Screening Rules Research Articles

Element Screening Engineering for High-Entropy Alloy Anodes: Achieving Fast and Robust Li-Storage With Optimal Working Potential.

While the high-entropy strategy is highly effective in enhancing the performance of materials across various fields, an optimal methodology for selecting component elements for performance optimization is still lacking. Here the findings on uncovering the element selection rules for rational design of high-entropy alloy anodes with exceptional lithium storage performance are reported. It is investigated high-entropy element screening rules by modifying stable diamond-structured Ge with P to induce a tetrahedrally coordinated sphalerite structure for enhanced metallic conductivity, further stabilized by incorporating Zn and other elements. Moreover, both theoretical and experimental results confirm that Li-storage performance improves with increasing atomic number: BZnGeP3 < AlZnGeP3 < GaZnGeP3 < InZnGeP3. InZnGeP3-based electrodes demonstrate the highest Li-ion affinity, fastest electronic and Li-ion transport, largest Li-storage capacity and reversibility, and best mechanical integrity. Further element screening based on the above criteria leads to high entropy alloy anodes with metallic conductivity like GaCuSnInZnGeP6, GaCu(or Sn)InZnGeP5, CuSnInZnGeP5, InZnGePSeS(or Te), InZnGeP2S(or Se) which show superior Li-storage performances. The excellent phase stability is attributed to their high configurational entropy. This study offers profound insights into element screening for high-entropy alloy-based anodes in Li-ion batteries, providing guidance and reference for the element combination and screening of other high-entropy functional materials.

Identification and screening of priority pollutants in printing and dyeing industry wastewater and the importance of these pollutants in environmental management in China

At present, priority pollutants in printing and dyeing wastewater have attracted increasing attention, but their types and limits in the wastewater discharge standards for China's dyeing and finishing industry are still lacking. This study selected 9 printing and dyeing enterprises in a region of Zhejiang Province as the research objects. Through literature collection, field investigations, and chemical analysis, combined with industry priority pollutant screening technology, 103 and 21 characteristic pollutants were selected as preliminary and supplementary lists of priority pollutants, respectively. The results of the pollutant category analysis revealed that 55% of the pollutants on the preliminary list included carcinogens, and 29% of the pollutants on the supplementary list included alcohols. According to the four rules for in-depth screening, a total of 23 indicators (excluding those in GB 4287-2012 and its revised versions) were selected as the final list of priority pollutants, most of which were polycyclic aromatic hydrocarbons (PAHs) but also included a small number of lipid substances, such as bis (2-ethylhexyl) phthalate. The screening of these indicators not only provides data support for the expansion of water pollution control indicators and the determination of their limits in China's printing and dyeing industry, but also promotes the supplementation and improvement of the sewage discharge standard system in related industries.

Type synthesis of 3-DOF spherical hybrid mechanisms with fixed centers of rotation

To improve the load-bearing capacity and the rotation range about the normal of the moving platform in a spherical parallel mechanism (SPM), a type synthesis method for spherical hybrid mechanisms (SHMs) with fixed centers of rotation is proposed by coupling a serial transmission chain with the central passive limb chain of the SPM near its center area. Based on the analysis of the spherical mechanism configuration’s research status with a fixed rotation center, a method for systematically synthesizing SHMs is given, and four types of limb chains are developed. The possible limb constraint systems provided by each type of limb chain are analyzed via screw theory, and the type synthesis of each type of limb chain is carried out. Screening rules are proposed to obtain the preferred limb chains from the limb chain configuration results. By selecting and permuting preferred limb chains based on the mechanism’s center position of rotation and the constraint type, a series of SHMs that rotate unrestricted about the normal of the moving platform are produced. Selecting the RBR-2RRR SHM as an example, its workspace, singularity, dexterity, and stiffness are analyzed to verify the effectiveness of the research. This work enriches the configuration types of SHMs and provides theoretical support for the design and applications of SHMs in engineering.

An Efficient Adaptive Multi-Kernel Learning With Safe Screening Rule for Outlier Detection

An Efficient Adaptive Multi-Kernel Learning With Safe Screening Rule for Outlier Detection

Element Screening of High-Entropy Silicon Anodes for Superior Li-Storage Performance of Li-Ion Batteries.

The high-entropy silicon anodes are attractive for enhancing electronic and Li-ionic conductivity while mitigating volume effects for advanced Li-ion batteries (LIBs), but are plagued by the complicated elements screening process. Inspired by the resemblances in the structure between sphalerite and diamond, we have selected sphalerite-structured SiP with metallic conductivity as the parent phase for exploring the element screening of high-entropy silicon-based anodes. The inclusion of the Zn in the sphalerite structure is crucial for improving the structural stability and Li-storage capacity. Within the same group, Li-storage performance is significantly improved with increasing atomic number in the order of BZnSiP3 < AlZnSiP3 < GaZnSiP3 < InZnSiP3. Thus, InZnSiP3-based electrodes achieved a high capacity of 719 mA h g-1 even after 1,500 cycles at 2,000 mA g-1, and a high-rate capacity of 725 mA h g-1 at 10,000 mA g-1, owing to its superior lithium-ion affinity, faster electronic conduction and lithium-ion diffusion, higher Li-storage capacity and reversibility, and mechanical integrity than others. Additionally, the incorporation of elements with larger atomic sizes leads to greater lattice distortion and more defects, further facilitating mass and charge transport. Following these screening rules, high-entropy disordered-cation silicon-based compounds such as GaCuSnInZnSiP6, GaCu(or Sn)InZnSiP5, and CuSnInZnSiP5, as well as high-entropy compounds with mixed-cation and -anion compositions, such as InZnSiPSeTe and InZnSiP2Se(or Te), are synthesized, demonstrating improved Li-storage performance with metallic conductivity. The phase formation mechanism of these compounds is attributed to the negative formation energies arising from elevated entropy.

A safe screening rule with bi-level optimization of [formula omitted] support vector machine

Support vector machine (SVM) has achieved many successes in machine learning, especially for a small sample problem. As a famous extension of the traditional SVM, the ν support vector machine (ν-SVM) has shown outstanding performance due to its great model interpretability. However, it still faces challenges in training overhead for large-scale problems. To address this issue, we propose a safe screening rule with bi-level optimization for ν-SVM (SRBO-ν-SVM) which can screen out inactive samples before training and reduce the computational cost without sacrificing the prediction accuracy. Our SRBO-ν-SVM is strictly deduced by integrating the Karush–Kuhn–Tucker (KKT) conditions, the variational inequalities of convex problems and the ν-property. Furthermore, we develop an efficient dual coordinate descent method (DCDM) to further improve computational speed. Finally, a unified framework for SRBO is proposed to accelerate many SVM-type models, and it is successfully applied to one-class SVM. Experimental results on 6 artificial data sets and 30 benchmark data sets have verified the effectiveness and safety of our proposed methods in supervised and unsupervised tasks.

ChemFH: an integrated tool for screening frequent false positives in chemical biology and drug discovery.

High-throughput screening rapidly tests an extensive array of chemical compounds to identify hit compounds for specific biological targets in drug discovery. However, false-positive results disrupt hit compound screening, leading to wastage of time and resources. To address this, we propose ChemFH, an integrated online platform facilitating rapid virtual evaluation of potential false positives, including colloidal aggregators, spectroscopic interference compounds, firefly luciferase inhibitors, chemical reactive compounds, promiscuous compounds, and other assay interferences. By leveraging a dataset containing 823391 compounds, we constructed high-quality prediction models using multi-task directed message-passing network (DMPNN) architectures combining uncertainty estimation, yielding an average AUC value of 0.91. Furthermore, ChemFH incorporated 1441 representative alert substructures derived from the collected data and ten commonly used frequent hitter screening rules. ChemFH was validated with an external set of 75 compounds. Subsequently, the virtual screening capability of ChemFH was successfully confirmed through its application to five virtual screening libraries. Furthermore, ChemFH underwent additional validation on two natural products and FDA-approved drugs, yielding reliable and accurate results. ChemFH is a comprehensive, reliable, and computationally efficient screening pipeline that facilitates the identification of true positive results in assays, contributing to enhanced efficiency and success rates in drug discovery. ChemFH is freely available via https://chemfh.scbdd.com/.

High-performance magnetic refrigeration materials: Prediction and realization

Magnetic refrigeration technology based on the magnetocaloric effect provides an optimal replacement for conventional refrigeration technology. However, an efficient approach to developing superior magnetic refrigeration materials to satisfy practical requirements is still lacking. Here, a feasible approach to develop magnetic refrigeration materials with a superior magnetocaloric effect is designed. Based on this screening rule, the 23 rare-earth-based compounds with inverse perovskite structures were revealed in space group Pm3̅m (No. 221). The First principle calculations are used to predict the magnetism, and electronic structure of candidate compounds, and there are 13 rare earth-based compounds in the candidate materials that have a ferromagnetic magnetic ground state. The Er3SnC, Ho3SnC, Gd3SnC, and Gd3AlC compounds were realized, and the magnetism, magnetocaloric effects were investigated. The experimental results demonstrate that the magnetic ground states of the prepared compounds are in agreement with theoretical predictions. The Er3SnC, Ho3SnC and Gd3SnC compounds with ferromagnetic states exhibit superior magnetocaloric effects.

Assessing portfolio diversification via two-sample graph kernel inference. A case study on the influence of ESG screening.

In this work we seek to enhance the frameworks practitioners in asset management and wealth management may adopt to asses how different screening rules may influence the diversification benefits of portfolios. The problem arises naturally in the area of Environmental, Social, and Governance (ESG) based investing practices as practitioners often need to select subsets of the total available assets based on some ESG screening rule. Once a screening rule is identified, one constructs a dynamic portfolio which is usually compared with another dynamic portfolio to check if it satisfies or outperforms the risk and return profile set by the company. Our study proposes a novel method that tackles the problem of comparing diversification benefits of portfolios constructed under different screening rules. Each screening rule produces a sequence of graphs, where the nodes are assets and edges are partial correlations. To compare the diversification benefits of screening rules, we propose to compare the obtained graph sequences. The method proposed is based on a machine learning hypothesis testing framework called the kernel two-sample test whose objective is to determine whether the graphs come from the same distribution. If they come from the same distribution, then the risk and return profiles should be the same. The fact that the sample data points are graphs means that one needs to use graph testing frameworks. The problem is natural for kernel two-sample testing as one can use so-called graph kernels to work with samples of graphs. The null hypothesis of the two-sample graph kernel test is that the graph sequences were generated from the same distribution, while the alternative is that the distributions are different. A failure to reject the null hypothesis would indicate that ESG screening does not affect diversification while rejection would indicate that ESG screening does have an effect. The article describes the graph kernel two-sample testing framework, and further provides a brief overview of different graph kernels. We then demonstrate the power of the graph two-sample testing framework under different realistic scenarios. Finally, the proposed methodology is applied to data within the SnP500 to demonstrate the workflow one can use in asset management to test for structural differences in diversification of portfolios under different ESG screening rules.

Trust exploration- and leadership incubation- based opinion dynamics model for social network group decision-making: A quantum theory perspective

Social network (SN) holds significant sway over the consciousness and behavior of involved individuals, serving as an evolutionary medium for opinion dynamics models. Consensus is a fundamental concept in group decision-making (GDM). How to effectively explore the opinion evolution during the consensus-reaching process through SN is of paramount significance for decision-making science. Therefore, a dual-mechanism containing trust exploration and leadership incubation is developed for modeling opinion dynamics, creating a favorable condition for achieving consensus. First, a novel mechanism for analyzing the completeness of SN is proposed, encompassing a trust propagation process that considers trust discount and stability, as well as a trust aggregation method grounded by quantum theory. Second, a trust screening rule is discussed to retain the valid indirect trust relationships (TRs), and then a leadership incubation mechanism is developed to promote the effective achievement of consensus opinions in group decision-making. Finally, a numerical study is presented to elucidate the superiority and rationality of the proposed methods, and some simulation experiments and comparative analyses demonstrating the effectiveness and advantages of which are covered.

Bayesian analysis of optimal burn‐in policy for heterogeneous components with minimal repair

AbstractThis study develops a Bayesian method to analyze optimal burn‐in policy for heterogeneous components with minimal repair. We use the non‐homogeneous Poisson processes with different power law intensity functions to model the minimal repair processes for heterogeneous components. Since the component from the weak subpopulation may fail more frequently, the total number of minimal repairs can be used for establishing a screening rule. By screening, we can decide whether the component after burn‐in should be eliminated or not. Considering the uncertainty of model parameters, we generate a minimally repaired data set of components by simulation. Then, we obtain the model parameters estimators by using Bayesian method combined with the existing data. We further analyze and get the optimal burn‐in settings under cost and performance optimization model. A numerical example is used to verify the effectiveness of the Bayesian method.

Properties and interaction of layers in board-biodegradable primer-printing ink screen-printed system

Surface interactions of the materials during and after the printing process are extremely important for understanding and optimizing the process of graphic reproduction. In screen printing on porous and absorbent substrates, mesh type and ink composition significantly influence the properties of the printed product. To protect the absorbent printing substrates such as board from moisture penetration and to ensure the optimal interaction of the printed ink layer and the substrate, board substrates can be coated with protective primers before printing. In this research, biodegradable primers made of poly(ɛ‐caprolactone) and poly(lactic acid) were applied on the board substrate which was then screen‐printed using two screen rulings of the mesh and two different types of water‐based printing inks on unprimed and primed board substrates. Printed ink layer thickness, surface roughness, water vapor transmission rate, surface free energy and adhesion parameters were measured/calculated on all produced samples. Microscopy of the printed elements was performed to visualize the influence of the primers on the printed line edge. Results of the research have shown that the primers influence the roughness reduction of the printed ink layer. Furthermore, thickness of the printed ink layers increased when the primers were applied on the substrate, pointing to the decreased permeability of the board, which was confirmed by the reduced water vapor transmission rate of the primed and printed substrates. The surface free energies of the tested surfaces and the adhesion parameters between biodegradable primers and prepared printing inks differed depending on the type of the ink and primer, pointing to the optimal combination of the primer and ink for the favorable acceptance of printing ink on the substrate. Results of this research have enabled the optimization of the quality of screen‐printed board product.

Automated mass screening and association rules analysis for comorbidities of psoriasis: A population-based case-control study.

Patients with psoriasis frequently have comorbidities, which are linked to higher mortality rates. An in-depth investigation of comorbidities and their effects on health can help improve the management of patients with psoriasis. We conducted a comprehensive and unbiased investigation of comorbidities in patients with psoriasis and explored the pattern of association between comorbidities. A nationwide population-based study included 384 914 patients with psoriasis and 384 914 matched controls between 2011 and 2021. We used automated mass screening of all diagnostic codes to identify psoriasis-associated comorbidities and applied association rule analysis to explore the patterns of comorbidity associations in patients with psoriasis. Patients with psoriasis had an increased risk of autoimmunity-related diseases such as inflammatory arthritis, Crohn's disease, type 1 diabetes, and acute myocardial infarction. The comorbidities of patients with psoriasis with a history of cardiovascular events demonstrated strong interrelationships with other cardiovascular risk factors including type 2 diabetes mellitus, essential hypertension, and dyslipidemia. We also found comorbidities, such as malignant skin tumors and kidney and liver diseases, which could have adverse effects of anti-psoriasis therapy. In contrast, patients with psoriasis showed a decreased association with upper respiratory tract infection. Our results imply that comorbidities in patients with psoriasis are associated with the systemic inflammation of psoriasis and the detrimental effects of its treatment. Furthermore, we found patterns of associations between the cardiovascular risk factors and psoriasis. Mass screening and association analyses using large-scale databases can be used to investigate impartially the comorbidities of psoriasis and other diseases.

Optimal burn-in for minimally repaired components from n subpopulations

Burn-in is a widely used technique to screen out defective components before they are put into field operation. In this paper, we propose a burn-in policy for the components from a heterogeneous population composed of [Formula: see text] ordered subpopulations. Assume that components can be minimally repaired when the failure occurs. We consider the total number of minimal repairs observed during burn-in process as a screening rule to remove defective components with poor reliability performance. First, we carry out the probabilistic analysis of the screening policy to show the effectiveness of burn-in. Second, we obtain the optimal burn-in settings which minimize the mean total cost or maximize the probability of passing the mission period. Finally, we give an example to illustrate the proposed model.

Multiview Structural Large Margin Classifier and Its Safe Acceleration Strategy.

Multiview learning (MVL) concentrates on the problem where each instance is represented by multiple different feature sets. Efficiently exploring and exploiting the common and complementary information among different views remains challenging in MVL. Nevertheless, many existing algorithms deal with multiview problems via pairwise strategies, which limit the exploration of relationships among different views and dramatically increase the computational cost. In this article, we propose a multiview structural large margin classifier (MvSLMC) that simultaneously satisfies the consensus and complementarity principles in all views. Specifically, on the one hand, MvSLMC employs a structural regularization term to promote cohesion within-class and separability between-class in each view. On the other hand, different views provide extra structural information to each other, which favors the diversity of the classifier. Moreover, the introduction of hinge loss in MvSLMC results in sample sparsity, which we leverage to construct a safe screening rule (SSR) for accelerating MvSLMC. To the best of our knowledge, this is the first attempt at safe screening in MVL. Numerical experimental results demonstrate the effectiveness of MvSLMC and its safe acceleration method.

Optimal dispatch of distributed renewable energy and energy storage systems via optimal configuration of mobile edge computing

AbstractThe access of distributed units leads to the rapid increase of power network information services, which brings great problems to the centralized dispatch of power system. To improve the efficiency of data processing and the flexibility of each unit dispatching, first, the areas are divided according to the load characteristics. An operating framework of distributed power system is presented based on offload strategy of mobile edge computing (MEC) and optimal allocation of computational quantity. Second, a novel hierarchical dispatching model is established based on the optimal configuration of MEC. In each area, the upper model takes the total energy consumption of the MEC server and the operating cost of the power system as the objective to calculate the output of each distributed unit and the computational quantity under the optimal offload strategy. The lower model ensures the complete accommodation of renewable energy and the optimal economic operation of the whole area through the introduction of electric vehicles (EVs) and secondary operation of energy storage systems under the constraint of MEC server computing delay. Third, the screening rules of the MEC system computing mode and the simulation strategies of EVs in different areas are proposed. Finally, the effectiveness and superiority of the model are verified by simulation experiments.

Enhanced defect detection in NDE using registration aided heterogeneous data fusion

The degradation of systems in service, including pipelines, over time highlights the critical need for reliable and accurate defect detection to ensure safe operations. However, single modality-based Nondestructive Evaluation (NDE) data used in practical applications often suffers from noise contamination and errors caused by various factors like lift-off/standoff distances, probe drift, scanning speed, variation in data acquisition rates, and poor sensor sensitivity. The presence of agnostic noise types poses a challenge in extracting defect signals. To address these challenges, this paper presents an automated NDE theory-based data fusion framework aimed at enhancing the detection of surface and near-surface defects in magnetizable and conductive specimens. The Magnetic Flux Leakage (MFL) and Eddy Current (EC) based NDE sensing methods demonstrate the highly heterogeneous nature of noise distributions. Given the heterogeneity of the inspection methods, a screening rule is proposed to determine the conditions under which fusion would be beneficial. An important aspect of the proposed fusion method is registration, which ensures accurate alignment of multi-sensor image data. Two registration methods are proposed in this study as performing blind fusion without registration leads to erroneous results. The first registration method is translational, whereas the second method is registration based on linear optimal transport (OT) which proves to be effective in the boundary conditions. Finally, the registered source images from the EC and MFL modalities are fused using pixel-based fusion algorithms, including transform domain and spatial domain-based methods. Qualitative and quantitative assessments demonstrate that the registered fusion results exhibit higher accuracy and reliability compared to unregistered fused results and source images. Although the fusion method is applied to MFL and EC data in this paper, it is also suitable for other NDE modalities.

Suspect and nontarget screening of mycotoxins and their modified forms in wheat products based on ultrahigh-performance liquid chromatography-high resolution mass spectrometry

Various forms of mycotoxins commonly exist in food and pose a significant risk to human health. Here a comprehensive suspect and nontarget screening strategy for both parent and modified mycotoxins was developed using ultrahigh-performance liquid chromatography-high resolution mass spectrometry (UHPLCHRMS). We constructed an in-house MS/MS database containing 82 mycotoxins in 8 categories. Then fragmentation characteristics of different classes of mycotoxins were rapidly extracted by a Python program “Fragmentation pattern screener (FPScreener)” and nontarget screening rules were determined by analyzing the frequencies and average intensities of fragmentation characteristics. Using the suspect and nontarget screening strategy, we successfully identified six parent mycotoxins and eight modified mycotoxins with different confidence levels in contaminated wheat and flour samples. This strategy enables screening of unknown parents and modified mycotoxins in food matrices with corresponding fragmentation characteristics.

Rule of Law Challenges of ‘Algorithmic Discretion’ &amp; Automation in EU Border Control

Abstract The European Travel Information and Authorisation System (ETIAS) will profile visa- exempt third-country nationals using a screening rules algorithm to make automated predictive risk assessments. By using the screening rules algorithm as a case study, this article analyses if the ETIAS Regulation, and the legal safeguards it establishes for the decision-making process, comply with formal and substantive legality requirements as defined by the Venice Commission and the Court of Justice of the European Union. Three legality challenges are identified: [1] The regulatory design of ETIAS raises issues in relation to the foreseeability, clarity and accessibility of law, as well as insufficient limits to discretion. [2] The ETIAS screening rules algorithm represents a development towards a new form of arbitrariness through ‘algorithmic discretion’. [3] The safeguard of manual (human) processing in case of a ‘hit’ is not a panacea for the identified legality challenges.

Oxytocin treatment for core symptoms in children with autism spectrum disorder: a systematic review and meta-analysis.

The purpose of this article is to examine the efficacy of oxytocin in treating core symptoms of autism spectrum disorder (ASD) with children. A systematic literature search was conducted to identify randomized controlled trials (RCTs) of oxytocin for the treatment of core symptoms in children with ASD. The search included studies published between January 1, 1999 and March 15, 2023, that were randomized, single or double-blinded, and included a placebo control group. Standard screening rules were applied to select relevant studies, resulting in the inclusion of five RCTs involving 486 children with ASD. Ultimately, a total of five RCTs, involving 486 children with ASD, were included in the review using standard screening rules.One of the included studies demonstrated a statistically significant improvement in Social Responsiveness Scale (SRS) and Repetitive Behavior Scale-Revised (RBS) scores when children with ASD were treated with oxytocin (24 IU/2 days for 6 weeks). The improvement in core symptoms persisted at the 6-month follow-up. The meta-analysis findings suggested that oxytocin might have a moderate effect in improving the core symptom of narrow interests and repetitive stereotyped behaviors in children with ASD. While the therapeutic value of oxytocin in treating core symptoms of ASD in children is not fully established, the results of this meta-analysis indicate a potential moderate effect. However, further studies with larger sample sizes and more robust RCTs are needed to directly demonstrate the efficacy of oxytocin. Future research should also focus on effect size and outcome evaluation accuracy while minimizing bias in RCT experiments.