Effect of external magnetic field on the instability of THz plasma waves in nanoscale graphene field-effect transistors

The instability of plasma waves in the channel of field-effect transistors will cause the electromagnetic waves with THz frequency.Based on a self-consistent quantum hydrodynamic model,the instability of THz plasmas waves in the channel of graphene field-effect transistors has been investigated with external magnetic field and quantum effects.We analyzed the influence of weak magnetic fields,quantum effects,device size,and temperature on the instability of plasma waves under asymmetric boundary conditions numerically.The results show that the magnetic fields,quantum effects,and the thickness of the dielectric layer between the gate and the channel can increase the radiation frequency.Additionally,we observed that increase in temperature leads to a decrease in both oscillation frequency and instability increment.The numerical results and accompanying images obtained from our simulations provide support for the above conclusions.

Effects of baffle position in serpentine flow channel on the performance of proton exchange membrane fuel cells

This study used a three-dimensional numerical model of a proton exchange membrane fuel cell with five types of channels:a smooth channel(Case 1);eight rectangular baffles were arranged in the upstream(Case 2),midstream(Case 3),downstream(Case 4),and the entire cathode flow channel(Case 5)to study the effects of baffle position on mass transport,power density,net power,etc.Moreover,the effects of back pressure and humidity on the voltage were investigated.Results showed that compared to smooth channels,the oxygen and water transport facilitation at the diffusion layer-channel interface were added 11.53%-20.60%and 7.81%-9.80%at 1.68 A·cm^(-2)by adding baffles.The closer the baffles were to upstream,the higher the total oxygen flux,but the lower the flux uniformity the worse the water removal.The oxygen flux of upstream baffles was 8.14%higher than that of downstream baffles,but oxygen flux uniformity decreased by 18.96%at 1.68 A·cm^(-2).The order of water removal and voltage improvement was Case 4>Case 5>Case 3>Case 2>Case 1.Net power of Case 4 was 9.87%higher than that of the smooth channel.To the Case 4,when the cell worked under low back pressure or high humidity,the voltage increments were higher.The potential increment for the back pressure of 0 atm was 0.9%higher than that of 2 atm(1 atm=101.325 kPa).The potential increment for the humidity of 100%was 7.89%higher than that of 50%.

Grouping tree species to estimate basal area increment in temperate multispecies forests in Durango,Mexico

Multispecies forests have received increased scientific attention,driven by the hypothesis that biodiversity improves ecological resilience.However,a greater species diversity presents challenges for forest management and research.Our study aims to develop basal area growth models for tree species cohorts.The analysis is based on a dataset of 423 permanent plots(2,500 m^(2))located in temperate forests in Durango,Mexico.First,we define tree species cohorts based on individual and neighborhood-based variables using a combination of principal component and cluster analyses.Then,we estimate the basal area increment of each cohort through the generalized additive model to describe the effect of tree size,competition,stand density and site quality.The principal component and cluster analyses assign a total of 37 tree species to eight cohorts that differed primarily with regard to the distribution of tree size and vertical position within the community.The generalized additive models provide satisfactory estimates of tree growth for the species cohorts,explaining between 19 and 53 percent of the total variation of basal area increment,and highlight the following results:i)most cohorts show a"rise-and-fall"effect of tree size on tree growth;ii)surprisingly,the competition index"basal area of larger trees"had showed a positive effect in four of the eight cohorts;iii)stand density had a negative effect on basal area increment,though the effect was minor in medium-and high-density stands,and iv)basal area growth was positively correlated with site quality except for an oak cohort.The developed species cohorts and growth models provide insight into their particular ecological features and growth patterns that may support the development of sustainable management strategies for temperate multispecies forests.

Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.

Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe operations.Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two datasets.In response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning algorithm.It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer.The algorithm also adaptively adjusts the portion of training data to balance the learning rate and training time.The proposed algorithm is evaluated and analyzed using ten benchmark datasets.Compared with other algorithms from existing works,SA-ITL improves the accuracy of all datasets.Ablation studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.

A Hybrid Optimization Approach of Single Point Incremental Sheet Forming of AISI 316L Stainless Steel Using Grey Relation Analysis Coupled with Principal Component Analysiss

We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response.

CNN-LSTM based incremental attention mechanism enabled phase-space reconstruction for chaotic time series prediction

To improve the prediction accuracy of chaotic time series and reconstruct a more reasonable phase space structure of the prediction network,we propose a convolutional neural network-long short-term memory(CNN-LSTM)prediction model based on the incremental attention mechanism.Firstly,a traversal search is conducted through the traversal layer for finite parameters in the phase space.Then,an incremental attention layer is utilized for parameter judgment based on the dimension weight criteria(DWC).The phase space parameters that best meet DWC are selected and fed into the input layer.Finally,the constructed CNN-LSTM network extracts spatio-temporal features and provides the final prediction results.The model is verified using Logistic,Lorenz,and sunspot chaotic time series,and the performance is compared from the two dimensions of prediction accuracy and network phase space structure.Additionally,the CNN-LSTM network based on incremental attention is compared with long short-term memory(LSTM),convolutional neural network(CNN),recurrent neural network(RNN),and support vector regression(SVR)for prediction accuracy.The experiment results indicate that the proposed composite network model possesses enhanced capability in extracting temporal features and achieves higher prediction accuracy.Also,the algorithm to estimate the phase space parameter is compared with the traditional CAO,false nearest neighbor,and C-C,three typical methods for determining the chaotic phase space parameters.The experiments reveal that the phase space parameter estimation algorithm based on the incremental attention mechanism is superior in prediction accuracy compared with the traditional phase space reconstruction method in five networks,including CNN-LSTM,LSTM,CNN,RNN,and SVR.

Research on the IL-Bagging-DHKELM Short-Term Wind Power Prediction Algorithm Based on Error AP Clustering Analysis

With the continuous advancement of China’s“peak carbon dioxide emissions and Carbon Neutrality”process,the proportion of wind power is increasing.In the current research,aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data,a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine(IL-Bagging-DHKELM)error affinity propagation cluster analysis is proposed.The algorithm effectively combines deep hybrid kernel extreme learning machine(DHKELM)with incremental learning(IL).Firstly,an initial wind power prediction model is trained using the Bagging-DHKELM model.Secondly,Euclidean morphological distance affinity propagation AP clustering algorithm is used to cluster and analyze the prediction error of wind power obtained from the initial training model.Finally,the correlation between wind power prediction errors and Numerical Weather Prediction(NWP)data is introduced as incremental updates to the initial wind power prediction model.During the incremental learning process,multiple error performance indicators are used to measure the overall model performance,thereby enabling incremental updates of wind power models.Practical examples show the method proposed in this article reduces the root mean square error of the initial model by 1.9 percentage points,indicating that this method can be better adapted to the current scenario of the continuous increase in wind power penetration rate.The accuracy and precision of wind power generation prediction are effectively improved through the method.

Evolution of mechanical parameters of Shuangjiangkou granite under different loading cycles and stress paths

Surrounding rocks at different locations are generally subjected to different stress paths during the process of deep hard rock excavation.In this study,to reveal the mechanical parameters of deep surrounding rock under different stress paths,a new cyclic loading and unloading test method for controlled true triaxial loading and unloading and principal stress direction interchange was proposed,and the evolution of mechanical parameters of Shuangjiangkou granite under different stress paths was studied,including the deformation modulus,elastic deformation increment ratios,fracture degree,cohesion and internal friction angle.Additionally,stress path coefficient was defined to characterize different stress paths,and the functional relationships among the stress path coefficient,rock fracture degree difference coefficient,cohesion and internal friction angle were obtained.The results show that during the true triaxial cyclic loading and unloading process,the deformation modulus and cohesion gradually decrease,while the internal friction angle gradually increases with increasing equivalent crack strain.The stress path coefficient is exponentially related to the rock fracture degree difference coefficient.As the stress path coefficient increases,the degrees of cohesion weakening and internal friction angle strengthening decrease linearly.During cyclic loading and unloading under true triaxial principal stress direction interchange,the direction of crack development changes,and the deformation modulus increases,while the cohesion and internal friction angle decrease slightly,indicating that the principal stress direction interchange has a strengthening effect on the surrounding rocks.Finally,the influences of the principal stress interchange direction on the stabilities of deep engineering excavation projects are discussed.

Unknown DDoS Attack Detection with Fuzzy C-Means Clustering and Spatial Location Constraint Prototype Loss

Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks.

Efficacy of a Diabetes Specific Nutritional Supplement (DSNS) on Glycemic Response in Prediabetic Adults: A Two-Armed, Open-Labelled Randomized Controlled Study

It is well known that Diabetes Specific Nutritional Supplements (DSNSs) are linked to improved glycemic control in individuals with diabetes. However, data on efficacy of DSNSs in prediabetics is limited. This was a two-armed, open-labelled, randomized controlled six-week study on 199 prediabetics [30 - 65 years;Glycosylated Hemoglobin (HbA1c) 5.7% - 6.4% and/or Fasting Blood Glucose (FBG) 100-125 mg/dl]. Two parallel phases were conducted: Acute Blood Glucose Response (ABGR) and Intervention phase. Prediabetic participants were randomized into test (n = 100) and control (n = 99). The primary objective was to assess the ABGR of DSNS versus an isocaloric snack, measured by incremental Area under the Curve (iAUC). Test and control received 60 g of DSNS and 56 g of isocaloric snack (cornflakes) respectively, both in 250 ml double-toned milk on visit days 1, 15, 29 and 43. Postprandial Blood Glucose (PPG) was estimated at 30, 60, 90, 120, 150 and 180 minutes. During the 4 weeks intervention phase, the test group received DSNS with lifestyle counselling (DSNS + LC) and was compared with the control receiving lifestyle counselling alone (LC alone). Impact was studied on FBG, HbA1C, anthropometry, body composition, blood pressure, nutrient intake, and physical activity. The impact of DSNS was also studied using CGM between two 14-day phases: CGM1 baseline (days 1 - 14) and CGM2 endline (days 28 - 42). DSNS showed significantly lower PPG versus isocaloric snack at 30 (p 12, and chromium were reported by DSNS + LC versus LC alone. No other significant changes were reported between groups. It may be concluded that DSNS may be considered as a snack for prediabetic or hyperglycemic individuals requiring nutritional support for improved glycemic control.

Approximation Maintenance When Adding a Conditional Value in Set-Valued Ordered Decision Systems

The integration of set-valued ordered rough set models and incremental learning signify a progressive advancement of conventional rough set theory, with the objective of tackling the heterogeneity and ongoing transformations in information systems. In set-valued ordered decision systems, when changes occur in the attribute value domain, such as adding conditional values, it may result in changes in the preference relation between objects, indirectly leading to changes in approximations. In this paper, we effectively addressed the issue of updating approximations that arose from adding conditional values in set-valued ordered decision systems. Firstly, we classified the research objects into two categories: objects with changes in conditional values and objects without changes, and then conducted theoretical studies on updating approximations for these two categories, presenting approximation update theories for adding conditional values. Subsequently, we presented incremental algorithms corresponding to approximation update theories. We demonstrated the feasibility of the proposed incremental update method with numerical examples and showed that our incremental algorithm outperformed the static algorithm. Ultimately, by comparing experimental results on different datasets, it is evident that the incremental algorithm efficiently reduced processing time. In conclusion, this study offered a promising strategy to address the challenges of set-valued ordered decision systems in dynamic environments.

Hybrid Seasonal Prediction of Meridional Temperature Gradient Associated with“Warm Arctic-Cold Eurasia”

The meridional gradient of surface air temperature associated with“Warm Arctic–Cold Eurasia”(GradTAE)is closely related to climate anomalies and weather extremes in the mid-low latitudes.However,the Climate Forecast System Version 2(CFSv2)shows poor capability for GradTAE prediction.Based on the year-to-year increment approach,analysis using a hybrid seasonal prediction model for GradTAE in winter(HMAE)is conducted with observed September sea ice over the Barents–Kara Sea,October sea surface temperature over the North Atlantic,September soil moisture in southern North America,and CFSv2 forecasted winter sea ice over the Baffin Bay,Davis Strait,and Labrador Sea.HMAE demonstrates good capability for predicting GradTAE with a significant correlation coefficient of 0.84,and the percentage of the same sign is 88%in cross-validation during 1983−2015.HMAE also maintains high accuracy and robustness during independent predictions of 2016−20.Meanwhile,HMAE can predict the GradTAE in 2021 well as an experiment of routine operation.Moreover,well-predicted GradTAE is useful in the prediction of the large-scale pattern of“Warm Arctic–Cold Eurasia”and has potential to enhance the skill of surface air temperature occurrences in the east of China.

Research on the MPPT of Photovoltaic Power Generation Based on the CSA-INC Algorithm

The existing Maximum Power Point Tracking(MPPT)method has low tracking efficiency and poor stability.It is easy to fall into the Local Maximum Power Point(LMPP)in Partial Shading Condition(PSC),resulting in the degradation of output power quality and efficiency.It was found that various bio-inspired MPPT based optimization algorithms employ different mechanisms,and their performance in tracking the Global Maximum Power Point(GMPP)varies.Thus,a Cuckoo search algorithm(CSA)combined with the Incremental conductance Algorithm(INC)is proposed(CSA-INC)is put forward for the MPPT method of photovoltaic power generation.The method can improve the tracking speed by more than 52%compared with the traditional Cuckoo Search Algorithm(CSA),and the results of the study using this algorithm are compared with the popular Particle Swarm Optimization(PSO)and the Gravitational Search Algorithm(GSA).CSA-INC has an average tracking efficiency of 99.99%and an average tracking time of 0.19 s when tracking the GMPP,which improves PV power generation’s efficiency and power quality.

Response of photosynthesis,growth,and acorn mass of pedunculate oak to diff erent levels of nitrogen in wet and dry growing seasons

The objective was to examine the effects of optimal leaf nitrogen levels>2.0%and suboptimal levels<2.0%,nitrogen nutrition on net photo synthetic rate,stem diameter increment,height growth increment and acorn mass of pedunculate oak during 2010 in the absence of drought stress and during 2011 under the impact of moderate drought stress.According to the results,moderate drought stress significantly reduced net photo synthetic rate,stem diameter increment and height growth increment,while acorn mass was not affected.Suboptimal nitrogen nutrition significantly reduced the net photo synthetic rate and stem diameter increment only in the wet year,acorn mass in both wet and dry years,while height growth increment was not significantly reduced by suboptimal nitrogen nutrition in either year.The results indicate that optimal nitrogen levels can stimulate photo synthetic rate and stem diameter increment of pedunculate oak only in the absence of moderate drought stress.Moreover,the results show that moderate drought stress is a more dominant stressor for photosynthesis and growth of pedunculate oak than suboptimal nitrogen nutrition,while for acorn development,it is the more dominant stressor.

Deformation and damage properties of rock-like materials subjected to multi-level loading-unloading cycles

In the process of engineering construction such as tunnels and slopes,rock mass is frequently subjected to multiple levels of loading and unloading,while previous research ignores the impact of unloading rate on the stability of rock mass.A number of uniaxial multi-level cyclic loading-unloading experiments were conducted to better understand the effect of unloading rate on the deformation behavior,energy evolution,and damage properties of rock-like material.The experimental results demonstrated that the unloading rate and relative cyclic number clearly influence the deformation behavior and energy evo-lution of rock-like samples.In particular,as the relative cyclic number rises,the total strain and reversible strain both increase linearly,while the total energy density,elastic energy density,and dissipated energy density all rise nonlinearly.In contrast,the irreversible strain first decreases quickly,then stabilizes,and finally rises slowly.As the unloading rate increases,the total strain and reversible strain both increase,while the irreversible strain decreases.The dissipated energy damage was examined in light of the aforementioned experimental findings.The accuracy of the proposed damage model,which takes into account the impact of the unloading rate and relative cyclic number,is then confirmed by examining the consistency between the model predicted and the experimental results.The proposed damage model will make it easier to foresee how the multi-level loading-unloading cycles will affect the rock-like materials.

Filter Bank Networks for Few-Shot Class-Incremental Learning

Deep Convolution Neural Networks(DCNNs)can capture discriminative features from large datasets.However,how to incrementally learn new samples without forgetting old ones and recognize novel classes that arise in the dynamically changing world,e.g.,classifying newly discovered fish species,remains an open problem.We address an even more challenging and realistic setting of this problem where new class samples are insufficient,i.e.,Few-Shot Class-Incremental Learning(FSCIL).Current FSCIL methods augment the training data to alleviate the overfitting of novel classes.By contrast,we propose Filter Bank Networks(FBNs)that augment the learnable filters to capture fine-detailed features for adapting to future new classes.In the forward pass,FBNs augment each convolutional filter to a virtual filter bank containing the canonical one,i.e.,itself,and multiple transformed versions.During back-propagation,FBNs explicitly stimulate fine-detailed features to emerge and collectively align all gradients of each filter bank to learn the canonical one.FBNs capture pattern variants that do not yet exist in the pretraining session,thus making it easy to incorporate new classes in the incremental learning phase.Moreover,FBNs introduce model-level prior knowledge to efficiently utilize the limited few-shot data.Extensive experiments on MNIST,CIFAR100,CUB200,andMini-ImageNet datasets show that FBNs consistently outperformthe baseline by a significantmargin,reporting new state-of-the-art FSCIL results.In addition,we contribute a challenging FSCIL benchmark,Fishshot1K,which contains 8261 underwater images covering 1000 ocean fish species.The code is included in the supplementary materials.

Comparison of seismic fragility analysis methods for arch dams

This study focuses on the seismic fragility analysis of arch dams.The multiple stripe analysis(MSA),cloud analysis(CLA),and incremental dynamic analysis(IDA)methods are compared.A comprehensive dam-reservoir-foundation rock system,which considers the opening of contraction joints,the nonlinearity of dam concrete and foundation rock,the radiation damping effect of semi-unbounded foundation,and the compressibility of reservoir water,is used as a numerical example.225,80,and 15 earthquake records are selected for MSA,CLA,and IDA,respectively.The results show that MSA provides satisfactory fragility analysis,while both CLA and IDA have assumptions that may lead to deviations.Therefore,MSA is the most reliable method among the three methods and is recommended for the fragility analysis of arch dams.It is also shown that the choice of demand level affects the reliability of fragility curves and the effect of the material uncertainty on the fragility of the dam is not significant.

Seismic response characteristics of nuclear island structure at generic soil and rock sites

The number of traditionally excellent coastal lithologic nuclear power plants is limited.It is a trend to develop nuclear power plants on soil sites in inland areas.Therefore,the seismic safety and adaptability of non-rock nuclear power plant(NPP)sites are the key concerns of nuclear safety researchers.Although the five site categories are clearly defined in the AP1000 design control documents,the effects of nuclear power five site conditions and soil nonlinearity on the seismic response characteristics of nuclear island buildings have not been systematically considered in previous related studies.In this study,targeting the AP1000 nuclear island structure as the research object,three-dimensional finite element models of a nuclear island structure at five types of sites(firm rock site(FR),soft rock site(SR),soft-to-medium soil site(SMS),upper bound soft-to-medium site(SMS-UB),and soft soil site(SS))are established.The partitioned analysis method of soil-structure interaction(PASSI)in the time-domain is used to investigate the effects of site hardness and nonlinearity on the acceleration,displacement,and acceleration response spectrum of the nuclear island structure under seismic excitation.The incremental equilibrium equation and explicit decoupling method are used to analyze the soil nonlinearity described by the Davidenkov model with simplified loading-reloading rules.The results show that,in the linear case,with the increase of site hardness,the peak ground acceleration(PGA)and the peak of acceleration response spectrum of the nuclear island structure increase except for the FR site,while the maximum displacement decreases.In nonlinear analysis,as the site hardness increases,the PGA,maximum displacement,and the peak of acceleration response spectrum of the nuclear island structure increase.The peak value of the acceleration response spectrum in the nonlinear case is greater than that in the linear case for FR,while smaller for SR and soil sites.The site nonlinearity reduces the peak values of the response spectrum for SR and soil sites much more as the site hardness decreases.The results of this study can provide a reference for design of nuclear island structures on soil and rock sites.

Design of the contingent royalty rate as related to the type of investment

This study investigates the design of the royalty rate in a first-price auction across three types of investments:incremental and lumpy with or without an exogenously given intensity.A bidder’s investment cost comprises private information.This,together with the stochastic evolution of the price of the output generated from the auctioned project,precludes the seller from setting the exact dates of investment with the winner.However,the seller can set the royalty rate to equate the winner’s royalty payment with the winner’s information rent so that the winner acts as if to maximize the seller’s revenue.We derive two main conclusions.First,compared with the case in which investment is lumpy with an exogenously given intensity,the seller can set a lower royalty rate on incremental investment because she can collect additional royalty payments from the winner,who has the option to later expand capacity.Second,the impact of output price uncertainty on the optimal royalty rate for the three types of investments exhibits two different patterns.When investment is either incremental or lumpy with an exogenously given intensity,greater output price uncertainty reduces the royalty rate.When investment is lumpy with variable intensity,greater output uncertainty raises the royalty rate.Our results imply that auctioneers may charge differential royalty rates for different types of investments.