Robust Alternatives Research Articles

Recent Advances and Opportunities of Eco-Friendly Ternary Copper Halides: A New Superstar in Optoelectronic Applications.

Recently, the newly-emerging lead-free metal-halide materials with less toxicity and superior optoelectronic properties have received wide attention as the safer and potentially more robust alternatives to lead-based perovskite counterparts. Among them, ternary copper halides (TCHs) have become a vital group due to their unique features, including abundant structural diversity, ease of synthesis, unprecedented optoelectronic properties, high abundance, and low cost. Although the recent efforts in this field have made certain progresses, some scientific and technological issues still remain unresolved. Herein, a comprehensive and up-to-date overview of recent progress on the fundamental characteristics of TCH materials and their versatile applications is presented, which contains topics such as: i) crystal and electronic structure features and synthesis strategies; ii) mechanisms of self-trapped excitons, luminescence regulation, and environmental stability; and iii) their burgeoning optoelectronic devices of phosphor-converted white light-emitting diodes (WLEDs), electroluminescent LEDs, anti-counterfeiting, X-ray scintillators, photodetectors, sensors, and memristors. Finally, the current challenges together with future perspectives on the development of TCH materials and applications are also critically described, which is considered to be critical for accelerating the commercialization of these rapidly evolving technologies.

Low-parameter supervised learning models can discriminate pseudoprogression and true progression in non-perfusion-based MRI.

Discrimination of pseudoprogression and true progression is one challenge to the treatment of malignant gliomas. Although some techniques such as circulating tumor DNA (ctDNA) and perfusion-weighted imaging (PWI) demonstrate promise in distinguishing PsP from TP, we investigate robust and replicable alternatives to distinguish the two entities based on more widely-available media. In this study, we use low-parametric supervised learning techniques based on geographically-weighted regression (GWR) to investigate the utility of both conventional MRI sequences as well as a diffusion-weighted sequence (apparent diffusion coefficient or ADC) in the discrimination of PsP v TP. GWR applied to MRI modality pairs is a unique approach for small sample sizes and is a novel approach in this arena. From our analysis, all modality pairs involving ADC maps, and those involving post-contrast T1 regressed onto T2 showed potential promise. This work on ADC data adds to a growing body of research suggesting the predictive benefits of ADC, and suggests further research on the relationships between post-contrast T1 and T2.Clinical relevance- Few studies have investigated predictive potential of conventional MRI and ADC to detect PsP. Our study adds to the growing research on the topic and presents a new perspective to research by exploiting the utility of ADC in PsP v TP distinction. In addition, our GWR methodology for low-parametric supervised computer vision models demonstrates a unique approach for image processing of small sample sizes.

Evaluating the accuracy and effectiveness of machine learning methods for rapidly determining the safety factor of road embankments

PurposeSlope stability analysis is essential for ensuring the safe design of road embankments. While various conventional methods, such as the finite element approach, are used to determine the safety factor of road embankments, there is ongoing interest in exploring the potential of machine learning techniques for this purpose.Design/methodology/approachWithin the study context, the outcomes of the ensemble machine learning models will be compared and benchmarked against the conventional techniques used to predict this parameter.FindingsGenerally, the study results have shown that the proposed machine learning models provide rapid and accurate estimates of the safety factor of road embankments and are, therefore, promising alternatives to traditional methods.Originality/valueAlthough machine learning algorithms hold promise for rapidly and accurately estimating the safety factor of road embankments, few studies have systematically compared their performance with traditional methods. To address this gap, this study introduces a novel approach using advanced ensemble machine learning techniques for efficient and precise estimation of the road embankment safety factor. Besides, the study comprehensively assesses the performance of these ensemble techniques, in contrast with established methods such as the finite element approach and empirical models, demonstrating their potential as robust and reliable alternatives in the realm of slope stability assessment.

Improvements to Statistical Characterization and Modeling, and a Caution to be Aware of Spurious Correlation in Statistical Simulation

This paper presents two improvements to, and a warning about a previously unrecognized issue with, statistical modeling, characterization, and simulation. We focus on mismatch, which is key for analog IC design, but the topics addressed are generic. For relative variation (i.e., % difference), we show that conventional measures have significant issues when the variation is large. We present two new measures of % difference between y2 and y1 and show that 100∙ln(y2/y1) is the only measure that is physically correct for mismatch. We review existing, but not widely known, robust alternatives to the textbook approach to estimate standard deviation, show the limitations of these, and propose a new method that is intermediate between, and overcomes limitations of, the textbook and robust approaches. We then show how the accuracy of mismatch simulation, critical for analog IC design, is affected by “spurious correlation” between statistical samples; we are not aware that the importance of this has been recognized previously.

Robust Z-Estimators for Semiparametric Moment Condition Models.

In the present paper, we introduce a class of robust Z-estimators for moment condition models. These new estimators can be seen as robust alternatives for the minimum empirical divergence estimators. By using the multidimensional Huber function, we first define robust estimators of the element that realizes the supremum in the dual form of the divergence. A linear relationship between the influence function of a minimum empirical divergence estimator and the influence function of the estimator of the element that realizes the supremum in the dual form of the divergence led to the idea of defining new Z-estimators for the parameter of the model, by using robust estimators in the dual form of the divergence. The asymptotic properties of the proposed estimators were proven, including here the consistency and their asymptotic normality. Then, the influence functions of the estimators were derived, and their robustness is demonstrated.

Robust Zinc(II)porphyrin Catalyst for Visible Light Induced C-H Arylation of Heteroarenes.

Zinc(II)porphyrin catalyzed light induced C-H arylation of heteroarenes from anilines is discussed. The method is nontoxic and efficient, using only 0.5 mol % of porphyrin catalyst to produce bi(hetero)aryls in good yields. This work demonstrates the potential use of porphyrin photocatalysts as efficient and robust alternatives to organic dyes.

Linear Convergence of a Proximal Alternating Minimization Method with Extrapolation for \(\boldsymbol{\ell_1}\) -Norm Principal Component Analysis

A popular robust alternative of the classic principal component analysis (PCA) is the -norm PCA (L1-PCA), which aims to find a subspace that captures the most variation in a dataset as measured by the -norm. L1-PCA has shown great promise in alleviating the effect of outliers in data analytic applications. However, it gives rise to a challenging nonsmooth, nonconvex optimization problem, for which existing algorithms are either not scalable or lack strong theoretical guarantees on their convergence behavior. In this paper, we propose a proximal alternating minimization method with extrapolation (PAMe) for solving a two-block reformulation of the L1-PCA problem. We then show that for both the L1-PCA problem and its two-block reformulation, the Kurdyka–Łojasiewicz exponent at any of the limiting critical points is . This allows us to establish the linear convergence of the sequence of iterates generated by PAMe and to determine the criticality of the limit of the sequence with respect to both the L1-PCA problem and its two-block reformulation. To complement our theoretical development, we show via numerical experiments on both synthetic and real-world datasets that PAMe is competitive with a host of existing methods. Our results not only significantly advance the convergence theory of iterative methods for L1-PCA but also demonstrate the potential of our proposed method in applications.

Generic Liquid Membranes for Electromembrane Extraction of Bases with Low or Moderate Hydrophilicity.

For the first time, this paper introduces the idea of generic extraction conditions in electromembrane extraction (EME), where the selection of the liquid membrane is based on the charge (z) and hydrophobicity (log P) of the analyte. A broad range of organic solvents were tested as liquid membranes, and 90 basic pharmaceuticals were used as model analytes (-4.2 < log P < 8.1). 2-Nitrophenyl octyl ether (NPOE) was confirmed as a highly efficient liquid membrane for mono- and dibases (+1.0 ≤ z ≤ +2.0) of low polarity in the log P range of 2.2-6.4. This log P range was set as the extraction window (operational range) of NPOE. NPOE provided very high operational stability. At 50 V, the current was at a 1 μA level, and gas formation and drifting pH due to electrolysis were insignificant. 2-Undecanone was discovered as a new and robust alternative. This solvent extracted monobasic analytes (z = +1) in the log P range of 1.0-5.8 and was efficient even for bases of moderate polarity. The current was at the 1-3 μA level when 2-undecanone was operated at 50 V. Tri(pentyl) phosphate emerged as another new alternative for bases in the log P range of 0.5 to 5.5, providing greater selectivity differences. This solvent provided a higher current (30-50 μA), but the EME system stability was not compromised. 2-Undecanone and tri(pentyl) phosphate extracted protonated bases mainly by hydrogen bond interactions. NPOE, on the other hand, extracted based on a combination of hydrogen bond and π-type interactions and was consequently less selective.

A New Algorithm on Pythagorean Fuzzy-Based Multi-Criteria Decision-Making and Its Application

The rapid growth in information technology (IT) and the implementation of IT tools has aided hospitals in delivering healthcare at an unprecedented pace. However, without the proper knowledge and understanding of software tools, healthcare establishments may find it difficult to select the best hospital management system software solution for their business. In this paper, a problem based on multi-criteria decision-making (MCDM) is modelled to identify the best hospital management software on the basis of a thorough analysis of every aspect of the healthcare needs and required software competencies, so that hospitals can select a robust hospital management software system. This article presents an extended MCDM method for estimating and selecting robust hospital management system alternatives using the Pythagorean fuzzy method based on the removal effects of criteria (MEREC) and stepwise weight assessment ratio analysis (SWARA). To do this, the objective weights and subjective weights are assessed by the MEREC and SWARA models, and the measurement alternatives and ranking according to compromise solution (MARCOS) method determines the order of preference of the alternatives on the basis of the Pythagorean fuzzy set. Moreover, after in-depth analysis of every aspect of healthcare needs and required software competencies in India, the proposed method is designed to identify the best hospital management software tool, so that the hospital can choose a robust hospital management system. Comparative discussion is presented to exhibit the rationality and sustainability of the established approach to prioritize hospital management software options.

Recent Advances in Alkali-Activated Materials with Seawater and Sea Sand.

The development of sustainable cementitious materials is essential and urgent for the construction industry. Benefiting from excellent engineering properties and a reduced greenhouse gas footprint, alkali-activated materials (AAM) are among the robust alternatives to Portland cement for civil infrastructure. Meanwhile, concrete production also accounts for around 20% of all industrial water consumption, and the global freshwater shortage is increasing. This review discusses recent investigations on seawater-mixed AAMs, including the effects of seawater on workability, reaction mechanism, shrinkage, short and long-term strength, binding of chloride and corrosion of steel reinforcement. Attention is also paid to the utilization of sea sand as aggregate, as well as discussions on the challenges and further research perspectives on the field application of AAMs with seawater and sea sand.

Synthesis and Photothermal Properties of UV-Plasmonic Group IV Transition Metal Carbide Nanoparticles

Refractory nanostructures can be low-cost and chemically and thermally robust alternatives to metal-based plasmonic materials. While transition metal nitrides have received much attention recently, there has been less emphasis on their closely related non-layered carbide counterparts. In this work, plasmonic group IV transition metal carbide (TiC, ZrC, and HfC) nanostructures were prepared using a facile magnesiothermic reduction method, which yielded a phase pure product. TiC, ZrC, and HfC with rock salt crystal structures and an average particle size of 24, 31, and 42 nm, respectively, were obtained by reacting corresponding metal oxide, magnesium, and biochar in the solid state. Calculations performed using a finite element method predicted these group IV carbide nanostructures to have localized surface plasmon resonance in the UV region between 150 and 175 nm. The photothermal transduction efficiency of each carbide was explored to further verify the plasmonic behavior. HfC was found to have the highest photothermal transduction efficiency of 73%, followed by ZrC (69%), and then TiC (60%) at 365 nm.

Stochastic model-based leakage prediction in water mains considering pipe condition uncertainties

Energy loss through leakage in water distribution networks (WDNs) has been estimated to add up to 20% of the freshwater supply in the US. One common practice to battle non-revenue water in WDNs is proactively pinpointing leak sources through both conventional and novel, data-driven methods. Conventional, in-practice leakage detection approaches suffer from costliness and laboriousness, and thus novel model-based techniques are becoming a robust, accurate, and cost-effective alternative. One of the existing limitations in the literature regarding novel leak localization methods and thus the novelty in this study is factoring in the stochastic behavior of water pipe conditions while predicting leakage through consumption and cyber-monitoring data. Hence, this paper presents a direct, data-driven, artificially intelligent predictive tool for leak localization and severity measurement. In this leakage prediction model, uncertain pipe conditions such as pipe diameters and roughness coefficients are stochastically approximated in a Monte Carlo simulations framework using water quality-included empirical formulation. A novel accuracy index is also developed to measure the precision of leak localization by employing a graph connectivity algorithm. Prediction results and sensitivity analyses on a large WDN benchmark demonstrate that the proposed stochastic leakage prediction model offers robustness in locating leak sources and measuring severity in various leak test cases. Not only does this research offer new numerical insights into the probabilistic nature of water pipelines’ dynamic parameters amid leakage, it also pushes the boundary of smart leak detection approaches at water utilities by offering cost-effective and data-driven horizons.

Robust nonparametric regression: Review and practical considerations

Nonparametric regression models offer a way to understand and quantify relationships between variables without having to identify an appropriate family of possible regression functions. Although many estimation methods for these models have been proposed in the literature, most of them can be highly sensitive to the presence of a small proportion of atypical observations in the training set. A review of outlier robust estimation methods for nonparametric regression models is provided, paying particular attention to practical considerations. Since outliers can also influence negatively the regression estimator by affecting the selection of bandwidths or smoothing parameters, a discussion of robust alternatives for this task is also included. Using many of the “classical” nonparametric regression estimators (and their robust counterparts) can be very challenging in settings with a moderate or large number of explanatory variables, so recently proposed robust nonparametric regression methods that scale well with a growing number of covariates are also discussed.

Robslopes: Efficient Computation of the (Repeated) Median Slope

Modern use of slope estimation often involves the (repeated) estimation of a large number of slopes on a large number of data points. Some of the most popular non-parametric and robust alternatives to the least squares estimator are the Theil-Sen and Siegel's repeated median slope estimators. The [robslopes](https://CRAN.R-project.org/package=robslopes) package contains fast algorithms for these slope estimators. The implemented randomized algorithms run in $\mathcal{O}(n\log(n))$ and $\mathcal{O}(n\log^2(n))$ expected time respectively and use $\mathcal{O}(n)$ space. They achieve speedups up to a factor $10^3$ compared with existing implementations for common sample sizes, as illustrated in a benchmark study, and they allow for the possibility of estimating the slopes on samples of size $10^5$ and larger thanks to the limited space usage. Finally, the original algorithms are adjusted in order to properly handle duplicate values in the data set.

The usefulness of ecotoxicological approaches to assess environmental impacts caused by oil spills

Accidents involving oil spills have a high potential to cause catastrophic environmental impacts. However, the Brazilian legislation and guidelines to plan and determine response actions in case of oil accidents are vague and generally do not include the assessment of toxic effects of oil on the exposed biota and ecosystems. Therefore, such actions are unable to adequately assess the environmental effects of oil in the short, medium and long terms. Ecotoxicological methods consist of cost-effective and robust alternatives to identify and quantify the effects of oil on the exposed ecosystems. Such approaches should be incorporated into response actions in episodes of oil spills in aquatic environments.

A Changeable Decision Support System Based on Data Models for Global Production Networks

Multiple influencing factors like changes in market demand or legal factors as well as risks, uncertainties, and dynamics have to be considered when deciding about the design and management of global production networks. To deal with the multitude of different influencing factors and possibilities of adaptations in global production networks, digital twins offer the possibility to support decisions and combine different application models. For the benefit across multiple decision support systems, the digital twins need to be standardized on the one hand, but also extendable on the other. The digital twins themselves are based on a defined concept and a clear modeling logic. With the help of the asset administration shell, data models for the description of global production networks, as well as for the influencing factors and their scenarios, are developed which enable the exchange of data via standardized interfaces. Using standardized interfaces, decision support systems in global production networks can make queries to the models via a service and receive the required information, like different demand scenarios, back. The decision support systems can then map a wide variety of these scenarios for the future. Based on this, the scenarios can be evaluated by target values and, if they are not met, an adjustment option can be sought. The foresight of all these scenarios can then help to find the most robust and resilient decision alternative. The findings and thus the optimal response to each contingency scenario can be fed back into the data model of the global production network and made available for other decision support systems. This paper proposes a theoretical framework, a possible implementation using asset administration shells, and the interaction of the different modules using an exemplary decision support system.

Robust clustering based on the most frequent value method

Assigning observations to highly separable although relatively homogeneous groups is still a challenging task despite the abundance of well-elaborated theories and effective, practical algorithms. Not just the aim of clustering then the underlying data itself influences the choice of method and the way of assessing the results. Outliers and non-normal data distribution can lead to surprising, unstable and many times undesirable clustering results especially in higher dimensions. This implies the importance of some human supervision in case of such unsupervised algorithms as well. In this paper a robust clustering alternative is presented based on the Most Frequent Value Method for crisp-type clustering in case of real-life data. The proposed approach is compared with the k-Medians algorithm. A favourable attribute of the applied procedure is its ease of application on multidimensional data sets where critical judgment of formed groups is particularly troublesome.

A rapid synthesis of molecularly imprinted polymer nanoparticles for the extraction of performance enhancing drugs (PIEDs).

It is becoming increasingly more significant to detect and separate hormones from water sources, with the development of synthetic recognition materials becoming an emerging field. The delicate nature of biological recognition materials such as the antibodies means the generation of robust viable synthetic alternatives has become a necessity. Molecularly imprinted nanoparticles (NanoMIPs) are an exciting class that has shown promise due the generation of high-affinity and specific materials. While nanoMIPs offer high affinity, robustness and reusability, their production can be tricky and laborious. Here we have developed a simple and rapid microwaveable suspension polymerisation technique to produce nanoMIPs for two related classes of drug targets, Selective Androgen Receptor Modulators (SARMs) and steroids. These nanoMIPs were produced using one-pot microwave synthesis with methacrylic acid (MAA) as the functional monomer and ethylene glycol dimethacrylate (EGDMA) as a suitable cross-linker, producing particles of an approximate range of 120-140 nm. With the SARMs-based nanoMIPs being able to rebind 94.08 and 94.46% of their target molecules (andarine, and RAD-140, respectively), while the steroidal-based nanoMIPs were able to rebind 96.62 and 96.80% of their target molecules (estradiol and testosterone, respectively). The affinity of nanoMIPs were investigated using Scatchard analysis, with Ka values of 6.60 × 106, 1.51 × 107, 1.04 × 107 and 1.51 × 107 M-1, for the binding of andarine, RAD-140, estradiol and testosterone, respectively. While the non-imprinted control polymer (NIP) shows a decrease in affinity with Ka values of 3.40 × 104, 1.01 × 104, 1.83 × 104, and 4.00 × 104 M-1, respectively. The nanoMIPs also demonstrated good selectivity and specificity of binding the targets from a complex matrix of river water, showing these functional materials offer multiple uses for trace compound analysis and/or sample clean-up.

An importance sampling approach for reliable and efficient inference in Bayesian ordinary differential equation models

Statistical models can involve implicitly defined quantities, such as solutions to nonlinear ordinary differential equations (ODEs), that unavoidably need to be numerically approximated in order to evaluate the model. The approximation error inherently biases statistical inference results, but the amount of this bias is generally unknown and often ignored in Bayesian parameter inference. We propose a computationally efficient method for verifying the reliability of posterior inference for such models, when the inference is performed using Markov chain Monte Carlo methods. We validate the efficiency and reliability of our workflow in experiments using simulated and real data and different ODE solvers. We highlight problems that arise with commonly used adaptive ODE solvers and propose robust and effective alternatives, which, accompanied by our workflow, can now be taken into use without losing reliability of the inferences.

Multi-objective robust decision-making for LIDs implementation under climatic change

Decision-makers in urban water systems have long searched for robust runoff management alternatives that can resist deviations from the performance for which they were designed. In this study, with the development of a new comprehensive framework consisting of Many- Objective Robust Decision-Making (MORDM) and Storm Water Management Model (SWMM), robust alternatives for the implementation of Low Impact Development (LID) practices are presented under future climate change. The proposed methodology has been applied to District 10 of the Tehran municipality, located in the central part of Tehran, Iran. In the first step, the performances of General Circulation Models (GCMs) in predicting rainfall in the base period (1986–2005) were compared, and MPI-ESM-LR was selected as the best model for predicting rainfall in the future period (2021–2040). In the next step, by developing the SWMM hydrological model, runoff quality and quantity were simulated under RCP8.5 and RCP2.6. By coupling SWMM with the NSGA-II multi-objective evolutionary algorithm and specifying objective functions, the most optimal LIDs implementation scenarios were produced. Then, well-characterized uncertainties were changed into deep uncertainties, and the robustness of each design alternative was quantified using two robustness assessment methods. Finally, system vulnerabilities were discovered using the patient rule induction method (PRIM) and classification and regression tree (CART). This study showed that scenarios with a higher percentage of LIDs are more robust against deep uncertainties. Using different RCP scenarios also revealed that methodological selection of future climate scenarios can affect the robustness of candidate alternatives. Implementing SWMM-MORDM enables decision-makers to effectively generate runoff management scenarios and visually evaluate their robustness against deep uncertainties.