The Social Surveillance and Generational Compliance in New Mediation

This article explores the social transformation on account of surveillance conception preoccupied through the technology diffusion thanks to the Web 2.0 novel features.The new mediation abstracts the social provisions into radical customs of surveillance.It primes the supposition of socialization hitherto to transpire the scrutiny of the mutual rehearses of vertical and horizontal surveillance.Hence,the ultimate conversion keeps on through self-exposition notion in views of social interaction which so far posit the question of the privacy and public boundary owing to the hypothetical undue freedom of self-expression through Web 2.0.Thus,this paper examines the Surveillance Society in a comprehensive scope of the social structure vis-à-vis the ideas of generational submission towards social transformation.Using the dichotomy of digital revolutions,the Digital“Natives”are classified as typically addicted digital consumptions bearing the community outlooks,while Digital“Immigrants”persist in semi obedience along with the traditional adherence.Ultimately,the Panopticon conceptualizes the certainty of social surveillance by dint of proliferation of information flows keen on social conducts automated in both online and offline paths through technological appliances.

Application of Multiple Mean Generational Function Method to Typhoon Prediction over the Western North Pacific

[ Objective] The multiple mean generational function （MMGF） method was applied to forecast the annual number of typhoons （TYs） over the Western North Pacific （WNP）. [Method]The method yields a number of predictors by mean generational function based on the rolling 50- year data of TYs frequency and sunspot number, and was repeated to generate forecasts year after year by optimal subset regression. [ Result] The results showed a reasonably high predictive ability dudng period 2000 -2010, with an average root mean square （RMSE） value of 1.92 and a mean absolute error （MAE） value of 1.64. [ Conclusion] Although the MMGF method needs further validation in the practical operation, it already has strong potential for the improvement of skill at forecasting annual frequency of TYs in the WNP.

Clinical supervision for novice millennial nurses in the perinatal setting: The need for generational sensitivity

This paper reports on a qualitative research study that examined the experience of expert and novice nurses participating in a new, reflective program of “clinical supervision”, intending to facilitate the transition of new graduate nurses into the workforce. Three patterns emerged during the constructivist inquiry: readiness to reflect, valuing of clinical supervision, and sustainability of the clinical supervision model. The researchers suggest generational sensitivity as a key perspective to consider when developing engaging workplace strategies for millennial nurses. The article offers recommendations for the implementation of clinical supervision and would be of interest to nurse leaders in a clinical setting.

Facilitating Multi-generational Talent Collaboration in a Context-Critical World Through Design Capabilities

One of the major concerns in today‘s business world is talent retention and development.Leading and working with multi-generational workforce in the age of digital transformation may seem daunting,but this paper argues that it has its advantages in creating opportunities.Through interviews and case studies,this paper discovers that tensions in multi-generational collaboration often occur during the process of setting priorities for a group because different generations might have brought in different levels of capacity and willingness to take risks,and different levels of trust in,and care for people and the organization.The role of design in this context focuses more on capabilities,including observing generational behavioral nuances through practicing empathetic view,inviting people from different age into conversation and actively listening to them through the practice of shifting perspectives,and communicating complex situations through visualization and materialization for people to feel together.If we look at different generations in an organization as natural continuum of knowledge flow,if we see multigenerational workforce as one of driving forces to maintain organizational balance rather than tearing forces,and if we approach generational attributes with honesty,we could steer away from stereotypes and find common grounds to thrive together.

Impact of the COVID-19 pandemic and generational heterogeneity on ecommerce shopping styles-A case study of Sacramento,California

The COVID pandemic has accelerated the growth of ecommerce and reshaped shopping patterns,which in turn impacts trip-making and vehicle miles traveled.The objectives of this study are to define shopping styles and quantify their prevalence in the population,investigate the impact of the pandemic on shopping style transition,understand the generational heterogeneity and other factors that influence shopping styles,and comment on the potential impact of the pandemic on long-term shopping behavior.Two months after the initial shutdown(May/June 2021),we collected ecommerce behavioral data from 313 Sacramento Region households using an online survey.A K-means clustering analysis of shopping behavior across eight commodity types identified five shopping styles,including ecommerce independent,ecommerce dependent,and three mixed modes in-between.We found that the share of ecommerce independent style shifted from 55%pre-pandemic to 27%during the pandemic.Overall,30%kept the same style as pre-pandemic,54%became more ecommerce dependent,and 16%became less ecommerce dependent,with the latter group more likely to view shopping an excuse to get out.Heterogeneity was found across generations.Pre-pandemic,Millennials and Gen Z were the most ecommerce dependent,but during the pandemic they made relatively small shifts toward increased ecommerce dependency.Baby Boomers and the Silent Generation were bimodal,either sticking to in-person shopping or shifting to ecommercedependency during the pandemic.Post-pandemic intentions varied across styles,with households who primarily adopt non-food ecommerce intending to reverse back to in-person shopping,while the highly ecommerce dependent intend to limit future in-store activities.

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。

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”.

Call for Papers:Special Issue of China Finance Review International Artificial Intelligence and Finance:Modern Approaches and Implications

Special Issue Guest Editors·Michael Gofman,Senior Lecturer in Finance at the Hebrew University of Jerusalem·Zhao Jin,Assistant Professor of Finance,CKGSB Special Issue Information Artificial intelligence(AI)is becoming an increasingly important tool for fund managers,CFOs,regulators,traders,investors,and entrepreneurs.The generative AI revolution that started with the ChatGPT,has spurred a gale of creative destruction that poses risks and opportunities to most firms in the world.

Vibronic effect study of ^(1)A_(2) state of H_(2)O and D_(2)O

The generalized oscillator strengths of the dipole-forbidden excitations of the ^(1)A_(2) of H_(2)O and D_(2)O were calculated with the time dependent density functional theory,by taking into account the vibronic effect.It is found that the vibronic effect converts the dipole-forbidden excitation of the ^(1)A_(2) into a dipole-allowed one,which enhances the intensities of the corresponding generalized oscillator strength in the small squared momentum transfer region.The present investigation shows that the vibronic effect of H_(2)O is slightly stronger than that of D_(2)O,which exhibits a clear isotopic effect.

Enhanced Spin–Orbit Torques in Graphene by Pt Adatoms Decoration

Graphene(Gr)with widely acclaimed characteristics,such as exceptionally long spin diffusion length at room temperature,provides an outstanding platform for spintronics.However,its inherent weak spin–orbit coupling(SOC)has limited its efficiency for generating the spin currents in order to control the magnetization switching process for applications in spintronics memories.Following the theoretical prediction on the enhancement of SOC in Gr by heavy atoms adsorption,here we experimentally observe a sizeable spin–orbit torques(SOTs)in Gr by the decoration of its surface with Pt adatoms in Gr/Pt(t Pt)/Fe Ni trilayers with the optimal damping-like SOT efficiency around 0.55 by 0.6-nm-thick Pt layer adsorption.The value is nearly four times larger than that of the Pt/Fe Ni sample without Gr and nearly twice the value of the Gr/Fe Ni sample without Pt adsorption.The efficiency of the enhanced SOT in Gr by Pt adatoms is also demonstrated by the field-free SOT magnetization switching process with a relatively low critical current density around 5.4 MA/cm^(2)in Gr/Pt/Fe Ni trilayers with the in-plane magnetic anisotropy.These findings pave the way for Gr spintronics applications,offering solutions for future low power consumption memories.

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.

Generalized load graphical forecasting method based on modal decomposition

In a“low-carbon”context,the power load is affected by the coupling of multiple factors,which gradually evolves from the traditional“pure load”to the generalized load with the dual characteristics of“load+power supply.”Traditional time-series forecasting methods are no longer suitable owing to the complexity and uncertainty associated with generalized loads.From the perspective of image processing,this study proposes a graphical short-term prediction method for generalized loads based on modal decomposition.First,the datasets are normalized and feature-filtered by comparing the results of Xtreme gradient boosting,gradient boosted decision tree,and random forest algorithms.Subsequently,the generalized load data are decomposed into three sets of modalities by modal decomposition,and red,green,and blue(RGB)images are generated using them as the pixel values of the R,G,and B channels.The generated images are diversified,and an optimized DenseNet neural network was used for training and prediction.Finally,the base load,wind power,and photovoltaic power generation data are selected,and the characteristic curves of the generalized load scenarios under different permeabilities of wind power and photovoltaic power generation are obtained using the density-based spatial clustering of applications with noise algorithm.Based on the proposed graphical forecasting method,the feasibility of the generalized load graphical forecasting method is verified by comparing it with the traditional time-series forecasting method.

Image segmentation of exfoliated two-dimensional materials by generative adversarial network-based data augmentation

Mechanically cleaved two-dimensional materials are random in size and thickness.Recognizing atomically thin flakes by human experts is inefficient and unsuitable for scalable production.Deep learning algorithms have been adopted as an alternative,nevertheless a major challenge is a lack of sufficient actual training images.Here we report the generation of synthetic two-dimensional materials images using StyleGAN3 to complement the dataset.DeepLabv3Plus network is trained with the synthetic images which reduces overfitting and improves recognition accuracy to over 90%.A semi-supervisory technique for labeling images is introduced to reduce manual efforts.The sharper edges recognized by this method facilitate material stacking with precise edge alignment,which benefits exploring novel properties of layered-material devices that crucially depend on the interlayer twist-angle.This feasible and efficient method allows for the rapid and high-quality manufacturing of atomically thin materials and devices.

A generalization of Banach's lemma and its applications to perturbations of bounded linear operators

Let X be a Banach space and let P:X→X be a bounded linear operator.Using an algebraic inequality on the spectrum of P,we give a new sufficient condition that guarantees the existence of(I-P)^(-1) as a bounded linear operator on X,and a bound on its spectral radius is also obtained.This generalizes the classic Banach lemma.We apply the result to the perturbation analysis of general bounded linear operators on X with commutative perturbations.

General multi-attack detection for continuous-variable quantum key distribution with local local oscillator

Continuous-variable quantum key distribution with a local local oscillator(LLO CVQKD)has been extensively researched due to its simplicity and security.For practical security of an LLO CVQKD system,there are two main attack modes referred to as reference pulse attack and polarization attack presently.However,there is currently no general defense strategy against such attacks,and the security of the system needs further investigation.Here,we employ a deep learning framework called generative adversarial networks(GANs)to detect both attacks.We first analyze the data in different cases,derive a feature vector as input to a GAN model,and then show the training and testing process of the GAN model for attack classification.The proposed model has two parts,a discriminator and a generator,both of which employ a convolutional neural network(CNN)to improve accuracy.Simulation results show that the proposed scheme can detect and classify attacks without reducing the secret key rate and the maximum transmission distance.It only establishes a detection model by monitoring features of the pulse without adding additional devices.

Thermodynamics in a quantum corrected Reissner-Nordstr?m-AdS black hole and its GUP-corrections

We calculate the thermodynamic quantities in the quantum corrected Reissner-Nordstr?m-AdS(RN-AdS)black hole,and examine their quantum corrections.By analyzing the mass and heat capacity,we give the critical state and the remnant state,respectively,and discuss their consistency.Then,we investigate the quantum tunneling from the event horizon of massless scalar particle by using the null geodesic method,and charged massive boson W^(±)and fermions by using the Hamilton-Jacob method.It is shown that the same Hawking temperature can be obtained from these tunneling processes of different particles and methods.Next,by using the generalized uncertainty principle(GUP),we study the quantum corrections to the tunneling and the temperature.Then the logarithmic correction to the black hole entropy is obtained.

Multi-distortion suppression for neutron radiographic images based on generative adversarial network

Neutron radiography is a crucial nondestructive testing technology widely used in the aerospace,military,and nuclear industries.However,because of the physical limitations of neutron sources and collimators,the resulting neutron radiographic images inevitably exhibit multiple distortions,including noise,geometric unsharpness,and white spots.Furthermore,these distortions are particularly significant in compact neutron radiography systems with low neutron fluxes.Therefore,in this study,we devised a multi-distortion suppression network that employs a modified generative adversarial network to improve the quality of degraded neutron radiographic images.Real neutron radiographic image datasets with various types and levels of distortion were built for the first time as multi-distortion suppression datasets.Thereafter,the coordinate attention mechanism was incorporated into the backbone network to augment the capability of the proposed network to learn the abstract relationship between ideally clear and degraded images.Extensive experiments were performed;the results show that the proposed method can effectively suppress multiple distortions in real neutron radiographic images and achieve state-of-theart perceptual visual quality,thus demonstrating its application potential in neutron radiography.

I-DCGAN and TOPSIS-IFP:A simulation generation model for radiographic flaw detection images in light alloy castings and an algorithm for quality evaluation of generated images

The intelligent detection technology driven by X-ray images and deep learning represents the forefront of advanced techniques and development trends in flaw detection and automated evaluation of light alloy castings.However,the efficacy of deep learning models hinges upon a substantial abundance of flaw samples.The existing research on X-ray image augmentation for flaw detection suffers from shortcomings such as poor diversity of flaw samples and low reliability of quality evaluation.To this end,a novel approach was put forward,which involves the creation of the Interpolation-Deep Convolutional Generative Adversarial Network(I-DCGAN)for flaw detection image generation and a comprehensive evaluation algorithm named TOPSIS-IFP.I-DCGAN enables the generation of high-resolution,diverse simulated images with multiple appearances,achieving an improvement in sample diversity and quality while maintaining a relatively lower computational complexity.TOPSIS-IFP facilitates multi-dimensional quality evaluation,including aspects such as diversity,authenticity,image distribution difference,and image distortion degree.The results indicate that the X-ray radiographic images of magnesium and aluminum alloy castings achieve optimal performance when trained up to the 800th and 600th epochs,respectively.The TOPSIS-IFP value reaches 78.7%and 73.8%similarity to the ideal solution,respectively.Compared to single index evaluation,the TOPSIS-IFP algorithm achieves higher-quality simulated images at the optimal training epoch.This approach successfully mitigates the issue of unreliable quality associated with single index evaluation.The image generation and comprehensive quality evaluation method developed in this paper provides a novel approach for image augmentation in flaw recognition,holding significant importance for enhancing the robustness of subsequent flaw recognition networks.

Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.

Prediction and driving factors of forest fire occurrence in Jilin Province,China

Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model,the geographical weighted logistic regression model,the Lasso regression model,the random forest model,and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province.The models,along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area.Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models.The accuracies of the random forest model,the support vector machine model,geographical weighted logistic regression model,the Lasso regression model,and logistic model were 88.7%,87.7%,86.0%,85.0%and 84.6%,respectively.Weather is the main factor affecting forest fires,while the impacts of topography factors,human and social-economic factors on fire occurrence were similar.