Search results for: claims database

Authors: Kaoutar Abbahaddou, Mohammed Salah Chiadmi

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The need to find a relevant way to predict the next-day price of a stock index is a real concern for many financial stakeholders and researchers. We have known across years the proliferation of several methods. Nevertheless, among all these methods, the most controversial one is a machine learning algorithm that claims to be reliable, namely neural networks. Thus, the purpose of this article is to study the prediction power of neural networks in the particular case of Islamic finance as it is an under-looked area. In this article, we will first briefly present a review of the literature regarding neural networks and Islamic finance. Next, we present the architecture and principles of artificial neural networks most commonly used in finance. Then, we will show its empirical application on two Islamic stock indexes. The accuracy rate would be used to measure the performance of the algorithm in predicting the right price the next day. As a result, we can conclude that artificial neural networks are a reliable method to predict the next-day price for Islamic indices as it is claimed for conventional ones.

Keywords: Islamic finance, stock price prediction, artificial neural networks, machine learning

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Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Geniece Mondé

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This article examines how formerly incarcerated mothers deploy strategic comparisons to support their claims of moral self-worth. In depth interviews with 69 ex-offender mothers show that although women occupy a morally ambiguous space, they frame their roles as “good” mothers as independent of past illegal activity. In substantiating the “good” mother narrative women draw comparisons with two groups of women. Some respondents identify individuals perceived as morally disadvantaged and draw comparisons that illustrate their comparative strength in relation to mothers who fail to adequately meet the needs of their children. Women also compared themselves to morally advantaged mothers and expressed a desire to embody the ideals and values of women they viewed as superior mothers. Findings reveal that respondents’ use of strategic comparisons substantiates their framing of personal moral identity, as well as their goals for the future. The paper concludes by examining the theoretical implications of strategic comparisons for the study of morality and identity construction.

Keywords: Ex-Offender, Rehabilitation, Incarceration, Motherhood

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Authors: Bernard Igoche Igoche, Olumuyiwa Matthew, Peter Bednar, Alexander Gegov

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This study presents an ontology-driven approach for knowledge discovery and validation from admission databases in Nigerian polytechnic institutions. The research aims to address the challenges of extracting meaningful insights from vast amounts of admission data and utilizing them for decision-making and process improvement. The proposed methodology combines the knowledge discovery in databases (KDD) process with a structural causal model (SCM) ontological framework. The admission database of Benue State Polytechnic Ugbokolo (Benpoly) is used as a case study. The KDD process is employed to mine and distill knowledge from the database, while the SCM ontology is designed to identify and validate the important features of the admission process. The SCM validation is performed using the conditional independence test (CIT) criteria, and an algorithm is developed to implement the validation process. The identified features are then used for machine learning (ML) modeling and prediction of admission status. The results demonstrate the adequacy of the SCM ontological framework in representing the admission process and the high predictive accuracies achieved by the ML models, with k-nearest neighbors (KNN) and support vector machine (SVM) achieving 92% accuracy. The study concludes that the proposed ontology-driven approach contributes to the advancement of educational data mining and provides a foundation for future research in this domain.

Keywords: admission databases, educational data mining, machine learning, ontology-driven knowledge discovery, polytechnic education, structural causal model

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Authors: Rima Al-Awadhi, Abdul Jaleel Tharayil

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On a regular basis, KOC engages into various types of Projects. However, due to very nature and complexity involved, each project experience generates a lot of ‘learnings’ that need to be factored into while drafting a new contract and thus avoid repeating the same mistakes. But, many a time these learnings are localized and remain as tacit leading to scope re-work, larger cycle time, schedule overrun, adjustment orders and claims. Also, these experiences are not readily available to new employees leading to steep learning curve and longer time to competency. This is to share our experience in designing and implementing a process driven architecture for the ‘lessons learnt’ knowledge sharing framework in KOC. It high-lights the ‘lessons learnt’ sharing process adopted, integration with the organizational processes, governance framework, the challenges faced and learning from our experience in implementing a ‘lessons learnt’ framework.

Keywords: lessons learnt, knowledge transfer, knowledge sharing, successful practices, Lessons Learnt Workshop, governance framework

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Authors: Suvarna Kumar Gogula, Sandhya Devi Gogula, P. Chanakya

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Augmented reality is defined as the collection of the digital (or) computer generated information like images, audio, video, 3d models, etc. and overlay them over the real time environment. Augmented reality can be thought as a blend between completely synthetic and completely real. Augmented reality provides scope in a wide range of industries like manufacturing, retail, gaming, advertisement, tourism, etc. and brings out new dimensions in the modern digital world. As it overlays the content, it makes the users enhance the knowledge by providing the content blended with real world. In this application, we integrated augmented reality with data analytics and integrated with cloud so the virtual content will be generated on the basis of the data present in the database and we used marker based augmented reality where every marker will be stored in the database with corresponding unique ID. This application can be used in wide range of industries for different business processes, but in this paper, we mainly focus on the marketing industry which helps the customer in gaining the knowledge about the products in the market which mainly focus on their prices, customer feedback, quality, and other benefits. This application also focuses on providing better market strategy information for marketing managers who obtain the data about the stocks, sales, customer response about the product, etc. In this paper, we also included the reports from the feedback got from different people after the demonstration, and finally, we presented the future scope of Augmented Reality in different business processes by integrating with new technologies like cloud, big data, artificial intelligence, etc.

Keywords: augmented reality, data analytics, catch room, marketing and sales

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Authors: Natcha Wattanaprapa, Alongkorn Taengtong, Phachaya Chaiwchan

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This study investigates and promotes the distribution of community enterprise products of Samut Songkhram province by using e-commerce web technology to help distribute the products. This study also aims to develop the information system to be able to operate on multiple platforms and promote the easy usability on smartphones to increase the efficiency and promote the distribution of community enterprise products of Samut Songkhram province in three areas including Baan Saraphi learning center, the learning center of Bang Noi Floating market as well as Bang Nang Li learning center. The main structure consists of spreading the knowledge regarding the tourist attraction in the area of community enterprise, e-commerce system of community enterprise products, and Chatbot. The researcher developed the system into an application form using the software package to create and manage the content on the internet. Connect management system (CMS) word press was used for managing web pages. Add-on CMS word press was used for creating the system of Chatbot, and the database of PHP My Admin was used as the database management system. The evaluation by the experts and users in 5 aspects, including the system efficiency, the accuracy in the operation of the system, the convenience and ease of use of the system, the design, and the promotion of product distribution in Samut Songkhram province by using questionnaires revealed that the result of evaluation in the promotion of product distribution in Samut Songkhram province was the highest with the mean of 4.20. When evaluating the efficiency of the developed system, it was found that the result of system efficiency was the highest level with a mean of 4.10.

Keywords: community enterprise, digital commerce, promotion of product distribution, Samut Songkhram province

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Authors: R. S. Remya, U. S. Sethulekshmi

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Detecting the authenticity of a video is an important issue in digital forensics as Video is used as a silent evidence in court such as in child pornography, movie piracy cases, insurance claims, cases involving scientific fraud, traffic monitoring etc. The biggest threat to video data is the availability of modern open video editing tools which enable easy editing of videos without leaving any trace of tampering. In this paper, we propose an efficient passive method for inter-frame video tampering detection, its type and location by estimating the optical flow of wavelet features of adjacent frames and thresholding the variation in the estimated feature. The performance of the algorithm is compared with the z-score thresholding and achieved an efficiency above 95% on all the tested databases. The proposed method works well for videos with dynamic (forensics) as well as static (surveillance) background.

Keywords: discrete wavelet transform, optical flow, optical flow variation, video tampering

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Authors: Jiajia Peng, Danni Cheng, Jianqing Qiu, Yufang Rao, Minzi Mao, Ke Qiu, Junhong Li, Fei Chen, Feng Liu, Jun Liu, Xiaosong Mu, Wenxin Yu, Wei Zhang, Wei Xu, Yu Zhao, Jianjun Ren

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Background: Currently, primary B-cell non-Hodgkin lymphoma (B-NHL) and T/NK-cell non-Hodgkin lymphoma (NKT-NHL) originated from the nasal cavity (NC), nasopharynx (NP) and nasal sinus (NS) distinguished unclearly in the clinic. Objective: We sought to compare the clinical and survival differences of B-NHL and NKT-NHL that occurred in NC, NP, and NS, respectively. Methods: Retrospective data of patients diagnosed with nasal cavity lymphoma (NCL), nasopharyngeal lymphoma (NPL), and nasal sinus lymphoma (NSL) between 1975 and 2017 from the Surveillance, Epidemiology, and End Results (SEER) database were collected. We identified the B/NKT-NHL patients based on the histological type and performed univariate, multivariate, and Kaplan-Meier analyses to investigate the survival rates. Results: Of the identified 3,101 B-NHL and 738 NKT-NHL patients, those with B-NHL in NP were the majority (43%) and had better cancer-specific survival than those in NC and NS from 2010 to 2017 (5-year-CSS, NC vs. NP vs. NS: 81% vs. 83% vs. 82%). In contrast, most of the NKT-NHL originated from NC (68%) and had the highest CSS rate in the recent seven years (2010-2017, 5-year-CSS: 63%). Additionally, the survival outcomes of patients with NKT-NHL-NP (HR: 1.34, 95% CI: 0.62-2.89, P=0.460) who had received surgery were much worse than those of patients with NKT-NHL-NC (HR: 1.07, 95% CI: 0.75-1.52, P=0.710) and NKT-NHL-NS (HR: 1.11, 95% CI: 0.59-2.07, P=0.740). NKT-NHL-NS patients who had radiation performed (HR: 0.38, 95% CI: 0.19-0.73, P=0.004) showed the highest survival rates, while chemotherapy performed (HR: 1.01, 95% CI: 0.43-2.37, P=0.980) presented opposite results. Conclusions: Although B-NHL and NKT-NHL originating from NC, NP and NS had similar anatomical locations, their clinical characteristics, treatment therapies, and prognoses were different in this study. Our findings may suggest that B-NHL and NKT-NHL in NC, NP, and NS should be treated as different diseases in the clinic.

Keywords: nasopharyngeal lymphoma, nasal cavity lymphoma, nasal sinus lymphoma, B-cell non-Hodgkin lymphoma, T/NK-cell non-Hodgkin lymphoma

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Authors: Kunle Oparinde

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The Covid-19 pandemic has seen several rumors and conspiracy theories overtake the truth in many online platforms across several African countries. Just as the coronavirus has travelled widely, misinformation has equally spread. Thus, it is important to launch investigations into these conspiracy theories in order to detect them early and as a result, implore health practitioners and agencies to be more proactive in repelling misinformation while at the same time provide the general populace with purely undiluted information regarding the virus. Through social media posts on platforms such as Twitter, Facebook, and WhatsApp, as well as online platforms such as Google, this study intends to draw as many instances as possible of infodemic and misinformation by reviewing and analyzing these texts and the resulting implication if the misinformation continues to gain popularity. The study discovers the use of conspiracy theories, rumors, hyperbolism, and unverified claims as elements of infodemic used during the coronavirus pandemic. Importantly, the findings of the study will assist the public to be cautious and vigilant against false information that are being peddled as original.

Keywords: infodemic, miscommunication, accuracy, social media, rumors, conspiracy

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Authors: Sanjeev Sharma, Suman Lata

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With the availability of diverse printed, electronic literature and web sites on medical and health related information, it is impossible for the medical professional to get the information he seeks in the shortest possible time. For all these problems information literacy is the only solution. Thus, information literacy is recognized as an important aspect of medical education. In the present study, an attempt has been made to know the information literacy skills of the faculty and students at medical colleges of Haryana, Punjab and Chandigarh. The scope of the study was confined to the 12 selected medical colleges of three States (Haryana, Punjab, and Chandigarh). The findings of the study were based on the data collected through 1018 questionnaires filled by the respondents of the medical colleges. It was found that Online Medical Websites (such as WebMD, eMedicine and Mayo Clinic etc.) were frequently used by 63.43% of the respondents of Chandigarh which is slightly more than Haryana (61%) and Punjab (55.65%). As well, 30.86% of the respondents of Chandigarh, 27.41% of Haryana and 27.05% of Punjab were familiar with the controlled vocabulary tool; 25.14% respondents of Chandigarh, 23.80% of Punjab, 23.17% of Haryana were familiar with the Boolean operators; 33.05% of the respondents of Punjab, 28.19% of Haryana and 25.14% of Chandigarh were familiar with the use and importance of the keywords while searching an electronic database; and 51.43% of the respondents of Chandigarh, 44.52% of Punjab and 36.29% of Haryana were able to make effective use of the retrieved information. For accessing information in electronic format, 47.74% of the respondents rated their skills high, while the majority of respondents (76.13%) were unfamiliar with the basic search technique i.e. Boolean operator used for searching information in an online database. On the basis of the findings, it was suggested that a comprehensive training program based on medical professionals information needs should be organized frequently. Furthermore, it was also suggested that information literacy may be included as a subject in the health science curriculum so as to make the medical professionals information literate and independent lifelong learners.

Keywords: information, information literacy, medical professionals, medical colleges

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Authors: Rahaf Suleiman Altallaa

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City planning involves making plans, organizing and dealing with the cities urban areas. It attempts to organize socio-spatial relationships at exceptional ranges of governance Urban planning offers the social, monetary and environmental effects of defining spatial obstacles and the influence on the spatial distribution of resources. The dreams and methods of reaching such dissemination vary extensively traditionally and geographically and are often challenged through traditional strategies that expose the political nature of application interventions and the bounds of technical know-how claims. Space, network, argument, and postcolonial debates address how present-day socio-spatial organization is formed, what needs to or should not trade, and the way it underscores whether or not a good plan will contribute to a given situation. Inside the absence of an agreed-upon technical justification for the planning exercise, the planning idea has a tendency to focus on normative processes, positioning making plans as an area for participatory democracy.

Keywords: environmental governance, environmental planning, environmental management, sustainable competitiveness, sustainability

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Authors: Rosyidah Muhamad

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Democracies are premised on the idea that citizens can hold their leaders accountable for their actions by voting for or against them in regular elections. However, in order this ideal to be realized, citizens must possess a minimum amount of information about their leaders’ performance. Citizens should be made aware of the performance of their elected representatives. This study seeks to analyse this critical information with special reference to Malaysian Parliamentarians (MPs). We adopted several existence Parliamentary Performance model with special reference to their performance inside the parliament. Among indicators used by the Scholastic for analysing this performance are the number of bills proposed by parliamentarian, the number of proposals that would benefit their constituency, the number of speeches made by the parliamentarian during plenary and the percentage of laws passed among the proposals made by certain parliamentary. The broad goals of the study include the analysis of the capacity of a representative body to accommodate the diverse claims and demands that are made on it. We find that overall performances of MPs are average. This is due to not only the background characteristic of individuals MPs but also the limitation of the mechanism provides in the Parliament itself.

Keywords: member of parliament, democracy, evaluation, Malaysia

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Authors: Michal Pachocki

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This study investigates the effects of mobility in the context of a different environment and work culture through analysing the learners perception of their international work experience. Since this kind of professional training abroad is becoming more popular in Europe, mainly due to the EU funding opportunities, it is of paramount importance to assess its long-term impact on educational and career paths of former students. Moreover, the tracer study aimed at defining what professional, social and intercultural competencies were gained or developed by the interns and to which extent those competences proved to be useful meeting the labor market requirements. Being a populous EU member state which actively modernizes its vocational education system (also with European funds), Poland can serve as an illustrative case study to investigate the above described research problems. However, the examined processes are most certainly universal, wherever mobility is included in the learning process. The target group of this research was the former mobility participants and the study was conducted using quantitative and qualitative methods, such as the online survey with over 2 600 questionnaires completed by the former mobility participants; -individual in-depth interviews (IDIs) with 20 Polish graduates already present in the labour market; - 5 focus group interviews (FGIs) with 60 current students of the Polish vocational schools, who have recently returned from the training abroad. As the adopted methodology included a data triangulation, the collected findings have also been supplemented with data obtained by the desk research (mainly contextual information and statistical summary of mobility implementation). The results of this research – to be presented in full scope within the conference presentation – include the participants’ perception of their work mobility. The vast majority of graduates agrees that such an experience has had a significant impact on their professional careers and claims that they would recommend training abroad to persons who are about to enter the labor market. Moreover, in their view, such form of practical training going beyond formal education provided them with an opportunity to try their hand in the world of work. This allowed them – as they accounted for them – to get acquainted with a work system and context different from the ones experienced in Poland. Although the work mobility becomes an important element of the learning process in the growing number of Polish schools, this study reveals that many sending institutions suffer from a lack of the coherent strategy for planning domestic and foreign training programmes. Nevertheless, the significant number of graduates claims that such a synergy improves the quality of provided training. Despite that, the research proved that the transnational mobilities exert an impact on their future careers and personal development. However, such impact is, in their opinion, dependant on other factors, such as length of the training period, the nature and extent of work, recruitment criteria and the quality of organizational arrangement and mentoring provided to learners. This may indicate the salience of the sending and receiving institutions organizational capacity to deal with mobility.

Keywords: learning mobility, transnational training, vocational education and training graduates, tracer study

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Authors: Arkadiusz Mezyk, Wojciech Klein, Mariusz Pawlak, Jacek Gnilka

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The aortic heart valve stents should fulfill many criterions. These criteria have a strong impact on the geometrical shape of the stent. Usually, the final construction of stent is a result of many year experience and knowledge. Depending on patents claims, different stent shapes are produced by different companies. This causes difficulties for biomechanics engineers narrowing the domain of feasible solutions. The paper present optimization method for stent geometry defining by a specific analytical equation based on various mathematical functions. This formula was implemented as APDL script language in ANSYS finite element environment. For the purpose of simulation tests, a few parameters were separated from developed equation. The application of the genetic algorithms allows finding the best solution due to selected objective function. Obtained solution takes into account parameters such as radial force, compression ratio and coefficient of expansion on the transverse axial.

Keywords: aortic stent, optimization process, geometry, finite element method

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Authors: Eun-Ji Lee, Hye-Sung Park, Chan-Jung Lee, Won-Sik Kong

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We analyzed the DNA sequence of the ITS (Internal Transcribed Spacer) region of the 18S ribosomal gene and compared it with the gene sequence of T. fuciformis and Hypoxylon sp. in the BLAST database. The sequences of collected T. fuciformis and Hypoxylon sp. have over 99% homology in the T. fuciformis and Hypoxylon sp. sequence BLAST database. In order to select the optimal medium for T. fuciformis, five kinds of a medium such as Potato Dextrose Agar (PDA), Mushroom Complete Medium (MCM), Malt Extract Agar (MEA), Yeast extract (YM), and Compost Extract Dextrose Agar (CDA) were used. T. fuciformis showed the best growth on PDA medium, and Hypoxylon sp. showed the best growth on MCM. So as to investigate the optimum pH and temperature, the pH range was set to pH4 to pH8 and the temperature range was set to 15℃ to 35℃ (5℃ degree intervals). Optimum culture conditions for the T. fuciformis growth were pH5 at 25℃. Hypoxylon sp. were pH6 at 25°C. In order to confirm the most suitable carbon source, we used fructose, galactose, saccharose, soluble starch, inositol, glycerol, xylose, dextrose, lactose, dextrin, Na-CMC, adonitol. Mannitol, mannose, maltose, raffinose, cellobiose, ethanol, salicine, glucose, arabinose. In the optimum carbon source, T. fuciformis is xylose and Hypoxylon sp. is arabinose. Using the column test, we confirmed sawdust a suitable for T. fuciformis, since the composition of sawdust affects the growth of fruiting bodies of T. fuciformis. The sawdust we used is oak tree, pine tree, poplar, birch, cottonseed meal, cottonseed hull. In artificial cultivation of T. fuciformis with sawdust medium, T. fuciformis and Hypoxylon sp. showed fast mycelial growth on mixture of oak tree sawdust, cottonseed hull, and wheat bran.

Keywords: cultivation, optimal condition, tremella fuciformis, nutritional source

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Authors: Iman A. Saad, Loay E. George

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Iris pattern is an important biological feature of human body; it becomes very hot topic in both research and practical applications. In this paper, an algorithm is proposed for iris recognition and a simple, efficient and fast method is introduced to extract a set of discriminatory features using first order gradient operator applied on grayscale images. The gradient based features are robust, up to certain extents, against the variations may occur in contrast or brightness of iris image samples; the variations are mostly occur due lightening differences and camera changes. At first, the iris region is located, after that it is remapped to a rectangular area of size 360x60 pixels. Also, a new method is proposed for detecting eyelash and eyelid points; it depends on making image statistical analysis, to mark the eyelash and eyelid as a noise points. In order to cover the features localization (variation), the rectangular iris image is partitioned into N overlapped sub-images (blocks); then from each block a set of different average directional gradient densities values is calculated to be used as texture features vector. The applied gradient operators are taken along the horizontal, vertical and diagonal directions. The low order norms of gradient components were used to establish the feature vector. Euclidean distance based classifier was used as a matching metric for determining the degree of similarity between the features vector extracted from the tested iris image and template features vectors stored in the database. Experimental tests were performed using 2639 iris images from CASIA V4-Interival database, the attained recognition accuracy has reached up to 99.92%.

Keywords: iris recognition, contrast stretching, gradient features, texture features, Euclidean metric

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Authors: Eran Viezel

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Critical biblical scholarship emerged in the early modern period, yet scholars frequently search for precursors to it among medieval commentators who adopted critical positions—and many mention Abraham Ibn Ezra (Spain–England, 1089–1164/7) in this context. Indeed, in several places, Ibn Ezra claims that there are verses in the Torah that were added to it after the time of Moses; and some major thinkers and scholars in the early modern period (for example, Baruch Spinoza) were aware of these remarks and influenced by them. However, Ibn Ezra’s belief that the Torah includes verses added at a later time is not based on the considerations that led the founders of critical biblical scholarship to their conclusion that Moses did not write the Torah. Ibn Ezra’s positions on the question of the Torah’s authorship are an example of the fact that similarity in conclusions and even in interpretive methodology should not obscure the different interpretive and attitudinal points of departure that distinguish traditional biblical interpretation from a critical biblical scholarship. Ultimately, a chasm exists between the views of Ibn Ezra and those of critical thinkers such as Spinoza.

Keywords: hebrew bible, Abraham Ibn Ezra, exegesis, biblical scholarship

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Authors: B. Gepts, E. Meex, E. Nuyts, E. Knaepen, G. Verbeeck

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The importance of resource efficiency and environmental impact assessment has raised the interest in knowing the amount of materials used in buildings. If no BIM model or energy performance certificate is available, material quantities can be obtained through an estimation or time-consuming calculation. For the interior wall area, no validated estimation method exists. However, in the case of environmental impact assessment or evaluating the existing building stock as future material banks, knowledge of the material quantities used in interior walls is indispensable. This paper presents a validated method for the estimation of the interior wall area for dwellings based on easy-to-collect building characteristics. A database of 4963 residential buildings spread all over Belgium is used. The data are collected through onsite measurements of the buildings during the construction phase (between mid-2010 and mid-2017). The interior wall area refers to the area of all interior walls in the building, including the inner leaf of exterior (party) walls, minus the area of windows and doors, unless mentioned otherwise. The two predictive modelling techniques used are 1) a (stepwise) linear regression and 2) a decision tree. The best estimation method is selected based on the best R² k-fold (5) fit. The research shows that the building volume is by far the most important variable to estimate the interior wall area. A stepwise regression based on building volume per building, building typology, and type of house provides the best fit, with R² k-fold (5) = 0.88. Although the best R² k-fold value is obtained when the other parameters ‘building typology’ and ‘type of house’ are included, the contribution of these variables can be seen as statistically significant but practically irrelevant. Thus, if these parameters are not available, a simplified estimation method based on only the volume of the building can also be applied (R² k-fold = 0.87). The robustness and precision of the method (output) are validated three times. Firstly, the prediction of the interior wall area is checked by means of alternative calculations of the building volume and of the interior wall area; thus, other definitions are applied to the same data. Secondly, the output is tested on an extension of the database, so it has the same definitions but on other data. Thirdly, the output is checked on an unrelated database with other definitions and other data. The validation of the estimation methods demonstrates that the methods remain accurate when underlying data are changed. The method can support environmental as well as economic dimensions of impact assessment, as it can be used in early design. As it allows the prediction of the amount of interior wall materials to be produced in the future or that might become available after demolition, the presented estimation method can be part of material flow analyses on input and on output.

Keywords: buildings as material banks, building stock, estimation method, interior wall area

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Authors: Adrian O'Hagan, Robert McLoughlin

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Considerable focus in the world of insurance risk quantification is placed on modeling loss values from lines of business (LOBs) that possess upper tail dependence. Copulas such as the Joe, Gumbel and Student-t copula may be used for this purpose. The copula structure imparts a desired level of tail dependence on the joint distribution of claims from the different LOBs. Alternatively, practitioners may possess historical or simulated data that already exhibit upper tail dependence, through the impact of catastrophe events such as hurricanes or earthquakes. In these circumstances, it is not desirable to induce additional upper tail dependence when modeling the joint distribution of the loss values from the individual LOBs. Instead, it is of interest to accurately assess the degree of tail dependence already present in the data. The empirical copula and its associated upper tail dependence coefficient are presented in this paper as robust, efficient means of achieving this goal.

Keywords: empirical copula, extreme events, insurance loss reserving, upper tail dependence coefficient

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Authors: Jerzy Banski

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Small towns represent a key element of settlement structure and serve a number of important functions associated with the servicing of rural areas that surround them. It is in light of this that scientific studies have paid considerable attention to the functional structure of centers of this kind, as well as the relationships with both surrounding rural areas and other urban centers. But a preliminary to such research has typically involved attempts at classifying the urban centers themselves, with this also assisting with the planning and shaping of development policy on different spatial scales. The purpose of the work is to test out the methods underpinning three different classifications of small urban centers, as well as to offer a preliminary interpretation of the outcomes obtained. Research took in 722 settlement units in Poland, granted town rights and populated by fewer than 20,000 inhabitants. A morphologically-based classification making reference to the database of topographic objects as regards land cover within the administrative boundaries of towns and cities was carried out, and it proved possible to distinguish the categories of “housing-estate”, industrial and R&R towns, as well as towns characterized by dichotomy. Equally, a functional/morphological approach taken with the same database allowed for the identification – via an alternative method – of three main categories of small towns (i.e., the monofunctional, multifunctional or oligo functional), which could then be described in far greater detail. A third, multi-criterion classification made simultaneous reference to the conditioning of a structural, a location-related, and an administrative hierarchy-related nature, allowing for distinctions to be drawn between small towns in 9 different categories. The results obtained allow for multifaceted analysis and interpretation of the geographical differentiation characterizing the distribution of Poland’s urban centers across space in the country.

Keywords: small towns, classification, local planning, Poland

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Authors: Hamdi Beji, Toufik Kanit, Tanguy Messager

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This study aims to construct a predictive model proficient in foreseeing the linear elastic and thermal characteristics of composite materials, drawing on a multitude of influencing parameters. These parameters encompass the shape of inclusions (circular, elliptical, square, triangle), their spatial coordinates within the matrix, orientation, volume fraction (ranging from 0.05 to 0.4), and variations in contrast (spanning from 10 to 200). A variety of machine learning techniques are deployed, including decision trees, random forests, support vector machines, k-nearest neighbors, and an artificial neural network (ANN), to facilitate this predictive model. Moreover, this research goes beyond the predictive aspect by delving into an inverse analysis using genetic algorithms. The intent is to unveil the intrinsic characteristics of composite materials by evaluating their thermomechanical responses. The foundation of this research lies in the establishment of a comprehensive database that accounts for the array of input parameters mentioned earlier. This database, enriched with this diversity of input variables, serves as a bedrock for the creation of machine learning and genetic algorithm-based models. These models are meticulously trained to not only predict but also elucidate the mechanical and thermal conduct of composite materials. Remarkably, the coupling of machine learning and genetic algorithms has proven highly effective, yielding predictions with remarkable accuracy, boasting scores ranging between 0.97 and 0.99. This achievement marks a significant breakthrough, demonstrating the potential of this innovative approach in the field of materials engineering.

Keywords: machine learning, composite materials, genetic algorithms, mechanical and thermal proprieties

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Authors: Herke Csongor

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The artificial intelligence (AI) has been used in the United States of America in the decisionmaking process of the criminal justice system for decades. In the field of law, including criminal law, AI can provide serious assistance in decision-making in many places. The paper reviews four main areas where AI still plays a role in the criminal justice system and where it is expected to play an increasingly important role. The first area is the predictive policing: a number of algorithms are used to prevent the commission of crimes (by predicting potential crime locations or perpetrators). This may include the so-called linking hot-spot analysis, crime linking and the predictive coding. The second area is the Big Data analysis: huge amounts of data sets are already opaque to human activity and therefore unprocessable. Law is one of the largest producers of digital documents (because not only decisions, but nowadays the entire document material is available digitally), and this volume can only and exclusively be handled with the help of computer programs, which the development of AI systems can have an increasing impact on. The third area is the criminal statistical data analysis. The collection of statistical data using traditional methods required enormous human resources. The AI is a huge step forward in that it can analyze the database itself, based on the requested aspects, a collection according to any aspect can be available in a few seconds, and the AI itself can analyze the database and indicate if it finds an important connection either from the point of view of crime prevention or crime detection. Finally, the use of AI during decision-making in both investigative and judicial fields is analyzed in detail. While some are skeptical about the future role of AI in decision-making, many believe that the question is not whether AI will participate in decision-making, but only when and to what extent it will transform the current decision-making system.

Keywords: artificial intelligence, international criminal cooperation, planning and organizing of the investigation, risk assessment

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Authors: Kasirga Yildirak, Ismail Gur

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We propose a model to measure hail risk of an Agricultural Insurance portfolio. Hail is one of the major catastrophic event that causes big amount of loss to an insurer. Moreover, it is very hard to predict due to its strange atmospheric characteristics. We make use of parcel based claims data on apricot damage collected by the Turkish Agricultural Insurance Pool (TARSIM). As our ultimate aim is to compute the loadings assigned to specific parcels, we build a portfolio risk model that makes use of PD and the severity of the exposures. PD is computed by Spherical-Linear and Circular –Linear regression models as the data carries coordinate information and seasonality. Severity is mapped into integer brackets so that Probability Generation Function could be employed. Individual regressions are run on each clusters estimated on different criteria. Loss distribution is constructed by Panjer Recursion technique. We also show that one risk-one crop model can easily be extended to the multi risk–multi crop model by assuming conditional independency.

Keywords: hail insurance, spherical regression, circular regression, spherical clustering

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Authors: Chi-Ching Lee, Po-Jung Huang, Kuo-Yang Huang, Petrus Tang

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Background: Recent advances in high-throughput research technologies such as new-generation sequencing and multi-dimensional liquid chromatography makes it possible to dissect the complete transcriptome and proteome in a single run for the first time. However, it is almost impossible for many laboratories to handle and analysis these “BIG” data without the support from a bioinformatics team. We aimed to provide a web-based analysis platform for users with only limited knowledge on bio-computing to study the functional genomics and proteomics. Method: We use NCI-60 as an example dataset to demonstrate the power of the web-based analysis platform and data delivering system: C-eXpress takes a simple text file that contain the standard NCBI gene or protein ID and expression levels (rpkm or fold) as input file to generate a distribution map of gene/protein expression levels in a heatmap diagram organized by color gradients. The diagram is hyper-linked to a dynamic html table that allows the users to filter the datasets based on various gene features. A dynamic summary chart is generated automatically after each filtering process. Results: We implemented an integrated database that contain pre-defined annotations such as gene/protein properties (ID, name, length, MW, pI); pathways based on KEGG and GO biological process; subcellular localization based on GO cellular component; functional classification based on GO molecular function, kinase, peptidase and transporter. Multiple ways of sorting of column and rows is also provided for comparative analysis and visualization of multiple samples.

Keywords: cancer, visualization, database, functional annotation

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Authors: Edgar Santoyo, Daniel Perez-Zarate, Agustin Acevedo, Lorena Diaz-Gonzalez, Mirna Guevara

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Four new gas geothermometers have been derived from a multivariate geo chemometric analysis of a geothermal fluid chemistry database, two of which use the natural logarithm of CO₂ and H2S concentrations (mmol/mol), respectively, and the other two use the natural logarithm of the H₂S/H₂ and CO₂/H₂ ratios. As a strict compilation criterion, the database was created with gas-phase composition of fluids and bottomhole temperatures (BHTM) measured in producing wells. The calibration of the geothermometers was based on the geochemical relationship existing between the gas-phase composition of well discharges and the equilibrium temperatures measured at bottomhole conditions. Multivariate statistical analysis together with the use of artificial neural networks (ANN) was successfully applied for correlating the gas-phase compositions and the BHTM. The predicted or simulated bottomhole temperatures (BHTANN), defined as output neurons or simulation targets, were statistically compared with measured temperatures (BHTM). The coefficients of the new geothermometers were obtained from an optimized self-adjusting training algorithm applied to approximately 2,080 ANN architectures with 15,000 simulation iterations each one. The self-adjusting training algorithm used the well-known Levenberg-Marquardt model, which was used to calculate: (i) the number of neurons of the hidden layer; (ii) the training factor and the training patterns of the ANN; (iii) the linear correlation coefficient, R; (iv) the synaptic weighting coefficients; and (v) the statistical parameter, Root Mean Squared Error (RMSE) to evaluate the prediction performance between the BHTM and the simulated BHTANN. The prediction performance of the new gas geothermometers together with those predictions inferred from sixteen well-known gas geothermometers (previously developed) was statistically evaluated by using an external database for avoiding a bias problem. Statistical evaluation was performed through the analysis of the lowest RMSE values computed among the predictions of all the gas geothermometers. The new gas geothermometers developed in this work have been successfully used for predicting subsurface temperatures in high-temperature geothermal systems of Mexico (e.g., Los Azufres, Mich., Los Humeros, Pue., and Cerro Prieto, B.C.) as well as in a blind geothermal system (known as Acoculco, Puebla). The last results of the gas geothermometers (inferred from gas-phase compositions of soil-gas bubble emissions) compare well with the temperature measured in two wells of the blind geothermal system of Acoculco, Puebla (México). Details of this new development are outlined in the present research work. Acknowledgements: The authors acknowledge the funding received from CeMIE-Geo P09 project (SENER-CONACyT).

Keywords: artificial intelligence, gas geochemistry, geochemometrics, geothermal energy

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Authors: Raghavi C. Janaswamy

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In the healthcare sector, heterogenous data elements such as patients, diagnosis, symptoms, conditions, observation text from physician notes, and prescriptions form the essentials of the Electronic Health Record (EHR). The data in the form of clear text and images are stored or processed in a relational format in most systems. However, the intrinsic structure restrictions and complex joins of relational databases limit the widespread utility. In this regard, the design and development of realistic mapping and deep connections as real-time objects offer unparallel advantages. Herein, a graph neural network-based classification of EHR data has been developed. The patient conditions have been predicted as a node classification task using a graph-based open source EHR data, Synthea Database, stored in Tigergraph. The Synthea DB dataset is leveraged due to its closer representation of the real-time data and being voluminous. The graph model is built from the EHR heterogeneous data using python modules, namely, pyTigerGraph to get nodes and edges from the Tigergraph database, PyTorch to tensorize the nodes and edges, PyTorch-Geometric (PyG) to train the Graph Neural Network (GNN) and adopt the self-supervised learning techniques with the AutoEncoders to generate the node embeddings and eventually perform the node classifications using the node embeddings. The model predicts patient conditions ranging from common to rare situations. The outcome is deemed to open up opportunities for data querying toward better predictions and accuracy.

Keywords: electronic health record, graph neural network, heterogeneous data, prediction

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Authors: Natacha Fery, Guilherme L. Dalledonne, Xiangyang Zheng, Cheng Tang, Roberto Mayerle

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A Decision Support System (DSS) for supporting decision makers in the management of the Shandong Peninsula has been developed. Emphasis has been given to coastal protection, coastal cage aquaculture and harbors. The investigations were done in the framework of a joint research project funded by the German Ministry of Education and Research (BMBF) and the Chinese Academy of Sciences (CAS). In this paper, a description of the DSS, the development of its components, and results of its application are presented. The system integrates in-situ measurements, process-based models, and a database management system. Numerical models for the simulation of flow, waves, sediment transport and morphodynamics covering the entire Bohai Sea are set up based on the Delft3D modelling suite (Deltares). Calibration and validation of the models were realized based on the measurements of moored Acoustic Doppler Current Profilers (ADCP) and High Frequency (HF) radars. In order to enable cost-effective and scalable applications, a database management system was developed. It enhances information processing, data evaluation, and supports the generation of data products. Results of the application of the DSS to the management of coastal protection, coastal cage aquaculture and harbors are presented here. Model simulations covering the most severe storms observed during the last decades were carried out leading to an improved understanding of hydrodynamics and morphodynamics. Results helped in the identification of coastal stretches subjected to higher levels of energy and improved support for coastal protection measures.

Keywords: coastal protection, decision support system, in-situ measurements, numerical modelling

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Authors: Jawaad Saleem, Hannah Wright, Peter Sommerville, Adrian Hopper

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Introduction: Inpatient falls are the most commonly reported patient safety incidents, and carry a significant burden on resources, morbidity, and mortality. Ensuring adequate post falls management of patients by staff is therefore paramount to maintaining patient safety especially in out of hours and resource stretched settings. Aims: This quality improvement project aims to improve the current practice of falls management at Guys St Thomas Hospital, London as compared to our 2016 Quality Improvement Project findings. Furthermore, it looks to increase current junior doctors confidence in managing falls and their use of new guidance protocols. Methods: Multifaceted Interventions implemented included: the development of new trust wide guidelines detailing management pathways for patients post falls, available for intranet access. Furthermore, the production of 2000 lanyard cards distributed amongst junior doctors and staff which summarised these guidelines. Additionally, a ‘safety signal’ email was sent from the Trust chief medical officer to all staff raising awareness of falls and the guidelines. Formal falls teaching was also implemented for new doctors at induction. Using an established incident database, 189 consecutive falls in 2017were retrospectively analysed electronically to assess and compared to the variables measured in 2016 post interventions. A separate serious incident database was used to analyse 50 falls from May 2015 to March 2018 to ascertain the statistical significance of the impact of our interventions on serious incidents. A similar questionnaire for the 2017 cohort of foundation year one (FY1) doctors was performed and compared to 2016 results. Results: Questionnaire data demonstrated improved awareness and utility of guidelines and increased confidence as well as an increase in training. 97% of FY1 trainees felt that the interventions had increased their awareness of the impact of falls on patients in the trust. Data from the incident database demonstrated the time to review patients post fall had decreased from an average of 130 to 86 minutes. Improvement was also demonstrated in the reduced time to order and schedule X-ray and CT imaging, 3 and 5 hours respectively. Data from the serious incident database show that ‘the time from fall until harm was detected’ was statistically significantly lower (P = 0.044) post intervention. We also showed the incidence of significant delays in detecting harm ( > 10 hours) reduced post intervention. Conclusions: Our interventions have helped to significantly reduce the average time to assess, order and schedule appropriate imaging post falls. Delays of over ten hours to detect serious injuries after falls were commonplace; since the intervention, their frequency has markedly reduced. We suggest this will lead to identifying patient harm sooner, reduced clinical incidents relating to falls and thus improve overall patient safety. Our interventions have also helped increase clinical staff confidence, management, and awareness of falls in the trust. Next steps include expanding teaching sessions, improving multidisciplinary team involvement to aid this improvement.

Keywords: patient safety, quality improvement, serious incidents, falls, clinical care

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Authors: Dogaru-Ulieru Valentin, Sălișteanu Ioan Corneliu, Ardeleanu Mihăiță Nicolae, Broscăreanu Ștefan, Sălișteanu Bogdan, Mihai Mihail

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The SiaMOTO system is a communications and data processing platform for vehicle traffic. The human factor is the most important factor in the generation of this data, as the driver is the one who dictates the trajectory of the vehicle. Like any trajectory, specific parameters refer to position, speed and acceleration. Constant knowledge of these parameters allows complex analyses. Roadways allow many vehicles to travel through their confined space, and the overlapping trajectories of several vehicles increase the likelihood of collision events, known as road accidents. Any such event has causes that lead to its occurrence, so the conditions for its occurrence are known. The human factor is predominant in deciding the trajectory parameters of the vehicle on the road, so monitoring it by knowing the events reported by the DiaMOTO device over time, will generate a guide to target any potentially high-risk driving behavior and reward those who control the driving phenomenon well. In this paper, we have focused on detailing the communication infrastructure of the DiaMOTO device with the traffic data collection server, the infrastructure through which the database that will be used for complex AI/DLM analysis is built. The central element of this description is the data string in CODEC-8 format sent by the DiaMOTO device to the SiaMOTO collection server database. The data presented are specific to a functional infrastructure implemented in an experimental model stage, by installing on a number of 50 vehicles DiaMOTO unique code devices, integrating ADAS and GPS functions, through which vehicle trajectories can be monitored 24 hours a day.

Keywords: DiaMOTO, Codec-8, ADAS, GPS, driver monitoring

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