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In view of the considerable applications of data clustering techniques in various fields, such as engineering, artificial intelligence, machine learning, clinical medicine, biology, ecology, disease diagnosis, and business marketing, many data clustering algorithms and methods have been developed to deal with complicated data. These techniques include supervised learning methods and unsupervised learning methods such as density-based clustering, K-means clustering, and K-nearest neighbor clustering. This book reviews recently developed data clustering techniques and algorithms and discusses the development of data clustering, including measures of similarity or dissimilarity for data clustering, data clustering algorithms, assessment of clustering algorithms, and data clustering methods recently developed for insurance, psychology, pattern recognition, and survey data.
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The present book discusses three significant challenges of the built environment, namely regional and global climate change, vulnerability, and survivability under the changing climate. Synergies between local climate change, energy consumption of buildings and energy poverty, and health risks highlight the necessity to develop mitigation strategies to counterbalance overheating impacts. The studies presented here assess the underlying issues related to urban overheating. Further, the impacts of temperature extremes on the low-income population and increased morbidity and mortality have been discussed. The increasing intensity, duration, and frequency of heatwaves due to human-caused climate change is shown to affect underserved populations. Thus, housing policies on resident exposure to intra-urban heat have been assessed. Finally, opportunities to mitigate urban overheating have been proposed and discussed.
Mediterranean --- semi-arid --- drought --- standardized precipitation evapotranspiration index (SPEI) --- climate warming --- soil moisture --- urban heat islands --- environmental justice --- climate change --- redlining --- heatwave --- diurnal temperature range --- time-series --- relative risk --- health --- transpiration cooling --- coastal cities --- sap flow --- subtropical desert climate --- urban overheating --- cluster analysis --- air temperature --- wind speed and wind directions --- synoptic conditions --- urban heat island --- mitigation --- resilience --- survivability --- low-income population
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Im vorliegenden Buch werden Gelingensbedingungen für einen erfolgreichen Berufseinstieg als Lehrperson rekonstruiert. Der Übergang mit den pädagogisch-praktischen Studien als Vorbereitung und der Induktionsphase als folgende und erste berufliche Professionalisierungsphase wird fokussiert, wobei der begleitende Mentoring-Prozess besondere Berücksichtigung findet. Das Habituskonzept (Bourdieu) mit der Trias wahrnehmen - denken - handeln bildet mit der Integration der Resonanz-Diskussion (Rosa) und dem Kontingenz-Phänomen die theoretische Grundlage bzw. die Kernidee der Rekonstruktion: Habitus als Konzept der Professionalität. Die Datengrundlage der empirischen Arbeit besteht aus Interviews mit 25 BerufsanfängerInnen und 15 SchulleiterInnen, die multimethodisch nach der Grounded Theory, der qualitativen Inhaltsanalyse und einer Clusteranalyse qualitativ untersucht werden. Die Arbeit schließt mit dem Ausblick auf konkrete Umsetzungsmöglichkeiten der gewonnenen Erkenntnisse in Curricula für LehrerInnenaus- und -fortbildung.
Mentoring --- Habitualisierung --- Lehramtsstudium --- Übergang --- Habitus --- Induktionsphase --- "Pädagogisch-praktische Studien" --- Berufseinstieg --- Pierre Bourdieu --- Lehrerbildung --- Lehrerinnenbildung --- Kontingenz --- Professionalisierung --- Contingency, continued professional development, curriculum, habitualization, induction phase, mentoring, professional habitus, professionalization, teacher training, uncertainty, Teaching Profession, Mentoring, Habitus, Professionalism, Empirical Investigation, Headmaster, Teacher Training, Habitualization, Induction, Contingency, School Type, Teacher Training, Professional Practice, Professional Biography, Practice, Transition Study – Profession, Professional Teacher Training, Curriculum, Pierre Bourdieu, Grounded Theory, Cluster Analysis, Qualitative Research, Interview, Austria, Teacher, Professionalization, Professional Entry, Teaching Profession, Mentoring, Habitus, Professionalism, Empirical Investigation, Headmaster, Teacher Training, Habitualization, Induction, Contingency, School Type, Teacher Training, Professional Practice, Professional Biography, Practice, Transition Study – Profession, Professional Teacher Training, Curriculum, Pierre Bourdieu, Grounded Theory, Cluster Analysis, Qualitative Research, Interview, Austria, Lehrer, Professionalisierung, Berufseintritt, Berufsanfänger, Lehrberuf, Mentoring, Habitus, Professionalität, Empirische Untersuchung, Schulleiter, Lehrerausbildung, Habitualisierung, Induktion, Kontingenz, Schulform, Lehramtsstudiengang, Lehrerbildung, Berufspraxis, Berufsbiografie, Praxis, Übergang Studium - Beruf, Profession, Lehrerfortbildung, Curriculum, Bourdieu, Pierre, Grounded Theory, Clusteranalyse, Qualitative Forschung, Interview, Österreich
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The analysis of power systems under various conditions represents one of the most important and complex tasks in electrical power engineering. Studies in this area are necessary to ensure that the reliability, efficiency, and stability of the power system is not adversely affected. This issue is devoted to reviews and applications of modern methods of signal processing used to analyze the operation of a power system and evaluate the performance of the system in all aspects. Smart grids as an emerging research field of the current decade is the focus of this issue. Monitoring capability with data integration, advanced analysis of support system control, enhanced power security and effective communication to meet the power demand, efficient energy consumption and minimum costs, and intelligent interaction between power-generating and -consuming devices depends on the selection and implementation of advanced signal analysis and processing techniques.
convolutional neural networks --- multi-headed CNN --- CNN-LSTM --- forecasting --- solar output --- sliding window --- renewable energy --- data mining --- cluster analysis --- power quality --- global power quality index --- electrical power network --- distributed generation --- mining industry --- ward algorithm --- different working conditions --- power supply restoration --- power supply outages --- failures --- time intervals --- obtaining information --- information recognition --- connection harmonization --- virtual power plant --- distributed energy resources --- energy storage systems --- grid codes --- power systems --- smart grids --- prosumer --- business model --- economic efficiency --- sensitivity analysis
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Today, a single laboratory can generate a vast amount of biological data. There is a wealth of data already available in public databases, which makes the modern life sciences almost dependent on bioinformatics. This book brings together an international team of experts to discuss the state-of-the-art from several fields of bioinformatics, from the automatic identification and classification of viruses to the analysis of the transcriptome of single cells and plants, including artificial intelligence algorithms to discover biomarkers and text mining approaches to help in the interpretation of the findings. Machine learning, pattern discovery and analysis, error correction, Bayesian inference and novel computational techniques to discover chromosomal rearrangements continue to play crucial roles in biological discovery, and all of them are explored in chapters of this book. In sum, this book contains high-quality chapters that provide excellent views into key topics of current bioinformatics research, topics that should remain important for the next several years.
Bioinformatics. --- Text Mining Gene Selection; Biological Big Data; Single-Cell RNA Sequencing; Large-Scale Structural Rearrangements in Chromosomes; Machine Learning Approaches; Biomarker Discovery; Gene Expression Data; Bayesian Inference of Gene Expression; Error-Correction Methodologies; Genome Sequencing Data; Plant Transcriptome Assembly; Aligned Pattern Clustering System; Pattern Analysis; Hidden Markov Models; Viral Classification and Discovery; Pattern Discovery and Disentanglement; Aligned Pattern Cluster Analysis; Protein Binding Complexes Detection
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This Special Issue, focusing on the value of mineralogical monitoring for the mining and minerals industry, should include detailed investigations and characterizations of minerals and ores of the following fields for ore and process control: Lithium ores—determination of lithium contents by XRD methods; Copper ores and their different mineralogy; Nickel lateritic ores; Iron ores and sinter; Bauxite and bauxite overburden; Heavy mineral sands. The value of quantitative mineralogical analysis, mainly by XRD methods, combined with other techniques for the evaluation of typical metal ores and other important minerals, will be shown and demonstrated for different minerals. The different steps of mineral processing and metal contents bound to different minerals will be included. Additionally, some processing steps, mineral enrichments, and optimization of mineral determinations using XRD will be demonstrated. Statistical methods for the treatment of a large set of XRD patterns of ores and mineral concentrates, as well as their value for the characterization of mineral concentrates and ores, will be demonstrated. Determinations of metal concentrations in minerals by different methods will be included, as well as the direct prediction of process parameters from raw XRD data.
barite --- mineralogy --- industrial application --- beneficiation --- specific gravity --- bauxite overburden --- Belterra Clay --- mineralogical quantification --- Rietveld analysis --- machine learning --- artificial intelligence --- mining --- mineralogical analysis --- bauxite --- available alumina --- reactive silica --- XRD --- PLSR --- lithium --- quantification --- clustering --- Rietveld --- cluster analysis --- spodumene --- petalite --- lepidolite --- triphylite --- zinnwaldite --- amblygonite --- chalcopyrite --- ore blending --- copper flotation --- nickel laterite --- ore sorting --- framboidal pyrite --- sulfide minerals --- flotation --- process mineralogy --- heavy minerals --- ilmenite --- titania slag --- rietveld --- Magneli phases --- n/a
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This book is published open access under a CC BY 4.0 license. It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm(DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures.The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining. Contents Approaches to Unsupervised Machine Learning Methods of Visualization of High-Dimensional Data Quality Assessments of Visualizations Behavior-Based Systems in Data Science Databionic Swarm (DBS) Target Groups Lecturers, students as well as non-professional users of data science, statistics, computer science, business mathematics, medicine, biology The Author Michael C. Thrun, Dipl.-Phys., successfully defended his Ph.D. in 2017 at the Philipps University of Marburg. Thrun’s advisor was the Chair of Neuroinformatics, Prof. Dr. rer. nat. Alfred G. H. Ultsch.
Computer science. --- Data structures (Computer science). --- Pattern recognition. --- Computer Science. --- Pattern Recognition. --- Data Structures. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Information structures (Computer science) --- Structures, Data (Computer science) --- Structures, Information (Computer science) --- Electronic data processing --- File organization (Computer science) --- Abstract data types (Computer science) --- Informatics --- Science --- Optical pattern recognition. --- Data structures (Computer scienc. --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Data structures (Computer science) --- Cluster Analysis --- Dimensionality Reduction --- Swarm Intelligence --- Visualization --- Unsupervised Machine Learning --- Data Science --- Knowledge Discovery --- 3D Printing --- Self-Organization --- Emergence --- Game Theory --- Advanced Analytics --- High-Dimensional Data --- Multivariate Data --- Analysis of Structured Data
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The renewable energy sector is one of the fastest growing branches of the economy in the world, including in Poland. Extensive investigation in research centers results in the increased efficiency of obtaining energy from renewable sources, as well as a decrease in the prices of renewable energy installations. The development of renewable energy motivates further research and the development of new technologies. Investments in renewable energy may also benefit the local community by increasing the attractiveness of the region to tourists, creating opportunities for professional activation (especially in areas with high unemployment), increasing the competitiveness of the local economy and its energy efficiency and obtaining raw materials from local producers, mainly farmers, which are an additional source of income for them. Another possible economic advantage is charging lease fees, for instance, for land under wind turbines or fees for ground easement, in order to ensure access to the construction of power lines, e.g., connecting turbines to the grid; lowering heat prices for residents of a given town; building investment plots in or near heat plants and biogas plants, with the provision of heat and electricity at competitive prices directly from these plants; investors covering the costs of modernizing local roads; and creating new transmission, power lines and supply points.
Technology: general issues --- History of engineering & technology --- Environmental science, engineering & technology --- waste management --- energy recovery --- model of energy recovery --- biogas --- fermentation --- combustion --- mini-grids --- energy access --- energy sustainability --- SDG 7 --- energy affordability --- green growth --- sustainable development --- environmental production --- relationships --- multicriteria taxonomy --- renewable energy sources --- household --- primary solid biofuels --- solar thermal system --- ambient pumps --- : CSR strategy --- financial performance --- energy sector --- : gross electricity production --- renewable sources --- energy transformation --- concentrationanalysis --- cluster analysis --- k-means --- European Union --- renewable energy sources (RES) --- the new EU member states --- Ward’s method: alternative energy sources --- photovoltaic systems --- wind systems --- hydropower systems --- biomass systems ---
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Severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) has spread worldwide from the beginning of 2020. The infection is mostly asymptomatic but some patients may develop COVID‑19 (coronavirus disease 2019) with a severe or critical course leading to pneumonia, acute respiratory distress syndrome, and multiorgan failure. Apart from the virus‑related damage of the lungs, pathomechanism of the disease seems to be linked to thromboembolism and inflammation accompanied by overproduction of proinflammatory cytokines, termed a cytokine storm, responsible for multiorgan damage and death. Since the development of a new therapeutic molecule, dedicated strictly to a particular virus is time‑consuming, physicians and scientists have started to test and repurpose old medications. Unfortunately, after one year of pandemics, there is still a lack of optimal therapy and no clear indicators of recovery. A major issue is also insufficient knowledge on predictors of the severe or deadly course of the disease, which could also help to switch from one therapeutic option to another. Due to many gaps still existing in the management of COVID-19, there is a need for the accumulation of new data particularly from real-world experience, which could be applicable to practice guidelines. The objective of this special issue of the Journal of Clinical Medicine is to provide an update on the mangement for the diagnostic workup and pharmacotherapy of SARS‑CoV‑2 infection.
Medicine --- COVID-19 --- SARS-CoV-2 --- interleukin-6 --- tocilizumab --- therapy --- coronavirus disease 2019 --- cytokines --- severity --- prognosis --- mortality --- kidney failure --- rapid diagnostic test --- antigen detection --- Cytomegalovirus --- co-infections --- critical care --- liver markers --- inflammation --- morbidity --- personalized medicine --- liver functional tests --- COVID-19 pneumonia --- meta-analysis --- trial sequential analysis --- children --- clinical presentation --- coronavirus disease 2019 (COVID-19) --- epidemiology --- severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) --- clinical outcome --- symptomatology --- pandemic --- angiotensin 1 receptor (AT1R) --- AT1R concentration --- angiotensin II --- symptoms’ severity --- diagnosis --- artificial intelligence --- medical imaging --- systematic umbrella review --- methodological credibility --- PCR test --- COVID-19 diagnosis --- Charlson Comorbidities Index --- cluster analysis --- longitudinal cluster --- individualized management --- n/a --- symptoms' severity
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A virus is considered a nanoscale organic material that can infect and replicate only inside the living cells of other organisms, ranging from animals and plants to microorganisms, including bacteria and archaea. The structure of viruses consists of two main parts: the genetic material from either DNA or RNA that carries genetic information, and a protein coat, called the capsid, which surrounds and protects the genetic material. By inserting the gene encoding functional proteins into the viral genome, the functional proteins can be genetically displayed on the protein coat to form bioengineered viruses. Therefore, viruses can be considered biological nanoparticles with genetically tunable surface chemistry and can serve as models for developing virus-like nanoparticles and even nanostructures. Via this process of viral display, bioengineered viruses can be mass-produced with lower cost and potentially used for energy and biomedical applications. This book highlights the recent developments and future directions of virus-based nanomaterials and nanostructures. The virus-based biomimetic materials formulated using innovative ideas were characterized for the applications of biosensors and nanocarriers. The research contributions and trends on virus-based materials covering energy harvesting devices to tissue regeneration in the last two decades are discussed.
virus-like particles --- glioblastoma --- convection-enhanced delivery --- tobacco mosaic virus --- bioconjugation --- doxorubicin --- drug delivery --- protein-based nanomaterials --- viral capsid --- VLPs --- hepatitis B virus capsid protein --- HBc --- viral self-assembly --- magnetic core --- HBcAg --- BmNPV bacmid --- nanobiomaterials --- Neospora caninum --- Neospora caninum profilin --- neosporosis --- silkworm expression system --- ZnS --- bio/inorganic hybrid materials --- hydrophobization --- polymer coupling --- virus --- tissue regeneration --- biomimetic nanocomposites --- phage display --- nano-vaccines --- HIV-1 Env trimers --- B-cell targeting --- intrastructural help --- VNPs --- Hsp60 --- IBD --- autoantibody --- inflammation --- diagnosis --- biosensor --- M13 bacteriophage --- color sensor --- energy generator --- piezoelectric --- self-assembly --- genetic engineering --- multi-array sensors --- hierarchical cluster analysis --- high selectivity --- piezoelectric materials --- organic materials --- biomaterials --- energy applications --- biomedical applications --- virus-based nanomaterials --- energy devices --- piezoelectric biomaterials
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