Spectrum Of Metrics Research Articles

An Analysis of Protein Crystals Grown under Microgravity Conditions

Microgravity has been shown to be an excellent tool for protein crystal formation. A retrospective analysis of all publicly available crystallization data, including many that have not yet been published, clearly demonstrates the value of the microgravity environment for producing superior protein crystals. The parameters in the database (the Butler Microgravity Protein Crystal Database, BμCDB) that were evaluated pertain to both crystal morphology and diffraction quality. Success metrics were determined as improvements in size, definition, uniformity, mosaicity, diffraction quality, resolution limits, and B factor. The proteins in the databases were evaluated by molecular weight, protein type, the number of subunits, space group, and Mattew’s Coefficient. Compared to ground experiments, crystals grown in a microgravity environment continue to show improvement across all metrics evaluated. General trends as well as numerical differences are included in the assessment of the BμCDB. The microgravity environment improves crystal formation across a spectrum of metrics and the datasets utilized for this investigation are excellent tools for this evaluation.

Optimizing self-compacting mortars with fillers from sustainable industrial by-products: evaluation of durability parameters

This study investigates the transformative potential of repurposing non-biodegradable industrial by-products, specifically glass, brick, and sanitary ceramic waste, as alternative fillers for self-compacting mortars (SCM). Positioned within the framework of sustainability and enhanced performance, we conduct an in-depth comparative analysis against traditional limestone fillers to ascertain the efficacy of these unconventional materials. Employing a comprehensive methodology, we conduct spreading tests, evaluate heat of hydration, and assess mechanical resistance. Additionally, we delve into key durability parameters, including water-accessible porosity and capillarity, to comprehensively understand the nuanced effects of diverse fillers on the characteristics of the resulting self-compacting mortars. The experimental timeline unfolds through a series of assessments, measuring compressive and tensile strengths at strategic intervals - 2, 7, 28, 90, 270, and 365 days post-application. After 270 days of maturation, our study rigorously examines durability parameters. The findings unequivocally reveal a significant enhancement in SCM performance when incorporating glass, brick, and sanitary ceramic waste as fillers, outperforming conventional limestone fillers. Of notable significance is the consistent superiority of ceramic fillers across a spectrum of metrics. This research significantly contributes to the understanding of sustainable repurposing of industrial by-products in construction. Moreover, it highlights the pivotal role played by ceramic fillers in elevating rheological, mechanical, and durability attributes of self-compacting mortars. Beyond its immediate implications, this study opens new avenues for environmentally responsible and economically viable construction materials, promising further advancements and innovation in the field.

From Policy to Impact: Advancing Economic Development and Tackling Social Inequities in Central and Eastern Europe

This study challenges the traditional reliance on GDP as the sole indicator of the success of the EU’s cohesion policy, aligning with the evolving academic discourse that calls for a broader spectrum of metrics incorporating social factors. The research aims to assess the impact of cohesion on economic performance and social progress at the regional level in Central and Eastern European countries, using regression analysis on panel data. Inspired by the call to move beyond GDP-focused assessments, this research re-evaluates cohesion policy within an expanded framework that prioritizes economic and social dimensions. Specifically, it addresses the escalating concerns of income disparity and poverty in Central and Eastern European nations. Utilizing panel data regression models, this study scrutinizes data from 2007 to 2018, covering two recent programming periods, to offer a comprehensive, multifaceted analysis of the impact of cohesion policy. It underscores the policy’s dual role in spurring economic growth and fostering social progress, particularly in mitigating income inequality and reducing poverty. The findings reveal that cohesion policies positively affect both economic performance and social progress, with notable impacts on narrowing the income gap and alleviating poverty in these regions. However, the economic benefits for poverty reduction materialize over a prolonged period, reflecting the gradual nature of policy impact and the time needed for investments to materialize. The study emphasizes the need for a long-term strategic vision in implementing cohesion policies. This includes enhanced data collection, a deeper focus on the social ramifications of policies, streamlined policy processes, capacity building, institutional strengthening, and prioritizing equitable opportunities to bridge income gaps effectively. This comprehensive approach aims to maximize the dual benefits of cohesion policies, promoting balanced economic and social progress across Central and Eastern Europe.

Efficient Approximation Framework for Attribute Recommendation

Trend analysis is a fundamental type of analytical query in online analytical processing (OLAP) systems. In trend analysis, a key step is to identify k valuable attributes whose distributions in two subsets under different predicates significantly differ for further investigation, where the difference is measured by metric functions. However, the exact solution that involves scanning all records is prohibitively expensive, particularly when handling large datasets in the era of big data. To minimize unnecessary data access, the existing state-of-the-art solution TopKAttr adopts sampling to avoid the expensive data scan. However, their solution still has two main drawbacks. Firstly, their solution is tailored only for two limited metric functions: the Earth Mover distance and Euclidean distance, and cannot be generalized to more complicated metric functions. Besides, their solution still aims to return the exact top-k answers via the sampling method, which still causes high running costs as shown in our experiment. Motivated by these limitations, we propose a general approximation framework for attribute recommendation that efficiently returns the top-k attributes with theoretical guarantees while supporting an extensive range of metric functions, such as the Kolmogorov-Smirnov test (KS-test), Chebyshev distance, the Earth Mover distance, Euclidean distance, and with the potential to more metrics. The key to our framework is a new bound estimation strategy that can be applied to a wide spectrum of metrics, as we listed above. Based on our estimation framework, we further devise an efficient approximation algorithm with theoretical guarantees to answer the top-k queries, which is widely used in attribute recommendation. Extensive experiments on four real large datasets show that our framework gains up to an order of magnitude speed-up and consistently high accuracy compared to TopKAttr, providing a promising alternative for attribute recommendation in OLAP systems.

Unveiling the impact and dual innovation of funded research

In the relentless pursuit of scientific advancement, comprehending the profound impact and innovation nature inherent in funded research projects assumes paramount significance. To illuminate this matter, I delve into the realm of research supported by the National Institutes of Health (NIH) and the National Science Foundation (NSF). The evaluative framework encompasses a spectrum of metrics, including citations by papers, patents, and Tweets, as markers of research impact. Moreover, I embrace ex-ante innovation (Novelty) and ex-post innovation (Disruption) as dual indispensable yardsticks for evaluating the innovative nature of research projects. Novelty denotes the manifestation of atypical combinations of existing knowledge, while Disruption signifies the extent of paradigm-shifting potential and the ability to exert a disruptive influence on future research endeavors. First, the analysis reveals that funded research projects manifest a conspicuously heightened impact in comparison to their non-funded counterparts. Second, I uncover a noteworthy finding: funded research demonstrates significantly higher levels of ex-ante innovation (Novelty). However, in a surprising twist, the impact of funding on ex-post innovation (Disruption) appears to be faint. Additionally, I undertake a meticulous scrutiny of the robustness of the research findings by scrutinizing patterns across years and fields. Despite the uneven distribution of NIH and NSF funded research and inconspicuous heterogeneity across fields, the patterns of the impact and dual innovation of funded research are consistent across almost all fields.

Enhancing of uniaxial compressive strength of travertine rock prediction through machine learning and multivariate analysis

Indirect methods for predicting material properties in rock engineering are vital for assessing elastic mechanical properties. Accurately predicting material properties holds significant importance in rock and geotechnical engineering, as it strongly influences decisions about the design and construction of infrastructure projects. Uniaxial compressive strength (UCS) is one of the most important elastic mechanical properties for understanding how rocks and geological formations respond to stress and deformation. However, the standard UCS test faces several challenges, including its destructive nature, high costs, time-consuming procedures, and the requirement for high-quality samples. Therefore, there is a growing demand for indirect methods to estimate UCS, which are invaluable tools for evaluating the elastic mechanical properties of materials. The study aimed to comprehensively analyze the relationships between UCS of travertine rock samples collected from the Dead Sea and Jordan Valley formations and seven different rock indices by utilizing parametric and non-parametric methods. The laboratory results indicate that the study area's travertine rock possesses high-quality and desirable properties. The results reveal that certain rock indices, such as Schmidt hammer, Leeb rebound hardness, and Point Load, strongly correlate with Uniaxial Compressive Strength (UCS). Conversely, other indices, specifically dry density, absorption, pulse velocity, and porosity, exhibit a considerably weaker or very weak relationship with UCS. The paper employs three machine learning techniques, namely the Tree model, k-nearest neighbors (KNN), and Artificial Neural Networks (ANN), to develop predictive models for rock strength. The models were trained on a dataset of rock properties and corresponding mechanical strength values. The study's results revealed that the M5 tree model is the most suitable method for predicting UCS. It demonstrates robust performance across a spectrum of metrics and boasts low prediction errors. Following the M5 tree model are the KNN, ANN, and regression methods in descending order of performance.

The energy spectrum of metrics on surfaces

Let (N,rho ) be a Riemannian manifold, S a surface of genus at least two and let f:S rightarrow N be a continuous map. We consider the energy spectrum of (N,rho ) (and f) which assigns to each point [J]in mathcal {T}(S) in the Teichmüller space of S the infimum of the Dirichlet energies of all maps (S,J)rightarrow (N,rho ) homotopic to f. We study the relation between the energy spectrum and the simple length spectrum. Our main result is that if N=S, f={{,mathrm{id},}} and rho is a metric of non-positive curvature, then the energy spectrum determines the simple length spectrum. Furthermore, we prove that the converse does not hold by exhibiting two metrics on S with equal simple length spectrum but different energy spectrum. As corollaries to our results we obtain that the set of hyperbolic metrics and the set of singular flat metrics induced by quadratic differentials satisfy energy spectrum rigidity, i.e. a metric in these sets is determined, up to isotopy, by its energy spectrum. We prove that analogous statements also hold true for Kleinian surface groups.

Cross-Adversarial Learning for Molecular Generation in Drug Design.

Molecular generation is an important but challenging task in drug design, as it requires optimization of chemical compound structures as well as many complex properties. Most of the existing methods use deep learning models to generate molecular representations. However, these methods are faced with the problems of generation validity and semantic information of labels. Considering these challenges, we propose a cross-adversarial learning method for molecular generation, CRAG for short, which integrates both the facticity of VAE-based methods and the diversity of GAN-based methods to further exploit the complex properties of Molecules. To be specific, an adversarially regularized encoder-decoder is used to transform molecules from simplified molecular input linear entry specification (SMILES) into discrete variables. Then, the discrete variables are trained to predict property and generate adversarial samples through projected gradient descent with corresponding labels. Our CRAG is trained using an adversarial pattern. Extensive experiments on two widely used benchmarks have demonstrated the effectiveness of our proposed method on a wide spectrum of metrics. We also utilize a novel metric named Novel/Sample to measure the overall generation effectiveness of models. Therefore, CRAG is promising for AI-based molecular design in various chemical applications.

All the three dimensional Lorentzian metrics admitting three Killing vectors

We obtain all the three-dimensional Lorentzian metrics which admit three Killing vectors. The classification has been made with the aid of the formalism which exploits the obstruction criteria for the Killing equations recently developed by the present authors. The current classification method does not rely on the transitivity property of the isometry group. It turns out that the Lorentzian manifold harbors a much richer spectrum of metrics with various Segre types, compared to the Riemannian case.

MalERA: An updated research agenda for characterising the reservoir and measuring transmission in malaria elimination and eradication.

This paper summarises key advances in defining the infectious reservoir for malaria and the measurement of transmission for research and programmatic use since the Malaria Eradication Research Agenda (malERA) publication in 2011. Rapid and effective progress towards elimination requires an improved understanding of the sources of transmission as well as those at risk of infection. Characterising the transmission reservoir in different settings will enable the most appropriate choice, delivery, and evaluation of interventions. Since 2011, progress has been made in a number of areas. The extent of submicroscopic and asymptomatic infections is better understood, as are the biological parameters governing transmission of sexual stage parasites. Limitations of existing transmission measures have been documented, and proof-of-concept has been established for new innovative serological and molecular methods to better characterise transmission. Finally, there now exists a concerted effort towards the use of ensemble datasets across the spectrum of metrics, from passive and active sources, to develop more accurate risk maps of transmission. These can be used to better target interventions and effectively monitor progress toward elimination. The success of interventions depends not only on the level of endemicity but also on how rapidly or recently an area has undergone changes in transmission. Improved understanding of the biology of mosquito–human and human–mosquito transmission is needed particularly in low-endemic settings, where heterogeneity of infection is pronounced and local vector ecology is variable. New and improved measures of transmission need to be operationally feasible for the malaria programmes. Outputs from these research priorities should allow the development of a set of approaches (applicable to both research and control programmes) that address the unique challenges of measuring and monitoring transmission in near-elimination settings and defining the absence of transmission.

Measuring university-based research in Europe: competing indicators and underlying assumptions

ABSTRACTModern societies have a growing need for information and numbers for governing social life. Numbers have the ability to represent a complex reality in a simplified and linear form, easily communicated. Far from being the product of a mere technical process, numbers are the result of a process that “is fundamentally social – an artifact of human action, imagination, ambition, accomplishment” (Espeland, W.N., and Stevens, M.L., 2008. A sociology of quantification. European journal of sociology, 49 (3), 401–436, p. 431). In the modern policy-making climate, numbers become key mechanisms for simplifying, classifying, comparing and evaluating. Along with this, the fields of visibility of evaluative objects, meanings and understandings (Dean, M., 2010. Governmentality. Power and rule in modern societies. 2nd ed. London: Sage) are re-framed consistently with what Clarke, J. (2004. Changing welfare, changing states. New direction in social policy. London: Sage) terms a ‘performance/evaluation nexus’ that links effort, values, purposes and self-understanding to measures and comparisons of outputs (Ball, S.J., 2012. Performativity, commodification and commitment: an I-Spy guide to the neoliberal university. British journal of educational studies, 601, 17–28). In this paper, we focus on the field of higher education (HE), where numbers, in the form of performance indicators, benchmarks and headline targets, are frequently used to strategically orient the sector towards the objectives and goals of the Bologna Process and of the overall Europe 2020 agenda (Waldow, F., 2014. From taylor to tyler to no child left behind: legitimating educational standards. Prospects, 45 (1), 49–62). We aim to offer a comparative overview of the complex spectrum of metrics, provided at the supranational level, within the field of higher education by focusing on the European Research Area (ERA) in order to map and analyse some of the crucial issues in play. A second ambition of this paper is to move from a mapping and analytical perspective to a deconstruction of a specific subset of research metrics, with the aim of challenging the ‘self-evident truths’ and the dominant conventional wisdom that define current European metrics in order to bring into question whether they contribute to restructuring the universities’ research environments, affecting research policies and procedures. Performance indicators are posited to be‘conceptual technologies’, encompassing theoretical and normative assumptions that shape the objects they aspire to measure.

Fair and Open Approach to Academic Publishing

The Journal Systemic Practice and Action Research (SPAR) aims to encourage into print authors and practitioners of systemic thinking and practice from all kinds of background. In this note we describe both the publishing world into which SPAR has emerged and the systemic and inclusive thinking behind the journal’s publishing policy. We set out our manifesto for a fair and open approach to academic publishing. “A rich and diverse set of potential bibliometric and scientometric predictors of research performance quality and importance are emerging today—from the classic metrics (publication counts, journal impact factors and individual article/author citation counts) to promising new online metrics such as download counts, hub/authority scores and growth/decay chronometrics. In and of themselves, however, metrics are circular: They need to be jointly tested and validated against what it is that they purport to measure and predict, with each metric weighted according to its contribution to their joint predictive power. The natural criterion against which to validate metrics is expert evaluation by peers; a unique opportunity to do this is offered by the 2008 UK Research Assessment Exercise, in which a full spectrum of metrics can be jointly tested, field by field, against peer rankings.” (Harnard 2008)

Validating research performance metrics against peer rankings

A rich and diverse set of potential bibliometric and scientometric predictors of research performance quality and importance are emerging today, from the classic metrics (publication counts, journal impact factors and individual article/author citation counts) to promising new online metrics such as download counts, hub/authority scores and growth/decay chronometrics. In and of themselves, however, metrics are circular: They need to be jointly tested and validated against what it is that they purport to measure and predict, with each metric weighted according to its contribution to their joint predictive power. The natural criterion against which to validate metrics is expert evaluation by peers, and a unique opportunity to do this is offered by the 2008 UK Research Assessment Exercise, in which a full spectrum of metrics can be jointly tested, field by field, against peer rankings.

An Index of Biotic Integrity (IBI) for Fish Assemblages in the Littoral Zone of Great Lakes' Areas of Concern

Karr's Index of Biotic Integrity (IBI) approach provides a biological measure of ecosystem health using a wide spectrum of metrics which can be extracted from fish catch data obtained using standardized methods. Extensive electrofishing surveys of littoral fish assemblages, conducted in three Great Lakes' Areas of Concern, provided the basis for developing a lacustrine IBI that was 12 metrics of three broad types: (i) species composition, (ii) trophic composition, and (iii) abundance and condition. In contrast with lotic IBIs where diversity and abundance metrics have mostly been used, several biomass metrics were adopted to accommodate the large size range of lentic fishes. The variability of repeated measures was low enough to allow valid testing of intertransect differences with three to five samples per transect. Comparisons among survey areas showed significant differences consistent with the varying levels of ecosystem degradation. Analyses of mean IBI values with measures of submerged vegetation density and cover by transect produced significant positive correlations. This IBI developed for the Great Lakes' littoral zone, both by design and by demonstrated correlations, integrates the effects of four main factors influencing fish assemblages and hence revealing ecosystem health: exotic fishes, water quality, physical habitat supply, and piscivore abundance.