23 results on 'LN cat08778a'
Search Results
2. Symmetric and Asymmetric Data in Solution Models.
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Kazimieras Zavadskas, Edmundas, Turskis, Zenonas, Antuchevičienė, Jurgita, and Kazimieras Zavadskas, Edmundas
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Information technology industries ,COVID-19 ,criteria importance through inter-criteria correlation (CRITIC) ,combined compromise solution (CoCoSo) ,gray values ,temporary hospital ,location selection ,ARAS ,interval-valued triangular fuzzy numbers ,e-learning courses ,MCDM ,neutrosophic sets ,quadripartitioned bipolar neutrosophic sets ,similarity measure ,decision making ,multiple-criteria decision-making ,neutrosophic ,single-valued neutrosophic sets ,TOPSIS ,Hamming distance ,Euclidean distance ,e-commerce development strategies ,Symmetry ,bibliometric analysis ,Web of Science ,co-citation ,burst detection analysis ,supply chain ,DANP-mV model ,performance analysis ,asymmetric underactuated ,rehabilitation ,robotic exoskeleton ,symmetric and asymmetric trajectory ,Bowden cable ,video processing data ,EOQ ,Wilson's formulation ,lot size ,reordering time ,visual analogue scales (VAS) ,criteria weighting ,matrix question ,survey ,WASPAS-SVNS ,entropy ,direct rating ,rehabilitation device ,electromyogram ,symmetry ,window parameters ,feature extraction ,pattern recognition ,sensitivity analysis ,reliability ,failure probability ,quantile ,civil engineering ,limit states ,mathematical model ,uncertainty ,cost overrun ,construction project ,fuzzy sets ,earned value management (EVM) ,artificial neural networks (ANNs) ,multiple regression analysis ,road industry ,buckling ,safety ,superquantile ,subquantile ,aerial imagery ,lossy compression ,qualitative evaluation ,WASPAS ,neutrosophic set ,Analytic Hierarchy Process ,fuzzy Analytic Hierarchy Process ,symmetric and asymmetric fuzzy numbers ,stability ,landscape ,micro factor ,macro factor ,real estate market ,Big Data analysis ,Big Data ,land price ,R and Python ,land Big Data ,symmetric data ,asymmetric data ,solution models - Abstract
Summary: This book is a Printed Edition of the Special Issue that covers research on symmetric and asymmetric data that occur in real-life problems. We invited authors to submit their theoretical or experimental research to present engineering and economic problem solution models that deal with symmetry or asymmetry of different data types. The Special Issue gained interest in the research community and received many submissions. After rigorous scientific evaluation by editors and reviewers, seventeen papers were accepted and published. The authors proposed different solution models, mainly covering uncertain data in multicriteria decision-making (MCDM) problems as complex tools to balance the symmetry between goals, risks, and constraints to cope with the complicated problems in engineering or management. Therefore, we invite researchers interested in the topics to read the papers provided in the book.
3. Software Engineering and Data Science.
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Tosi, Davide and Tosi, Davide
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Information technology industries ,COVID-19 ,SARS-CoV-2 ,data analytics ,schools' impact ,Google mobility impact ,feature selection ,ontology ,text classification ,machine-learning ,SARS-COV-2 ,Bayesian regression ,changepoint detection ,European football championship ,big data ,delay-tolerant network (DTN) ,multi-attribute decision making ,public transport ,energy consumption ,software development process ,operations ,software engineering ,information system development ,team structure ,Software Library Recommendation ,graph filters ,dependency graphs ,link prediction ,n/a - Abstract
Summary: This reprint focuses on data-driven software solutions and their impact on research and development at the academic, industry, business, and government levels to exploit the hidden knowledge and big data that can be offered to cities and citizens in the future. Data-driven software solutions are different from "traditional" software development projects, as the focus of the main development core is on managing the data (e.g., data store and data quality) and designing behavioral models with the aid of artificial intelligence and machine learning techniques. To this end, new life cycles, algorithms, methods, processes, and tools are required. This reprint is centered on the recent trends and advancements in the field of engineering data-intensive software solutions to address the challenges in developing, testing, and maintaining such data-driven systems, with a focus on the application of data-driven solutions to real-life problems and techniques and algorithms addressing the different challenges of data-driven software engineering.
4. Inaugural Section Special Issue. Key Topics and Future Perspectives in Natural Hazards Research.
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Tapete, Deodato and Tapete, Deodato
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Research & information: general ,big data ,disaster management ,review ,natural hazards ,disaster ,scientometrics ,bibliometrics ,citation analysis ,NatCatSERVICE ,Sigma Explorer ,Oroville Dam ,spillway ,incident ,flood control ,flood-frequency analysis ,dam operation ,drought ,impacts ,exposure ,vulnerability ,risk ,policy ,risk assessment ,earthquake risk ,energy security ,reliability of power supply ,Eurasian Economic Union (EAEU) ,integration process ,common electricity market ,masonry aggregates ,vulnerability assessment ,vulnerability curves ,damage scenarios ,local hazard effect ,psychological representation of earthquakes ,open-ended and closed-questions surveys ,children ,seismic hazard assessment ,emotions ,emotional prevention ,African easterly wave ,attractor coexistence ,chaos ,hurricane ,limit cycle ,Lorenz model ,predictability ,recurrence ,extended range weather prediction ,Jakarta basin ,site effects ,shear-wave velocity ,urban fabrics ,seismic vulnerability ,critic analysis ,cost modelling ,urban preservation programming ,building works programming ,natural hazard ,earthquake ,dam spillway ,psychology ,cyber-infrastructure - Abstract
Summary: This book collects selected high-quality papers published in 2018-2020 to inaugurate the "Natural Hazards" Section of the Geosciences journal. The topics encompass: trends in publications at international level in the field of natural hazards research; the role of Big Data in natural disaster management; assessment of seismic risk through the understanding and quantification of its different components; climatic/hydro-meteorological hazards; and finally, the scientific analysis and disaster forensics of recent natural hazard events. The target audience includes not only specialists, but also graduate students who wish to approach the challenging, but also fascinating
5. Open Data and Energy Analytics.
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Nastasi, Benedetto, Manfren, Massimiliano, Noussan, Michel, and Nastasi, Benedetto
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Research & information: general ,data envelopment analysis ,Kohonen self-organizing maps ,factor analysis ,multiple regression ,energy efficiency ,social media ,energy-consuming activities ,energy consumption ,machine learning ,ontology ,energy performance certificate ,heating energy demand ,buildings ,data mining ,classification ,regression ,decision tree ,support vector machine ,random forest ,artificial neural network ,open data ,electrification modelling ,Malawi ,OnSSET ,MESSAGEix ,reproducibility ,collaborative work ,open modelling and data ,data-handling ,integrated assessment modelling ,data pre- and post-processing ,space heating ,domestic hot water ,market assessment ,EU28 ,district heating ,data analytics ,big data ,forecasting ,energy ,polygeneration ,clustering ,kNN ,pattern recognition ,heating ,building stock ,heat map ,spatial analysis ,heat density map ,building performance simulation ,parametric modelling ,energy management ,model calibration ,Passive House ,energy planning ,energy potential mapping ,urban energy atlas ,urban energy transition ,energy data ,data-aware planning ,spatial planning ,open data analytics ,smart cities ,open energy governance ,urban database ,energy mapping ,building dataset ,energy modelling - Abstract
Summary: Open data and policy implications coming from data-aware planning entail collection and pre- and postprocessing as operations of primary interest. Before these steps, making data available to people and their decision-makers is a crucial point. Referring to the relationship between data and energy, public administrations, governments, and research bodies are promoting the construction of reliable and robust datasets to pursue policies coherent with the Sustainable Development Goals, as well as to allow citizens to make informed choices. Energy engineers and planners must provide the simplest and most robust tools to collect, process, and analyze data in order to offer solid data-based evidence for future projections in building, district, and regional systems planning. This Special Issue aims at providing the state-of-the-art on open-energy data analytics; its availability in the different contexts, i.e., country peculiarities; and its availability at different scales, i.e., building, district, and regional for data-aware planning and policy-making. For all the aforementioned reasons, we encourage researchers to share their original works on the field of open data and energy analytics. Topics of primary interest include but are not limited to the following: 1. Open data and energy sustainability; 2. Open data science and energy planning; 3. Open science and open governance for sustainable development goals; 4. Key performance indicators of data-aware energy modelling, planning, and policy; 5. Energy, water, and sustainability database for building, district, and regional systems; 6. Best practices and case studies.
6. Comprehensive Systems Biomedicine.
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Pietro Lio and Enrico Capobianco
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inference ,systems biomedicine ,big data ,translational science ,paradigm shift - Abstract
Summary: Systems Biomedicine is a field in perpetual development. By definition a translational discipline, it emphasizes the role of quantitative systems approaches in biomedicine and aims to offer solutions to many emerging problems characterized by levels and types of complexity and uncertainty unmet before. Many factors, including technological and societal ones, need to be considered. In particular, new technologies are providing researchers with the data deluge whose management and exploitation requires a reinvention of cross-disciplinary team efforts. The advent of "omics" and high-content imaging are examples of advances de facto establishing the necessity of systems approaches. Hypothesis-driven models and in silico validation tools in support to all the varieties of experimental applications call for a profound revision. The focus on phases like mining and assimilating the data has substantially increased so to allow for interpretable knowledge to be inferred. Notably, to be able to tackle the newly generated data dimensionality, heterogeneity and complexity, model-free and data-driven intensive applications are increasingly shaping the computational pipelines and architectures that quant specialists set aside of the high-throughput genomics, transcriptomics, proteomics platforms. As for the societal aspects, in many advanced societies health care needs now more than in the past to address the problem of managing ageing populations and their complex morbidity patterns. In parallel, there is a growing research interest on the impact that cross-disciplinary clinical, epidemiological and quantitative modelling studies can have in relation to outcomes potentially affecting the quality of life of many people. Complex systems, including those characterizing biomedicine, are assessed in both their functionality and stability, and also relatively to the capacity of generating information from diversity, variation, and complexity. Due to the combined interactions and effects, such systems embed prediction power available for instance in both target identification or marker discovery, or more generally for conducting inference about patients' pathological states, i.e. normal versus disease, diagnostic or prognostic analysis, and preventive assessment (e.g., risk evaluation). The ultimate goal, personalized medicine, will be achieved based on the confluence of the system's predictive power to patient-specific profiling.
7. Digital innovation in Multiple Sclerosis Management.
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Ziemssen, Tjalf, Haase, Rocco, and Ziemssen, Tjalf
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Medicine ,multiple sclerosis ,digital health ,eHealth ,intervention ,patient management ,chronic disease ,disease management ,Patient Reported Outcomes ,e-health ,app ,communication ,digital tools ,patient empowerment ,health information seeking ,user-centered design ,patient portal ,master's program ,education ,multiple sclerosis management ,Dresden International University ,digitization ,icompanion ,icobrain ,digital health technology ,mobile application ,patient reported outcomes ,magnetic resonance imaging ,mHealth ,telemonitoring ,longitudinal assessment ,rehabilitation ,fatigue ,walking ,cognition ,software as a medical device ,participatory health ,monitoring ,smartphone-based assessments ,clinical validation ,technical validation ,MS apps ,digital health solution development ,digital biomarkers ,AI ,(early) Health Technology Assessment ,home monitoring ,MS disease activity ,MS disease progression ,early detection ,disease modelling ,digital therapeutics ,gait analysis ,mobility ,digital tools and applications ,precision medicine ,personalized therapy ,big data ,digital twin ,relapsing-remitting multiple sclerosis (RRMS) ,magnetic resonance imaging (MRI) ,brain MRI analysis software ,non-evidence of disease activity (NEDA) ,Markov model ,n/a - Abstract
Summary: Due to innovation in technology, a new type of patient has been created, the e-patient, characterized by the use of electronic communication tools and commitment to participate in their own care. The extent to which the world of digital health has changed during the COVID-19 pandemic has been widely recognized. Remote medicine has become part of the new normal for patients and clinicians, introducing innovative care delivery models that are likely to endure even if the pendulum swings back to some degree in a post-COVID age. The development of digital applications and remote communication technologies for patients with multiple sclerosis has increased rapidly in recent years. For patients, eHealth apps have been shown to improve outcomes and increase access to care, disease information, and support. For HCPs, eHealth technology may facilitate the assessment of clinical disability, analysis of lab and imaging data, and remote monitoring of patient symptoms, adverse events, and outcomes. It may allow time optimization and more timely intervention than is possible with scheduled face-to-face visits. The way we measure the impact of MS on daily life has remained relatively unchanged for decades, and is heavily reliant on clinic visits that may only occur once or twice each year.These benefits are important because multiple sclerosis requires ongoing monitoring, assessment, and management.The aim of this Special Issue is to cover the state of knowledge and expertise in the field of eHealth technology applied to multiple sclerosis, from clinical evaluation to patient education.
8. Algorithms in Decision Support Systems.
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García-Díaz, Vicente and García-Díaz, Vicente
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History of engineering & technology ,semi-supervised learning ,transfer learning ,radar emitter ,decision support systems ,population health management ,big data ,machine learning ,deep learning ,personalized patient care ,Nonlinear regression ,interactive platform ,component-based approach ,software architecture ,Eclipse-RCP (Rich Client Platform) ,spatial prediction ,rule-based expert systems ,tennis hitting technique ,computer algebra systems ,Groebner bases ,Boolean logic ,data envelopment analysis ,dimensionality reduction ,ensembles ,exhaustive state space search ,entropy ,associative classification ,class association rule ,vertical data representation ,classification ,algorithm evaluation ,parallel algorithms ,multi-objective optimization ,train rescheduling ,very large-scale decision support systems ,very large-scale data and program cores of information systems ,meta-database ,teleological meta-database ,thematic list ,indicators list ,computational methods list ,geographically dispersed systems ,external sources - Abstract
Summary: This book aims to provide a new vision of how algorithms are the core of decision support systems (DSSs), which are increasingly important information systems that help to make decisions related to unstructured and semi-unstructured decision problems that do not have a simple solution from a human point of view. It begins with a discussion of how DSSs will be vital to improving the health of the population. The following article deals with how DSSs can be applied to improve the performance of people doing a specific task, like playing tennis. It continues with a work in which authors apply DSSs to insect pest management, together with an interactive platform for fitting data and carrying out spatial visualization. The next article improves how to reschedule trains whenever disturbances occur, together with an evaluation framework. The final works focus on different relevant areas of DSSs: 1) a comparison of ensemble and dimensionality reduction models based on an entropy criterion; 2) a radar emitter identification method based on semi-supervised and transfer learning; 3) design limitations, errors, and hazards in creating very large-scale DSSs; and 4) efficient rule generation for associative classification. We hope you enjoy all the contents in the book.
9. The Economics of Big Science. Essays by Leading Scientists and Policymakers.
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Beck, Hans Peter, Charitos, Panagiotis, and Beck, Hans Peter
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Particle & high-energy physics ,Research & development management ,Astronautics ,Databases ,Investment & securities ,Particle and Nuclear Physics ,R & D/Technology Policy ,Space Sciences (including Extraterrestrial Physics, Space Exploration and Astronautics) ,Big Data ,Investment Appraisal ,Nuclear and Particle Physics ,Economics ,Space Physics ,Finance ,Investing in fundamental science ,Societal benefits / value of science ,Measuring socio-economic impact of science ,Benefits from fundamental research ,Big science projects finance/costs ,Cost of large-scale scientific projects ,Societal value of fundamental science ,Open Access - Abstract
Summary: The essays in this open access volume identify the key ingredients for success in capitalizing on public investments in scientific projects and the development of large-scale research infrastructures. Investment in science - whether in education and training or through public funding for developing new research tools and technologies - is a crucial priority. Authors from big research laboratories/organizations, funding agencies and academia discuss how investing in science can produce societal benefits as well as identifying future challenges for scientists and policy makers. The volume cites different ways to assess the socio-economic impact of Research Infrastructures and their role as hubs of global collaboration, creativity and innovation. It highlights the different benefits stemming from fundamental research at the local, national and global level, while also inviting us to rethink the notion of "benefit" in the 21st century. Public investment is required to maintain the pace of technological and scientific advancements over the next decades. Far from advocating a radical transformation and massive expansion in funding, the authors suggest ways for maintaining a strong foundation of science and research to ensure that we continue to benefit from the outputs. The volume draws inspiration from the first "Economics of Big Science" workshop, held in Brussels in 2019 with the aim of creating a new space for dialogue and interaction between representatives of Big Science organizations, policy makers and academia. It aspires to provide useful reading for policy makers, scientists and students of science, who are increasingly called upon to explain the value of fundamental research and adopt the language and logic of economics when engaging in policy discussions.
10. Industry 4.0 for SMEs. Challenges, Opportunities and Requirements.
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Matt, Dominik T., Modrák, Vladimír, Zsifkovits, Helmut, and Matt, Dominik T.
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Research & development management ,Business mathematics & systems ,Small businesses & self-employed ,Business ,Management science ,Management ,Industrial management ,Small business ,Big data - Abstract
Summary: This open access book explores the concept of Industry 4.0, which presents a considerable challenge for the production and service sectors. While digitization initiatives are usually integrated into the central corporate strategy of larger companies, smaller firms often have problems putting Industry 4.0 paradigms into practice. Small and medium-sized enterprises (SMEs) possess neither the human nor financial resources to systematically investigate the potential and risks of introducing Industry 4.0. Addressing this obstacle, the international team of authors focuses on the development of smart manufacturing concepts, logistics solutions and managerial models specifically for SMEs. Aiming to provide methodological frameworks and pilot solutions for SMEs during their digital transformation, this innovative and timely book will be of great use to scholars researching technology management, digitization and small business, as well as practitioners within manufacturing companies.
11. Applications in Electronics Pervading Industry, Environment and Society. Sensing Systems and Pervasive Intelligence.
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Saponara, Sergio, De Gloria, Alessandro, Bellotti, Francesco, and Saponara, Sergio
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Technology: general issues ,model-based design ,FPGA ,HDL code generation ,wearable sensors ,embedded devices ,face recognition ,face verification ,biometric sensors ,deep learning ,distillation ,convolutional neural networks ,spatial transformer network ,video coding ,discrete cosine transform ,directional transform ,VLSI ,alternative representations to float numbers ,posit arithmetic ,Deep Neural Networks (DNNs) ,neural network activation functions ,surface electromyography ,event-driven ,functional electrical stimulation ,embedded system ,resampling ,interpolating polynomial ,polyphase filter ,digital circuit design ,ASIC ,bitmap indexing ,processing in memory ,memory wall ,big data ,internet of things ,intelligent sensors ,autonomous driving ,cyber security ,HW accelerator ,on-chip random number generator (RNG) ,SHA2 ,ASIC standard-cell ,machine learning ,edge computing ,edge analytics ,ANN ,k-NN ,SVM ,decision trees ,ARM ,X-Cube-AI ,STM32 Nucleo ,rad-hard ,PLL (phase-locked loop) ,SEE (single event effects) ,Spacefibre ,TID (total ionization dose) ,charge pump ,phase/frequency detector ,frequency divider ,ring oscillator ,LC-tank oscillator ,SpaceFibre ,rad-hard circuits ,radiation effects ,high-speed data transfer ,support attitude ,inertial measurement unit ,coal mining ,unscented Kalman filter ,quaternion ,gradient descent ,research data collection and sharing ,connected and automated driving ,deployment and field testing ,vehicular sensors ,impact assessment ,knowledge management ,collaborative project methodology ,n/a - Abstract
Summary: This book features the manuscripts accepted for the Special Issue "Applications in Electronics Pervading Industry, Environment and Society-Sensing Systems and Pervasive Intelligence" of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the "Applications in Electronics Pervading Industry, Environment and Society" (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs.
12. The Application of Computer Techniques to ECG Interpretation.
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Macfarlane, Peter and Macfarlane, Peter
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Medicine ,electrocardiographic imaging (ECGI) ,heart failure (HF) ,cardiac resynchronization therapy (CRT) ,ultrasound ,strain ,speckle tracking echocardiography ,in silico ,electrophysiology ,electrocardiogram ,ECG ,cardiac disease ,arrhythmia ,ischemia ,standardization ,computerized ECG ,personalized medicine ,telemedicine ,digital ECG data interchange protocol ,eHealth ,ECG equipment ,computerized electrocardiograph ,ECG analysis algorithms ,computerized ECG interpretation ,interatrial block ,partial interatrial block ,advanced interatrial block ,atypical patterns ,electrocardiogram (ECG) ,automated ECG analysis ,CSE study ,age ,sex ,race ,historical aspects ,electronic cohort ,mortality ,big data ,telehealth ,alarm fatigue ,annotation of ECG data ,arrhythmia alarms ,intensive care unit ,patient monitoring ,ambulatory ECG ,machine learning ,deep learning ,pattern recognition ,noise reduction ,Holter ECG ,ECG interpretation ,artificial intelligence ,body surface mapping ,electrocardiographic imaging ,image processing ,clinical applications ,n/a - Abstract
Summary: This book presents some of the latest available information on automated ECG analysis written by many of the leading researchers in the field. It contains a historical introduction, an outline of the latest international standards for signal processing and communications and then an exciting variety of studies on electrophysiological modelling, ECG Imaging, artificial intelligence applied to resting and ambulatory ECGs, body surface mapping, big data in ECG based prediction, enhanced reliability of patient monitoring, and atrial abnormalities on the ECG. It provides an extremely valuable contribution to the field.
13. Review Papers for Journal of Risk and Financial Management (JRFM)
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McAleer, Michael and McAleer, Michael
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Technology: general issues ,big data ,computational science ,economics ,finance ,management ,theoretical models ,econometric and statistical models ,applications ,n/a ,bank regulation ,capital adequacy standards ,regulatory complexity ,US banking crises ,supply chain management ,supply chain finance ,working capital ,factors ,outcomes ,solutions ,optimisation ,portfolio selection ,risk measure ,fat tail ,Copula ,shrinkage ,semi-variance ,CVaR ,excess returns ,efficient market hypothesis ,data snooping ,investment and capital markets ,market efficiency ,price-volume ,adaptive market hypothesis ,time-varying or adaptive market efficiency ,cross section of country equity returns ,country-level stock market anomalies ,empirical asset pricing ,international equity markets ,return predictability ,bank regulatory capital requirements ,marketing ,psychology ,price-volume relationship ,adaptive market efficiency ,covariance matrix estimation ,portfolio risk measurement ,stock investment ,country equity returns - Abstract
Summary: The Journal of Risk and Financial Management (JRFM) was inaugurated in 2008 and has successfully continued publishing, with Volume 13 in 2020. Since the journal was established, JRFM has published in excess of 580 topical and interesting theoretical and empirical papers in financial economics, financial econometrics, banking, finance, mathematical finance, statistical finance, accounting, decision sciences, information management, tourism economics and finance, international rankings of journals in financial economics, and bibliometric rankings of journals in cognate disciplines. Papers published in the journal range from novel technical and theoretical papers to innovative empirical contributions. The journal wishes to encourage critical review papers on topical subjects in any of the topics mentioned above in financial economics and in cognate disciplines.
14. Big Data in Dental Research and Oral Healthcare.
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Joda, Tim and Joda, Tim
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Medicine ,digital transformation ,rapid prototyping ,augmented and virtual reality (AR/VR) ,artificial intelligence (AI) ,machine learning (ML) ,personalized dental medicine ,tele-health ,patient-centered outcomes ,integrated care, medical-dental integration, simulation model, dental research ,oral medicine ,oral healthcare ,dentistry ,gerodontology ,elderly patient ,big data ,Big Data ,digital dentistry ,oral health ,ethical issues ,dental education ,augmented reality (AR) ,virtual reality (VR) ,artificial intelligence ,AI ,machine learning ,ML ,cone beam computed tomography (CBCT) ,intraoral scanning ,facial scanning ,healthcare cost ,medical healthcare cost ,dental healthcare cost ,zero-inflated model ,neural network - Abstract
Summary: Progress in information technology has fostered a global explosion of data generation. Accumulated big data are now estimated to be 4.4 zettabytes in the digital universe; and trends predict an exponential increase in the future. Health data are gathered from professional routine care and other expanded sources including the social determinants of health, such as Internet of Things. Biomedical research has recently moved through three stages in digital healthcare: (1) data collection; (2) data sharing; and (3) data analytics. With the explosion of stored health data, dental medicine is edging into its fourth stage of digitization using new technologies including augmented and virtual reality, artificial intelligence, and blockchain. Big data collaborations involve interactions between a diverse range of stakeholders with analytical, technical and political focus. In oral healthcare, data technology has many areas of application: prognostic analysis and predictive modeling, the identification of unknown correlations of diseases, clinical decision support for novel treatment concepts, public health surveys and population-based clinical research, as well as the evaluation of healthcare systems. The objective of this Special Issue is to provide an update on the current knowledge with state-of-the-art theory and practical information on human and social perspectives that determine the uptake of technological innovations in big data science in the field of dental medicine. Moreover, it will focus on the identification of future research needs to manage the continuous increase in health data and to accomplish its clinical translation for patient-centered research and personalized dentistry. This Special Issue welcomes all types of studies and reviews considering the perspectives of different stakeholders on technological innovations for big data science in all dental disciplines. Kind regards,
15. Clinical Studies, Big Data, and Artificial Intelligence in Nephrology and Transplantation.
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Cheungpasitporn, Wisit, Thongprayoon, Charat, Kaewput, Wisit, and Cheungpasitporn, Wisit
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Medicine ,tacrolimus ,C/D ratio ,tacrolimus metabolism ,everolimus ,conversion ,kidney transplantation ,gut microbiome ,renal transplant recipient ,diarrhea ,immunosuppressive medication ,gut microbiota ,16S rRNA sequencing ,butyrate-producing bacteria ,Proteobacteria ,torquetenovirus ,immunosuppression ,transplantation ,immunosuppressed host ,outcome ,renal transplantation ,Goodpasture syndrome ,anti-GBM disease ,epidemiology ,hospitalization ,outcomes ,acute kidney injury ,risk prediction ,artificial intelligence ,patent ductus arteriosus ,conservative management ,blood pressure ,eradication ,interferon-free regimen ,hepatitis C infection ,kidney transplant ,allograft steatosis ,lipopeliosis ,transplant numbers ,live donors ,public awareness ,Google TrendsTM ,machine learning ,big data ,nephrology ,chronic kidney disease ,NLR ,PLR ,RPGN ,predictive value ,hemodialysis ,withdrawal ,cellular crescent ,global sclerosis ,procurement kidney biopsy ,glomerulosclerosis ,minimally-invasive donor nephrectomy ,robot-assisted surgery ,laparoscopic surgery ,organ donation ,living kidney donation ,MeltDose® ,LCPT ,renal function ,liver transplantation ,metabolism ,erythropoietin ,fibroblast growth factor 23 ,death ,weekend effect ,in-hospital mortality ,comorbidity ,dialysis ,elderly ,klotho ,α-Klotho ,FGF-23 ,kidney donor ,Nephrology ,CKD-MBD ,CKD-Mineral and Bone Disorder ,deceased donor ,Eurotransplant Senior Program ,risk stratification ,intensive care ,kidney transplant recipients ,long-term outcomes ,graft failure ,cardiovascular mortality ,lifestyle ,inflammation ,vascular calcification ,bone mineral density ,dual-energy X-ray absorptiometry ,living donation ,repeated kidney transplantation ,graft survival ,prolonged ischaemic time ,patient survival ,pre-emptive transplantation ,metabolomics ,urine ,acute rejection ,allograft ,cystatin C ,hyperfiltration ,kidney injury molecule (KIM)-1 ,tubular damage ,genetic polymorphisms ,(cardiac) surgery ,inflammatory cytokines ,clinical studies ,chronic kidney disease (CKD) ,no known kidney disease (NKD) ,ICD-10 billing codes ,phenotyping ,electronic health record (EHR) ,estimated glomerular filtration rate (eGFR) ,machine learning (ML) ,generalized linear model network (GLMnet) ,random forest (RF) ,artificial neural network (ANN), clinical natural language processing (clinical NLP) ,discharge summaries ,laboratory values ,area under the receiver operating characteristic (AUROC) ,area under the precision-recall curve (AUCPR) ,fibrosis ,extracellular matrix ,collagen type VI ,living-donor kidney transplantation ,ethnic disparity - Abstract
Summary: In recent years, artificial intelligence has increasingly been playing an essential role in diverse areas in medicine, assisting clinicians in patient management. In nephrology and transplantation, artificial intelligence can be utilized to enhance clinical care, such as through hemodialysis prescriptions and the follow-up of kidney transplant patients. Furthermore, there are rapidly expanding applications and validations of comprehensive, computerized medical records and related databases, including national registries, health insurance, and drug prescriptions. For this Special Issue, we made a call to action to stimulate researchers and clinicians to submit their invaluable works and present, here, a collection of articles covering original clinical research (single- or multi-center), database studies from registries, meta-analyses, and artificial intelligence research in nephrology including acute kidney injury, electrolytes and acid-base, chronic kidney disease, glomerular disease, dialysis, and transplantation that will provide additional knowledge and skills in the field of nephrology and transplantation toward improving patient outcomes.
16. Cultural Heritage Storytelling, Engagement and Management in the Era of Big Data and the Semantic Web.
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Dimoulas, Charalampos and Dimoulas, Charalampos
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Film, TV & radio ,3D modeling ,3D reconstruction ,event detection ,Twitter ,spectral clustering ,cultural heritage ,social media ,news ,journalism ,semantic analysis ,big data ,data center ,digital marketing ,eco-friendly ,environmental communication ,green websites ,green culture ,green hosting ,sustainability ,software sustainability ,multimedia tools ,static analysis ,evolution analytics ,interactive documentary ,audience engagement ,digital storytelling ,intangible heritage ,media users' engagement ,marine heritage ,biocultural heritage ,heritage management ,heritage communication ,digital narrative ,Instagram ,UNESCO ,marine protected areas of outstanding universal value ,soundscapes ,audiovisual heritage ,semantic audio ,data-driven storytelling ,content crowdsourcing ,requirements engineering ,authoring tools ,3D content ,IEEE 830 standard ,semantic indexing ,text classification ,Greek literature ,TextRank ,BERT ,smart cities ,energy transition ,Évora ,POCITYF ,relation extraction ,distant supervision ,deep neural networks ,Transformers ,Greek NLP ,literary fiction ,metadata extraction ,Katharevousa ,n/a - Abstract
Summary: The current Special Issue launched with the aim of further enlightening important CH areas, inviting researchers to submit original/featured multidisciplinary research works related to heritage crowdsourcing, documentation, management, authoring, storytelling, and dissemination. Audience engagement is considered very important at both sites of the CH production-consumption chain (i.e., push and pull ends). At the same time, sustainability factors are placed at the center of the envisioned analysis. A total of eleven (11) contributions were finally published within this Special Issue, enlightening various aspects of contemporary heritage strategies placed in today's ubiquitous society. The finally published papers are related but not limited to the following multidisciplinary topics:Digital storytelling for cultural heritage;Audience engagement in cultural heritage;Sustainability impact indicators of cultural heritage;Cultural heritage digitization, organization, and management;Collaborative cultural heritage archiving, dissemination, and management;Cultural heritage communication and education for sustainable development;Semantic services of cultural heritage;Big data of cultural heritage;Smart systems for Historical cities - smart cities;Smart systems for cultural heritage sustainability.
17. New Horizons for a Data-Driven Economy. A Roadmap for Usage and Exploitation of Big Data in Europe.
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Cavanillas, José María, Curry, Edward, Wahlster, Wolfgang, and Cavanillas, José María
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Library & information sciences ,Information technology industries ,Engineering: general ,Computer science ,Coins, banknotes, medals, seals (numismatics) ,Information Storage and Retrieval ,Innovation/Technology Management ,Computer Applications ,Computers and Society ,Big data - Abstract
Summary: Information Storage and Retrieval; Innovation/Technology Management; Computer Applications; Computers and Society; Big data
18. Document sobre bioètica i Big Data de salut: explotació i comercialització de les dades dels usuaris de la sanitat pública. Document on bioethics and Big Data: exploitation and commercialisation of user data in public health care.
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María Casado, Observatori de Bioètica i Dret, M. Rosa Llácer Matacás, and Lídia Buisan Espeleta
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Bioètica ,Dades massives ,Bioethics ,Big data ,Salut pública ,Public health - Abstract
Summary: En aquest volum del Grup d'Opinió de l'Observatori de Bioètica i Dret, coordinat per les doctores María Casado, Maria Rosa Llàcer i Lídia Buisan, s'analitzen, des de la perspectiva bioètica, els inconvenients de l'explotació i la comercialització de dades dels usuaris de la sanitat pública arran dels problemes detectats en el projecte VISC+ de la Generalitat de Catalunya, tant en relació amb possibles vulneracions dels drets dels ciutadans com amb la manca de transparència i de debat públic informat en una qüestió de tanta importància com és el tràfic de dades personals. El document posa de manifest que la implementació de les tecnologies Big Data en l'àmbit sanitari, associada a una eventual comercialització d'aquestes dades, impacta directament en el nostre sistema sanitari i investigador i afecta de ple a l'àmbit privat dels ciutadans.
19. The Financial Industry 4.0.
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Ngo, Thanh, Guegan, Dominique, Vo, Dinh-Tri, and Ngo, Thanh
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Coins, banknotes, medals, seals (numismatics) ,stochastic volatility with co-jumps ,threshold GARCH ,RiskMetrics ,validation ,cryptocurrency market ,technology ,banking 4.0 ,industry 4.0 ,roadmap ,digitalization ,big data ,blockchain ,disruptive technology ,corporate governance ,corporate voting ,tokenisation ,smart contracts ,artificial intelligence ,digital financial inclusion ,finance ,digital financial services ,digital credit ,betting ,financial distress ,coping strategies ,welfare outcomes - Abstract
Summary: We invite you to read the Special Issue on The Financial Industry 4.0. It is a collection of 13 articles published in a Special Issue of International Journal of Financial Studies (MDPI) in 2020-2022. The main emphasis of this reprint is on The Financial Industry 4.0 to provide insightful understanding about the benefits as well as the challenges that financial institutions are facing under the Industry 4.0 era. The articles in this Special Issue discussed the potential of blockchain technology, the impact of fintech on financial inclusion in developing countries, the role of fintech in the insurance industry, and so on. It highlights the benefits of fintech, such as improved efficiency, accuracy, and customer experience, but also notes the challenges and risks involved, such as data privacy and security concerns. Collaboration between financial institutions, regulators, and technology firms is seen as necessary to promote innovation and ensure the stability and security of the financial system while addressing the challenges and risks associated with fintech.
20. Sustainable Real Estate: Management, Assessment and Innovations.
- Author
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De Paola, Pierfrancesco, Tajani, Francesco, Locurcio, Marco, and De Paola, Pierfrancesco
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Information technology industries ,big data ,decision-making ,feasibility study ,fuzzy theory ,high-rise building ,mixed-use development ,urban tree canopy (UTC) ,hedonic price model ,two-stage spatial model ,multi-level mixed model ,varying effect ,customer gender ,women ,tenure choice ,sustainable housing ,housing market ,mass appraisal techniques ,evaluation model ,hedonic price method ,geographically weighted regression ,evolutionary polynomial regression ,market value ,smart building ,smart energy system ,renewable energy resources ,energy storage ,reserve power system ,investor motives ,investment profitability ,smart readiness indicator ,discounted cash flow analysis ,natural landscape ,views ,visual perception ,housing price ,quantile regression ,marginal impact ,wealth inequality ,growth management ,sustainable development ,transit-oriented development ,contingent valuation method ,retirement ,housing downsizing ,housing consumption ,housing tenure choice ,consumption ,housing wealth effect ,financial wealth effect ,multi-step causality ,ESG ,real estate companies ,ratings ,sustainability ,energy efficiency ,sustainable decision-making ,sustainable social housing management ,multi-criteria decision-making (MCDM) ,AHP ,WASPAS ,COPRAS ,social cohesion ,uncertainty ,U.S. housing markets ,local projection method ,impulse response functions ,n/a - Abstract
Summary: Production and consumption activities have determined a weakness of the sustainable real estate economy. The main problems are the subordination of public decision making, which is subjected to pressure from big companies; inefficient appraisal procedures; excessive use of financial leverage in investment projects; the atypical nature of markets; income positions in urban transformations; and the financialization of real estate markets, with widespread negative effects. A delicate role in these complex problems is assigned to real estate appraisal activities, called to make value judgments on real estate goods and investment projects, the prices of which are often formed in atypical real estate markets, giving ever greater importance to sustainable development and transformation issues. This Special Issue is dedicated to developing and disseminating knowledge and innovations related to most recent real estate evaluation methodologies applied in the fields of architecture and civil, building, environmental, and territorial engineering. Suitable works include studies on econometric models, sustainable building management, building costs, risk management and real estate appraisal, mass appraisal methods applied to real estate properties, urban and land economics, transport economics, the application of economics and financial techniques to real estate markets, the economic valuation of real estate investment projects, the economic effects of building transformations or projects on the environment, and sustainable real estate.
21. Data Science for Economics and Finance. Methodologies and Applications.
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Consoli, Sergio, Reforgiato Recupero, Diego, Saisana, Michaela, and Consoli, Sergio
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Data mining ,Machine learning ,Business mathematics & systems ,Public administration ,Information retrieval ,Data Mining and Knowledge Discovery ,Machine Learning ,Business Information Systems ,Big Data/Analytics ,Computer Appl. in Administrative Data Processing ,Information Storage and Retrieval ,IT in Business ,Computer and Information Systems Applications ,Open Access ,Data Mining ,Big Data ,Data Analytics ,Decision Support Systems ,Semantics and Reasoning ,Expert systems / knowledge-based systems ,Information technology: general issues ,Data warehousing - Abstract
Summary: This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.
22. Data Science in Healthcare.
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Hulsen, Tim and Hulsen, Tim
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Medicine ,Pharmacology ,data sharing ,data management ,data science ,big data ,healthcare ,depression ,psychological treatment ,task sharing ,primary care ,pilot study ,non-specialist health worker ,training ,digital technology ,mental health ,COVID-19 ,SARS-CoV-2 ,pneumonia ,computed tomography ,case fatality rate ,social distancing ,smoking ,metabolically healthy obese phenotype ,metabolic syndrome ,obesity ,coronavirus ,machine learning ,social media ,apache spark ,Twitter ,Arabic language ,distributed computing ,smart cities ,smart healthcare ,smart governance ,Triple Bottom Line (TBL) ,thoracic pain ,tree classification ,cross-validation ,hand-foot-and-mouth disease ,early-warning model ,neural network ,genetic algorithm ,sentinel surveillance system ,outbreak prediction ,artificial intelligence ,vascular access surveillance ,arteriovenous fistula ,end stage kidney disease ,dialysis ,kidney failure ,chronic kidney disease (CKD) ,end-stage kidney disease (ESKD) ,kidney replacement therapy (KRT) ,risk prediction ,naïve Bayes classifiers ,precision medicine ,machine learning models ,data exploratory techniques ,breast cancer diagnosis ,tumors classification ,n/a - Abstract
Summary: Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management.
23. Sustainable Marketing, Branding and CSR in the Digital Economy.
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Ozuem, Wilson, Ranfagni, Silvia, and Ozuem, Wilson
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Information technology industries ,advertising copy ,smartphone consumer ,consumer typology ,sustainable marketing ,unobserved heterogeneity ,business analytics ,social media ,CSR ,strategy formulation ,strategic planning ,governance ,celebrity-brand association ,real-life setting on social media ,para-social interaction ,self-brand connection ,brand quality ,advertising ,emotions ,emotional states ,regions ,emotional appeal ,adolescents ,SNS ,emojis ,self-presentation ,symbolic value ,playfulness ,need for uniqueness ,Internet of Things ,business models ,smart cities ,big data ,consumer data ,n/a - Abstract
Summary: Sustainable marketing practice is essential for developing a more comprehensive understanding of consumers' purchase decisions in dynamic digital marketing environments. Scholars and practitioners conceive sustainable marketing practices as episodic, predicated on temporal practices in response to emerging digital environments. Consumers are increasingly becoming aware of the ecological issues that their consumption creates in the marketplace. Despite the importance of sustainable practices, when and how sustainability occurs regarding the consumer's purchase decision remains largely unexplored. In part, this is because the practices of sustainability in the emerging computer-mediated marketing environments (CMMEs) are difficult to anticipate and study. Much of what we know about sustainable marketing practice is mainly focused on customer-brand relationships. Prior literature examining sustainable marketing practice through CMMEs remains sparse, despite consistent emphasis on the benefits of sustainable marketing practices in the emerging digital world.
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