General Model Research Articles

Groundwater potential zoning using Logistics Model Trees based novel ensemble machine learning model

In this work, the main aim is to map the potential zones of groundwater in Central Highlands (Vietnam) using a novel ensemble machine learning model, namely CG-LMT, which is a combination of two advanced techniques, namely Cascade Generalization (CG) and Logistics Model Trees (LMT). For this, a total of 501 wells data and a set of twelve affecting factors were gathered and selected to generate training and testing datasets used for building and validating the model. Validation of the models was implemented utilizing various quantitative indices, including ROC curve. Results of the present study indicated that the novel ensemble model performed well for groundwater potential mapping and modeling (AUC = 0.742), and its predictive capability is even better than a single LMT model (AUC = 0.727). Thus, the CG-LMT is a promising tool for accurately predicting potential groundwater areas. In addition, the potential map of groundwater generated from the CG-LMT model is a helpful tool for better-studying water resource management in the area.

Comparison of COSMO and ICON-LAM high-resolution numerical forecast for Romanian territory: Case studies and evaluation

This paper aims to offer the first detailed inter-comparison of the performance from the numerical weather prediction models Consortium for Small-scale Modeling (COSMO) and Icosahedral Nonhydrostatic General Circulation Model, Limited Area Mode (ICON-LAM), integrated for the Romanian territory at the horizontal resolution of 2.8 km. As the ICON numerical model is set to replace COSMO, a comparison is carried out between the operational configurations of the two models. First, two cases with severe weather (strong atmospheric instability, observed heavy precipitation, and strong wind) are analyzed: February 3-6, 2020 (heavy precipitation [snow] and very strong wind) and May 3-5, 2020 (heavy precipitation). This is followed by a statistical inter-comparison between the two models for the summer of 2020 (June, July, August). A statistical evaluation of the forecast quality from the two models is performed objectively, through statistical scores computed using surface observations from all available meteorological stations on the Romanian territory. Stratifications depending on station altitude and location are carried out, with emphasis on stations of particular interest, depending on the evolution of the synoptic situation. Following the statistical evaluation, results for the precipitation forecast do not show significant improvement in favor of either model. However, ICON-2.8 km mostly outperforms COSMO-2.8 km for surface parameters.

Improved Online Scheduling of Moldable Task Graphs under Common Speedup Models

We consider the online scheduling problem of moldable task graphs on multiprocessor systems for minimizing the overall completion time (or makespan). Moldable job scheduling has been widely studied in the literature, in particular when tasks have dependencies (i.e., task graphs) or when tasks are released on-the-fly (i.e., online). However, few studies have focused on both (i.e., online scheduling of moldable task graphs). In this article, we design a new online scheduling algorithm for this problem and derive constant competitive ratios under several common yet realistic speedup models (i.e., roofline, communication, Amdahl, and a general combination). These results improve the ones we have shown in the preliminary version of the article. We also prove, for each speedup model, a lower bound on the competitiveness of any online list scheduling algorithm that allocates processors to a task based only on the task’s parameters and not on its position in the graph. This lower bound matches exactly the competitive ratio of our algorithm for the roofline, communication, and Amdahl’s model, and is close to the ratio for the general model. Finally, we provide a lower bound on the competitive ratio of any deterministic online algorithm for the arbitrary speedup model, which is not constant but depends on the number of tasks in the longest path of the graph.

Improving access and use of climate projections for ecological research through the use of a new Python tool

Over the past decade, the use of future climate projections from the coupled model intercomparison project (CMIP) has become central in biodiversity science. Pre‐packaged datasets containing future projections of the widely used bioclimatic variables, for different times and socio‐economic pathways, have contributed immensely to the study of climate change implications for biodiversity. However, these datasets lack the flexibility to obtain projections to other target years, and the use of raw data requires coding and spatial information systems expertise. The Python tool, ‘chelsa‐cmip6', developed by Karger et al., provides the flexibility needed by allowing users to generate bioclimatic variables for the time of their choice provided the selected general circulation model and socioeconomic pathway combination exists. This is a fantastic step forward in bringing flexibility to the use of climate datasets in biodiversity and will allow for more widespread use of data provided by CMIP6. We hope it also will prompt the development of more user‐friendly tools for the study of the effects of climate change on biodiversity.

Disk‐Integrated Earth’s Outgoing Longwave Radiation Viewed From a Moon‐Based Platform: Model Simulations

AbstractA Moon‐based sensor can observe the Earth as a single point and achieve disk‐integrated measurements of outgoing longwave radiation (OLR), which significantly differs from low orbital, geostationary, and Sun–Earth L1 point platforms. In this study, a scheme of determining the disk‐integrated Earth’s OLR based on a Moon‐based platform is proposed. The observational solid angle was theoretically derived based on the Earth’s ellipsoid model and the disk‐integrated observational anisotropic factor was estimated to eliminate the effects of the Earth’s radiant anisotropy. The simulated disk‐integrated Earth's OLR obtained from a Moon‐based platform varies periodically, due to changes in the observation geometry and Earth's scene distribution within the observed Earth’s disk. Clouds, meteorological parameters, and the land cover distribution notably affect the disk‐integrated Earth’s OLR. By analyzing the disk‐integrated Earth’s OLR from a Moon‐based platform, significant variabilities were investigated. Additionally, the Earth’s shape and radiant anisotropy that affecting the disk‐integrated Earth’s OLR were estimated. In conclusion, a more realistic Earth’s shape, the latest version of the angular distribution model (ADM), and accurate land cover and meteorological datasets are needed when determining the disk‐integrated Earth’s OLR. It is expected the unique variability captured by this platform and its ability to complement traditional satellite data make it a valuable tool for studying Earth’s radiation budget and energy cycle, and contributing to diagnostic of the climate General Circulation Models (GCM) performance.

A General Model of Impact Sensitivity for Nitrogen-Rich Energetic Materials: A Combined Incremental Theory and Genetic Function Approximation Study.

In this paper, we report the first example of impact sensitivity prediction based on the genetic function approximation (GFA) as a regression method. The prediction is applicable for a wide variety of chemical families, which include nitro compounds, peroxides, nitrogen-rich salts, heterocycles, etc. Within this work, we have obtained 7 empirical models (with 27-32 basis functions), which all provide 0.80≤R2≤0.83 and 7.2 J≤RMSE≤7.8 J (for 450 training set compounds) and 0.64≤R2≤0.70 and 11.2 J≤RMSE≤12.4 J (for 170 test set compounds). The models were developed using Friedman Lack-of-Fit as a scoring function, which allows avoiding an overfitting. All the models have simple descriptors as basis functions and include linear splines. Furthermore, the applied descriptors do not require expensive calculation procedures, namely, non-empirical quantum-chemical calculations, complex iterative procedures, real space electron density analysis, etc. Most descriptors are based on structural and topological analysis and a part of them require very cheap semi-empirical PM6 calculations. The prediction takes a few minutes as an average, and most of the time is for the structure preparation and manual calculation of the descriptor "Increment", which is based on our recent incremental theory.

Scattering spectra models for physics.

Physicists routinely need probabilistic models for a number of tasks such as parameter inference or the generation of new realizations of a field. Establishing such models for highly non-Gaussian fields is a challenge, especially when the number of samples is limited. In this paper, we introduce scattering spectra models for stationary fields and we show that they provide accurate and robust statistical descriptions of a wide range of fields encountered in physics. These models are based on covariances of scattering coefficients, i.e. wavelet decomposition of a field coupled with a pointwise modulus. After introducing useful dimension reductions taking advantage of the regularity of a field under rotation and scaling, we validate these models on various multiscale physical fields and demonstrate that they reproduce standard statistics, including spatial moments up to fourth order. The scattering spectra provide us with a low-dimensional structured representation that captures key properties encountered in a wide range of physical fields. These generic models can be used for data exploration, classification, parameter inference, symmetry detection, and component separation.

Mechanism for feature learning in neural networks and backpropagation-free machine learning models.

Understanding how neural networks learn features, or relevant patterns in data, for prediction is necessary for their reliable use in technological and scientific applications. In this work, we presented a unifying mathematical mechanism, known as average gradient outer product (AGOP), that characterized feature learning in neural networks. We provided empirical evidence that AGOP captured features learned by various neural network architectures, including transformer-based language models, convolutional networks, multilayer perceptrons, and recurrent neural networks. Moreover, we demonstrated that AGOP, which is backpropagation-free, enabled feature learning in machine learning models, such as kernel machines, that a priori could not identify task-specific features. Overall, we established a fundamental mechanism that captured feature learning in neural networks and enabled feature learning in general machine learning models.

Tongue feature dataset construction and real-time detection.

Tongue diagnosis in traditional Chinese medicine (TCM) provides clinically important, objective evidence from direct observation of specific features that assist with diagnosis. However, the current interpretation of tongue features requires a significant amount of manpower and time. TCM physicians may have different interpretations of features displayed by the same tongue. An automated interpretation system that interprets tongue features would expedite the interpretation process and yield more consistent results. This study applied deep learning visualization to tongue diagnosis. After collecting tongue images and corresponding interpretation reports by TCM physicians in a single teaching hospital, various tongue features such as fissures, tooth marks, and different types of coatings were annotated manually with rectangles. These annotated data and images were used to train a deep learning object detection model. Upon completion of training, the position of each tongue feature was dynamically marked. A large high-quality manually annotated tongue feature dataset was constructed and analyzed. A detection model was trained with average precision (AP) 47.67%, 58.94%, 71.25% and 59.78% for fissures, tooth marks, thick and yellow coatings, respectively. At over 40 frames per second on a NVIDIA GeForce GTX 1060, the model was capable of detecting tongue features from any viewpoint in real time. This study constructed a tongue feature dataset and trained a deep learning object detection model to locate tongue features in real time. The model provided interpretability and intuitiveness that are often lacking in general neural network models and implies good feasibility for clinical application.

Macroprudential policies and Brexit: A welfare analysis

AbstractBrexit will have implications on financial stability and the implementation of macroprudential policies. The United Kingdom (UK) will no longer be subject to the jurisdiction of the European Systemic Risk Board. This paper studies the welfare implications of this change of regime. By means of a dynamic stochastic general equilibrium model, I compare the pre‐Brexit scenario with the new one, in which the UK sets macroprudential policy independently. I find that, after Brexit, the UK is better off by setting its own macroprudential policy without taking into account Europe's welfare as a whole.

High-resolution dynamic downscaling of historical and future climate projections over Central Asia

Climate change poses various challenges for agriculture and water management practices in Central Asia (CA). Central to these challenges are cryosphere dynamics, fragile mountain ecosystems, and ongoing natural hazards that highlight the need for robust projections of regional climate change. For the first time, dynamic downscaling was conducted in Central Asia at a spatial resolution of 5 km. This produced a regional dataset that incorporated periods between 1980 and 2000 and 2076 to 2096. Results show that dynamic downscaling significantly improves the simulation of temperature and precipitation across CA compared to General Circulation Models (GCMs) and other Regional Climate Models (RCMs) due to better representation of topography and related meteorological fields. Our analysis shows that there will be a significant warming trend in Central Asia with a projected increase of 6°C under the Shared Socioeconomic Pathway (SSP) scenario SSP5-8.5 from 2076 to 2096. Pronounced warming is detected over mountainous areas of CA from autumn to spring, which can be explained by the snow-albedo feedback. Precipitation increases are projected from winter to spring and decreases are projected from summer to autumn.

General model of nonradiative excitation energy migration on a spherical nanoparticle with attached chromophores

Theory of multistep excitation energy migration within the set of chemically identical chromophores distributed on the surface of a spherical nanoparticle is presented. The Green function solution to the master equation is expanded as a diagrammatic series. Topological reduction of the series leads to the expression for emission anisotropy decay. The solution obtained behaves very well over the whole time range and it remains accurate even for a high number of the attached chromophores. Emission anisotropy decay depends strongly not only on the number of fluorophores linked to the spherical nanoparticle but also on the ratio of critical radius to spherical nanoparticle radius, which may be crucial for optimal design of antenna-like fluorescent nanostructures. The results for mean squared excitation displacement are provided as well. Excellent quantitative agreement between the theoretical model and Monte–Carlo simulation results was found. The current model shows clear advantage over previously elaborated approach based on the Padé approximant.

Managerial Mental Accounting and Downstream Project Decisions

Project leaders are responsible for planning, controlling, and revising projects. As a project unfolds, the leader evaluates the project’s progress by comparing ongoing costs and scope to a baseline plan and considers potential revisions. We offer a general model of managerial mental accounting, which includes loss aversion, reference point updating, and narrow framing, and examine how it impacts downstream decisions. Our model predicts insufficient adjustments of project scope and cost at revision, resulting in reduced financial profit. We show that the choice of measure to quantify the project progress—planned, actual, or earned—affects the updating of reference points, and hence the downstream decisions. Thus, progress measures could be wisely employed to mitigate insufficient adjustments. It turns out that measuring progress via planned scope is often advantageous, whereas utilizing earned value for cost is never advisable. This paper was accepted by David Simchi-Levi, behavioral economics and decision analysis. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2021.02929 .

SparseRNAfolD: optimized sparse RNA pseudoknot-free folding with dangle consideration

MotivationComputational RNA secondary structure prediction by free energy minimization is indispensable for analyzing structural RNAs and their interactions. These methods find the structure with the minimum free energy (MFE) among exponentially many possible structures and have a restrictive time and space complexity (O(n3)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^3)$$\\end{document} time and O(n2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^2)$$\\end{document} space for pseudoknot-free structures) for longer RNA sequences. Furthermore, accurate free energy calculations, including dangle contributions can be difficult and costly to implement, particularly when optimizing for time and space requirements.ResultsHere we introduce a fast and efficient sparsified MFE pseudoknot-free structure prediction algorithm, SparseRNAFolD, that utilizes an accurate energy model that accounts for dangle contributions. While the sparsification technique was previously employed to improve the time and space complexity of a pseudoknot-free structure prediction method with a realistic energy model, SparseMFEFold, it was not extended to include dangle contributions due to the complexity of computation. This may come at the cost of prediction accuracy. In this work, we compare three different sparsified implementations for dangle contributions and provide pros and cons of each method. As well, we compare our algorithm to LinearFold, a linear time and space algorithm, where we find that in practice, SparseRNAFolD has lower memory consumption across all lengths of sequence and a faster time for lengths up to 1000 bases.ConclusionOur SparseRNAFolD algorithm is an MFE-based algorithm that guarantees optimality of result and employs the most general energy model, including dangle contributions. We provide a basis for applying dangles to sparsified recursion in a pseudoknot-free model that has the potential to be extended to pseudoknots.

The global challenge of reducing mercury contamination from artisanal and small-scale gold mining (ASGM): evaluating solutions using generic theories of change

Mercury contamination from artisanal and small-scale gold mining (ASGM) currently accounts for 37% of the global total, often affecting tropical regions where regulations, if they exist, are often poorly enforced. Ingestion by people and other animals damages the nervous, reproductive, and cognitive systems. Despite the efforts of many organizations and governments to curb mercury releases from ASGM, it is increasing globally. There are many possible interventions, all with significant complexity and cost. Therefore, we recommend taking an established systematic approach to articulate the current situation and construct theories of change (ToC) for different possible interventions for any government or organization trying to solve this problem. Here we present a high-level situation analysis and generic ToC to support a more coordinated approach that explicitly builds upon previous experience to identify organization- and situation-appropriate engagement on this issue. We then illustrate the use of these generic models to construct a specific ToC with a policy-focused entry point. This includes interventions through (1) engagement with the global Minamata Convention on Mercury; (2) support for existing national laws and policies connected to ASGM and mercury contamination; and (3) engagement of indigenous people and local communities with governments to meet the governments’ legal obligations. By methodically articulating assumptions about interventions, connections among actions, and desired outcomes, it is possible to create a more effective approach that will encourage more coordination and cooperation among governments and other practitioners to maximize their investments and support broad environmental and socio-political outcomes necessary to address this pernicious problem.

Reliability and validity of the PROMIS-29 health profile in Ankylosing Spondylitis patients: A cross-sectional study.

The Patient-Reported Outcomes Measurement Information System 29-Item Health Profile (PROMIS-29) is a generic measure of health-related quality of life that is not well-studied in Ankylosing Spondylitis (AS) patients. Our objective was to investigate the reliability and validity of the PROMIS-29 in AS. About 169 consecutive AS patients were enrolled from 2017 to 2022 with 167/169 patients fully completing the PROMIS-29 in this cross-sectional study. Test-retest reliability and internal consistency was assessed using intraclass correlation coefficients (ICC) and Cronbach alpha, respectively. We studied structural validity with confirmatory factor analysis (CFA) of our hypothesized and general population models. We evaluated model fit by Chi-squared goodness-of-fit-test (χ2), comparative fit index, and root mean square error of approximation. A χ2 test was used to compare nested models. PROMIS-29 convergent validity was studied by Spearman correlation coefficients with AS-legacy measures. PROMIS-29 domains showed good test-retest reliability (intraclass correlation coefficients (ICC) > 0.7) and excellent internal consistency with Cronbach alpha > 0.9 in all subscales. CFA of only the general population model met our model fit cutoffs (χ2 goodness-of-fit P-value of 0.21, comparative fit index of 0.99, and root mean square error of approximation of 0.05). Furthermore, a nested χ2 test was not significantly different between our hypothesized (full) and general (reduced) model [χ2 (1) = 0.754, P > .38]. AS legacy measures showed a strong correlation (rho > |0.7|) with the extracted physical health factor. The PROMIS-29 demonstrated good reliability and construct validity in AS patients with the general population model. Further study is required to determine its clinical and research utility in AS patients.

Developing a General Daily Lake Evaporation Model and Demonstrating Its Application in the State of Texas

AbstractOpen water evaporation, which often consumes a large fraction of annual storage (especially in arid and semi‐arid regions), is a controlling variable for modern water resource management. Developing a daily reservoir evaporation data set is necessary for reservoir operations to consider the influence of evaporation in a timely manner. However, over the past few decades, the quantification of reservoir evaporation has primarily relied on monthly Class A pan evaporation observations, which might lead to largely biased estimations because they do not incorporate the effects of heat storage and fetch. In this study, we developed a general Daily Lake Evaporation Model (DLEM) based on Penman's equation combined with daily atmospheric reanalysis data sets, to improve the validity and frequency of reservoir evaporation monitoring. Compared with daily evaporation estimates from eddy covariance systems, the DLEM showed good quality and reliability, with R2 values ranging from 0.50 to 0.73 and RMSE values ranging from 1.13 to 1.97 mm day−1 in four different sites. By applying DLEM to all 188 major reservoirs in Texas, we generated a long‐term data set of (1 January 1981–31 December 2021) daily evaporation rates. The results reveal a clear geographic distribution and strong seasonality of evaporation throughout Texas, where the mean evaporation rate is highest during July, with 6.85 mm day−1. Trend analysis indicates that the annual average evaporation rate has significantly increased since 1981 at a rate of 0.076 mm day−1 decade−1. This study provides sufficient data support for water resource operations and management, and regional water planning.

Wave-based signal-to-noise enhancement and the shape of cochlear filters

Cochlear transfer functions measured at one location—a.k.a. “cochlear filters”—are characterized by a steep roll-off as the frequency increases above the local best (responding) frequency (BF). The functional role of the sharp “cut-off” is not well understood, although it was previously hypothesized that it plays a major role in encoding sound frequency. Less hypothetical in nature, our work elucidates the role of the steep cut-off in improving signal detection in cochlear-like amplification strategies. We study signal amplification in generic active gain media models and physics-based cochlear models as well. We demonstrate that the optimal strategy for boosting the signal-to-noise ratio (SNR) at a given location “x” requires high gain basal to “x,” followed by rapid attenuation (i.e., sharp cut-off) beyond it. This strategy of amplification followed by a sharp cut-off is precisely how the cochlea processes traveling waves of given frequencies; it boils down to a peculiar form of spatial filtering where waves coming from the “signal side” are amplified, while waves coming from the direction where there is noise but no signal are squelched. This noise “squelching” action manifests in the cochlear filters as the steep high-frequency roll-off.

Access to primary eye care for people living with dementia: a call to action for primary care practitioners to 'think vision'.

Access to allied health services offers significant benefits for people living with dementia, yet access is currently fragmented and inconsistent. The 2023-2024 budget allocated AU$445million to further enable general practice-led, multidisciplinary teams, with integrated care located within practices, including employment of allied health professionals. Such team care models are recognised by The Royal Australian College of General Practitioners as vital to delivery of high-quality care for older adults. They are especially relevant for over 250,000 Australians who live with dementia in the community. However, not all allied health professionals are currently based within general practices. Future, sustainable general practice-led models of multidisciplinary care that connect patients with external allied health providers could be considered for a comprehensive and collaborative approach to care. Our focus is on people living with dementia, who are at greater risk of preventable vision impairment. Poor vision and/or ocular health can be detected and managed through regular eye examinations, which are predominantly delivered by community-based optometrists in Australia, in a primary care capacity. However, people living with dementia are also less likely to have regular eye examinations. In this paper, we highlight the value of ensuring access to primary eye care services as part of post-diagnosis dementia care. We illustrate the important role of primary care practitioners in building and sustaining connections with allied health professions, like optometry, through effective referral and interprofessional communication systems. This can help break down access barriers to dementia-friendly eye care, through promoting the importance of regular eye tests for people living with dementia.

POMDP-BCI: A Benchmark of (Re)Active BCI Using POMDP to Issue Commands.

Past research in Brain-Computer Interfaces (BCI) have presented different decoding algorithms for different modalities. Meanwhile, highly specific decision making processes have been developed for some of these modalities, while others lack such a component in their classic pipeline. The present work proposes a model based on Partially Observable Markov Decission Process (POMDP) that works as a high-level decision making framework for three different active/reactive BCI modalities. We tested our approach on three different BCI modalities using publicly available datasets. We compared the general POMDP model as a decision making process with state of the art methods for each BCI modality. Accuracy, false positive (FP) trials, no-action (NA) trials and average decision time are presented as metrics. Our results show how the presented POMDP models achieve comparable or better performance to the presented baseline methods, while being usable for the three proposed experiments without significant changes. Crucially, it offers the possibility of taking no-action (NA) when the decoding does not perform well. The present work implements a flexible POMDP model that acts as a sequential decision framework for BCI systems that lack such a component, and perform comparably to those that include it. We believe the proposed POMDP framework provides several interesting properties for future BCI developments, mainly the generalizability to any BCI modality and the possible integration of other physiological or brain data pipelines under a unified decision-making framework.