Effects of surface chlorine atoms on charge distribution and reaction barriers for photocatalytic CO_(2)reduction

Photocatalytic CO_(2)reduction to produce high value-added carbon-based fuel has been proposed as a promising approach to mitigate global warming issues.However,the conversion efficiency and product selectivity are still low due to the sluggish dynamics of transfer processes involved in proton-assisted multi-electron reactions.Lowering the formation energy barriers of intermediate products is an effective method to enhance the selectivity and productivity of final products.In this study,we aim to regulate the surface electronic structure of Bi_(2)WO_(6)by doping surface chlorine atoms to achieve effective photocatalytic CO_(2)reduction.Surface Cl atoms can enhance the absorption ability of light,affect its energy band structure and promote charge separation.Combined with DFT calculations,it is revealed that surface Cl atoms can not only change the surface charge distribution which affects the competitive adsorption of H_(2)O and CO_(2),but also lower the formation energy barrier of intermediate products to generate more intermediate*COOH,thus facilitating CO production.Overall,this study demonstrates a promising surface halogenation strategy to enhance the photocatalytic CO_(2)reduction activity of a layered structure Bi-based catalyst.

Designing ultrastable P2/O3-type layered oxides for sodium ion batteries by regulating Na distribution and oxygen redox chemistry

P2/O3-type Ni/Mn-based layered oxides are promising cathode materials for sodium-ion batteries(SIBs)owing to their high energy density.However,exploring effective ways to enhance the synergy between the P2 and 03 phases remains a necessity.Herein,we design a P2/O3-type Na_(0.76)Ni_(0.31)Zn_(0.07)Mn_(0.50)Ti_(0.12)0_(2)(NNZMT)with high chemical/electrochemical stability by enhancing the coupling between the two phases.For the first time,a unique Na*extraction is observed from a Na-rich O3 phase by a Na-poor P2 phase and systematically investigated.This process is facilitated by Zn^(2+)/Ti^(4+)dual doping and calcination condition regulation,allowing a higher Na*content in the P2 phase with larger Na^(+)transport channels and enhancing Na transport kinetics.Because of reduced Na^(+)in the O3 phase,which increases the difficulty of H^(+)/Na^(+) exchange,the hydrostability of the O3 phase in NNZMT is considerably improved.Furthermore,Zn^(2+)/Ti^(4+)presence in NNZMT synergistically regulates oxygen redox chemistry,which effectively suppresses O_(2)/CO_(2) gas release and electrolyte decomposition,and completely inhibits phase transitions above 4.0 V.As a result,NNZMT achieves a high discharge capacity of 144.8 mA h g^(-1) with a median voltage of 3.42 V at 20 mA g^(-1) and exhibits excellent cycling performance with a capacity retention of 77.3% for 1000 cycles at 2000 mA g^(-1).This study provides an effective strategy and new insights into the design of high-performance layered-oxide cathode materials with enhanced structure/interface stability forSIBs.

The New Mixed Generalized Erlang Distribution

In probability theory, the mixture distribution M has a density function for the collection of random variables and weighted by wi ≥ 0 and . These mixed distributions are used in various disciplines and aim to enrich the collection distribution to more parameters. A more general mixture is derived by Kadri and Halat, by proving the existence of such mixture by wi ∈ R, and maintaining . Kadri and Halat provided many examples and applications for such new mixed distributions. In this paper, we introduce a new mixed distribution of the Generalized Erlang distribution, which is derived from the Hypoexponential distribution. We characterize this new distribution by deriving simply closed expressions for the related functions of the probability density function, cumulative distribution function, moment generating function, reliability function, hazard function, and moments.

Analysis of College Students’ Test Scores Based on Two-Component Mixed Generalized Normal Distribution

In order to improve the fitting accuracy of college students’ test scores, this paper proposes two-component mixed generalized normal distribution, uses maximum likelihood estimation method and Expectation Conditional Maxinnization (ECM) algorithm to estimate parameters and conduct numerical simulation, and performs fitting analysis on the test scores of Linear Algebra and Advanced Mathematics of F University. The empirical results show that the two-component mixed generalized normal distribution is better than the commonly used two-component mixed normal distribution in fitting college students’ test data, and has good application value.

Federated Learning Model for Auto Insurance Rate Setting Based on Tweedie Distribution

In the assessment of car insurance claims,the claim rate for car insurance presents a highly skewed probability distribution,which is typically modeled using Tweedie distribution.The traditional approach to obtaining the Tweedie regression model involves training on a centralized dataset,when the data is provided by multiple parties,training a privacy-preserving Tweedie regression model without exchanging raw data becomes a challenge.To address this issue,this study introduces a novel vertical federated learning-based Tweedie regression algorithm for multi-party auto insurance rate setting in data silos.The algorithm can keep sensitive data locally and uses privacy-preserving techniques to achieve intersection operations between the two parties holding the data.After determining which entities are shared,the participants train the model locally using the shared entity data to obtain the local generalized linear model intermediate parameters.The homomorphic encryption algorithms are introduced to interact with and update the model intermediate parameters to collaboratively complete the joint training of the car insurance rate-setting model.Performance tests on two publicly available datasets show that the proposed federated Tweedie regression algorithm can effectively generate Tweedie regression models that leverage the value of data fromboth partieswithout exchanging data.The assessment results of the scheme approach those of the Tweedie regressionmodel learned fromcentralized data,and outperformthe Tweedie regressionmodel learned independently by a single party.

A Novel Defender-Attacker-Defender Model for Resilient Distributed Generator Planning with Network Reconfiguration and Demand Response

To improve the resilience of a distribution system against extreme weather,a fuel-based distributed generator(DG)allocation model is proposed in this study.In this model,the DGs are placed at the planning stage.When an extreme event occurs,the controllable generators form temporary microgrids(MGs)to restore the load maximally.Simultaneously,a demand response program(DRP)mitigates the imbalance between the power supply and demand during extreme events.To cope with the fault uncertainty,a robust optimization(RO)method is applied to reduce the long-term investment and short-term operation costs.The optimization is formulated as a tri-level defenderattacker-defender(DAD)framework.At the first level,decision-makers work out the DG allocation scheme;at the second level,the attacker finds the optimal attack strategy with maximum damage;and at the third level,restoration measures,namely distribution network reconfiguration(DNR)and demand response are performed.The problem is solved by the nested column and constraint generation(NC&CG)method and the model is validated using an IEEE 33-node system.Case studies validate the effectiveness and superiority of the proposed model according to the enhanced resilience and reduced cost.

Demonstration of a small‐scale power generator using supercritical CO_(2)

The supercritical CO_(2)(sCO_(2))power cycle could improve efficiencies for a wide range of thermal power plants.The sCO_(2)turbine generator plays an important role in the sCO_(2)power cycle by directly converting thermal energy into mechanical work and electric power.The operation of the generator encounters challenges,including high temperature,high pressure,high rotational speed,and other engineering problems,such as leakage.Experimental studies of sCO_(2)turbines are insufficient because of the significant difficulties in turbine manufacturing and system construction.Unlike most experimental investigations that primarily focus on 100 kW‐or MW‐scale power generation systems,we consider,for the first time,a small‐scale power generator using sCO_(2).A partial admission axial turbine was designed and manufactured with a rated rotational speed of 40,000 rpm,and a CO_(2)transcritical power cycle test loop was constructed to validate the performance of our manufactured generator.A resistant gas was proposed in the constructed turbine expander to solve the leakage issue.Both dynamic and steady performances were investigated.The results indicated that a peak electric power of 11.55 kW was achieved at 29,369 rpm.The maximum total efficiency of the turbo‐generator was 58.98%,which was affected by both the turbine rotational speed and pressure ratio,according to the proposed performance map.

Application of neutrophil-lymphocyte ratio and red blood cell distribution width in diabetes mellitus complicated with heart failure

BACKGROUND Accumulating clinical evidence has shown that diabetes mellitus(DM)is a serious risk factor for cardiovascular disorders and an important factor for adverse cardiovascular events.AIM To explore the value of the combined determination of the neutrophil-lymphocyte ratio(NLR)and red blood cell distribution width(RDW)in the early diagnosis and prognosis evaluation of DM complicated with heart failure(HF).METHODS We retrospectively analyzed clinical data on 65 patients with type 2 DM(T2DM)complicated with HF(research group,Res)and 60 concurrent patients with uncomplicated T2DM(control group,Con)diagnosed at Zhejiang Provincial People’s Hospital between January 2019 and December 2021.The NLR and RDW values were determined and comparatively analyzed,and their levels in T2DM+HF patients with different cardiac function grades were recorded.The receiver operating characteristic(ROC)curves were plotted to determine the NLR and RDW values(alone and in combination)for the early diagnosis of HF.The correlation between NLR and RDW with the presence or absence of cardiac events was also investigated.RESULTS Higher NLR and RDW levels were identified in the Res vs the Con groups(P<0.05).The NLR and RDW increased gradually and synchronously with the deterioration of cardiac function in the Res group,with marked differences in their levels among patients with grade II,III,and IV HF(P<0.05).ROC curve analysis revealed that NLR combined with RDW detection had an area under the curve of 0.915,a sensitivity of 76.9%,and a specificity of 100%for the early diagnosis of HF.Furthermore,HF patients with cardiac events showed higher NLR and RDW values compared with HF patients without cardiac events.CONCLUSION NLR and RDW were useful laboratory indicators for the early diagnosis of DM complicated with HF,and their joint detection was beneficial for improving diagnostic efficiency.Additionally,NLR and RDW values were directly proportional to patient outcomes.

MOLECULES AND NEW INTERACTIONAL STRUCTURES FOR A(2+1)-DIMENSIONAL GENERALIZED KONOPELCHENKO-DUBROVSKY-KAUP-KUPERSHMIDT EQUATION

Soliton molecules(SMs)of the(2+1)-dimensional generalized KonopelchenkoDubrovsky-Kaup-Kupershmidt(gKDKK)equation are found by utilizing a velocity resonance ansatz to N-soliton solutions,which can transform to asymmetric solitons upon assigning appropriate values to some parameters.Furthermore,a double-peaked lump solution can be constructed with breather degeneration approach.By applying a mixed technique of a resonance ansatz and conjugate complexes of partial parameters to multisoliton solutions,various kinds of interactional structures are constructed;There include the soliton molecule(SM),the breather molecule(BM)and the soliton-breather molecule(SBM).Graphical investigation and theoretical analysis show that the interactions composed of SM,BM and SBM are inelastic.

Robust multi-task distributed estimation based on generalized maximum correntropy criterion

False data injection(FDI) attacks are common in the distributed estimation of multi-task network environments, so an attack detection strategy is designed by combining the generalized maximum correntropy criterion. Based on this, we propose a diffusion least-mean-square algorithm based on the generalized maximum correntropy criterion(GMCC-DLMS)for multi-task networks. The algorithm achieves gratifying estimation results. Even more, compared to the related work,it has better robustness when the number of attacked nodes increases. Moreover, the assumption about the number of attacked nodes is relaxed, which is applicable to multi-task environments. In addition, the performance of the proposed GMCC-DLMS algorithm is analyzed in the mean and mean-square senses. Finally, simulation experiments confirm the performance and effectiveness against FDI attacks of the algorithm.

Brain Functional Network Generation Using Distribution-Regularized Adversarial Graph Autoencoder with Transformer for Dementia Diagnosis

The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia disorders.The brain functional network is suitable to bridge the correlation between abnormal connectivities and dementia disorders.However,it is challenging to access considerable amounts of brain functional network data,which hinders the widespread application of data-driven models in dementia diagnosis.In this study,a novel distribution-regularized adversarial graph auto-Encoder(DAGAE)with transformer is proposed to generate new fake brain functional networks to augment the brain functional network dataset,improving the dementia diagnosis accuracy of data-driven models.Specifically,the label distribution is estimated to regularize the latent space learned by the graph encoder,which canmake the learning process stable and the learned representation robust.Also,the transformer generator is devised to map the node representations into node-to-node connections by exploring the long-term dependence of highly-correlated distant brain regions.The typical topological properties and discriminative features can be preserved entirely.Furthermore,the generated brain functional networks improve the prediction performance using different classifiers,which can be applied to analyze other cognitive diseases.Attempts on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset demonstrate that the proposed model can generate good brain functional networks.The classification results show adding generated data can achieve the best accuracy value of 85.33%,sensitivity value of 84.00%,specificity value of 86.67%.The proposed model also achieves superior performance compared with other related augmentedmodels.Overall,the proposedmodel effectively improves cognitive disease diagnosis by generating diverse brain functional networks.

The application of Gaussian distribution deconvolution method to separate the overlapping signals in the 2D NMR map

The 2D NMR(T_(1)-T_(2))mapping technique,which can be used to separate different proton populations from various sources(hydroxyls,solid organic matter,free water,and free HC)has gained attention in petroleum industry.To separate proton contributions,a fixed straight line is commonly employed to separate different regions representing proton sources on the map.However,some of these regions(Region 1 and 2)might overlap which makes extracting the NMR signal amplitude from these regions inaccurate.In order to solve this issue,in this study,we applied the Gaussian distribution deconvolution method to separate the T_(1)and T_(2)relaxation distributions and then derived the signal amplitude of each region instead of following the common fixed line approach.Next,we employed this method to analyze several shale samples from the literature and compared the results following both methods to verify our methodology.Finally,samples from the Bakken Shale were studied to separate signals from Region 1 and Region 2 and corelated the results with geochemical properties that were obtained from programmed(Rock Eval)pyrolysis.Results demonstrated an improvement in their relation when our approach is employed compared to the fixed line technique to differentiate signal from overlapping regions.This means the Gaussian distribution deconvolution method can be used with confidence to provide us with more accurate petrophysical and geochemical understanding of complex formations.

Semigroup of Weakly Continuous Operators Associated to a Generalized Schrödinger Equation

In this work, we prove the existence and uniqueness of the solution of the generalized Schrödinger equation in the periodic distributional space P’. Furthermore, we prove that the solution depends continuously respect to the initial data in P’. Introducing a family of weakly continuous operators, we prove that this family is a semigroup of operators in P’. Then, with this family of operators, we get a fine version of the existence and dependency continuous theorem obtained. Finally, we provide some consequences of this study.

Group of Weakly Continuous Operators Associated to a Generalized Schrödinger Type Homogeneous Model

In this work, we prove the existence and uniqueness of the solution of the generalized Schrödinger type homogeneous model in the periodic distributional space P’. Furthermore, we prove that the solution depends continuously respect to the initial data in P’. Introducing a family of weakly continuous operators, we prove that this family is a group of operators in P’. Then, with this family of operators, we get a fine version of the existence and dependency continuous theorem obtained. Finally, we give some remarks derived from this study.

Statistical analysis of generalized exponential distribution under progressive censoring with binomial removals

The estimation of generalized exponential distribution based on progressive censoring with binomial removals is presented, where the number of units removed at each failure time follows a binomial distribution. Maximum likelihood estimators of the parameters and their confidence intervals are derived. The expected time required to complete the life test under this censoring scheme is investigated. Finally, the numerical examples are given to illustrate some theoretical results by means of Monte-Carlo simulation.

SPECTRUM DISTRIBUTION OF THE SEDOND ORDER GENERALIZED DISTRIBUTED PARAMETER SYSTEMS

Spectrum distribution of the second order generalized distributed parameter system was discussed via the functional analysis and operator theory in Hilbert space. The solutions of the problem and the constructive expression of the solutions are given by the generalized inverse one of bounded linear operator. This is theoretically important for studying the stabilization and asymptotic stability of the second order generalized distributed parameter system.

GENERALIZED OPERATORS AND OPERATOR-VALUED DISTRIBUTIONS IN QUANTUM FIELD THEORY

The relation between generalized operators and operator-valued distributions is discussed so that these two viewpoints can be used alternatively to explain quantum fields.

GENERALIZED PARETO DISTRIBUTION FIT TO MEDICAL INSURANCE CLAIMS DATA

How to choose an optimal threshold is a key problem in the generalized Pareto distribution （GPD） model. This paper attains the exact threshold by testing for GPD,and shows that GPD model allows the actuary to easily estimate high quantiles and the probable maximum loss from the medical insurance claims data.

The exponentiated generalized power Lindley distribution: Properties and applications

In this paper, we introduce a new extension of the power Lindley distribution, called the exponentiated generalized power Lindley distribution. Several mathematical properties of the new model such as the shapes of the density and hazard rate functions, the quantile function, moments, mean deviations, Bonferroni and Lorenz curves and order statistics are derived. Moreover, we discuss the parameter estimation of the new distribution using the maximum likelihood and diagonally weighted least squares methods. A simulation study is performed to evaluate the estimators. We use two real data sets to illustrate the applicability of the new model. Empirical findings show that the proposed model provides better fits than some other well-known extensions of Lindley distributions.

Prediction Based on Generalized Order Statistics from a Mixture of Rayleigh Distributions Using MCMC Algorithm

This article considers the problem in obtaining the maximum likelihood prediction (point and interval) and Bayesian prediction (point and interval) for a future observation from mixture of two Rayleigh (MTR) distributions based on generalized order statistics (GOS). We consider one-sample and two-sample prediction schemes using the Markov chain Monte Carlo (MCMC) algorithm. The conjugate prior is used to carry out the Bayesian analysis. The results are specialized to upper record values. Numerical example is presented in the methods proposed in this paper.