Analytical Evaluation of Non-Elementary Integrals Involving Some Exponential, Hyperbolic and Trigonometric Elementary Functions and Derivation of New Probability Measures Generalizing the Gamma-Type and Gaussian-Type Distributions

The non-elementary integrals involving elementary exponential, hyperbolic and trigonometric functions, and where α,η and β are real or complex constants are evaluated in terms of the confluent hypergeometric function 1F1 and the hypergeometric function 1F2. The hyperbolic and Euler identities are used to derive some identities involving exponential, hyperbolic, trigonometric functions and the hypergeometric functions 1F1 and 1F2. Having evaluated, these non-elementary integrals, some new probability measures generalizing the gamma-type and Gaussian distributions are also obtained. The obtained generalized probability distributions may, for example, allow to perform better statistical tests than those already known (e.g. chi-square (x2) statistical tests and other statistical tests constructed based on the central limit theorem (CLT)), while avoiding the use of computational approximations (or methods) which are in general expensive and associated with numerical errors.

A Note on the Operator Equation Generalizing the Notion of Slant Hankel Operators

The operator equation λMz^-X = XMzk, for k ≥ 2,λ∈ C, is completely solved. Further, some algebraic and spectral properties of the solutions of the equation are discussed.

Complementary roles of glial cells in generating region-specific neuroinflammatory responses and phagocytosis in Parkinson’s disease

Neuroinflammation is associated with Parkinson’s disease:Reactive gliosis and neuroinflammation are hallmarks of Parkinson’s disease(PD),a multisystem neurodegenerative disorder characterized by a progressive loss of dopaminergic neurons.Neuroinflammation has long been considered a mere consequence of neuronal loss,but whether it promotes PD or is a key player in disease progression remains to be determined.Human leukocyte antigen.

Multiparameter generalized universal characters and multiparameter generalized B-type universal characters

We construct the quantum fields presentation of the generalized universal character and the generalized B-type universal character,and by acting the quantum fields presentations to the constant 1,the generating functions are derived.Furthermore,we introduce two integrable systems known as the generalized UC(GUC)hierarchy and the generalized Btype UC(GBUC)hierarchy satisfied by the generalized universal character and the generalized B-type universal character,respectively.Based on infinite sequences of complex numbers,we further establish the multiparameter generalized universal character and the multiparameter generalized B-type universal character,which have been proved to be solutions of the GUC hierarchy and the GBUC hierarchy,respectively.

基于ADRV9009的雷达干扰系统设计

如何快速高效地实现高灵活性、高集成度、可配置性好、低成本且易于维护的雷达干扰系统是当前雷达对抗领域的研究重点。针对以上需求,采用高集成度宽带射频收发器ADRV9009和FPGA芯片XC7Z100来设计雷达干扰系统,实现从射频到基带的全链路信号收发,提高了系统的集成度。通过串口通信对雷达干扰参数进行实时配置,提高了雷达干扰系统的灵活性和可配置性。采用System Generator开发工具对雷达干扰系统进行层次化、模块化设计,缩短了系统开发周期,提高了系统的可维护性。

A Generative Adversarial Network with an Attention Spatiotemporal Mechanism for Tropical Cyclone Forecasts

Tropical cyclones(TCs)are complex and powerful weather systems,and accurately forecasting their path,structure,and intensity remains a critical focus and challenge in meteorological research.In this paper,we propose an Attention Spatio-Temporal predictive Generative Adversarial Network(AST-GAN)model for predicting the temporal and spatial distribution of TCs.The model forecasts the spatial distribution of TC wind speeds for the next 15 hours at 3-hour intervals,emphasizing the cyclone's center,high wind-speed areas,and its asymmetric structure.To effectively capture spatiotemporal feature transfer at different time steps,we employ a channel attention mechanism for feature selection,enhancing model performance and reducing parameter redundancy.We utilized High-Resolution Weather Research and Forecasting(HWRF)data to train our model,allowing it to assimilate a wide range of TC motion patterns.The model is versatile and can be applied to various complex scenarios,such as multiple TCs moving simultaneously or TCs approaching landfall.Our proposed model demonstrates superior forecasting performance,achieving a root-mean-square error(RMSE)of 0.71 m s^(-1)for overall wind speed and 2.74 m s^(-1)for maximum wind speed when benchmarked against ground truth data from HWRF.Furthermore,the model underwent optimization and independent testing using ERA5reanalysis data,showcasing its stability and scalability.After fine-tuning on the ERA5 dataset,the model achieved an RMSE of 1.33 m s^(-1)for wind speed and 1.75 m s^(-1)for maximum wind speed.The AST-GAN model outperforms other state-of-the-art models in RMSE on both the HWRF and ERA5 datasets,maintaining its superior performance and demonstrating its effectiveness for spatiotemporal prediction of TCs.

Determinants of generalized anxiety and construction of a predictive model in patients with chronic obstructive pulmonary disease

BACKGROUND Patients with chronic obstructive pulmonary disease(COPD)frequently experience exacerbations requiring multiple hospitalizations over prolonged disease courses,which predispose them to generalized anxiety disorder(GAD).This comorbidity exacerbates breathing difficulties,activity limitations,and social isolation.While previous studies predominantly employed the GAD 7-item scale for screening,this approach is somewhat subjective.The current literature on predictive models for GAD risk in patients with COPD is limited.AIM To construct and validate a GAD risk prediction model to aid healthcare professionals in preventing the onset of GAD.METHODS This retrospective analysis encompassed patients with COPD treated at our institution from July 2021 to February 2024.The patients were categorized into a modeling(MO)group and a validation(VA)group in a 7:3 ratio on the basis of the occurrence of GAD.Univariate and multivariate logistic regression analyses were utilized to construct the risk prediction model,which was visualized using forest plots.The model’s performance was evaluated using Hosmer-Lemeshow(H-L)goodness-of-fit test and receiver operating characteristic(ROC)curve analysis.RESULTS A total of 271 subjects were included,with 190 in the MO group and 81 in the VA group.GAD was identified in 67 patients with COPD,resulting in a prevalence rate of 24.72%(67/271),with 49 cases(18.08%)in the MO group and 18 cases(22.22%)in the VA group.Significant differences were observed between patients with and without GAD in terms of educational level,average household income,smoking history,smoking index,number of exacerbations in the past year,cardiovascular comorbidities,disease knowledge,and personality traits(P<0.05).Multivariate logistic regression analysis revealed that lower education levels,household income<3000 China yuan,smoking history,smoking index≥400 cigarettes/year,≥two exacerbations in the past year,cardiovascular comorbidities,complete lack of disease information,and introverted personality were significant risk factors for GAD in the MO group(P<0.05).ROC analysis indicated that the area under the curve for predicting GAD in the MO and VA groups was 0.978 and 0.960.The H-L test yieldedχ^(2) values of 6.511 and 5.179,with P=0.275 and 0.274.Calibration curves demonstrated good agreement between predicted and actual GAD occurrence risks.CONCLUSION The developed predictive model includes eight independent risk factors:Educational level,household income,smoking history,smoking index,number of exacerbations in the past year,presence of cardiovascular comorbidities,level of disease knowledge,and personality traits.This model effectively predicts the onset of GAD in patients with COPD,enabling early identification of high-risk individuals and providing a basis for early preventive interventions by nursing staff.

Magneto-Electro-Elastic Analysis of Doubly-Curved Shells: Higher-Order Equivalent Layer-Wise Formulation

Recent engineering applications increasingly adopt smart materials,whose mechanical responses are sensitive to magnetic and electric fields.In this context,new and computationally efficient modeling strategies are essential to predict the multiphysic behavior of advanced structures accurately.Therefore,the manuscript presents a higher-order formulation for the static analysis of laminated anisotropic magneto-electro-elastic doubly-curved shell structures.The fundamental relations account for the full coupling between the electric field,magnetic field,and mechanical elasticity.The configuration variables are expanded along the thickness direction using a generalized formulation based on the Equivalent Layer-Wise approach.Higher-order polynomials are selected,allowing for the assessment of prescribed values of the configuration variables at the top and bottom sides of solids.In addition,an effective strategy is provided for modeling general surface distributions of mechanical pressures and electromagnetic external fluxes.The model is based on a continuum-based formulation which employs an analytical homogenization of the multifield material properties,based on Mori&Tanaka approach,of a magneto-electro-elastic composite material obtained from a piezoelectric and a piezomagnetic phase,with coupled magneto-electro-elastic effects.A semi-analytical Navier solution is applied to the fundamental equations,and an efficient post-processing equilibrium-based procedure is here used,based on the numerical assessment with the Generalized Differential Quadrature(GDQ)method,to recover the response of three-dimensional shells.The formulation is validated through various examples,investigating the multifield response of panels of different curvatures and lamination schemes.An efficient homogenization procedure,based on the Mori&Tanaka approach,is employed to obtain the three-dimensional constitutive relation of magneto-electro-elastic materials.Each model is validated against three-dimensional finite-element simulations,as developed in commercial codes.Furthermore,the full coupling effect between the electric and magnetic response is evaluated via a parametric investigation,with useful insights for design purposes of many engineering applications.The paper,thus,provides a formulation for the magneto-electro-elastic analysis of laminated structures,with a high computational efficiency,since it provides results with three-dimensional capabilities with a two-dimensional formulation.The adoption of higher-order theories,indeed,allows us to efficiently predict not only the mechanical response of the structure as happens in existing literature,but also the through-the-thickness distribution of electric and magnetic variables.A novel higher-order theory has been proposed in this work for the magneto-electro-elastic analysis of laminated shell structures with varying curvatures.This theory employs a generalized method to model the distribution of the displacement field components,electrostatic,and magneto-static potential,accounting for higher-order polynomials.The thickness functions have been defined to prescribe the arbitrary values of configuration variables at the top and bottom surfaces,even though the model is ESL-based.The fundamental governing equations have been derived in curvilinear principal coordinates,considering all coupling effects among different physical phenomena,including piezoelectric,piezomagnetic,and magneto-electric effects.A homogenization algorithm based on a Mori&Tanaka approach has been adopted to obtain the equivalent magneto-electro-mechanical properties of a two-phase transversely isotropic composite.In addition,an effective method has been adopted involving the external loads in terms of surface tractions,as well as the electric and magnetic fluxes.In the post-processing stage,a GDQ-based procedure provides the actual 3D response of a doubly-curved solid.The model has been validated through significant numerical examples,showing that the results of this semi-analytical theory align well with those obtained from 3D numerical models from commercial codes.In particular,the accuracy of the model has been verified for lamination schemes with soft layers and various curvatures under different loading conditions.Moreover,this formulation has been used to predict the effect of combined electric and magnetic loads on the mechanical response of panels with different curvatures and lamination schemes.As a consequence,this theory can be applied in engineering applications where the combined effect of electric and magnetic loads is crucial,thus facilitating their study and design.An existing limitation of this study is that the solution is that it is derived only for structures with uniform curvature,cross-ply lamination scheme,and simply supported boundary conditions.Furthermore,it requires that each lamina within the stacking sequence exhibits magneto-electro-elastic behavior.Therefore,at the present stage,it cannot be used for multifield analysis of classical composite structures with magneto-electric patches.A further enhancement of the research work could be the derivation of a solution employing a numerical technique,to overcome the limitations of the Navier method.In this way,the same theory may be adopted to predict the multifield response of structures with variable curvatures and thickness,as well as anisotropic materials and more complicated boundary conditions.Acknowledgement:The authors are grateful to the Department of Innovation Engineering of Univer-sity of Salento for the support.

Hygro-Thermo-Mechanical Equivalent Layer-Wise Theory of Laminated Shell Structures

This study presents a generalized two-dimensional model for evaluating the stationary hygro-thermo-mechanical response of laminated shell structures made of advanced materials.It introduces a generalized kinematic model,enabling the assessment of arbitrary values of temperature variation and mass concentration variation for the unvaried configuration at the top and bottom surfaces.This is achieved through the Equivalent Layer-Wise description of the unknown field variable using higher-order polynomials and zigzag functions.In addition,an elastic foundation is modeled utilizing the Winkler-Pasternak theory.The fundamental equations,derived from the total free energy of the system,are solved analytically using Navier’s method.Then,the Fourier-based generalized differential quadrature numerical method is adopted to efficiently recover the through-the-thickness distribution of secondary variables in agreement with the hygro-thermal loading conditions.The formulation is applied in some examples of investigation where the response of panels of different curvature and lamination schemes is evaluated under external hygro-thermal fluxes and prescribed values of temperature and moisture concentration.In addition,this study investigates the effect of the hygro-thermal coupling due to Dufour and Soret effect.The present formulation is verified to be a valuable tool for reducing computational effort and determining the effect on the mechanical response of laminated structures in a thermal and hygrometric environment.

Improve Strategy for Transient Power Angle Stability Control of VSG Combining Frequency Difference Feedback and Virtual Impedance

As the penetration rate of distributed energy increases,the transient power angle stability problem of the virtual synchronous generator(VSG)has gradually become prominent.In view of the situation that the grid impedance ratio(R/X)is high and affects the transient power angle stability of VSG,this paper proposes a VSG transient power angle stability control strategy based on the combination of frequency difference feedback and virtual impedance.To improve the transient power angle stability of the VSG,a virtual impedance is adopted in the voltage loop to adjust the impedance ratio R/X;and the PI control feedback of the VSG frequency difference is introduced in the reactive powervoltage link of theVSGto enhance the damping effect.Thesecond-orderVSGdynamic nonlinearmodel considering the reactive power-voltage loop is established and the influence of different proportional integral(PI)control parameters on the system balance stability is analyzed.Moreover,the impact of the impedance ratio R/X on the transient power angle stability is presented using the equal area criterion.In the simulations,during the voltage dips with the reduction of R/X from 1.6 to 0.8,Δδ_(1)is reduced from 0.194 rad to 0.072 rad,Δf_(1)is reduced from 0.170 to 0.093 Hz,which shows better transient power angle stability.Simulation results verify that compared with traditional VSG,the proposedmethod can effectively improve the transient power angle stability of the system.

A fractional-order improved FitzHugh–Nagumo neuron model

We propose a fractional-order improved Fitz Hugh–Nagumo(FHN)neuron model in terms of a generalized Caputo fractional derivative.Following the existence of a unique solution for the proposed model,we derive the numerical solution using a recently proposed L1 predictor–corrector method.The given method is based on the L1-type discretization algorithm and the spline interpolation scheme.We perform the error and stability analyses for the given method.We perform graphical simulations demonstrating that the proposed FHN neuron model generates rich electrical activities of periodic spiking patterns,chaotic patterns,and quasi-periodic patterns.The motivation behind proposing a fractional-order improved FHN neuron model is that such a system can provide a more nuanced description of the process with better understanding and simulation of the neuronal responses by incorporating memory effects and non-local dynamics,which are inherent to many biological systems.

Evaluating the impact of virtual reality on reducing restrictive practices in psychiatric wards

This manuscript critically evaluates the randomized controlled trial(RCT)conducted by Phiri et al,which assessed the effectiveness of virtual reality(VR)training for psychiatric staff in reducing restrictive practices(RPs).Specifically,this RCT investigated the impact of VR on the handling of aggressive patients by psychiatric staff compared to traditional training methods.Despite significant reductions in perceived discrimination in the VR group,there were no major improvements in self-efficacy or anxiety levels.The system usability scale rated the VR platform highly,but it did not consistently outperform traditional training methods.Indeed,the study shows the potential for VR to reduce RPs,although fluctuations in RP rates suggest that external factors,such as staff turnover,influenced the outcomes.This manuscript evaluates the study’s methodology,results,and broader implications for mental health training.Additionally,it highlights the need for more comprehensive research to establish VR as a standard tool for psychiatric staff education,focusing on patient care outcomes and real-world applicability.Finally,this study explores future research di-rections,technological improvements,and the potential impact of policies that could enhance the integration of VR in clinical training.

A Novel Control Strategy Based onπ-VSG for Inter-Face Converter in Hybrid Microgrid

The rapid development of new energy power generation technology and the transformation of power electronics in the core equipment of source-grid-load drives the power system towards the“double-high”development pattern of“high proportion of renewable energy”and“high proportion of power electronic equipment”.To enhance the transient performance of AC/DC hybrid microgrid(HMG)in the context of“double-high,”aπtype virtual synchronous generator(π-VSG)control strategy is applied to bidirectional interface converter(BIC)to address the issues of lacking inertia and poor disturbance immunity caused by the high penetration rate of power electronic equipment and new energy.Firstly,the virtual synchronous generator mechanical motion equations and virtual capacitance equations are used to introduce the virtual inertia control equations that consider the transient performance of HMG;based on the equations,theπ-type equivalent control model of the BIC is established.Next,the inertia power is actively transferred through the BIC according to the load fluctuation to compensate for the system’s inertia deficit.Secondly,theπ-VSG control utilizes small-signal analysis to investigate howthe fundamental parameters affect the overall stability of the HMG and incorporates power step response curves to reveal the relationship between the control’s virtual parameters and transient performance.Finally,the PSCAD/EMTDC simulation results show that theπ-VSG control effectively improves the immunity of AC frequency and DC voltage in the HMG system under the load fluctuation condition,increases the stability of the HMG system and satisfies the power-sharing control objective between the AC and DC subgrids.

Computational Experiments for Complex Social Systems:Experiment Design and Generative Explanation

Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system emergence.To address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating laws.Finally,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.

基于System Generator的卷积加速结构设计与实现

为解决卷积神经网络中卷积运算耗时长、运算复杂的问题,针对卷积运算的并行性特征,提出一种基于分块的流水线加速方法,并基于该方法在System Generator上进行了电路设计。通过在FPGA(Field-programmable Gate Array)上进行实验验证,该设计模型能正确输出卷积运算结果;在结构和输入数据相同的情况下,该设计模型在计算速度上相比于普通CPU最高可加速258倍,相比于服务器级CPU提高了近40倍,具有良好的加速效果。

May ChatGPT be a tool producing medical information for common inflammatory bowel disease patients’questions?An evidencecontrolled analysis

Artificial intelligence is increasingly entering everyday healthcare.Large language model(LLM)systems such as Chat Generative Pre-trained Transformer(ChatGPT)have become potentially accessible to everyone,including patients with inflammatory bowel diseases(IBD).However,significant ethical issues and pitfalls exist in innovative LLM tools.The hype generated by such systems may lead to unweighted patient trust in these systems.Therefore,it is necessary to understand whether LLMs(trendy ones,such as ChatGPT)can produce plausible medical information(MI)for patients.This review examined ChatGPT’s potential to provide MI regarding questions commonly addressed by patients with IBD to their gastroenterologists.From the review of the outputs provided by ChatGPT,this tool showed some attractive potential while having significant limitations in updating and detailing information and providing inaccurate information in some cases.Further studies and refinement of the ChatGPT,possibly aligning the outputs with the leading medical evidence provided by reliable databases,are needed.

Correcting Climate Model Sea Surface Temperature Simulations with Generative Adversarial Networks:Climatology,Interannual Variability,and Extremes

Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections.Addressing these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning methods.Here,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth System Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST extremes.Notably,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to rectify.Additionally,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32.This enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales variabilities.Our study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.

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.

Permeability and selectivity synergistically enhanced nanofluidic membrane for osmotic energy harvesting

For the porous‐membrane‐based osmotic energy generator,the potential synergistic enhancement mechanism of various key parameters is still controversial,especially because optimizing the trade‐off between permeability and selectivity is still a challenge.Here,to construct a permeability and selectivity synergistically enhanced osmotic energy generator,the twodimensional porous membranes with tunable charge density are prepared by inserting sulfonated polyether sulfone into graphene oxide.Influences of charge density and pore size on the ion transport are explored,and the ionic behaviors in the channel are calculated by numerical simulations.The mechanism of ion transport in the process is studied in depth,and the fundamental principles of energy conversion are revealed.The results demonstrate that charge density and pore size should be matched to construct the optimal ion channel.This collaborative enhancement strategy of permeability and selectivity has significantly improved the output power in osmotic energy generation;compared to the pure graphene oxide membrane,the composite membrane presents almost 20 times improvement.

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.