Unobservable Processes Research Articles

Digital tools as a key element of human resource management technology in the conditions of digital transformation

Aim. To identify technological features of human resource management (HRM) in the conditions of digital transformation.Objectives. To describe the main directions of HRM technology transformation in modern conditions; to define the concept of "digital tools"; to systematize the digital tools used in modern conditions; to identify promising directions of using digital tools on the way of digital trans- formation.Methods. The authors used in the research process the methods of contextual analysis, modeling terms and models, induction and deduction, synthesis and description, applied methods of comparative, system and structural analysis.Results. The outcome of the digital transformation of business is a progressive process, which in the end will radically affect the functioning of personnel. In this regard, the concept of "digital tools" of HRM is defined, their relationship with HRM technologies is revealed. It is revealed what digital tools are most often used today in Russian companies, whether there is their classification by forms of application and whether digital tools affect the technological process of HRM. The peculiarity of applying HRM tools from the possible set of tools is its scientific substantiation on the one hand and the practice of its application on the other. The basis is the orientation on the solution of a certain problem faced by the company in the current period. In order to get an effective effect, it is necessary to refuse from haphazard ap- plication of tools. A technological process of HRM should be formed, which will be based on a clearly structured sequence of actions that are interchangeable. An important element that should be paid close attention to is the understanding that the new information reality will place increased demands on the competencies of personnel. The article emphasizes that the digital transformation of business should be carried out progressively, taking into account the timely mastering of new digital competencies by employees.Conclusions. The digital transformation of business taking place today launches new, previously unobserved processes that cover all spheres of functioning of real commercial enterprises. Business can get a new quality both in the aspect of formation of management decisions on an objec- tive basis and in the framework of HRM. In this regard, it is safe to say that we are waiting for a systemic restructuring of the main business processes and, as a consequence, the introduction of new HRM tools, on the basis of which it will be possible to design digital HRM technologies.

Single-Molecule FRET at 10 MHz Count Rates.

A bottleneck in many studies utilizing single-molecule Förster resonance energy transfer is the attainable photon count rate, as it determines the temporal resolution of the experiment. As many biologically relevant processes occur on time scales that are hardly accessible with currently achievable photon count rates, there has been considerable effort to find strategies to increase the stability and brightness of fluorescent dyes. Here, we use DNA nanoantennas to drastically increase the achievable photon count rates and observe fast biomolecular dynamics in the small volume between two plasmonic nanoparticles. As a proof of concept, we observe the coupled folding and binding of two intrinsically disordered proteins, which form transient encounter complexes with lifetimes on the order of 100 μs. To test the limits of our approach, we also investigated the hybridization of a short single-stranded DNA to its complementary counterpart, revealing a transition path time of 17 μs at photon count rates of around 10 MHz, which is an order-of-magnitude improvement compared to the state of the art. Concomitantly, the photostability was increased, enabling many seconds long megahertz fluorescence time traces. Due to the modular nature of the DNA origami method, this platform can be adapted to a broad range of biomolecules, providing a promising approach to study previously unobservable ultrafast biophysical processes.

A Bayesian functional approach to test models of life course epidemiology over continuous time.

Life course epidemiology examines associations between repeated measures of risk and health outcomes across different phases of life. Empirical research, however, is often based on discrete-time models that assume that sporadic measurement occasions fully capture underlying long-term continuous processes of risk. We propose (i) the functional relevant life course model (fRLM), which treats repeated, discrete measures of risk as unobserved continuous processes, and (ii) a testing procedure to assign probabilities that the data correspond to conceptual models of life course epidemiology (critical period, sensitive period and accumulation models). The performance of the fRLM is evaluated with simulations, and the approach is illustrated with empirical applications relating body mass index (BMI) to mRNA-seq signatures of chronic kidney disease, inflammation and breast cancer. Simulations reveal that fRLM identifies the correct life course model with three to five repeated assessments of risk and 400 subjects. The empirical examples reveal that chronic kidney disease reflects a critical period process and inflammation and breast cancer likely reflect sensitive period mechanisms. The proposed fRLM treats repeated measures of risk as continuous processes and, under realistic data scenarios, the method provides accurate probabilities that the data correspond to commonly studied models of life course epidemiology. fRLM is implemented with publicly-available software.

A decade-long journey shed light on chemical composition and field determination of acid mine drainage in Brazil.

Regular monitoring of Acid Mine Drainage (AMD) is essential for understanding its extent and impact on water resources. Traditional manual sampling methods have limitations, such as limited representativeness and delayed lab analysis. High-frequency monitoring offers an alternative, enabling real-time analysis of AMD fluctuations and determination of constituents in the field. This study assessed a decade-long environmental monitoring database from watersheds impacted by coal mining in Brazil to analyze the relationships between physical properties and constituents from different water sources affected by AMD. Samples were grouped into four categories based on location and contamination levels. Results revealed that water samples from the two groups not affected by AMD exhibited near-neutral pH, low metal and sulfate concentrations, and a large portion of samples below the quantification limit for Mn and Al. In contrast, samples from groups affected by AMD displayed high metal and sulfate concentrations and acidic pH, with the highest contamination observed in the underground mine discharges group (AMD UMD). Spearman correlation analyzes between field (pH and electrical conductivity (EC)) and lab (SO42-, Fe, Mn, and Al) parameters showed no significant correlations in non-AMD-affected groups, but significant correlations in AMD-affected groups, particularly the Streams group. A regression model between sulfate and EC was identified as the best predictor for AMD, enabling continuous, low-cost monitoring of contaminated streams and providing insight into previously unobserved AMD processes, such as variations in contamination during storm events and river flushing.

Oscillatory response of Larsen C Ice Shelf flow to the calving of iceberg A-68

Abstract The collapse of several ice shelves in the Antarctic Peninsula since the late 20th century has resulted in the upstream acceleration of multiple formerly buttressed outlet glaciers, raising questions about the stability of Antarctica's remaining ice shelves and the effects their demise may have upon inland ice. Here, we use high temporal resolution Sentinel-1A/B synthetic aperture radar-derived observations to assess the velocity response of Larsen C Ice Shelf (LCIS) to the calving of colossal iceberg A-68 in 2017. We find marked oscillations in ice-shelf flow across LCIS in the months following A-68's calving, beginning with a near-ice-shelf-wide slowdown of 11.3 m yr−1 on average. While falling close to the limits of detectability, these ice-flow variations appear to have been presaged by similar oscillations in the years prior to A-68's breakaway, associated primarily with major rifting events, together reflecting potentially hitherto unobserved ice-shelf mechanical processes with important implications for ice-shelf weakening. Such ice-flow oscillations were, however, short-lived, with more recent observations suggesting a deceleration below longer-term rates of ice flow. Collectively, our observations reveal complex spatial-temporal patterns of ice-flow variability at LCIS. Similarly abrupt fluctuations may have important implications for the stability of other ice shelves, necessitating the continued, close observation of Antarctica's coastline in the future.

Inferring delays in partially observed gene regulation processes.

Cell function is regulated by gene regulatory networks (GRNs) defined by protein-mediated interaction between constituent genes. Despite advances in experimental techniques, we can still measure only a fraction of the processes that govern GRN dynamics. To infer the properties of GRNs using partial observation, unobserved sequential processes can be replaced with distributed time delays, yielding non-Markovian models. Inference methods based on the resulting model suffer from the curse of dimensionality. We develop a simulation-based Bayesian MCMC method employing an approximate likelihood for the efficient and accurate inference of GRN parameters when only some of their products are observed. We illustrate our approach using a two-step activation model: an activation signal leads to the accumulation of an unobserved regulatory protein, which triggers the expression of observed fluorescent proteins. With prior information about observed fluorescent protein synthesis, our method successfully infers the dynamics of the unobserved regulatory protein. We can estimate the delay and kinetic parameters characterizing target regulation including transcription, translation, and target searching of an unobserved protein from experimental measurements of the products of its target gene. Our method is scalable and can be used to analyze non-Markovian models with hidden components. Our code is implemented in R and is freely available with a simple example data at https://github.com/Mathbiomed/SimMCMC.

Instructors' Gestural Accuracy Affects Geology Learning in Interaction with Students' Spatial Skills.

Complex and often unobservable STEM constructs and processes are represented using a variety of representations, including iconic gestures in which the body is configured or moved to resemble a referent's spatial properties or actions. Earlier researchers have suggested links between gesturing and expertise, leading some to recommend instructional gestures. Earlier research, however, has been largely correlational; furthermore, some gestures may be made with misleading positions or movements. Using the illustrative topic of strike in structural geology, we investigated the existence and impact of inaccurate instructional gestures. In Study 1, we examined videotapes of participants who had been asked to explain strikes to another person. We observed inaccurate (non-horizontal) strike gestures not only among novices (first introduced to strike during the study itself, n = 68) but also among participants who had greater expertise in geology (n = 21). In Study 2, we randomly assigned novices (N = 167) to watch video lessons in which the instructor accompanied verbal explanations of strikes with accurate, inaccurate, or no iconic gestures and tested students' learning on a strike-mapping task. Students with low spatial-perception skills showed no impact of their gestural condition on performance. Students with high spatial-perception skills showed no advantage from accurate gestures but performed significantly worse in the inaccurate-gesture condition. Findings suggest that recommendations to use gestures during instruction should include professional development programs that reduce the occurrence of inaccurate gestures.

Global Temperature Projections from a Statistical Energy Balance Model Using Multiple Sources of Historical Data

Abstract This paper estimates a two-component energy balance model as a linear state-space system (EBM-SS model) using historical data. It is a joint model for the temperature in the mixed layer, the temperature in the deep ocean layer, and radiative forcing. The EBM-SS model allows for the modeling of nonstationarity in forcing and the incorporation of multiple data sources for the unobserved processes. We estimate the EBM-SS model using historical datasets at the global level for the period 1955–2020 by maximum likelihood. We show in the empirical estimation and in simulations that using multiple data sources for the unobserved processes reduces parameter estimation uncertainty. When fitting the EBM-SS model to six observational global mean surface temperature (GMST) anomaly series, the GMST projections under representative concentration pathway scenarios are comparable to those from Coupled Model Intercomparison Project models. The results show that a simple statistical climate model estimated on the historical period can produce GMST projections compatible with output from large-scale Earth system models. Significance Statement We develop a statistical model to understand Earth’s energy balance and how it impacts temperature across the planet’s surface and deep oceans. Unlike previous approaches, ours is fully statistical. This means that we use maximum likelihood to calculate the estimates and evaluate the level of uncertainty in the model’s parameters. By applying the model to historical data from 1955 to 2020, we are able to make predictions about the average global surface temperature that are consistent with those from the Coupled Model Intercomparison Project. Our findings support current predictions about global temperatures using a straightforward statistical climate model, grounded in historical data.

Lithium in felsic magmas: a volcanological perspective

Volcanic eruptions are unpredictable phenomena that pose a challenge to crisis management, owing to the fact that contrasted eruptive styles (explosive versus effusive) exhibited at the surface depend on unobservable deep processes occurring in the reservoir and the volcanic conduit. Constricting the behaviour of magma during ascent, and the degassing in particular, allows for a clearer understanding of the relationships between petrological and volcano monitoring signals, and hence a better description of the volcanic hazard. To this aim, lithium (Li) has been used to track magmatic and post-eruptive processes, as a geospeedometer for processes operating on short time scales due to its high mobility in silicate melts and crystals. Yet, the accurate use of Li to assess syn- and post-eruptive processes still lack complete dataset. We propose a review of our current knowledge on Li behavior, with an emphasis on felsic (andesitic to rhyolitic) magmas whose explosive behavior during volcanic eruptions is still poorly understood. We present current knowledge regarding the Li concentration and isotopic compositions, intracrystalline diffusion, and crystal-melt-fluid partition coefficients discovered in felsic magmas and primary crystals. We describe difficulties in interpreting Li data to investigate the differentiation, degassing, ascent rate, volatile fluxing, and cooling of magmas. Finally, we suggest future directions for expanding our understanding of Li behavior.

Latent factor model for multivariate functional data.

For multivariate functional data, a functional latent factor model is proposed, extending the traditional latent factor model for multivariate data. The proposed model uses unobserved stochastic processes to induce the dependence among the different functions, and thus, for a large number of functions, may provide a more parsimonious and interpretable characterization of the otherwise complex dependencies between the functions. Sufficient conditions are provided to establish the identifiability of the proposed model. The performance of the proposed model is assessed through simulation studies and an application to electroencephalographydata.

Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations

Abstract. Soil moisture is a key variable in monitoring climate and an important component of the hydrological, carbon, and energy cycles. Satellite products ameliorate the sparsity of field measurements but are inherently limited to observing the near-surface layer, while water available in the unobserved root-zone controls critical processes like plant water uptake and evapotranspiration. A variety of approaches exist for modelling root-zone soil moisture (RZSM), including approximating it from surface layer observations. While the number of available RZSM datasets is growing, they usually do not contain estimates of their uncertainty. In this paper we derive a long-term RZSM dataset (2002–2020) from the Copernicus Climate Change Service (C3S) surface soil moisture (SSM) COMBINED product via the exponential filter (EF) method. We identify the optimal value of the method's model parameter T, which controls the level of smoothing and delaying applied to the surface observations, by maximizing the correlation of RZSM estimates with field measurements from the International Soil Moisture Network (ISMN). Optimized T-parameter values were calculated for four soil depth layers (0–10, 10–40, 40–100, and 100–200 cm) and used to calculate a global RZSM dataset. The quality of this dataset is then globally evaluated against RZSM estimates of the ERA5-Land reanalysis. Results of the product comparison show satisfactory skill in all four layers, with the median Pearson correlation ranging from 0.54 in the topmost to 0.28 in the deepest soil layer. Temporally dynamic product uncertainties for each of the RZSM product layers are estimated by applying standard uncertainty propagation to SSM input data and by estimating structural uncertainties in the EF method from ISMN ground reference measurements taken at the surface and at varying depths. Uncertainty estimates were found to exhibit both realistic absolute magnitudes and temporal variations. The product described here is, to the best of our knowledge, the first global, long-term, uncertainty-characterized, and purely observation-based product for RZSM estimates up to 2 m depth.

CEBoosting: Online sparse identification of dynamical systems with regime switching by causation entropy boosting.

Regime switching is ubiquitous in many complex dynamical systems with multiscale features, chaotic behavior, and extreme events. In this paper, a causation entropy boosting (CEBoosting) strategy is developed to facilitate the detection of regime switching and the discovery of the dynamics associated with the new regime via online model identification. The causation entropy, which can be efficiently calculated, provides a logic value of each candidate function in a pre-determined library. The reversal of one or a few such causation entropy indicators associated with the model calibrated for the current regime implies the detection of regime switching. Despite the short length of each batch formed by the sequential data, the accumulated value of causation entropy corresponding to a sequence of data batches leads to a robust indicator. With the detected rectification of the model structure, the subsequent parameter estimation becomes a quadratic optimization problem, which is solved using closed analytic formulas. Using the Lorenz 96 model, it is shown that the causation entropy indicator can be efficiently calculated, and the method applies to moderately large dimensional systems. The CEBoosting algorithm is also adaptive to the situation with partial observations. It is shown via a stochastic parameterized model that the CEBoosting strategy can be combined with data assimilation to identify regime switching triggered by the unobserved latent processes. In addition, the CEBoosting method is applied to a nonlinear paradigm model for topographic mean flow interaction, demonstrating the online detection of regime switching in the presence of strong intermittency and extreme events.

Going Beyond Observable Actions

Abstract: Pauses represented in process data captured from digital learning and assessment tasks are defined as the time elapsed between two subsequent events. Pauses are used as a marker of unobservable cognitive processes, such as encoding, problem-solving, and planning, that underlie test takers’ subsequent observable actions in educational assessment. To make valid inferences about the underlying cognitive processes represented by pauses, we argue that applying a task-specific cognitive modeling approach is required. We discuss and demonstrate how to apply a task-specific, theoretical cognitive modeling approach for interpreting pauses. We believe that this approach will have value for educational researchers seeking to make valid, task-general inferences about test-taker cognition from pauses represented in process data.

Semiparametric predictive inference for failure data using first-hitting-time threshold regression.

The progression of disease for an individual can be described mathematically as a stochastic process. The individual experiences a failure event when the disease path first reaches or crosses a critical disease level. This happening defines a failure event and a first hitting time or time-to-event, both of which are important in medical contexts. When the context involves explanatory variables then there is usually an interest in incorporating regression structures into the analysis and the methodology known as threshold regression comes into play. To date, most applications of threshold regression have been based on parametric families of stochastic processes. This paper presents a semiparametric form of threshold regression that requires the stochastic process to have only one key property, namely, stationary independent increments. As this property is frequently encountered in real applications, this model has potential for use in many fields. The mathematical underpinnings of this semiparametric approach for estimation and prediction are described. The basic data element required by the model is a pair of readings representing the observed change in time and the observed change in disease level, arising from either a failure event or survival of the individual to the end of the data record. An extension is presented for applications where the underlying disease process is unobservable but component covariate processes are available to construct a surrogate disease process. Threshold regression, used in combination with a data technique called Markov decomposition, allows the methods to handle longitudinal time-to-event data by uncoupling a longitudinal record into a sequence of single records. Computational aspects of the methods are straightforward. An array of simulation experiments that verify computational feasibility and statistical inference are reported in an online supplement. Case applications based on longitudinal observational data from The Osteoarthritis Initiative (OAI) study are presented to demonstrate the methodology and its practical use.

MiCellAnnGELo: annotate microscopy time series of complex cell surfaces with 3D virtual reality.

Advances in 3D live cell microscopy are enabling high-resolution capture of previously unobserved processes. Unleashing the power of modern machine learning methods to fully benefit from these technologies is, however, frustrated by the difficulty of manually annotating 3D training data. MiCellAnnGELo virtual reality software offers an immersive environment for viewing and interacting with 4D microscopy data, including efficient tools for annotation. We present tools for labelling cell surfaces with a wide range of applications, including cell motility, endocytosis and transmembrane signalling. MiCellAnnGELo employs the cross-platform (Mac/Unix/Windows) Unity game engine and is available under the MIT licence at https://github.com/CellDynamics/MiCellAnnGELo.git, together with sample data. MiCellAnnGELo can be run in desktop mode on a 2D screen or in 3D using a standard VR headset with a compatible GPU. Supplementary data are available at Bioinformatics online.

Decoding cognition in real-time.

How can we study unobservable cognitive processes that cannot be measured directly? This has been an enduring challenge for cognitive scientists. In this essay we discuss advances in neurotechnology that could allow cognitive processes to be decoded in real-time and the implications that this may have for cognitive science and the treatment of neuropsychiatric disease.

Toward improved lumped groundwater level predictions at catchment scale: Mutual integration of water balance mechanism and deep learning method

Model development in groundwater simulation and physics informed deep learning (DL) has been advancing separately with limited integration. This study develops a general hybrid model for groundwater level (GWL) simulations, wherein water balance-based groundwater processes are embedded as physics constrained recurrent neural layers into prevalent DL architectures. Because of the automatic parameterizing process, physics-informed deep learning algorithm (DLA) equips the hybrid model with enhanced abilities of inferring geological structures of catchment and unobserved groundwater-related processes implicitly. The main purposes of this study are: 1) to explore an optimized data-driven method as alternative to complicated groundwater models; 2) to improve the awareness of hydrological knowledge of DL model for lumped GWL simulation; and 3) to explore the lumped data-driven groundwater models for cross-region applications. The 91 illustrative cases of GWL modeling across the middle eastern continental United States (CONUS) demonstrate that the hybrid model outperforms the pure DL models in terms of prediction accuracy, generality, and robustness. More specifically, the hybrid model outperforms the pure DL models in 78 % of catchments with the improved Δ NSE = 0.129. Meanwhile, the hybrid model simulates more stably with different input strategies. This study reveals the superiority and powerful simulation ability of the DL model with physical constraints, which increases trust in data-driven approaches on groundwater modellings.

Theorizing with Microhistory

Management and organization studies have long been interested in the social contexts and enduring consequences of individual and collective action. Yet empirically observing both the situated nature of actions and their ultimate consequences remains challenging. In this paper, we describe microhistory as a complementary approach to grounded and longitudinal studies that reconciles situated action in time with its broader consequences over time. Microhistorical research involves the reflexive use of dual temporal frames: a microtemporal frame suited for an empirically grounded study of individuals in time and a macrotemporal frame accounting for processes of continuity and change in social structures over time. We describe the epistemology, method, and form inherent in theorizing with microhistory and consider its potential for management researchers. Microhistory's approach, we recognize, is well-suited to several phenomena that remain elusive to contemporaneous and longitudinal studies. For example, exceptional normal actions, unintended consequences, non-linear and emergent processes, contingent process, and unobserved or inconceivable processes. Finally, we consider how microhistory's reflexive temporality offers management scholars opportunities to situate ourselves and our own theorizing in time and to account for their evolving consequences over time.

Quantum vacuum processes in the extremely intense light of relativistic plasma mirror sources

The advent of petawatt-class laser systems allows generating electromagnetic fields of unprecedented strength in a controlled environment, driving increasingly more efforts to probe yet unobserved processes through their interaction with the quantum vacuum. Still, the lowest intensity scale governing these effects lies orders of magnitude beyond foreseen capabilities, so that such endeavor is expected to remain extremely challenging. In recent years, however, plasma mirrors have emerged as a promising bridge across this gap, by enabling the conversion of intense infrared laser pulses into coherently focused Doppler harmonic beams lying in the X-UV range. In this work, we present predictions on the quantum vacuum signatures produced when such beams are focused to intensities between 1024 and 1028 W cm−2, specifically photon–photon scattering and electron–positron pair creation. These signatures are computed via the stimulated vacuum formalism, combined with a model of perfectly focused beam built from PIC-generated harmonics spectra, and implemented on state-of-the-art massively parallel numerical tools. In view of identifying experimentally favorable configurations, we also consider the coupling of the focused harmonic beam with an auxiliary optical beam, and provide comparison with other established schemes. Our results show that a single coherently focused harmonic beam can produce as much scattered photons as two infrared pulses in head-on collision, and confirm that the coupling of the harmonic beam to an auxiliary beam gives rise to significant levels of inelastic scattering, and hence holds the potential to strongly improve the attainable signal to noise ratios in experiments.

Drift-diffusion cognitive models: description, applications and perspectives (Modelos cognitivos de deriva-difusión: descripción, aplicaciones y perspectivas)

ABSTRACT Drift-diffusion models (DDM) have been proposed as a way to study unobservable cognitive processes underlying decision-making. DDM have been widely used and developed in psychology and cognitive neuroscience, but there are no general references in Spanish on the subject. The purpose of this review is to develop reference materials for Spanish-speaking researchers, professionals and scholars in the area of cognitive sciences who are interested in the experimental study of decision-making in the psychology and neuroscience fields. It will also explore the standard description of DDM, its parameters, mathematical expression and applications in cognitive tasks. Recent publications in the fields of cognitive psychology and neuroscience that use DDM are reviewed, and their prospects for new and future applications are discussed, in light of current discussions of cognitive processes.