Underlying dimensions of knowledge assessment : factor analysis of the knowledge assessment methodology data

The Knowledge Assessment Methodology (KAM) database measures variables that may be used to assess the readiness of countries for the knowledge economy and has many policy uses. Formal analysis using KAM data is faced with the problem of which variables to choose and why. Rather than make these decisions in an ad hoc manner, the authors recommend factor-analytic methods to distill the information contained in the many KAM variables into a smaller set of"factors."Their main objective is to quantify the factors for each country, and to do so in a way that allows comparisons of the factor scores over time. The authors investigate both principal components as well as true factor analytic methods, and emphasize simple structures that help provide a clear political-economic meaning of the factors, but also allow comparisons over time.Statistical&Mathematical Sciences,Econometrics,Economic Theory&Research,Education for Development (superceded),Innovation

Extracting and analyzing the warming trend in global and hemispheric temperatures

This article offers an updated and extended attribution analysis based on recently published versions of temperature and forcing datasets. It shows that both temperature and radiative forcing variables can be best represented as trend stationary processes with structural changes occurring in the slope of their trend functions and that they share a common secular trend and common breaks, largely determined by the anthropogenic radiative forcing. The common nonlinear trend is isolated, and further evidence on the possible causes of the current slowdown in warming is presented. Our analysis offers interesting results in relation to the recent literature. Changes in the anthropogenic forcings are directly responsible for the hiatus, while natural variability modes such as the Atlantic Multidecadal Oscillation, as well as new temperature adjustments, contribute to weaken the signal. In other words, natural variability and data adjustments do not explain in any way the hiatus; they simply mask its presence

FINE SCALE MAPPING OF LAURENTIAN MIXED FOREST NATURAL HABITAT COMMUNITIES USING MULTISPECTRAL NAIP AND UAV DATASETS COMBINED WITH MACHINE LEARNING METHODS

Natural habitat communities are an important element of any forest ecosystem. Mapping and monitoring Laurentian Mixed Forest natural communities using high spatial resolution imagery is vital for management and conservation purposes. This study developed integrated spatial, spectral and Machine Learning (ML) approaches for mapping complex vegetation communities. The study utilized ultra-high and high spatial resolution National Agriculture Imagery Program (NAIP) and Unmanned Aerial Vehicle (UAV) datasets, and Digital Elevation Model (DEM). Complex natural vegetation community habitats in the Laurentian Mixed Forest of the Upper Midwest. A detailed workflow is presented to effectively process UAV imageries in a dense forest environment where the acquisition of ground control points (GCPs) is extremely difficult. Statistical feature selection methods such as Joint Mutual Information Maximization (JMIM) which is not that widely used in the natural resource field and variable importance (varImp) were used to discriminate spectrally similar habitat communities. A comprehensive approach to training set delineation was implemented including the use of Principal Components Analysis (PCA), Independent Components Analysis (ICA), soils data, and expert image interpretation. The developed approach resulted in robust training sets to delineate and accurately map natural community habitats. Three ML algorithms were implemented Random Forest (RF), Support Vector Machine (SVM), and Averaged Neural Network (avNNet). RF outperformed SVM and avNNet. Overall RF accuracies across the three study sites ranged from 79.45-87.74% for NAIP and 87.31-93.74% for the UAV datasets. Different ancillary datasets including spectral enhancement and image transformation techniques (PCA and ICA), GLCM-Texture, spectral indices, and topography features (elevation, slope, and aspect) were evaluated using the JMIM and varImp feature selection methods, overall accuracy assessment, and kappa calculations. The robustness of the workflow was evaluated with three study sites which are geomorphologically unique and contain different natural habitat communities. This integrated approach is recommended for accurate natural habitat community classification in ecologically complex landscapes

Remote sensing studies and morphotectonic investigations in an arid rift setting, Baja California, Mexico

The Gulf of California and its surrounding land areas provide a classic example of recently rifted continental lithosphere. The recent tectonic history of eastern Baja California has been dominated by oblique rifting that began at ~12 Ma. Thus, extensional tectonics, bedrock lithology, long-term climatic changes, and evolving surface processes have controlled the tectono-geomorphological evolution of the eastern part of the peninsula since that time. In this study, digital elevation data from the Shuttle Radar Topography Mission (SRTM) from Baja California were corrected and enhanced by replacing artifacts with real values that were derived using a series of geostatistical techniques. The next step was to generate accurate thematic geologic maps with high resolution (15-m) for the entire eastern coast of Baja California. The main approach that we used to clearly represent all the lithological units in the investigated area was objectoriented classification based on fuzzy logic theory. The area of study was divided into twenty-two blocks; each was classified independently on the basis of its own defined membership function. Overall accuracies were 89.6 %, indicating that this approach was highly recommended over the most conventional classification techniques. The third step of this study was to assess the factors that affected the geomorphologic development along the eastern side of Baja California, where thirty-four drainage basins were extracted from a 15-m-resolution absolute digital elevation model (DEM). Thirty morphometric parameters were extracted; these parameters were then reduced using principal component analysis (PCA). Cluster analysis classification defined four major groups of basins. We extracted stream length-gradient indices, which highlight the differential rock uplift that has occurred along fault escarpments bounding the basins. Also, steepness and concavity indices were extracted for bedrock channels within the thirty-four drainage basins. The results were highly correlated with stream length-gradient indices for each basin. Nine basins, exhibiting steepness index values greater than 0.07, indicated a strong tectonic signature and possible higher uplift rates in these basins. Further, our results indicated that drainage basins in the eastern rift province of Baja California could be classified according to the dominant geomorphologic controlling factors (i.e., faultcontrolled, lithology-controlled, or hybrid basins)

Data Mining and Machine Learning in Astronomy

We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black-box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those where data mining techniques directly resulted in improved science, and important current and future directions, including probability density functions, parallel algorithms, petascale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm, and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra figures, some minor additions to the tex

Psychometrics in Behavioral Software Engineering: A Methodological Introduction with Guidelines

A meaningful and deep understanding of the human aspects of software engineering (SE) requires psychological constructs to be considered. Psychology theory can facilitate the systematic and sound development as well as the adoption of instruments (e.g., psychological tests, questionnaires) to assess these constructs. In particular, to ensure high quality, the psychometric properties of instruments need evaluation. In this paper, we provide an introduction to psychometric theory for the evaluation of measurement instruments for SE researchers. We present guidelines that enable using existing instruments and developing new ones adequately. We conducted a comprehensive review of the psychology literature framed by the Standards for Educational and Psychological Testing. We detail activities used when operationalizing new psychological constructs, such as item pooling, item review, pilot testing, item analysis, factor analysis, statistical property of items, reliability, validity, and fairness in testing and test bias. We provide an openly available example of a psychometric evaluation based on our guideline. We hope to encourage a culture change in SE research towards the adoption of established methods from psychology. To improve the quality of behavioral research in SE, studies focusing on introducing, validating, and then using psychometric instruments need to be more common.Comment: 56 pages (pp. 1-36 for the main paper, pp. 37-56 working example in the appendix), 8 figures in the main paper. Accepted for publication at ACM TOSE

Multimorbidity patterns in people with HIV

PURPOSE OF REVIEW: With the progressive aging of populations of people with HIV (PWH), multimorbidity is increasing. Multimorbidity patterns, that is groups of comorbidities that are likely to co-occur, may suggest shared causes or common risk factors. We review the literature regarding multimorbidity patterns identified with data-driven approaches and discuss the methodology and potential implications of the findings. RECENT FINDINGS: Despite the substantial heterogeneity in the methods used to identify multimorbidity patterns, patterns of mental health problems, cardiovascular diseases, metabolic disorders and musculoskeletal problems are consistently reported in the general population, with patterns of mental health problems, cardiovascular diseases or metabolic disorders commonly reported in PWH. In addition to these, patterns of lifestyle-related comorbidities, such as sexually transmitted diseases, substance use (alcohol, recreational drugs and tobacco smoking) or their complications, seem to occur among PWH. SUMMARY: Multimorbidity patterns could inform the development of appropriate guidelines for the prevention, monitoring and management of multiple comorbidities in PWH. They can also help to generate new hypotheses on the causes underlying previously known and unknown associations between comorbidities and facilitate the identification of risk factors and biomarkers for specific patterns

EFFECT OF DYSTROPHIN DEFICIENCY ON SELECTED INTRINSIC LARYNGEAL MUSCLES OF THE mdx MOUSE

The intrinsic laryngeal muscles are recognized as a highly specialized allotype of skeletal muscle. To date, much of the research examining the properties of this muscle group has been conducted on 2 primary muscles: the thyroarytenoid and posterior cricoarytenoid. Consequently, it is unknown whether the remaining intrinsic laryngeal muscles evidence this highly refined phenotype or if they retain a phenotype more similar to prototypical skeletal muscle. The purpose of this study was to further define the biologic properties of the interarytenoid (IA) and cricothyroid (CT) muscles of the larynx using the dystrophin deficient mdx mouse model. Previous work in this model has demonstrated sparing of select craniofacial muscles in the disease. Interestingly, a vast body of literature also supports the uniqueness of these spared muscles in a number of other areas including: fiber types, motor unit size, proprioceptive mechanisms, myosin isoform expression, remodeling behaviors, and sarcomeric structure. It follows, then, that muscle response to dystrophin deficiency serves as a sensitive marker of a muscles level of biological specialization and its similarity to or departure from classic limb muscle. Larynges and gastrocnemius muscles from 8 mdx and 8 C57BL control mice were examined histologically for typical markers of dystrophinopathy. Immunocytochemical testing examined the distribution of dystrophin and its homolog, utrophin, in control and mdx muscles. Results demonstrated that despite the absence of dystrophin, the laryngeal muscles did not show the classic markers of disease. The mdx superior cricoarytenoid muscle (SCA; mouse counterpart of human IA) demonstrated no evidence of damage, inflammation, necrosis, or regeneration. The mdx CT evidenced subtle markers of regeneration (eg, slight increase in centrally nucleated fibers) but no evidence of degeneration. The authors concluded that the SCA was spared from the effects of dystrophin deficiency, while the CT was strongly protected. The results demonstrate that the SCA and CT muscles of the larynx possess a specialized nature that separates them from prototypical limb muscle. Information from the study offers insight into the unique biology of the laryngeal muscles and holds implications for the translational study of voice and voice disorders

Multispectral and Hyperspectral Remote Sensing Data for Mineral Exploration and Environmental Monitoring of Mined Areas

In recent decades, remote sensing technology has been incorporated in numerous mineral exploration projects in metallogenic provinces around the world. Multispectral and hyperspectral sensors play a significant role in affording unique data for mineral exploration and environmental hazard monitoring. This book covers the advances of remote sensing data processing algorithms in mineral exploration, and the technology can be used in monitoring and decision-making in relation to environmental mining hazard. This book presents state-of-the-art approaches on recent remote sensing and GIS-based mineral prospectivity modeling, offering excellent information to professional earth scientists, researchers, mineral exploration communities and mining companies

A Data Mining Methodology for Vehicle Crashworthiness Design

This study develops a systematic design methodology based on data mining theory for decision-making in the development of crashworthy vehicles. The new data mining methodology allows the exploration of a large crash simulation dataset to discover the underlying relationships among vehicle crash responses and design variables at multiple levels and to derive design rules based on the whole-vehicle safety requirements to make decisions about component-level and subcomponent-level design. The method can resolve a major issue with existing design approaches related to vehicle crashworthiness: that is, limited abilities to explore information from large datasets, which may hamper decision-making in the design processes. At the component level, two structural design approaches were implemented for detailed component design with the data mining method: namely, a dimension-based approach and a node-based approach to handle structures with regular and irregular shapes, respectively. These two approaches were used to design a thin-walled vehicular structure, the S-shaped beam, against crash loading. A large number of design alternatives were created, and their responses under loading were evaluated by finite element simulations. The design variables and computed responses formed a large design dataset. This dataset was then mined to build a decision tree. Based on the decision tree, the interrelationships among the design parameters were revealed, and design rules were generated to produce a set of good designs. After the data mining, the critical design parameters were identified and the design space was reduced, which can simplify the design process. To partially replace the expensive finite element simulations, a surrogate model was used to model the relationships between design variables and response. Four machine learning algorithms, which can be used for surrogate model development, were compared. Based on the results, Gaussian process regression was determined to be the most suitable technique in the present scenario, and an optimization process was developed to tune the algorithm’s hyperparameters, which govern the model structure and training process. To account for engineering uncertainty in the data mining method, a new decision tree for uncertain data was proposed based on the joint probability in uncertain spaces, and it was implemented to again design the S-beam structure. The findings show that the new decision tree can produce effective decision-making rules for engineering design under uncertainty. To evaluate the new approaches developed in this work, a comprehensive case study was conducted by designing a vehicle system against the frontal crash. A publicly available vehicle model was simplified and validated. Using the newly developed approaches, new component designs in this vehicle were generated and integrated back into the vehicle model so their crash behavior could be simulated. Based on the simulation results, one can conclude that the designs with the new method can outperform the original design in terms of measures of mass, intrusion and peak acceleration. Therefore, the performance of the new design methodology has been confirmed. The current study demonstrates that the new data mining method can be used in vehicle crashworthiness design, and it has the potential to be applied to other complex engineering systems with a large amount of design data