Adjustment Approach Research Articles

Geometryczne dane niwelacyjne i niektóre błędy systematyczne w ich przetwarzaniu

The aim of this article is to illuminate some latent systematic faults in the mathematical treatment of precise levelling data. The first one is associated with the use of the average of both measurements of the height differences between the terminal benchmarks in levelling lines. Another weak point in the classical treatment of levelling data is the incomplete minimization of the impact of the spatial network configuration on the produced mean standard errors of the nodal benchmarks from the adjustment. Generating sixty random paired samples of size 1000, derived from three continuous distributions, e.g. Normal (0, 1), Uniform (-1.732, 1.732) and Gamma (1, 1), it was found that the average of two same distributed and ordered observations is very nearby to the theoretical expectation, in comparison to both observations, only in approximately 27-30% of all cases. Contrary, in other 70-74% of cases, either the “first” or the “second” observation is in close proximity to the expectation. The miss of this fact leads to a statistically significant deterioration of the final accuracy of the levelling networks. In the current study, it is also shown that the minimization of the standard errors of the adjusted normal heights of the nodal benchmarks in the Bulgarian Levelling Network 1980 cannot be achieved with the weights w=const.L-1, which are the most popular and used type of weights in the adjustment of geometric levelling networks. Finally, it is illustrated that taking into account the above marks and applying an appropriate adjustment algorithm, the mean of the standard errors of the adjusted heights of the nodal benchmarks in the analysed network is possible to be less than 1mm. The standard error of the adjusted height of the most remoted benchmark “Pushkarov”, which is 598 km far away from the datum point located in Varna, is equal to 1.40mm. The obtained from the adjustment mean standard error for the weight unit is estimated to be 0.164 mm/√km. In comparison, the adjustment mean standard error for the weight unit, but yielded by the classical approach of adjustment of the analysed network, is 1.289 mm/√km or almost 9 times higher. Despite being tedious and time-consuming, it is not on point of discarding the precise geometric levelling as a main geodetic method for solving of a couple of scientific and engineering tasks, where differences in heights have to be determined with the highest accuracy.

Modulating zero charge potential of Zn-doped N, P-containing carbon electrodes for efficient copper ion electrosorption

Nitrogen/phosphorus (N/P) doped carbon materials exhibit enhanced electro adsorption capability in capacitive deionization (CDI) applications. However, the incorporation of N/P atoms may introduce electron-withdrawing groups to the electrode surface, impacting the potential of zero charge (EPZC) and thereby influencing its electro adsorption efficiency. This research explores the modulation of electrode surface charge by varying the N/P doping composition. Specifically, it presents a Zn and N/P co-doped carbon material employed as the CDI cathode, showcasing selective adsorption of Cu2+ ions. Under a voltage of 1.2 V, the adsorption capacity achieves 250 mg/g in a 500 mg/L Cu2+ ion solution. Furthermore, it demonstrates outstanding cycling stability, maintaining an adsorption capacity of 233 mg/g even after 21 cycles. This investigation introduces an innovative approach for EPZC adjustment and offers a fresh theoretical framework for fabricating high-performance CDI electrodes.

Exploring the contribution of low‐frequency internal variability modes to global mean sea surface temperature variability based on large ensembles

AbstractBased on three large ensemble (LE) historical simulations, this study explored the contribution of low‐frequency internal variability (IV) modes to global mean sea surface temperature anomaly (GmSST) during 1920–2010. The dominant contributions of low‐frequency IV modes to GmSST in the three large ensembles are quite different. The low‐frequency IV modes in the Indo‐Pacific Ocean (i.e., IPO and IOBW) dominate the low‐frequency GmSST signal for the CESM2 and MPI LEs (the ensemble median contribution value reaches 70%–80%), while the low‐frequency IV modes in the North Atlantic dominate for the FGOALS‐g3 LE (contribute more than 50%). CESM2 LE and MPI LE can reasonably reproduce the impact of low‐frequency modes in the Indo‐Pacific ocean as observed, but FGOALS‐g3 LE underestimates this impact and overestimates the extratropical impact (on GmSST) of the North Atlantic. After removing the external forcing, it still retains the excessive signs in low‐frequency IV modes in FGOALS‐g3 LE over the North Atlantic. Furthermore, we used a pattern adjustment approach to revise the surplus effect in the North Atlantic. After revision, the contribution of decadal IVs in the North Atlantic to GmSST is reduced by 30%. Meanwhile, the tropical contribution in the Indo‐Pacific Ocean is increased and that is closer to the observed one. This approach can be employed to revise the weak or strong IV signals in other regions or LEs, which is meaningful for reducing the uncertainty of IV signals.

How Resilient are Lucid Motivators? Endeavoring Reforms for Effects of Psycho-social Factors on Workers Health Through Concurrent Engineering

BackgroundAs the tremendous impact of extreme workloads, arduous working conditions, and disorganization disrupt humane job definitions in some industries, the need for workplace re-articulation was interfered to ameliorate psycho-social factors and suggest organizational intervention strategies. Especially for colossally wounded health-care (HC) systems, today it is now even more unrealizable to retain workforce resilience considering the immense impact of overwhelming working conditions. MethodsThis study introduces employment of concurrent engineering tools to re-design humane workplaces annihilating abatement over devoured resources. The study handles HC-workforce resilience in a pioneering motive to introduce transformation of well-known motivators and proposes solutions for retention and resilience issues grounding on HC workers’ own voice. ResultsThe proposed adjustable approach introduces integral use of focus group studies, SWARA, and QFD methods, and was practiced on a real-world case regarding Turkish HC workforce. The paper also presents widespread effects of findings by tendering generalized psycho-social rehabilitation strategies. Results confirmed the modifications of the most potent incessant motivators. Conclusion“Burn out issues” and “Challenging work” were found as the most important motivator and satisfier, respectively, to be exigently fulfilled. Corrective interventions, required resolutions, and workplace articulation connotations were arbitrated in terms of entire outcomes on four dimensions in three different planning periods considering the current status, repercussions of pandemic, and contingency of similar catastrophes. Descriptive illustrations were additionally presented to support deducted interpretations.

A Cost of Misclassification Adjustment Approach for Estimating Optimal Cut-Off Point for Classification

Classification is one of the main areas of machine learning, where the target variable is usually categorical with at least two levels. This study focuses on deducing an optimal cut-off point for continuous outcomes (e.g., predicted probabilities) resulting from binary classifiers. To achieve this aim, the study modified univariate discriminant functions by incorporating the error cost of misclassification penalties involved. By doing so, we can systematically shift the cut-off point within its measurement range till the optimal point is obtained. Extensive simulation studies were conducted to investigate the performance of the proposed method in comparison with existing classification methods under the binary logistic and Bayesian quantile regression frameworks. The simulation results indicate that logistic regression models incorporating the proposed method outperform the existing ordinary logistic regression and Bayesian regression models. We illustrate the proposed method with a practical dataset from the finance industry that assesses default status in home equity.

Consensus adjustment for multi-attribute group decision making based on cross-allocation

Consensus adjustment is crucial in group decision making (GDM), aiming to reduce discrepancies in decision makers’ (DMs’) judgments for final decision results. The consensus adjustments made by DMs are interactive rather than independent. However, few studies investigate the consensus adjustment problem from the perspective of interactions among DMs. This study first constructs a model to determine the total minimum consensus adjustment under the requirements of consensus threshold and individual-collective judgment bias. Then, a consensus adjustment approach based on cross-allocation scheme is proposed to address the inconsistency between individual and collective minimum adjustments, which concerns all DMs’ individual favorable allocation results under the total minimum consensus adjustment. The cross-allocation scheme ensures unanimous support for the same allocation result among all rational DMs, thereby eliminating the discrepancies among them. Subsequently, the optimization model-based minimum consensus judgment penalty is formulated to handle the non-cooperative behaviors of DMs. Furthermore, we show how to determine the unique consensus adjustment result for the non-cooperative DMs using the cross-allocation scheme. Therefore, our study contributes by developing a new GDM method that conducts consensus adjustment allocation by considering interactions among DMs. Finally, a case study is employed to demonstrate the specific application of the new method and discuss its efficiency. Meanwhile, comparison analysis and simulation experiments are provided.

Occupant behavioral adjustments and thermal comfort with torso and/or foot warming in two cold indoor environments

Personal warming systems have been shown to improve occupant thermal comfort in cold indoor environments. Currently, there are almost no published comprehensive studies on the examination of occupant behavioral patterns in relation to the use of personal warming systems. Thus, this study presented a human trial study to investigate the effects of torso and foot warming on thermal comfort of young female occupants in two cold indoor environments (15 °C and 17 °C), based on a water warming suit (WWS) and HYB (a WWS combined with heated socks). Besides, occupant behavior adjustments regarding water temperature setpoints for WWS and electrical heating levels for heated socks were thoroughly investigated. WWS and HYB improved occupant thermal comfort at 15 °C and 17 °C, with ≥85.7 % of ‘No Change’ OTPVs (overall thermal preference votes) observed during the last 20 min of exposure. Foot heating can significantly reduce heating demand on the upper torso, both in terms of water setting temperatures and total heating time. HYB exhibited a 25.4 % and 43.3 % shorter total duration with water setting temperatures of ≥40 °C compared to WWS at 15 °C and 17 °C, respectively. Further, occupants have employed a trial-and-error adjustment approach to determine the heating temperature/level that offers optimal thermal comfort in the cold. Lastly, the use of two PWS combinations has the potential to reduce energy consumption for indoor heating by 62.9 %, 32.1 %, and 53.7 % in cities such as San Francisco, Phoenix, and Minneapolis (baseline heating setpoint: 21.5 °C).

A Comprehensive Review and Tutorial on Confounding Adjustment Methods for Estimating Treatment Effects Using Observational Data

Controlling for confounding bias is crucial in causal inference. Causal inference using data from observational studies (e.g., electronic health records) or imperfectly randomized trials (e.g., imperfect randomization or compliance) requires accounting for confounding variables. Many different methods are currently employed to mitigate bias due to confounding. This paper provides a comprehensive review and tutorial of common estimands and confounding adjustment approaches, including outcome regression, g-computation, propensity score, and doubly robust methods. We discuss bias and precision, advantages and disadvantages, and software implementation for each method. Moreover, approaches are illustrated empirically with a reproducible case study. We conclude that different scientific questions are better addressed by certain estimands. No estimand is uniformly more appropriate. Upon selecting an estimand, decisions on which estimator can be driven by performance and available background knowledge.

A dilated convolution‐based method with time series fine tuning for data‐driven crack length estimation

AbstractThis paper presents a novel crack size estimation method based on dilated convolution and time series fine tuning using in‐situ Lamb wave. The contributions of this proposed method are twofold: (1) a dilated convolution‐based neural network which is an effective representation learning methods with larger receptive fields for periodic signals and (2) a crack length adjustment approach based on previous estimations with a time series fine tuning method. The time series fine tuning method utilizes the monotonicity of the crack length with respect to time. A new evaluation metric has been adopted, which considers prediction errors, time errors, and monotonicity errors. The proposed method outperforms the previous feature extraction‐based methods with or without time series fine tuning in this new evaluation metric.

Can stormwater runoff measurements be used for weather radar rainfall adjustment?

ABSTRACT Predicting the response to rainfall in urban hydrological applications requires accurate precipitation estimates with a high spatiotemporal resolution to reflect the natural variability of rainfall. However, installing rain gauges under nearly ideal measurement conditions is often difficult in urban areas, if not impossible. This paper demonstrates the potential of deriving rainfall measurements in urban areas and bias-adjusting weather radar rainfall measurements using stormwater runoff measurements. As a supplement to point rainfall measurements from rain gauges, the developed bias adjustment approach uses catchment runoff-rainfall estimates derived from water level measurements of a stormwater detention pond. The study shows that the bias-adjusted radar product correlates highly with rain gauge measurements in the catchment. Moreover, the presented approach enables rainfall measurements within a catchment independent of rain gauges located in the catchment, making the technique highly applicable for increasing the density of ground observations and thus improving weather radar precipitation estimates over urban areas. The method also derives the catchment-specific runoff coefficient independently of expensive flow measurements in the catchment, making the method very scalable. This paper highlights the potential of using easily achievable catchment runoff-rainfall measurements to increase the density of available ground observations and thereby improve weather radar precipitation estimates.

Virtual reality games for cognitive rehabilitation of older adults: a review of adaptive games, domains and techniques

In recent decades, the senior adults population worldwide has increased, as well as the medical conditions related to aging, such as cognitive decline. Virtual reality (VR) games are a valuable addition to conventional cognitive rehabilitation as they increase engagement to the therapy through customization, socialization, immersion, and feedback. This review, performed according to PRISMA protocol, addresses the following questions: How VR games have been used for cognitive rehabilitation?, What cognitive domains have been addressed by VR games and in which populations have these games been used?, Which features have been considered to improve engagement in VR games for cognitive rehabilitation?, How is the difficulty adjustment of exercises carried out in VR games for cognitive rehabilitation?. We found 25 scientific works related to these questions, 92% of them treating one cognitive domain at a time, despite the fact that the related literature recognizes the value of training multiple domains simultaneously. Our review indicates that, despite the existence of serious VR games for working memory training, such as those described in Flak et al. (Front Psychol 10:807, 2019. https://doi.org/10.3389/fpsyg.2019.00807), to our knowledge, there are no applications that simultaneously address multiple cognitive domains and incorporate dynamic difficulty adjustment, which are important to ensure ecological validity of therapy and therapy adherence, respectively. In addition, we found that games themselves could be used to monitor the user’s progression. It is also important to determine the impact of multiplayer interactions in the game, test difficulty adjustment approaches that use physiological variables, and define difficulty-skill relationships aligned with the user’s preferences. This paper concludes that the main barriers to implement dynamic difficulty adjustment in VR games for cognitive rehabilitation are: (i) the absence of metrics to estimate when the game offers to the players a challenge adapted their skills, and (ii) the lack of a conceptual framework that integrates relevant theories such as state of flow, cognitive load, cognitive rehabilitation, and feedback systems.

A sample average approximation-based heuristic for the stochastic production routing problem

AbstractThe Production Routing Problem under demand uncertainty is an integrated problem containing production, inventory, and distribution decisions. At the planning level, the aim is to meet retailers demand, when only the demand distribution is known in advance, while minimizing the corresponding costs. In this study, a two-stage formulation is presented in which the routing can be adjusted at short notice. In the first stage, only production decisions are made, while delivery and inventory quantities and retailer visit schedules are determined in the second stage. To handle a large number of scenarios, two solution methods based on Sample Average Approximation are introduced. Furthermore, the impact of the routing quality is explored by applying a simple heuristic and an effective metaheuristic on the routing part. It is shown that, on average, the simple heuristic within an adjustable Sample Average Approximation approach provides better objective function values than the metaheuristic within a non-adjustable approach. Also all solution approaches outperform an expected value based approach in terms of runtime and objective function value.

Gendered health effects of cooking fuel technologies in southern Ghana

The introduction of clean cooking technologies in many developing countries ensures environmental quality and improved well-being through reduction in indoor air pollution. This study examines the adoption of cooking fuel technologies and its effect on health outcomes using panel data from two districts in the Greater Accra region of Ghana. The inverse probability weighting regression adjustment (IPWRA) approach was used to examine the effect of cooking fuel technologies on health outcomes. The empirical results show that factors such as tenancy, kitchen design, assets, gender, education, access to internet and tarred roads influence the adoption of cooking fuel technologies, including clean cooking fuels. The study finds that clean cooking fuels decrease female illness incidence but not male illness incidence, with large improvement for adults. The findings suggest that policies and programs aimed at promoting clean cooking fuel technologies can significantly improve well-being of females in developing countries.

GAD-PVI: A General Accelerated Dynamic-Weight Particle-Based Variational Inference Framework

Particle-based Variational Inference (ParVI) methods approximate the target distribution by iteratively evolving finite weighted particle systems. Recent advances of ParVI methods reveal the benefits of accelerated position update strategies and dynamic weight adjustment approaches. In this paper, we propose the first ParVI framework that possesses both accelerated position update and dynamical weight adjustment simultaneously, named the General Accelerated Dynamic-Weight Particle-based Variational Inference (GAD-PVI) framework. Generally, GAD-PVI simulates the semi-Hamiltonian gradient flow on a novel Information-Fisher-Rao space, which yields an additional decrease on the local functional dissipation. GAD-PVI is compatible with different dissimilarity functionals and associated smoothing approaches under three information metrics. Experiments on both synthetic and real-world data demonstrate the faster convergence and reduced approximation error of GAD-PVI methods over the state-of-the-art.

Uncovering the Effect of SARS-CoV-2 on Liver Metabolism via Genome-Scale Metabolic Modeling for Reprogramming and Therapeutic Strategies.

Genome-scale metabolic models (GEMs) are promising computational tools that contribute to elucidating host-virus interactions at the system level and developing therapeutic strategies against viral infection. In this study, the effect of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on liver metabolism was investigated using integrated GEMs of human hepatocytes and SARS-CoV-2. They were generated for uninfected and infected hepatocytes using transcriptome data. Reporter metabolite analysis resulted in significant transcriptional changes around several metabolites involved in xenobiotics, drugs, arachidonic acid, and leukotriene metabolisms due to SARS-CoV-2 infection. Flux balance analysis and minimization of metabolic adjustment approaches unraveled possible virus-induced hepatocellular reprogramming in fatty acid, glycerophospholipid, sphingolipid cholesterol, and folate metabolisms, bile acid biosynthesis, and carnitine shuttle among others. Reaction knockout analysis provided critical reactions in glycolysis, oxidative phosphorylation, purine metabolism, and reactive oxygen species detoxification subsystems. Computational analysis also showed that administration of dopamine, glucosamine, D-xylose, cysteine, and (R)-3-hydroxybutanoate contributes to alleviating viral infection. In essence, the reconstructed host-virus GEM helps us understand metabolic programming and develop therapeutic strategies to battle SARS-CoV-2.

Integration of rupture directivity models for the US National Seismic Hazard Model

Several rupture directivity models (DMs) have been developed in recent years to describe the near-source spatial variations in ground-motion amplitudes related to propagation of rupture along the fault. We recently organized an effort toward incorporating these directivity effects into the US Geological Survey (USGS) National Seismic Hazard Model (NSHM), by first evaluating the community’s work and potential methods to implement directivity adjustments into probabilistic seismic hazard analysis (PSHA). Guided by this evaluation and comparison among the considered DMs, we selected an approach that can be readily implemented into the USGS hazard software, which provides an azimuthally varying adjustment to the median ground motion and its aleatory variability. This method allows assessment of the impact on hazard levels and provides a platform to test the DM amplification predictions using a generalized coordinate system, necessary for consistent calculation of source-to-site distance terms for complex ruptures. We give examples of the directivity-related impact on hazard, progressing from a simple, hypothetical rupture, to more complex fault systems, composed of multiple rupture segments and sources. The directivity adjustments were constrained to strike–slip faulting, where DMs have good agreement. We find that rupture directivity adjustments using a simple median and aleatory adjustment approach can affect hazard both from a site-specific perspective and on a regional scale, increasing ground motions off the end of the fault trace up to 30%–40% and potentially reducing it for sites along strike. Statewide hazard maps of California show that the change in shaking along major faults can be a factor to consider for assessing long-period [Formula: see text] near-source effects within the USGS NSHM going forward, reaching up to 10%–20%. Finally, we suggest consideration of minimum parameter ranges and baseline requirements as future DMs are developed to minimize single approach adaptations to enable more consistent application within both ground motion and hazard studies.

A new approach for drought index adjustment to clay-shrinkage-induced subsidence over France: advantages of the interactive leaf area index

Abstract. Clay shrinkage, which consists of a reduction in the volume of clay soils during dry periods, can affect buildings and cause subsidence damage. In France, losses due to subsidence are estimated at more than EUR 16 billion for the period 1989–2021 (CCR, 2021) and are expected to increase under the effect of climate warming. This work aims to improve the current understanding of the conditions triggering subsidence by proposing an innovative drought index. We use a daily soil wetness index (SWI) to develop a new annual drought index that can be related to subsidence damage. The SWI is derived from simulations of soil moisture profiles from the interactions between soil–biosphere–atmosphere (ISBA) land surface model developed by Météo-France. The ability of the drought index to correlate with insurance claim data is assessed by calculating the Kendall rank correlation over 20 municipalities in France. The insurance data, aggregated by year and municipality, are provided by the Caisse Centrale de Réassurance (CCR). A total of 1200 configurations of the drought index are considered. They are generated by combining different calculation methods, ISBA simulation settings, soil model layers, and drought percentile thresholds. The analysis includes a comparison with the independent claim data of six additional municipalities and with a record of official “CatNat” (the French national natural disaster compensation scheme) decrees, useful for the analysis. The best results are obtained for drought magnitudes based on SWI values of the 0.8-to-1.0 m deep soil layer, an ISBA simulation with interactive leaf area index (LAI), and consideration of low drought SWI percentile thresholds. Comparison with claim data shows that drought magnitude is able to identify subsidence events while being spatially consistent. This drought magnitude index provides more insight into subsidence triggers while benefiting from advanced land surface modeling schemes (interactive LAI, multilayer soil). This work paves the way for more reliable damage estimates.

Categorisation of continuous covariates for stratified randomisation: How should we adjust?

To obtain valid inference following stratified randomisation, treatment effects should be estimated with adjustment for stratification variables. Stratification sometimes requires categorisation of a continuous prognostic variable (eg, age), which raises the question: should adjustment be based on randomisation categories or underlying continuous values? In practice, adjustment for randomisation categories is more common. We reviewed trials published in general medical journals and found none of the 32 trials that stratified randomisation based on a continuous variable adjusted for continuous values in the primary analysis. Using data simulation, this article evaluates the performance of different adjustment strategies for continuous and binary outcomes where the covariate-outcome relationship (via the link function) was either linear or non-linear. Given the utility of covariate adjustment for addressing missing data, we also considered settings with complete or missing outcome data. Analysis methods included linear or logistic regression with no adjustment for the stratification variable, adjustment for randomisation categories, or adjustment for continuous values assuming a linear covariate-outcome relationship or allowing for non-linearity using fractional polynomials or restricted cubic splines. Unadjusted analysis performed poorly throughout. Adjustment approaches that misspecified the underlying covariate-outcome relationship were less powerful and, alarmingly, biased in settings where the stratification variable predicted missing outcome data. Adjustment for randomisation categories tends to involve the highest degree of misspecification, and so should be avoided in practice. To guard against misspecification, we recommend use of flexible approaches such as fractional polynomials and restricted cubic splines when adjusting for continuous stratification variables in randomised trials.

Statistical inference for diagnostic test accuracy studies with multiple comparisons.

Diagnostic accuracy studies assess the sensitivity and specificity of a new index test in relation to an established comparator or the reference standard. The development and selection of the index test are usually assumed to be conducted prior to the accuracy study. In practice, this is often violated, for instance, if the choice of the (apparently) best biomarker, model or cutpoint is based on the same data that is used later for validation purposes. In this work, we investigate several multiple comparison procedures which provide family-wise error rate control for the emerging multiple testing problem. Due to the nature of the co-primary hypothesis problem, conventional approaches for multiplicity adjustment are too conservative for the specific problem and thus need to be adapted. In an extensive simulation study, five multiple comparison procedures are compared with regard to statistical error rates in least-favourable and realistic scenarios. This covers parametric and non-parametric methods and one Bayesian approach. All methods have been implemented in the new open-source R package cases which allows us to reproduce all simulation results. Based on our numerical results, we conclude that the parametric approaches (maxT and Bonferroni) are easy to apply but can have inflated type I error rates for small sample sizes. The two investigated Bootstrap procedures, in particular the so-called pairs Bootstrap, allow for a family-wise error rate control in finite samples and in addition have a competitive statistical power.

A Dual Population Collaborative Harmony Search Algorithm with Adaptive Population Size for the System Reliability-Redundancy Allocation Problems

Abstract Aiming at the problem that the diversity of the current double population algorithm with dynamic population size reduction cannot be guaranteed in real time in iteration and is easy to fall into local optimum, this study presents a dual population collaborative harmony search algorithm with adaptive population size (DPCHS). Firstly, we propose a dual population algorithm framework for improving the algorithm global search capability. Within this framework, the guidance selection strategy and information interaction mechanism are integrated to strengthen the competition and cooperation among populations, and achieving a good balance between exploration and exploitation. A population state assessment method is designed to monitor population changes in real-time for enhancing population real-time self-regulation. Additionally, population size adjustment approach is designed to adopted to effectively streamline population resources and improve population quality. Comprehensive experiment results demonstrate that DPCHS effectively addresses system reliability-redundancy allocation problems with superior performance and robust convergence compared to other HS variants and algorithms from different categories.