Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-fault diagnosis for wind turbines based on principal component analysis and support vector machines

The reliability requirements of wind turbine (WT) components have increased significantly in recent years in the search for a lower impact on the cost of energy. In addition, the trend towards larger WTs installed in offshore locations has significantly increased the cost of repair of the components. In the wind industry, therefore, condition monitoring is crucial for maximum availability. In this work, without using specific tailored devices for condition monitoring but only increasing the sampling frequency in the already available sensors of the SCADA system, a data-driven multi-fault diagnosis strategy is contributed. The advanced WT benchmark proposed by [1] is used. That is a 5 MW modern WT simulated with the FAST [2] software and subject to various actuators and sensors faults of different type. The measurement noise at each sensor is modeled as a Gaussian white noise. First, the SCADA measurements are pre-processed and feature transformation based on multiway principal component analysis (MPCA) is realized. Then, 10-fold cross validation support vector machines (SVM) based classification is applied. In this work, SVMs were used as a first choice for fault detection as they have proven their robustness for some particular faults [3-5] but never accomplished, to the authors’ knowledge, at the same time the detection and classification of all the proposed faults taken into account in this work. To this end, the choice of the features as well as the selection of data are of primary importance. Simulation results show that all studied faults are detected and classified with an overall accuracy of 98%. Finally, it is noteworthy that the prediction speed allows this strategy to be deployed for real-time condition monitoring in WTs.
Expert opinion [accounting]

The infrastructure of a corporate enterprise information system is concerned with acquiring, transferring, converting, and selling economic resources such as cash, inventory, and supplies. Accounting systems with individual computerized modules, such as: payroll, accounts payable and receivable, job costing, order entry, and general ledgers, have traditionally tracked such data, and accounting system designers usually have had a preemptive call on the basic schemes used to type economic data. This is because of a need to meet either statutory reporting requirements for governmental agencies, or private reporting requirements for creditors and shareholders. If accountants use these preemptive privileges to force traditional account coding onto the organization database as its fundamental classification architecture (that is, to require early bookkeeper filtering of transaction data), then many dysfunctional effects arise. Our remedy for these deficiencies is to apply the REA (economic resources, events, and agents) conceptual model when analyzing, designing, implementing, and operating an enterprise information system. REA specifically incorporates the semantics of economic objects into a firm's information architecture. Such embedding facilitates an information system's initial design, as well as couples its production use with knowledge based decision support systems.
Improvement of the sensory and autonomous capability of robots through olfaction: the IRO Project

Olfaction is a valuable source of information about the environment that has not been sufficiently exploited in mobile robotics yet. Certainly, odor information can contribute to other sensing modalities, e.g. vision, to successfully accomplish high-level robot activities, such as task planning or execution in human environments. This paper describes the developments carried out in the scope of the IRO project, which aims at making progress in this direction by investigating mechanisms that exploit odor information (usually coming in the form of the type of volatile and its concentration) in problems like object recognition and scene-activity understanding. A distinctive aspect of this research is the special attention paid to the role of semantics within the robot perception and decisionmaking processes. The results of the IRO project have improved the robot capabilities in terms of efficiency, autonomy and usefulness. Copyright c © CEA.
Preclinical MRI and NMR Biomarkers of Alzheimer’s Disease: Concepts and Applications

Alzheimer's disease is an important social and economic issue for our societies. The development of therapeutics against this severe dementia requires assessing the effects of new drugs in animal models thanks to dedicated biomarkers. This review fi rst overviews Alzheimer's disease and its models as well as the concept of biomarkers. It then focuses on MRI and NMR biomarkers of Alzheimer's disease in animals. Anatomical markers such as atrophy and angiography are useful to phe- notype newly developed models of Alzheimer's disease, even if the alterations in these animals are not as severe as in humans. Amyloid plaques imaging is a promising marker of the pathology in animals, and is a rapidly evolving fi eld of MRI. Functional methods such as perfusion and diffusion imaging or spectroscopy are able to detect alterations in transgenic mice mimicking Alzheimer and also to show similar alterations than in humans. They can thus be good translational markers of the disease. Manganese-Enhanced MRI shows a reduction of neuronal transportation in transgenic models of Alzheimer and it allows monitoring improvements induced by treatments of the disease. It is thus a promising biomarker of the pathology in animals.
Monitoring of intra-ER free Ca2+

The importance of calcium signaling in cell health and disease is the major driving force in current research of intracellular calcium homeostasis. Ca2+ release from the endoplasmic reticulum (ER) and other calcium stores seems to be the crucial factor in the activation of many cellular functions. Significant changes in ER Ca2+ content and dynamics have been implicated in the activation of the ER stress response, abnormal autophagy, and cell death which leads to a variety of pathological conditions. For example, in acute pancreatitis, an inflammatory disease of the exocrine pancreas caused primarily by bile stones or alcohol, excessive intracellular calcium overload due to Ca2+ release from internal stores followed by store operated Ca2+ entry (SOCE) leads to the premature activation of digestive proenzymes within pancreatic acinar cells. Recent data show that SOCE channel blockers are capable of substantially reducing the intracellular Ca2+ overload and subsequent cell necrosis without major alteration of ER Ca2+ content. We also demonstrate here that indirect ER measurements can be misleading and only direct intra-ER Ca2+ monitoring offers reliable conclusions. In this respect, it is essential to summarize the methods available and provide examples of direct measurements of free Ca2+ concentration [Ca2+] in the ER lumen in pancreatic acinar cells. This article is aimed at highlighting the major techniques for monitoring ER Ca2+ with reference to their advantages, limitations, and views for future improvements. WIREs Membr Transp Signal 2014,3:63–71. doi: 10.1002/wmts.106 For further resources related to this article, please visit the WIREs website. Conflict of interest: The authors have declared no conflicts of interest for this article.
The elements of technical writing

Preface Part One The Seven Principles of Technical Writing 1 Know Your Purpose and Your Writing Situation Your Purpose Your Situation Activities Chapter Assignments 2 Know Your Audience and Their Situation Concerns and Characteristics Education, Experience, and Location Attitudes Toward Purpose and Information Activities Chapter Assignments 3 Choose and Organize Your Content Around Your Purpose and Audience Researching Content Choosing Content Organizing Your Content Chronological Classification and Division Mechanism Descriptions Outlines Activities Chapter Assignments 4 Write Clearly and Precisely Paragraph for Readers Revise Sentences and Words for Readers Use Language Appropriate for Your Readers Prefer the Active Voice Use Personal Pronouns Use Action Verbs Don't Introduce Unnecessary Complication Writing and Revising with Others Activities Chapter Assignments 5 Use Good Page Design Use Default Layout Templates Cautiously Provide Headings Phrasing Headings Making Headings Noticeable and Distinctive Use Headers and Footers Choose an Appropriate Type Size and Typeface Type Size Typeface Use Lists and Informal Tables Use Emphasis Carefully Leave Ample White Space Margins Medium-Length Lines Proper Spacing Activities Chapter Assignments 6 Think Visually Getting and Showing Objects Photographs Drawings Showing Processes Showing Data Tables Graphs Principles of Tables and Graphs Considering Cultural Differences Activities Chapter Assignments 7 Write Ethically Don't Hide or Suppress Unfavorable Data Don't Exaggerate Favorable Data Don't Make False Implications Don't Plagiarize Credit Your Sources Internet Documentation Construct Ethical Graphs Don't Lie Activities Chapter Assignments Part Two The Formats of Technical Writing 8 Elements of Reports Title Page Letter of Transmittal Preface Table of Contents List of Illustrations Glossary List of Symbols Abstracts and Summaries Introduction Discussion Conclusions and Recommendations Appendixes Documentation Copyright Activities Chapter Assignments 9 Formats of Reports Instructions Introduction List of Equipment and Materials How-To Instructions Warnings Theory Glossary Analytical Reports Format Discussion Sections Proposals Unsolicited Proposals Student Proposals Progress Reports Empirical Research Reports Abstract Introduction Literature Review Materials and Methods Results Discussion Conclusions Activities Chapter Assignments 10 Formats of Correspondence Formal Elements of Letters Letterhead Date Line Inside Address Re Line Subject Line Salutation Body Complimentary Close Signature Block End Notations Continuation Page Simplified Letters Memorandums E-mail Correspondence of the Job Search Resumes Letters of Application Other Correspondence Further Information Activities Chapter Assignments Chapter Notes Index
Exploring the Consistency of the Quality Scores with Machine Learning for Next-Generation Sequencing Experiments

Background Next-generation sequencing enables massively parallel processing, allowing lower cost than the other sequencing technologies. In the subsequent analysis with the NGS data, one of the major concerns is the reliability of variant calls. Although researchers can utilize raw quality scores of variant calling, they are forced to start the further analysis without any preevaluation of the quality scores. Method We presented a machine learning approach for estimating quality scores of variant calls derived from BWA+GATK. We analyzed correlations between the quality score and these annotations, specifying informative annotations which were used as features to predict variant quality scores. To test the predictive models, we simulated 24 paired-end Illumina sequencing reads with 30x coverage base. Also, twenty-four human genome sequencing reads resulting from Illumina paired-end sequencing with at least 30x coverage were secured from the Sequence Read Archive. Results Using BWA+GATK, VCFs were derived from simulated and real sequencing reads. We observed that the prediction models learned by RFR outperformed other algorithms in both simulated and real data. The quality scores of variant calls were highly predictable from informative features of GATK Annotation Modules in the simulated human genome VCF data (R2: 96.7%, 94.4%, and 89.8% for RFR, MLR, and NNR, respectively). The robustness of the proposed data-driven models was consistently maintained in the real human genome VCF data (R2: 97.8% and 96.5% for RFR and MLR, respectively).
Intonation Units in Japanese Conversation: Syntactic, Informational and Functional Structures

This book explores how speakers of Japanese organize their messages into coherent units as they jointly and interactively construct conversational discourse. Specifically, it investigates the syntactic, informational, and functional structures of intonation units (IUs) as basic units of discourse production and information flow in spoken communication. It addresses various research topics: clause vs. phrase centrality, relationship between IUs and clauses, functions of independent NPs, preferred argument/clause structure and transitivity, interrelationship among functional components, and the role of new and interactional information in the shaping of IU syntax. Overall, it tries to elucidate not only the preferred IU structures that are typical of the way Japanese speakers talk in connected discourse, but also possible relationships between the structures and their implications. Besides three main chapters discussing the results of quantitative and qualitative analyses, it also includes an introductory chapter comprehensively covering key issues in research on information flow in spoken discourse in general. Thus the book will be useful to all students and researchers of functional linguistics and discourse analysis.
Hypothesis margin based weighting for feature selection using boosting: Theory, algorithms and applications

Feature selection (FS) is a preprocessing process aimed at identifying a small subset of highly predictive features out of a large set of raw input variables that are possibly irrelevant or redundant. It plays a fundamental role in the success of many learning tasks where high dimensionality arises as a big challenge. Many endeavors to cope with this problem have been attempted and various outstanding feature selection methods have been proposed. Recently, there has been a growing line of research in utilizing the concept of hypothesis margins to measure the quality of a set of features. However, most previous feature selection algorithms have been developed under the large hypothesis margin principles of the 1-NN algorithm, such as Simba. Little attention has been paid so far to exploiting the hypothesis margins of boosting to evaluate features. Boosting is well known to maximize the training examples’ hypothesis margins, in particular, the average margin that considers the whole margin distribution and thus include more information. In this thesis, we took an unusual approach for using boosting as an effective FS by utilizing the training examples’ mean margins. A weight criterion, termed Margin Fraction (MF), is assigned to each feature that contributes to the margin distribution combined in the final output produced by boosting. We argue that using the MF is more favorable for several reasons. First, boosting hypothesis margins have been used both for theoretical generalization bounds and as guidelines for algorithm design, and thus, a natural goal is to find learners (features) that achieve a maximum margin. Second, current boosting-based feature selection methods measure the relative importance of features based on the Confidence Ratio (CR) of the learned base hypothesis. However, while a feature may have a large CR, it will not contribute to a good overall margin unless its “conditional” margin is also large. The thesis mainly consists of two parts. In part one, we establish a rigorous theoretical and mathematical basis for the proposed weighting and selection methodology, and we describe how to extend this methodology to handle the presence of imbalanced data; by defining a new weight metric, termed AUC Margin Fraction (AMF), that characterize the quality of a set of features based on the maximized Area Under ROC curve (AUC) margin it induces during the process of learning with boosting. Based on this we design two different embedded-based FS algorithms, the SBS-MF and the SBS-AMF. We then investigate the effectiveness of the proposed methods through extensive comparisons with other algorithms using real-world data. In part two, we apply the proposed SBS-AMF method to design a real intrusion detection system (IDS) of virtual server environments utilizing only information available from the perspective of the
METHOD OF GENERATING RECOMMENDATIONS LISTS WITH CONSIDERING ACTIVITY INDEXES OF USERS IN A RECOMMENDATION SYSTEM

Предметом вивчення у статті є процес генерації списків рекомендацій для користувачів рекомендаційної системи. Метою є розробка нового методу побудови рекомендаційних систем для підвищення якості списків рекомендацій, підвищення покриття простору користувачів та покриття каталогу об’єктів за допомогою врахування інформації про показники активності користувачів; розробка гібриду даного методу з методом колаборативної фільтрації. Завдання: розробити метод побудови рекомендаційних систем на основі врахування показників активності користувачів системи, розробити програмне забезпечення для тестування даного методу, провести експерименти на розробленому програмному забезпеченні для перевірки ефективності застосування розробленого методу, визначення якості його роботи та порівняння даного методу зі стандартним методом колаборативної фільтрації. Використовуваними методами є: теорія графів, математична статистика, теорія алгоритмів, об’єктно-орієнтоване програмування. Отримані такі результати: розроблено експертно-орієнтований метод побудови рекомендаційних систем на основі врахування показників активності користувачів та розрахунку коефіцієнтів експертів, розроблено гібрид даного методу з методом колаборативної фільтрації, розроблено програмне забезпечення для реалізації даного методу та гібриду, проведено експерименти на розробленому програмному забезпеченні для тестування розробленого методу та гібриду. Висновки. Досліджено можливість використання інформації про активність користувачів у рекомендаційних системах для покращення якості списків рекомендацій. Запропоновано розрахунок коефіцієнтів експертів для доповнення до коефіцієнтів подоби у рекомендаційних системах. Розроблено експертно-орієнтований метод побудови рекомендаційних систем на основі врахування показників активності користувачів та його гібрид з колаборативною фільтрацією. Проведені на розробленому програмному забезпеченні експерименти показали, що розроблений метод суттєво підвищує такі показники роботи рекомендаційної системи, як покриття простору користувачів, покриття каталогу об’єктів, та дозволяє створювати більш якісні списки рекомендацій без суттєвих коливань точності та повноти роботи рекомендаційних систем, а в деяких випадках навіть підвищує ці показники, що залежить від особливостей вхідних даних.
Clinical features of lenvatinib for unresectable hepatocellular carcinoma in real‐world conditions: Multicenter analysis

Presently, there are no therapeutic options for unresectable hepatocellular carcinoma (u‐HCC) patients who are intolerant to sorafenib or regorafenib failure. There have been no reports with detailed clinical findings of lenvatinib (LEN), a newly developed first‐line tyrosine kinase inhibitor (TKI), obtained in real‐world practice. We aimed to elucidate the therapeutic efficacy of LEN.
OptimalCutpoints: An R Package for Selecting Optimal Cutpoints in Diagnostic Tests

Continuous diagnostic tests are often used for discriminating between healthy and diseased populations. For the clinical application of such tests, it is useful to select a cutpoint or discrimination value c that defines positive and negative test results. In general, individuals with a diagnostic test value of c or higher are classified as diseased. Several search strategies have been proposed for choosing optimal cutpoints in diagnostic tests, depending on the underlying reason for this choice. This paper introduces an R package, known as OptimalCutpoints, for selecting optimal cutpoints in diagnostic tests. It incorporates criteria that take the costs of the different diagnostic decisions into account, as well as the prevalence of the target disease and several methods based on measures of diagnostic test accuracy. Moreover, it enables optimal levels to be calculated according to levels of given (categorical) covariates. While the numerical output includes the optimal cutpoint values and associated accuracy measures with their confidence intervals, the graphical output includes the receiver operating characteristic (ROC) and predictive ROC curves. An illustration of the use of OptimalCutpoints is provided, using a real biomedical dataset.
Improved Parallel Legalization Schemes for Standard Cell Placement with Obstacles

In standard cell placement, a circuit is given consisting of cells with a standard height, (different widths) and the problem is to place the cells in the standard rows of a chip area so that no overlaps occur and some target function is optimized. The process is usually split into at least two phases. In a first pass, a global placement algorithm distributes the cells across the circuit area, while in the second step, a legalization algorithm aligns the cells to the standard rows of the power grid and alleviates any overlaps. While a few legalization schemes have been proposed in the past for the basic problem formulation, few obstacle-aware extensions exist. Furthermore, they usually provide extreme trade-offs between time performance and optimization efficiency. In this paper, we focus on the legalization step, in the presence of pre-allocated modules acting as obstacles. We extend two known algorithmic approaches, namely Tetris and Abacus, so that they become obstacle-aware. Furthermore, we propose a parallelization scheme to tackle the computational complexity. The experiments illustrate that the proposed parallelization method achieves a good scalability, while it also efficiently prunes the search space resulting in a superlinear speedup. Furthermore, this time performance comes at only a small cost (sometimes even improvement) concerning the typical optimization metrics.
Breast cancer susceptibility genes: Options for those carrying BRCA1 mutations

The discovery of the association between breast and ovarian cancer and the BRCA genes and the development of methods for genetic testing made it possible to screen women for genetic predisposition to develop hereditary breast cancer (HBC). Parallelly, prevention strategies, including clinical surgical and medical interventions become available in order to reduce cancer risk. In a meantime, we became aware of limitations of genetic testing from the aspect of BRCA gene penetrance, negative result interpretation etc. All of these, together with data that invasive prevention strategies such as prophylactic surgery demonstrate better results in risk reduction than regimens including self and clinical-examination, face BRCA mutation carriers with difficult choice for risk reduction options. Therefore, the patients at high risk of HBC can best make informed decisions when guided by a multidisciplinary genetic counseling team.