Empirical Discovery Research Articles

Synergistic Machine Learning Accelerated Discovery of Nanoporous Inorganic Crystals as Non-Absorbable Oral Drugs.

Machine learning (ML) has taken drug discovery to new heights, where effective ML training requires vast quantities of high-quality experimental data as input. Non-absorbable oral drugs (NODs) have unique safety advantage for chronic diseases due to their zero systemic exposure, but their empirical discovery is still time-consuming and costly. Here, a synergistic ML method, integrating small data-driven multi-layer unsupervised learning, in silico quantum-mechanical computations, and minimal wet-lab experiments is devised to identify the finest NODs from massive inorganic materials to achieve multi-objective function (high selectivity, large capacity, and stability). Based on this method, a NH4-form nanoporous zeolite with merlinoite (MER) framework (NH4-MER) is discovered for the treatment of hyperkalemia. In three different animal models, NH4-MER shows a superior safety and efficacy profile in reducing blood K+ without Na+ release, which is an unmet clinical need in chronic kidney disease and Gordon's syndrome. This work provides a synergistic ML method to accelerate the discovery of NODs and other shape-selective materials.

The empirical discovery of domains of assembly and communion

We consider chanting, or joint speech, which is ubiquitous, but not evenly distributed, in human activity. Taking an observational stance motivated by embodied cognitive science, we approach this topic without assumptions of the structure of persons, social formations, culture, or nature. This restrictive starting point motivates the use of a simple empirical definition of joint speech (the utterance by multiple persons of the same sounds at the same time) to allow us to induce four distinguished domains of assembly and communion among persons. These may be loosely indicated by the familiar terms of ritual, sports, protest and primary education. We use our empirical definition to induce these domains, and then consider how they might be regarded jointly. We are not aware of any social or psychological theory that would generate these four domains, and we suggest that our restricted mode of observation may be of use in the collective consideration of human patterning, without the common assumptions of cognitivism.

Forecasting Chinese provincial CO2 emissions: A universal and robust new-information-based grey model

Under China's new development philosophy, CO2 emissions forecasting is becoming more and more crucial for attaining carbon peaking and carbon neutrality targets. In light of this, this research suggests a new-information-based grey model built on cutting-edge methodologies that integrates the inventive damping accumulative generating operator, the data smoothing index, and particle swarm optimization. These three updates allow readers to anticipate the Chinese 30 provincial carbon emissions, which are characterized by insufficient information, regional heterogeneity, and complex patterns. For verification purposes, we investigate the robustness test referring to Monte-Carlo simulation and probability density analysis except as the multi-step-ahead forecasting. Extensive provincial experiments demonstrate that this proposed model considerably outperforms various prevailing competitors with remarkable universality, including grey models, artificial intelligence methods, and statistical models. Moreover, our critical empirical discovery is that this new model's performance fluctuates minimally across 30 provinces, with the average MAPE values less than 5% and 10% in the in-sample and out-of-sample periods, respectively, whereas the other benchmarks display unsteady simulation and prediction results. Furthermore, we employ the elite model to estimate Chinese 30 provincial CO2 emissions for the following three years, whose projections are endorsed by other studies and international organizations. Most importantly, our results provide insights for practitioners in formulating and implementing decarbonization policies and plans for specific provinces.

The “Social Science of Practice”: An Introductory Summary and Analysis

The “theory of practice” was developed mainly in order to rise above the common tendency to treat the subjective and the objective as an either/or binary. We might rely mainly on subjective presuppositions to arrive at our scholarly conclusions, or engage only in the compilation and accumulation of empirical evidence, but practice is unavoidably born of the interaction between the two dimensions. Precisely for this reason, we suggest that scholarly research proceed from actual practice, in order to rise above the either/or subjective/objective binary, and attend to both theoretical construction and empirical discovery, to focus deliberately on the inter-relating of the two. This article attempts to use actual scholarly practices as concrete examples to illustrate what is meant by the “social science of practice” approach to research.

Anomalous Scaling of Aeolian Sand Transport Reveals Coupling to Bed Rheology

Predicting transport rates of windblown sand is a central problem in aeolian research, with implications for climate, environmental, and planetary sciences. Though studied since the 1930s, the underlying many-body dynamics is still incompletely understood, as underscored by the recent empirical discovery of an unexpected third-root scaling in the particle-fluid density ratio. Here, by means of grain-scale simulations and analytical modeling, we elucidate how a complex coupling between grain-bed collisions and granular creep within the sand bed yields a dilatancy-enhanced bed erodibility. Our minimal saltation model robustly predicts both the observed scaling and a new undersaturated steady transport state that we confirm by simulations for rarefied atmospheres.

The model for non-Abelian field topology for the multilayer fractional quantum anomalous Hall device

From the recent empirical discovery of the quantum anomalous Hall effect (QAHE), the interaction of the particle with spin–orbit coupling (SOC) plays an essential role in the cause of the QAHE, which includes three terms: external, internal, and chiral symmetric terms. Then, the non-Abelian quantum field theory was adopted to analyze and prove the conjecture on the causes that can lead to the fractional quantum Hall effect (FQHE). The spontaneously topological chiral symmetry breaking is the main contribution to the FQHE, which also includes two terms: the hopping of sublattice and Coulomb energy by the interaction of many-body particles. More generally, this exciton possesses an intermediate characteristic between the Wannier regimes and displays a peculiar two-dimensional wavefunction in the three-dimensional FQHE states. Finally, a bilayer three-dimensional model is proposed to implement the FQHE on the lattice by incorporating ferromagnetic dopants into three-dimensional topological insulative thin films. This study theoretically predicts the FQHE on the basis of other reports that have experimentally verified the rationality of the proposed model in magnetic topological insulators.

The Mechanistic Determination of Doping Contrast from Fermi Level Pinned Surfaces in the Scanning Electron Microscope Using Energy-Filtered Imaging and Calculated Potential Distributions

Abstract Secondary electron (SE) doping contrast in the scanning electron microscope is correlated with Fermi level pinned surfaces of Si samples prepared using HF-based wet-chemical treatment or focused ion beam (FIB) micromachining en route to quantitative dopant profiling. Using energy-resolved SE imaging techniques and finite-element analyses of surface states and surface junction potentials, we clarified the surface band-bending effects post-NH4F-treatment, consistent with brighter p-contrast from degenerately doped (>1019 cm−3) regions. In general, SE spectromicroscopy scan measurements unambiguously indicated heavy suppression of patch fields, while the empirical discovery of scan frequency-modulated contrast inversion due to Chee et al. [Springer Proceedings in Physics, 120, pp. 407–410 (2008)] is ascribable to competing fixed oxide charge and dynamic charge injection phenomena (particularly at dwell times >29 μs). Leveraging numerical simulations of electric potentials and variable-voltage experimental data, the theoretical model based on amorphization damage-mediated Fermi level pinning is elucidated for Ga+ FIB-processed site-specific doping contrast on patch field-free surfaces. This work successfully argues against the notion that doping contrast ultimately or exclusively entails patch fields or adventitious metal–semiconductor contacts.

Influence of Rural Social Capital and Production Mode on the Subjective Well-Being of Farmers and Herdsmen: Empirical Discovery on Farmers and Herdsmen in Inner Mongolia.

Rural areas are crucial to the realization of sustainable development goals (SDGs). Rural social capital is indispensable for these areas to fulfil the SDGs. As China pursues rural revitalization, it is essential to achieve the sustainable development of rural areas within the agropastoral transition zone (APTZ) in northern China. The same applies to the SDGs’ realization in other APTZs across the globe. From the micro perspective of individual farmers and herdsmen, this article collected 732 microscopic datapoints through repeated rural surveys, and adopted the multivariate ordered probit model to empirically analyze how the subjective well-being of farmers and herdsmen in northern China’s APTZ was affected by the individual (person) and collective (community) layers of rural social capital. Specifically, the production mode was introduced to study the relationship between social capital and subjective well-being, and social capital was measured by a self-designed theoretical analysis framework, which covered six dimensions and two layers (person and community). It was verified that the individual social capital and collective social capital were mutually replaceable in terms of the effect on the subjective well-being of farmers and herdsmen. Additionally, this article examined the influence of different production modes on the social capital and subjective well-being of farmers and herdsmen, and discussed how the same amount of social capital contributes differently to the subjective well-being of farmers and herdsmen under different production models. The results showed that: (1) Social capital significantly promoted the subjective well-being of farmers and herdsmen, and social network was the leading contributor among the dimensions of individual social capital, while social trust was the leading contributor among the dimensions of collective social capital. By the contribution to the subjective well-being of farmers and herdsmen, the six dimensions of social capital can be ranked as social network > social trust > social participation > social standard > social fame > common vision. (2) Individual social capital and collective social capital were mutually replaceable in terms of enhancing the subjective well-being of farmers and herdsmen; when the individual social capital was insufficient, the collective social capital would exert a much greater influence on the subjective well-being of farmers and herdsmen; when the individual social capital grows, the farmers and herdsmen would depend less on collective social capital. (3) The same amount of social capital contributes differently to the subjective well-being of farmers and herdsmen under different production models; by contribution strength, the production models can be ranked as pure herdsmen (PH) > pure farmers (PF) > non-farmers/herdsmen (NFH) > farmers + herdsmen (FH). Finally, several policy suggestions were provided to improve the subjective well-being of farmers and herdsmen in APTZ. The results show that collective social capital eliminates the negative effect of individual social capital shortage on the subjective well-being of farmers and herdsmen. Thus, it is suggested to consider not only the construction of macroscopic hardware environment, but also to roll out policies and measures to improve the subjective well-being from the micro perspective of farmers and herdsmen. These suggestions are concentrated on the implementation of China’s rural revitalization strategy, and the creation of relevant institutions and cultural environments, as well as the optimization of the internal hierarchy of farmers and herdsmen’s social capital. The research results may help to promote the subjective well-being of farmers and herdsmen in rural areas within the APTZs of China and the world, and provide a reference and a path to realize SDGs in China and similar places across the globe.

Nature’s Perfection: Aristotle and Descartes on Motion and Purpose

Descartes holds that, insofar as nature is a purposeless, unthinking, extended substance, there could be no final causes in physics. Descartes’ derivation of his three laws of motion from the perfections of God thus underwrites a rejection of Aristotle’s conception of natural self-motion and teleology. Aristotle derived his conception of the purposeful action of sublunar creatures from his notion that superlunar bodies are perfect, eternal, living beings, via the thesis that circular motion is more complete or perfect than rectilinear motion. Descartes’ reduction of circular motion to rectilinear motion, achieved through his theological foundation of the laws of motion, thus marks a crucial break from Aristotle’s philosophy of nature. This paper argues that the shift from the Aristotelian conception of nature as self-moving and teleological to the Cartesian conception of nature as purposeless and inert, is not an empirical discovery but is rooted in differing conceptions of where perfection lies in nature.

From mechanism-based to data-driven approaches in materials science

A time-honored approach in theoretical materials science revolves around the search for basic mechanisms that should incorporate key feature of the phenomenon under investigation. Recent years have witnessed an explosion across areas of science of a data-driven approach fueled by recent advances in machine learning. Here we provide a brief perspective on the strengths and weaknesses of mechanism based and data-driven approaches in the context of the mechanics of materials. We discuss recent literature on dislocation dynamics, atomistic plasticity in glasses focusing on the empirical discovery of governing equations through artificial intelligence. We conclude highlighting the main open issues and suggesting possible improvements and future trajectories in the fields.

Mental Disorder and Suicide: What’s the Connection?

This paper offers a philosophical analysis of the connection between mental disorder and suicide risk. In contemporary psychiatry, it is commonly suggested that this connection is a causal connection that has been established through empirical discovery. Herein, I examine the extent to which this claim can be sustained. I argue that the connection between mental disorder and increased suicide risk is not wholly causal but is partly conceptual. This in part relates to the way suicidality is built into the definitions of some psychiatric diagnoses. It also relates to the broader normative assumption that suicidal behavior is by definition mentally disordered behavior. The above has significant epistemological implications, which I explore. I propose that the claim that suicide is connected with mental disorder cannot be justified solely by appealing to empirical evidence but also warrants a justification on conceptual and normative grounds.

Confronting Machine Learning with Financial Research

This article aims to examine the challenges and applications of machine learning for financial research. Machine learning algorithms have been developed for certain data environments that substantially differ from the one we encounter in finance. Not only do difficulties arise owing to some of the idiosyncrasies of financial markets, there is also a fundamental tension between the underlying paradigm of machine learning and the research philosophy in financial economics. Given the peculiar features of financial markets and the empirical framework within social science, various adjustments must be made to the conventional machine learning methodology. The authors discuss some of the main challenges of machine learning in finance and examine how these could be accounted for. Despite the challenges, the authors argue that machine learning could be unified with financial research to become a robust complement to the econometrician’s toolbox. Moreover, the authors discuss the various applications of machine learning in the research process, such as estimation, empirical discovery, testing, causal inference, and prediction. TOPICS:Big data/machine learning, financial crises and financial market history, portfolio theory Key Findings ▪ The authors compare machine learning to conventional quantitative research methodologies in finance. ▪ The authors discuss the idiosyncrasies of finance and the challenges that financial markets pose to machine learning methodologies. ▪ The authors examine the opportunities (and applications) that machine learning offers for financial research.

The Use of Case Study Design in Learning Management System Research: A Label of Convenience?

Case study design is often characterized as a poorly applied and understood approach to conduct research into information systems. This literature review examined 28 empirical studies of Learning Management Systems (LMS) that were self-declared as case studies. This paper’s main aim was to establish whether these studies actually applied case study techniques or simply utilized the term “case study” as a label of convenience. Alarmingly, more than one-third of the examined articles contained little or no justification for adopting a case study design and did not meet the established criteria for identification as a case study as outlined in this review. The lack of an appropriate discussion on the characteristics and applicability of the case study approach to empirical discovery brings into question the robustness of a study’s stated methodology. It is not sufficient to assume that the label “case study” by itself will portray a universal understanding of the research design of an empirical LMS study. This paper proposes that a robust description of what constitutes a case study in a LMS context will assist in ensuring that future studies on LMS (or other information systems) research that intend to adopt a case study approach, adequately present appropriate justifications for adopting this methodology. It is hoped that our definition and description of LMS case study methodology will serve to inspire further reflection on the characteristics of this robust research approach that might lead to a set of guidelines for applying this methodology to information systems research.

High-throughput analysis suggests differences in journal false discovery rate by subject area and impact factor but not open access status

BackgroundA low replication rate has been reported in some scientific areas motivating the creation of resource intensive collaborations to estimate the replication rate by repeating individual studies. The substantial resources required by these projects limits the number of studies that can be repeated and consequently the generalizability of the findings. We extend the use of a method from Jager and Leek to estimate the false discovery rate for 94 journals over a 5-year period using p values from over 30,000 abstracts enabling the study of how the false discovery rate varies by journal characteristics.ResultsWe find that the empirical false discovery rate is higher for cancer versus general medicine journals (p = 9.801E−07, 95% CI: 0.045, 0.097; adjusted mean false discovery rate cancer = 0.264 vs. general medicine = 0.194). We also find that false discovery rate is negatively associated with log journal impact factor. A two-fold decrease in journal impact factor is associated with an average increase of 0.020 in FDR (p = 2.545E−04). Conversely, we find no statistically significant evidence of a higher false discovery rate, on average, for Open Access versus closed access journals (p = 0.320, 95% CI − 0.015, 0.046, adjusted mean false discovery rate Open Access = 0.241 vs. closed access = 0.225).ConclusionsOur results identify areas of research that may need additional scrutiny and support to facilitate replicable science. Given our publicly available R code and data, others can complete a broad assessment of the empirical false discovery rate across other subject areas and characteristics of published research.

Mysticism and the Fine Structure Constant

The number $\pi/(2\cdot6^3)$, suggested as the value of the fine structure constant $\alpha$ by Werner Heisenberg in 1935, is modified by "quantizing" $\pi$. This obtains, by empirical discovery, a new number which is much closer to the current measured value of the fine structure constant and within the range of variation of the fine structure constant reported by astronomers from their observation of the spectra of distant quasars. The expression of the reciprocal of this number in base 6 arithmetic yields further evidence for the surprising connection between the number 137 and Kabbalah first noted by Gershom Scholem in the 1950s. The results are interpreted in the hermeneutic tradition of the Pauli-Jung collaboration (relating, in particular, tothe World Clock dream) and Pythagorean mysticism. Some connections of the number $137$ to the golden ratio and the Fibonacci sequence are also explored.

Exploring the collective human behavior in cascading systems: a comprehensive framework

The collective behavior describing spontaneously emerging social processes and events is ubiquitous in both physical society and online social media. The knowledge of collective behavior is critical in understanding and predicting social movements, fads, riots, and so on. However, detecting, quantifying, and modeling the collective behavior in cascading systems at large scale are seldom explored. In this paper, we examine a real-world online social media with more than 1.7 million information spreading records. We observe evident collective behavior in information cascading systems and then propose metrics to quantify the collectivity. We find that previous information cascading models cannot capture the collective behavior in the real-world data and thus never utilize it. Furthermore, we propose a comprehensive generative framework with a latent user interest layer to capture the collective behavior. Our framework accurately models the information cascades with respect to dynamics, popularity, structure, and collectivity. By leveraging the knowledge behind collective behavior, our model successfully predicts the popularity and participants of information cascades without temporal features or early stage information. Our framework may serve as a more generalized one in modeling cascading systems, and, together with empirical discovery and applications, advance our understanding of human behavior.

Against Block on attention and mental paint

ABSTRACT In two papers, Ned Block has argued that representationalists have trouble with the empirical discovery that differences in the degree of visual attention to an object can lead to a difference in how the object looks. I here respond on behalf of the representationalist. Against the argument of Block, I give a “companions-in-guilt” argument that the dilemma Block claims to find for the representationalist will, in fact, arise for anyone who accepts that attention can alter appearance in the described manner. I conclude that the facts about attention’s effects on appearance give no specific reason to reject representationalism alone. Meanwhile, I argue that Block fails to adequately motivate a key claim, the ‘phenomenal precision principle’ upon which he relies. I conclude that the representationalist has nothing to fear from.

On the Empirical Determination of a Gas–Liquid Supercritical Mesophase and its Phenomenological Definition

We respond to recent articles (Int. J. Thermophysics 39 139 2018, ibid.40 21 2019) that seek to deny the veracity of the empirical discovery and thermodynamic description of a gas–liquid supercritical mesophase. These IJT articles are misleading because they are based upon a false premise that the mesophase is a hypothetical concept. There is no “mesophase hypothesis” as wrongly stated in the title of article IJT, 40 21, 2019. Unlike the critical point continuity hypothesis of van der Waals (1873), or the universal critical point scaling hypothesis advocated by Anisimov and Sengers, and others since 1965, the supercritical mesophase is an empirical entity. If a hypothetical van der Waals fluid with the properties of a critical-state point with divergent isochoric heat capacity, and with the scaling properties of “universality” theory, were to exist, it could be used to vitiate the second law of thermodynamics. By contrast, the supercritical mesophase, discovered originally from computer experiments, is an empirically established equilibrium fluid region, of two-state systems of gas plus liquid, within a single Gibbs phase; the properties of which are determined by the laws of thermodynamics. Here, we provide a phenomenological definition of the gas–liquid supercritical mesophase that accords with the body of compelling experimental results over a 150-year period since van der Waals including modern computer experiments.

Experimental Research Methods in SociolinguisticsKatieDrager Bloomsbury Academic “Research Methods in Linguistics” series. London, UK: Bloomsbury Academic. 2018. 216 pp. PB 35.95 USD list price 23.39 GBP; HB 100 USD list price 67.50 GBP; EPUB/MOBI/PDF Ebook 32.35 USD list price 22.45 GBP.

For many of its research community, the study of sociolinguistics is quintessentially about understanding the use and variability of language in its natural social context, a context which is about as far removed as possible from what goes on in a laboratory setting. However, with the social‐indexical properties of speech and language now prominent in a great deal of theoretical debate around speech production, processing, and acquisition, many sociolinguists are now equally at home carrying out research in the laboratory as well as in the field, thereby opening up a rich seam of empirical sociolinguistic research and discovery.

Resilience and coevolution of preferential interdependent networks

We propose a new model for the study of resilience of coevolving multiplex scale-free networks. Our network model, called preferential interdependent networks, is a novel continuum over scale-free networks parameterized by their correlation $$\rho , 0 \le \rho \le 1$$ . Our failure and recovery model ties the propensity of a node, both to fail and to assist in recovery, to its importance. We show, analytically, that our network model can achieve any $$\gamma , 2 \le \gamma \le 3$$ for the exponent of the power law of the degree distribution; this is superior to existing multiplex models and allows us better fidelity in representing real-world networks. Our failure and recovery model is also a departure from the much studied cascading error model based on the giant component; it allows for surviving important nodes to send assistance to the damaged nodes to enable their recovery. This better reflects the reality of recovery in man-made networks such as social networks and infrastructure networks. Our main finding, based on simulations, is that resilient preferential interdependent networks are those in which the layers are neither completely correlated ( $$\rho = 1$$ ) nor completely uncorrelated ( $$\rho =0$$ ) but instead semi-correlated ( $$\rho \approx 0.1 - 0.3$$ ). This finding is consistent with the real-world experience where complex man-made networks typically bounce back quickly from stress. In an attempt to explain our intriguing empirical discovery, we present an argument for why semi-correlated multiplex networks can be the most resilient. Our argument can be seen as an explanation of plausibility or as an incomplete mathematical proof subject to certain technical conjectures that we make explicit.