walkable city along the pathway pedestrian corridor of Dukuh Atas area are formulated. Achievement of the results of this study amounted to 80% which needs to be done further review of the results of the analysis. The work of this research is expected to be a recommendation or input for the government in the development of pedestrian paths in maximizing the use of public transportation modes.

Keywords: design, global walkability index, mass rapid transit, walkable city

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Authors: Amandeep Kaur, Ns Atri, Munruchi Kaur

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Mushrooms have shaped our environment in ways that we are only beginning to understand. The weather patterns, topography, flora and fauna of Punjab state in India create favorable growing conditions for thousands of species of mushrooms, but the complete region was unexplored when it comes to coprophilous mushrooms growing on herbivorous dung. Coprophilous mushrooms are the most specialized fungi ecologically, which germinate and grow directly on different types of animal dung or on manured soil. In the present work, the diversity of coprophilous mushrooms' of Family Psathyrellaceae of the order Agaricales is explored, their relationship to the human world is sketched out, and their supreme significance to life on this planet is revealed. During the investigation, different dung localities from 16 districts of Punjab state have been explored for the collection of material. The macroscopic features of the collected mushrooms were documented on the Field key. The hand cut sections of the various parts of carpophore, such as pileus, gills, stipe and the basidiospores details, were studied microscopically under different magnification. Various authentic publications were consulted for the identification of the investigated taxa. The classification, authentic names and synonyms of the investigated taxa are as per the latest version of Dictionary of Fungi and the MycoBank. The present work deals with the taxonomy of 81 collections belonging to 39 species spread over 05 coprophilous genera, namely Psathyrella, Panaeolus, Parasola, Coprinopsis, and Coprinellus of family Psathyrellaceae. In the text, the investigated taxa have been arranged as they appear in the key to the genera and species investigated. In this work, have been thoroughly examined for their macroscopic, microscopic, ecological, and chemical reaction details. The authors dig deeper to give indication of their ecology and the dung type where they can be obtained. Each taxon is accompanied by a detailed listing of its prominent features and an illustration with habitat photographs and line drawings of morphological and anatomical features. Taxa are organized as per their status in the keys, which allow easy recognition. All the taxa are compared with similar taxa. The study has shown that dung is an important substrate which serves as a favorable niche for the growth of a variety of mushrooms. This paper shows an insight what short-lived coprophilous mushrooms can teach us about sustaining life on earth!

Keywords: abundance, basidiomycota, biodiversity, seasonal availability, systematics

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Authors: Sanjay Rathee, Arti Kashyap

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Extraction of useful information from large datasets is one of the most important research problems. Association rule mining is one of the best methods for this purpose. Finding possible associations between items in large transaction based datasets (finding frequent patterns) is most important part of the association rule mining. There exist many algorithms to find frequent patterns but Apriori algorithm always remains a preferred choice due to its ease of implementation and natural tendency to be parallelized. Many single-machine based Apriori variants exist but massive amount of data available these days is above capacity of a single machine. Therefore, to meet the demands of this ever-growing huge data, there is a need of multiple machines based Apriori algorithm. For these types of distributed applications, MapReduce is a popular fault-tolerant framework. Hadoop is one of the best open-source software frameworks with MapReduce approach for distributed storage and distributed processing of huge datasets using clusters built from commodity hardware. However, heavy disk I/O operation at each iteration of a highly iterative algorithm like Apriori makes Hadoop inefficient. A number of MapReduce-based platforms are being developed for parallel computing in recent years. Among them, two platforms, namely, Spark and Flink have attracted a lot of attention because of their inbuilt support to distributed computations. Earlier we proposed a reduced- Apriori algorithm on Spark platform which outperforms parallel Apriori, one because of use of Spark and secondly because of the improvement we proposed in standard Apriori. Therefore, this work is a natural sequel of our work and targets on implementing, testing and benchmarking Apriori and Reduced-Apriori and our new algorithm ReducedAll-Apriori on Apache Flink and compares it with Spark implementation. Flink, a streaming dataflow engine, overcomes disk I/O bottlenecks in MapReduce, providing an ideal platform for distributed Apriori. Flink's pipelining based structure allows starting a next iteration as soon as partial results of earlier iteration are available. Therefore, there is no need to wait for all reducers result to start a next iteration. We conduct in-depth experiments to gain insight into the effectiveness, efficiency and scalability of the Apriori and RA-Apriori algorithm on Flink.

Keywords: apriori, apache flink, Mapreduce, spark, Hadoop, R-Apriori, frequent itemset mining

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Authors: Nicholas S. Norbert Jr., John E. Bischoff, Christopher J. Willy

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Knowledge Management (KM) is globally recognized as a crucial element in securing competitive advantage through building and maintaining organizational memory, codifying and protecting intellectual capital and business intelligence, and providing mechanisms for collaboration and innovation. KM frameworks and approaches have been developed and defined identifying critical success factors for conducting KM within numerous industries ranging from scientific to business, and for ranges of organization scales from small groups to large enterprises. However, engineering and technical teams operating within restricted environments are subject to unique barriers and KM challenges which cannot be directly treated using the approaches and tools prescribed for other industries. This research identifies barriers in conducting KM within Hardware Development Engineering (HDE) teams and statistically compares significance to barriers upholding the four KM pillars of organization, technology, leadership, and learning for HDE teams. HDE teams suffer from restrictions in knowledge sharing (KS) due to classification of information (national security risks), customer proprietary restrictions (non-disclosure agreement execution for designs), types of knowledge, complexity of knowledge to be shared, and knowledge seeker expertise. As KM evolved leveraging information technology (IT) and web-based tools and approaches from Web 1.0 to Enterprise 2.0, KM may also seek to leverage emergent tools and analytics including expert locators and hybrid recommender systems to enable KS across barriers of the technical teams. The research will test hypothesis statistically evaluating if KM barriers for HDE teams affect the general set of expected benefits of a KM System identified through previous research. If correlations may be identified, then generalizations of success factors and approaches may also be garnered for HDE teams. Expert elicitation will be conducted using a questionnaire hosted on the internet and delivered to a panel of experts including engineering managers, principal and lead engineers, senior systems engineers, and knowledge management experts. The feedback to the questionnaire will be processed using analysis of variance (ANOVA) to identify and rank statistically significant barriers of HDE teams within the four KM pillars. Subsequently, KM approaches will be recommended for upholding the KM pillars within restricted environments of HDE teams.

Keywords: engineering management, knowledge barriers, knowledge management, knowledge sharing

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Authors: M. A. C. S. Sampath Fernando, James M. Curran, Renate Meyer

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Forensic DNA analysis has received much attention over the last three decades, due to its incredible usefulness in human identification. The statistical interpretation of DNA evidence is recognised as one of the most mature fields in forensic science. Peak heights in an Electropherogram (EPG) are approximately proportional to the amount of template DNA in the original sample being tested. A stutter is a minor peak in an EPG, which is not masking as an allele of a potential contributor, and considered as an artefact that is presumed to be arisen due to miscopying or slippage during the PCR. Stutter peaks are mostly analysed in terms of stutter ratio that is calculated relative to the corresponding parent allele height. Analysis of mixture profiles has always been problematic in evidence interpretation, especially with the presence of PCR artefacts like stutters. Unlike binary and semi-continuous models; continuous models assign a probability (as a continuous weight) for each possible genotype combination, and significantly enhances the use of continuous peak height information resulting in more efficient reliable interpretations. Therefore, the presence of a sound methodology to distinguish between stutters and real alleles is essential for the accuracy of the interpretation. Sensibly, any such method has to be able to focus on modelling stutter peaks. Bayesian nonparametric methods provide increased flexibility in applied statistical modelling. Mixture models are frequently employed as fundamental data analysis tools in clustering and classification of data and assume unidentified heterogeneous sources for data. In model-based clustering, each unknown source is reflected by a cluster, and the clusters are modelled using parametric models. Specifying the number of components in finite mixture models, however, is practically difficult even though the calculations are relatively simple. Infinite mixture models, in contrast, do not require the user to specify the number of components. Instead, a Dirichlet process, which is an infinite-dimensional generalization of the Dirichlet distribution, is used to deal with the problem of a number of components. Chinese restaurant process (CRP), Stick-breaking process and Pólya urn scheme are frequently used as Dirichlet priors in Bayesian mixture models. In this study, we illustrate an infinite mixture of simple linear regression models for modelling stutter ratio and introduce some modifications to overcome weaknesses associated with CRP.

Keywords: Chinese restaurant process, Dirichlet prior, infinite mixture model, PCR stutter

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Authors: Natalia Sypion-Dutkowska

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Abstract — The main research objective of the paper is to discover the relationship between the age groups of residents and crime in particular districts of a large city. The basic analytical tool is specific crime rates, calculated not in relation to the total population, but for age groups in a different social situation - property, housing, work, and representing different generations with different behavior patterns. They are the communities from which criminals and victims of crimes come. The analysis of literature and national police reports gives rise to hypotheses about the ability of a given age group to generate crime as a source of offenders and as a group of victims. These specific indicators are spatially differentiated, which makes it possible to detect socio-demographic determinants of spatial patterns of urban crime. A multi-feature classification of districts was also carried out, in which specific crime rates are the diagnostic features. In this way, areas with a similar structure of socio-demographic determinants of spatial patterns on urban crime were designated. The case study is the city of Szczecin in Poland. It has about 400,000 inhabitants and its area is about 300 sq km. Szczecin is located in the immediate vicinity of Germany and is the economic, academic and cultural capital of the region. It also has a seaport and an airport. Moreover, according to ESPON 2007, Szczecin is the Transnational and National Functional Urban Area. Szczecin is divided into 37 districts - auxiliary administrative units of the municipal government. The population of each of them in 2015-17 was divided into 8 age groups: babes (0-2 yrs.), children (3-11 yrs.), teens (12-17 yrs.), younger adults (18-30 yrs.), middle-age adults (31-45 yrs.), older adults (46-65 yrs.), early older (66-80) and late older (from 81 yrs.). The crimes reported in 2015-17 in each of the districts were divided into 10 groups: fights and beatings, other theft, car theft, robbery offenses, burglary into an apartment, break-in into a commercial facility, car break-in, break-in into other facilities, drug offenses, property damage. In total, 80 specific crime rates have been calculated for each of the districts. The analysis was carried out on an intra-city scale, this is a novel approach as this type of analysis is usually carried out at the national or regional level. Another innovative research approach is the use of specific crime rates in relation to age groups instead of standard crime rates. Acknowledgments: This research was funded by the National Science Centre, Poland, registration number 2019/35/D/HS4/02942.

Keywords: age groups, determinants of crime, spatial crime pattern, urban crime

Procedia PDF Downloads 151

Authors: Yarra Sulina, Nunna Tagore Sai Priya, Ankhi Banerjee

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With the rapid growth in tourist arrivals, there has been a huge demand for the growth of infrastructure in the destinations. With the increasing preference of tourists to stay near attractions, there has been a considerable change in the land use around tourist sites. However, with the inclusion of certain regulations and guidelines provided by the authorities based on the nature of tourism activity and geographical constraints, the pattern of growth of built form is different for various tourist sites. Therefore, this study explores the patterns of growth of built-up for a decade from 2009 to 2019 through two-dimensional and three-dimensional analysis. Land use maps are created through supervised classification of satellite images obtained from LANDSAT 4-5 and LANDSAT 8 for 2009 and 2019, respectively. The overall expansion of the built-up area in the region is analyzed in relation to the distance from the city's geographical center and the tourism-related growth regions are identified which are influenced by the proximity of tourist attractions. The primary tourist sites of various destinations with different geographical characteristics and tourism activities, that have undergone a significant increase in built-up area and are occupied with tourism-related infrastructure are selected for further study. Proximity analysis of the tourism-related growth sites is carried out to delineate the influence zone of the tourist site in a destination. Further, a temporal analysis of volumetric growth of built form is carried out to understand the morphology of the tourist precincts over time. The Digital Surface Model (DSM) and Digital Terrain Model (DTM) are used to extract the building footprints along with building height. Factors such as building height, and building density are evaluated to understand the patterns of three-dimensional growth of the built area in the region. The study also explores the underlying reasons for such changes in built form around various tourist sites and predicts the impact of such growth patterns in the region. The building height and building density around tourist site creates a huge impact on the appeal of the destination. The surroundings that are incompatible with the theme of the tourist site have a negative impact on the attractiveness of the destination that leads to negative feedback by the tourists, which is not a sustainable form of development. Therefore, proper spatial measures are necessary in terms of area and volume of the built environment for a healthy and sustainable environment around the tourist sites in the destination.

Keywords: sustainable tourism, growth patterns, land-use changes, 3-dimensional analysis of built-up area

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Authors: Orkide Donma, Mustafa M. Donma

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Obesity is associated with increased fat mass as well as fat percentage. Minerals are the elements, which are of vital importance. In this study, the relationships between body as well as bone mineral profile and the percentage as well as mass values of fat, fat-free portion, protein, skeletal muscle were evaluated in adult men with normal body mass index (N-BMI), and those classified according to different stages of obesity. A total of 103 adult men classified into five groups participated in this study. Ages were within 19-79 years range. Groups were N-BMI (Group 1), overweight (OW) (Group 2), first level of obesity (FLO) (Group 3), second level of obesity (SLO) (Group 4) and third level of obesity (TLO) (Group 5). Anthropometric measurements were performed. BMI values were calculated. Obesity degree, total body fat mass, fat percentage, basal metabolic rate (BMR), visceral adiposity, body mineral mass, body mineral percentage, bone mineral mass, bone mineral percentage, fat-free mass, fat-free percentage, protein mass, protein percentage, skeletal muscle mass and skeletal muscle percentage were determined by TANITA body composition monitor using bioelectrical impedance analysis technology. Statistical package (SPSS) for Windows Version 16.0 was used for statistical evaluations. The values below 0.05 were accepted as statistically significant. All the groups were matched based upon age (p > 0.05). BMI values were calculated as 22.6 ± 1.7 kg/m², 27.1 ± 1.4 kg/m², 32.0 ± 1.2 kg/m², 37.2 ± 1.8 kg/m², and 47.1 ± 6.1 kg/m² for groups 1, 2, 3, 4, and 5, respectively. Visceral adiposity and BMR values were also within an increasing trend. Percentage values of mineral, protein, fat-free portion and skeletal muscle masses were decreasing going from normal to TLO. Upon evaluation of the percentages of protein, fat-free portion and skeletal muscle, statistically significant differences were noted between NW and OW as well as OW and FLO (p < 0.05). However, such differences were not observed for body and bone mineral percentages. Correlation existed between visceral adiposity and BMI was stronger than that detected between visceral adiposity and obesity degree. Correlation between visceral adiposity and BMR was significant at the 0.05 level. Visceral adiposity was not correlated with body mineral mass but correlated with bone mineral mass whereas significant negative correlations were observed with percentages of these parameters (p < 0.001). BMR was not correlated with body mineral percentage whereas a negative correlation was found between BMR and bone mineral percentage (p < 0.01). It is interesting to note that mineral percentages of both body as well as bone are highly affected by the visceral adiposity. Bone mineral percentage was also associated with BMR. From these findings, it is plausible to state that minerals are highly associated with the critical stages of obesity as prominent parameters.

Keywords: bone, men, minerals, obesity

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Authors: Giorgio Bertolazzi, Panayiotis Benos, Michele Tumminello, Claudia Coronnello

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MicroRNAs are small non-coding RNAs that post-transcriptionally regulate the expression levels of messenger RNAs. MicroRNA regulation activity depends on the recognition of binding sites located on mRNA molecules. ComiR (Combinatorial miRNA targeting) is a user friendly web tool realized to predict the targets of a set of microRNAs, starting from their expression profile. ComiR incorporates miRNA expression in a thermodynamic binding model, and it associates each gene with the probability of being a target of a set of miRNAs. ComiR algorithms were trained with the information regarding binding sites in the 3’UTR region, by using a reliable dataset containing the targets of endogenously expressed microRNA in D. melanogaster S2 cells. This dataset was obtained by comparing the results from two different experimental approaches, i.e., inhibition, and immunoprecipitation of the AGO1 protein; this protein is a component of the microRNA induced silencing complex. In this work, we tested whether including coding region binding sites in the ComiR algorithm improves the performance of the tool in predicting microRNA targets. We focused the analysis on the D. melanogaster species and updated the ComiR underlying database with the currently available releases of mRNA and microRNA sequences. As a result, we find that the ComiR algorithm trained with the information related to the coding regions is more efficient in predicting the microRNA targets, with respect to the algorithm trained with 3’utr information. On the other hand, we show that 3’utr based predictions can be seen as complementary to the coding region based predictions, which suggests that both predictions, from 3'UTR and coding regions, should be considered in a comprehensive analysis. Furthermore, we observed that the lists of targets obtained by analyzing data from one experimental approach only, that is, inhibition or immunoprecipitation of AGO1, are not reliable enough to test the performance of our microRNA target prediction algorithm. Further analysis will be conducted to investigate the effectiveness of the tool with data from other species, provided that validated datasets, as obtained from the comparison of RISC proteins inhibition and immunoprecipitation experiments, will be available for the same samples. Finally, we propose to upgrade the existing ComiR web-tool by including the coding region based trained model, available together with the 3’UTR based one.

Keywords: AGO1, coding region, Drosophila melanogaster, microRNA target prediction

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Authors: Newton Muhury, Armando A. Apan, Tek Maraseni

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This study modelled the relationships between vegetation response and available water below the soil surface using the Terra’s Moderate Resolution Imaging Spectroradiometer (MODIS) generated Normalised Difference Vegetation Index (NDVI) and soil water content (SWC) data. The Soil & Water Assessment Tool (SWAT) interface known as ArcSWAT was used in ArcGIS for the groundwater analysis. The SWAT model was calibrated and validated in SWAT-CUP software using 10 years (2001-2010) of monthly streamflow data. The average Nash-Sutcliffe Efficiency during the calibration and validation was 0.54 and 0.51, respectively, indicating that the model performances were good. Twenty years (2001-2020) of monthly MODIS NDVI data for three different types of vegetation (forest, shrub, and grass) and soil water content for 43 sub-basins were analysed using the WEKA, machine learning tool with a selection of two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF). The modelling results show that different types of vegetation response and soil water content vary in the dry and wet season. For example, the model generated high positive relationships (r=0.76, 0.73, and 0.81) between the measured and predicted NDVI values of all vegetation in the study area against the groundwater flow (GW), soil water content (SWC), and the combination of these two variables, respectively, during the dry season. However, these relationships were reduced by 36.8% (r=0.48) and 13.6% (r=0.63) against GW and SWC, respectively, in the wet season. On the other hand, the model predicted a moderate positive relationship (r=0.63) between shrub vegetation type and soil water content during the dry season, which was reduced by 31.7% (r=0.43) during the wet season. Our models also predicted that vegetation in the top location (upper part) of the sub-basin is highly responsive to GW and SWC (r=0.78, and 0.70) during the dry season. The results of this study indicate the study region is suitable for seasonal crop production in dry season. Moreover, the results predicted that the growth of vegetation in the top-point location is highly dependent on groundwater flow in both dry and wet seasons, and any instability or long-term drought can negatively affect these floodplain vegetation communities. This study has enriched our knowledge of vegetation responses to groundwater in each season, which will facilitate better floodplain vegetation management.

Keywords: ArcSWAT, machine learning, floodplain vegetation, MODIS NDVI, groundwater

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Authors: Uraib Sharaha, Ahmad Salman, Eladio Rodriguez-Diaz, Elad Shufan, Klaris Riesenberg, Irving J. Bigio, Mahmoud Huleihel

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Antimicrobial drugs have an important role in controlling illness associated with infectious diseases in animals and humans. However, the increasing resistance of bacteria to a broad spectrum of commonly used antibiotics has become a global health-care problem. Rapid determination of antimicrobial susceptibility of a clinical isolate is often crucial for the optimal antimicrobial therapy of infected patients and in many cases can save lives. The conventional methods for susceptibility testing like disk diffusion are time-consuming and other method including E-test, genotyping are relatively expensive. Fourier transform infrared (FTIR) microscopy is rapid, safe, and low cost method that was widely and successfully used in different studies for the identification of various biological samples including bacteria. The new modern infrared (IR) spectrometers with high spectral resolution enable measuring unprecedented biochemical information from cells at the molecular level. Moreover, the development of new bioinformatics analyses combined with IR spectroscopy becomes a powerful technique, which enables the detection of structural changes associated with resistivity. The main goal of this study is to evaluate the potential of the FTIR microscopy in tandem with machine learning algorithms for rapid and reliable identification of bacterial susceptibility to antibiotics in time span of few minutes. The bacterial samples, which were identified at the species level by MALDI-TOF and examined for their susceptibility by the routine assay (micro-diffusion discs), are obtained from the bacteriology laboratories in Soroka University Medical Center (SUMC). These samples were examined by FTIR microscopy and analyzed by advanced statistical methods. Our results, based on 550 E.coli samples, were promising and showed that by using infrared spectroscopic technique together with multivariate analysis, it is possible to classify the tested bacteria into sensitive and resistant with success rate higher than 85% for eight different antibiotics. Based on these preliminary results, it is worthwhile to continue developing the FTIR microscopy technique as a rapid and reliable method for identification antibiotic susceptibility.

Keywords: antibiotics, E. coli, FTIR, multivariate analysis, susceptibility

Procedia PDF Downloads 237

Authors: Valentina Marutyan

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Healthcare data mining is a rapidly developing field at the intersection of technology and medicine that has the potential to change our understanding and approach to providing healthcare. Healthcare and data mining is the process of examining huge amounts of data to extract useful information that can be applied in order to improve patient care, treatment effectiveness, and overall healthcare delivery. This field looks for patterns, trends, and correlations in a variety of healthcare datasets, such as electronic health records (EHRs), medical imaging, patient demographics, and treatment histories. To accomplish this, it uses advanced analytical approaches. Predictive analysis using historical patient data is a major area of interest in healthcare data mining. This enables doctors to get involved early to prevent problems or improve results for patients. It also assists in early disease detection and customized treatment planning for every person. Doctors can customize a patient's care by looking at their medical history, genetic profile, current and previous therapies. In this way, treatments can be more effective and have fewer negative consequences. Moreover, helping patients, it improves the efficiency of hospitals. It helps them determine the number of beds or doctors they require in regard to the number of patients they expect. In this project are used models like logistic regression, random forests, and neural networks for predicting diseases and analyzing medical images. Patients were helped by algorithms such as k-means, and connections between treatments and patient responses were identified by association rule mining. Time series techniques helped in resource management by predicting patient admissions. These methods improved healthcare decision-making and personalized treatment. Also, healthcare data mining must deal with difficulties such as bad data quality, privacy challenges, managing large and complicated datasets, ensuring the reliability of models, managing biases, limited data sharing, and regulatory compliance. Finally, secret code of data mining in healthcare helps medical professionals and hospitals make better decisions, treat patients more efficiently, and work more efficiently. It ultimately comes down to using data to improve treatment, make better choices, and simplify hospital operations for all patients.

Keywords: data mining, healthcare, big data, large amounts of data

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Authors: Areti Angeliki Veroniki, Sofia Tsokani, Evangelos Paraskevaidis, Dimitris Mavridis

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The plethora of diagnostic test accuracy (DTA) studies has led to the increased use of systematic reviews and meta-analysis of DTA studies. Clinicians and healthcare professionals often consult DTA meta-analyses to make informed decisions regarding the optimum test to choose and use for a given setting. For example, the human papilloma virus (HPV) DNA, mRNA, and cytology can be used for the cervical intraepithelial neoplasia grade 2+ (CIN2+) diagnosis. But which test is the most accurate? Studies directly comparing test accuracy are not always available, and comparisons between multiple tests create a network of DTA studies that can be synthesized through a network meta-analysis of diagnostic tests (DTA-NMA). The aim is to summarize the DTA-NMA methods for at least three index tests presented in the methodological literature. We illustrate the application of the methods using a real data set for the comparative accuracy of HPV DNA, HPV mRNA, and cytology tests for cervical cancer. A search was conducted in PubMed, Web of Science, and Scopus from inception until the end of July 2019 to identify full-text research articles that describe a DTA-NMA method for three or more index tests. Since the joint classification of the results from one index against the results of another index test amongst those with the target condition and amongst those without the target condition are rarely reported in DTA studies, only methods requiring the 2x2 tables of the results of each index test against the reference standard were included. Studies of any design published in English were eligible for inclusion. Relevant unpublished material was also included. Ten relevant studies were finally included to evaluate their methodology. DTA-NMA methods that have been presented in the literature together with their advantages and disadvantages are described. In addition, using 37 studies for cervical cancer obtained from a published Cochrane review as a case study, an application of the identified DTA-NMA methods to determine the most promising test (in terms of sensitivity and specificity) for use as the best screening test to detect CIN2+ is presented. As a conclusion, different approaches for the comparative DTA meta-analysis of multiple tests may conclude to different results and hence may influence decision-making. Acknowledgment: This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning 2014-2020» in the context of the project “Extension of Network Meta-Analysis for the Comparison of Diagnostic Tests ” (MIS 5047640).

Keywords: colposcopy, diagnostic test, HPV, network meta-analysis

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Authors: Jillian M. Hagel, Joseph E. Tucker, Peter J. Facchini

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With the aim of establishing a holistic approach for the treatment of central nervous system (CNS) disorders, we are pursuing a drug development program rapidly progressing through discovery and characterization phases. The drug candidates identified in this program are referred to as neuroplastogens owing to their ability to mediate neuroplasticity, which can be beneficial to patients suffering from anxiety, depression, or posttraumatic stress disorder. These and other related neuropsychiatric conditions are associated with the onset of neuronal atrophy, which is defined as a reduction in the number and/or productivity of neurons. The stimulation of neuroplasticity results in an increase in the connectivity between neurons and promotes the restoration of healthy brain function. We have synthesized a substantial catalogue of proprietary indolethylamine derivatives based on the general structures of serotonin (5-hydroxytryptamine) and psychedelic molecules such as N,N-dimethyltryptamine (DMT) and psilocin (4-hydroxy-DMT) that function as neuroplastogens. A primary objective in our screening protocol is the identification of derivatives associated with a significant reduction in hallucination, which will allow administration of the drug at a dose that induces neuroplasticity and triggers other efficacious outcomes in the treatment of targeted CNS disorders but which does not cause a psychedelic response in the patient. Both neuroplasticity and hallucination are associated with engagement of the 5HT2A receptor, requiring drug candidates differentially coupled to these two outcomes at a molecular level. We use novel and proprietary artificial intelligence algorithms to predict the mode of binding to the 5HT2A receptor, which has been shown to correlate with the hallucinogenic response. Hallucination is tested using the mouse head-twitch response model, whereas mouse marble-burying and sucrose preference assays are used to evaluate anxiolytic and anti-depressive potential. Neuroplasticity is assays using dendritic outgrowth assays and cell-based ELISA analysis. Pharmacokinetics and additional receptor-binding analyses also contribute the selection of lead candidates. A summary of the program is presented.

Keywords: neuroplastogen, non-hallucinogenic, drug development, anxiety, depression, PTSD, indolethylamine derivatives, psychedelic-inspired, 5-HT2A receptor, computational chemistry, head-twitch response behavioural model, neurite outgrowth assay

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Authors: Natalia Rudeli, Elisabeth Viles, Adrian Santilli

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Delays in the construction industry are a global phenomenon. Many construction projects experience extensive delays exceeding the initially estimated completion time. The main purpose of this study is to identify construction projects typical behaviors in order to develop a prognosis and management tool. Being able to know a construction projects schedule tendency will enable evidence-based decision-making to allow resolutions to be made before delays occur. This study presents an innovative approach that uses Cluster Analysis Method to support predictions during Earned Value Analyses. A clustering analysis was used to predict future scheduling, Earned Value Management (EVM), and Earned Schedule (ES) principal Indexes behaviors in construction projects. The analysis was made using a database with 90 different construction projects. It was validated with additional data extracted from literature and with another 15 contrasting projects. For all projects, planned and executed schedules were collected and the EVM and ES principal indexes were calculated. A complete linkage classification method was used. In this way, the cluster analysis made considers that the distance (or similarity) between two clusters must be measured by its most disparate elements, i.e. that the distance is given by the maximum span among its components. Finally, through the use of EVM and ES Indexes and Tukey and Fisher Pairwise Comparisons, the statistical dissimilarity was verified and four clusters were obtained. It can be said that construction projects show an average delay of 35% of its planned completion time. Furthermore, four typical behaviors were found and for each of the obtained clusters, the interim milestones and the necessary rhythms of construction were identified. In general, detected typical behaviors are: (1) Projects that perform a 5% of work advance in the first two tenths and maintain a constant rhythm until completion (greater than 10% for each remaining tenth), being able to finish on the initially estimated time. (2) Projects that start with an adequate construction rate but suffer minor delays culminating with a total delay of almost 27% of the planned time. (3) Projects which start with a performance below the planned rate and end up with an average delay of 64%, and (4) projects that begin with a poor performance, suffer great delays and end up with an average delay of a 120% of the planned completion time. The obtained clusters compose a tool to identify the behavior of new construction projects by comparing their current work performance to the validated database, thus allowing the correction of initial estimations towards more accurate completion schedules.

Keywords: cluster analysis, construction management, earned value, schedule

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Authors: Nina Ponikvar, Katja Zajc Kejžar

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While the positive impacts of heritage on a broad societal spectrum have long been recognized and measured, the economic effects of the heritage sector are often less visible and frequently underestimated. At macro level, economic effects are usually studied based on one of the two mainstream approach, i.e. either the multiplier approach or the value chain approach. Consequently, there is limited comparability of the empirical results due to the use of different methodological approach in the literature. Furthermore, it is also not clear on which criteria the used approach was selected. Our aim is to bring the attention to the difference in the scope of effects that are encompassed by the two most frequent methodological approaches to valuation of economic effects of cultural heritage on macroeconomic level, i.e. the multiplier approach and the value chain approach. We show that while the multiplier approach provides a systematic, theory-based view of economic impacts but requires more data and analysis, the value chain approach has less solid theoretical foundations and depends on the availability of appropriate data to identify the contribution of cultural heritage to other sectors. We conclude that the multiplier approach underestimates the economic impact of cultural heritage, mainly due to the narrow definition of cultural heritage in the statistical classification and the inability to identify part of the contribution of cultural heritage that is hidden in other sectors. Yet it is not possible to clearly determine whether the value chain method overestimates or underestimates the actual economic impact of cultural heritage since there is a risk that the direct effects are overestimated and double counted, but not all indirect and induced effects are considered. Accordingly, these two approaches are not substitutes but rather complementary. Consequently, a direct comparison of the estimated impacts is not possible and should not be done due to the different scope. To illustrate the difference of the impact assessment of the cultural heritage, we apply both approaches to the case of Slovenia in the 2015-2022 period and measure the economic impact of cultural heritage sector in terms of turnover, gross value added and employment. The empirical results clearly show that the estimation of the economic impact of a sector using the multiplier approach is more conservative, while the estimates based on value added capture a much broader range of impacts. According to the multiplier approach, each euro in cultural heritage sector generates an additional 0.14 euros in indirect effects and an additional 0.44 euros in induced effects. Based on the value-added approach, the indirect economic effect of the “narrow” heritage sectors is amplified by the impact of cultural heritage activities on other sectors. Accordingly, every euro of sales and every euro of gross value added in the cultural heritage sector generates approximately 6 euros of sales and 4 to 5 euros of value added in other sectors. In addition, each employee in the cultural heritage sector is linked to 4 to 5 jobs in other sectors.

Keywords: economic value of cultural heritage, multiplier approach, value chain approach, indirect effects, slovenia

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Authors: Misuk Kim, Seokho Kang, Jehyuk Lee, Hyunchang Cho, Sungzoon Cho

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In semiconductor manufacturing, virtual metrology (VM) refers to methods to predict properties of a wafer based on machine parameters and sensor data of the production equipment, without performing the (costly) physical measurement of the wafer properties (Wikipedia). Additional benefits include avoidance of human bias and identification of important factors affecting the quality of the process which allow improving the process quality in the future. It is however rare to find VM applied to other areas of manufacturing. In this work, we propose to use VM to copper clad laminate (CCL) manufacturing. CCL is a core element of a printed circuit board (PCB) which is used in smartphones, tablets, digital cameras, and laptop computers. The manufacturing of CCL consists of three processes: Treating, lay-up, and pressing. Treating, the most important process among the three, puts resin on glass cloth, heat up in a drying oven, then produces prepreg for lay-up process. In this process, three important quality factors are inspected: Treated weight (T/W), Minimum Viscosity (M/V), and Gel Time (G/T). They are manually inspected, incurring heavy cost in terms of time and money, which makes it a good candidate for VM application. We developed prediction models of the three quality factors T/W, M/V, and G/T, respectively, with process variables, raw material, and environment variables. The actual process data was obtained from a CCL manufacturer. A variety of variable selection methods and learning algorithms were employed to find the best prediction model. We obtained prediction models of M/V and G/T with a high enough accuracy. They also provided us with information on “important” predictor variables, some of which the process engineers had been already aware and the rest of which they had not. They were quite excited to find new insights that the model revealed and set out to do further analysis on them to gain process control implications. T/W did not turn out to be possible to predict with a reasonable accuracy with given factors. The very fact indicates that the factors currently monitored may not affect T/W, thus an effort has to be made to find other factors which are not currently monitored in order to understand the process better and improve the quality of it. In conclusion, VM application to CCL’s treating process was quite successful. The newly built quality prediction model allowed one to reduce the cost associated with actual metrology as well as reveal some insights on the factors affecting the important quality factors and on the level of our less than perfect understanding of the treating process.

Keywords: copper clad laminate, predictive modeling, quality control, virtual metrology

Procedia PDF Downloads 332