Game theory attack pricing for mining pools in blockchain-based IoT

The malicious mining pool can sacrifice part of its revenue to employ the computing power of blockchain network.The employed computing power carries out the pool mining attacks on the attacked mining pool.To realize the win-win game between the malicious mining pool and the employee,the paper proposes an Employment Attack Pricing Algorithm(EAPA)of mining pools in blockchain based on game theory.In the EAPA,the paper uses mathematical formulas to express the revenue of malicious mining pools under the employment attack,the revenue increment of malicious mining pools,and the revenue of the employee.It establishes a game model between the malicious mining pool and the employee under the employment attack.Then,the paper proposes an optimal computing power price selection strategy of employment attack based on model derivation.In the strategy,the malicious mining pool analyzes the conditions for the employment attack,and uses the derivative method to find the optimal utilization value of computing power,employees analyze the conditions for accepting employment,and use the derivative method to find the optimal reward value of computing power.Finally,the strategy finds the optimal employment computing power price to realize Nash equilibrium between the malicious mining pool and the employee under the current computing power allocation.The simulation results show that the EAPA could find the employment computing power price that realizes the win-win game between the malicious mining pool and the employee.The EAPA also maximizes the unit computing power revenue of employment and the unit computing power revenue of honest mining in malicious mining pool at the same time.The EAPA outperforms the state-of-the-art methods such as SPSUCP,DPSACP,and FPSUCP.

基于改进Faster R-CNN的热轧带钢表面缺陷检测

热轧带钢是钢铁行业的重要产品,其表面缺陷是影响产品质量的重要因素。针对传统缺陷检测算法存在的过程繁琐、精度不足和效率低下等问题,提出一种基于改进更快速区域卷积神经网络(faster region-based convolutional neural network,Faster R-CNN)的检测算法,实现对热轧带钢表面缺陷的高效、高精度检测。首先,采用特征相加的方法对底层细节特征和高层语义特征进行融合;然后,采用精准的感兴趣区域池化(precise region of interest pooling,Precise ROI Pooling)获取固定大小的特征向量,避免特征出现位置偏差;最后,利用均值偏移聚类算法对带钢数据集进行聚类,获得适用于热轧带钢表面缺陷检测的先验框尺寸。实验结果表明,所提算法在热轧带钢表面缺陷检测数据集上的平均精度均值达到了85.34%,检测速度为23.5帧/s,且鲁棒性良好,满足实际的工业检测需求。

An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

Understanding Simulated Causes of Damaging Surface Winds in a Derecho-Producing Mesoscale Convective System near the East China Coast Based on Convection-Permitting Simulations

A mesoscale convective system(MCS) occurred over the East China coastal provinces and the East China Sea on 30April 2021, producing damaging surface winds near the coastal city Nantong with observed speeds reaching 45 m s^(–1). A simulation using the Weather Research and Forecasting model with a 1.5-km grid spacing generally reproduces the development and subsequent organization of this convective system into an MCS, with an eastward protruding bow segment over the sea. In the simulation, an east-west-oriented high wind swath is generated behind the gust front of the MCS. Descending dry rear-to-front inflows behind the bow and trailing gust front are found to feed the downdrafts in the main precipitation regions. The inflows help to establish spreading cold outflows and enhance the downdrafts through evaporative cooling. Meanwhile, front-to-rear inflows from the south are present, associated with severely rearward-tilted updrafts initially forming over the gust front. Such inflows descend behind(north of) the gust front, significantly enhancing downdrafts and near-surface winds within the cold pool. Consistently, calculated trajectories show that these parcels that contribute to the derecho originate primarily from the region ahead(south) of the east-west-oriented gust front, and dry southwesterly flows in the low-to-middle levels contribute to strong downdrafts within the MCS. Moreover, momentum budget analyses reveal that a large westward-directed horizontal pressure gradient force within the simulated cold pool produced rapid flow acceleration towards Nantong. The analyses enrich the understanding of damaging wind characteristics over coastal East China and will prove helpful to operational forecasters.

Local Torrential Rainfall Event within a Mei-Yu Season Mesoscale Convective System:Importance of Back-Building Processes

An extreme rainfall event occurred over Hangzhou,China,during the afternoon hours on 24 June 2013.This event occurred under suitable synoptic conditions and the maximum 4-h cumulative rainfall amount was over 150 mm.This rainfall event had two major rainbands.One was caused by a quasi-stationary convective line,and the other by a backbuilding convective line related to the interaction of the outflow boundary from the first rainband and an existing low-level mesoscale convergence line associated with a mei-yu frontal system.The rainfall event lasted 4 h,while the back-building process occurred in 2 h when the extreme rainfall center formed.So far,few studies have examined the back-building processes in the mei-yu season that are caused by the interaction of a mesoscale convergence line and a convective cold pool.The two rainbands are successfully reproduced by the Weather Research and Forecasting(WRF)model with fourlevel,two-way interactive nesting.In the model,new cells repeatedly occur at the west side of older cells,and the backbuilding process occurs in an environment with large CAPE,a low LFC,and plenty of water vapor.Outflows from older cells enhance the low-level convergence that forces new cells.High precipitation efficiency of the back-building training cells leads to accumulated precipitation of over 150 mm.Sensitivity experiments without evaporation of rainwater show that the convective cold pool plays an important role in the organization of the back-building process in the current extreme precipitation case.

Effects of pooling,specialization,and discretionary task completion on queueing performance

Pooling,unpooling/specialization,and discretionary task completion are typical operational strategies in queueing systems that arise in healthcare,call centers,and online sales.These strategies may have advantages and disadvantages in different operational environments.This paper uses the M/M/1 and M/M/2 queues to study the impact of pooling,specialization,and discretionary task completion on the average queue length.Closed-form solutions for the average M/M/2 queue length are derived.Computational examples illustrate how the average queue length changes with the strength of pooling,specialization,and discretionary task completion.Finally,several conjectures are made in the paper.

An improved deep dilated convolutional neural network for seismic facies interpretation

With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural networks.These intelligent and automated methods significantly reduce manual labor,particularly in the laborious task of manually labeling seismic facies.However,the extensive demand for training data imposes limitations on their wider application.To overcome this challenge,we adopt the UNet architecture as the foundational network structure for seismic facies classification,which has demonstrated effective segmentation results even with small-sample training data.Additionally,we integrate spatial pyramid pooling and dilated convolution modules into the network architecture to enhance the perception of spatial information across a broader range.The seismic facies classification test on the public data from the F3 block verifies the superior performance of our proposed improved network structure in delineating seismic facies boundaries.Comparative analysis against the traditional UNet model reveals that our method achieves more accurate predictive classification results,as evidenced by various evaluation metrics for image segmentation.Obviously,the classification accuracy reaches an impressive 96%.Furthermore,the results of seismic facies classification in the seismic slice dimension provide further confirmation of the superior performance of our proposed method,which accurately defines the range of different seismic facies.This approach holds significant potential for analyzing geological patterns and extracting valuable depositional information.

An Improved UNet Lightweight Network for Semantic Segmentation of Weed Images in Corn Fields

In cornfields,factors such as the similarity between corn seedlings and weeds and the blurring of plant edge details pose challenges to corn and weed segmentation.In addition,remote areas such as farmland are usually constrained by limited computational resources and limited collected data.Therefore,it becomes necessary to lighten the model to better adapt to complex cornfield scene,and make full use of the limited data information.In this paper,we propose an improved image segmentation algorithm based on unet.Firstly,the inverted residual structure is introduced into the contraction path to reduce the number of parameters in the training process and improve the feature extraction ability;secondly,the pyramid pooling module is introduced to enhance the network’s ability of acquiring contextual information as well as the ability of dealing with the small target loss problem;and lastly,Finally,to further enhance the segmentation capability of the model,the squeeze and excitation mechanism is introduced in the expansion path.We used images of corn seedlings collected in the field and publicly available corn weed datasets to evaluate the improved model.The improved model has a total parameter of 3.79 M and miou can achieve 87.9%.The fps on a single 3050 ti video card is about 58.9.The experimental results show that the network proposed in this paper can quickly segment corn weeds in a cornfield scenario with good segmentation accuracy.

Bayesian partial pooling to reduce uncertainty in overcoring rock stress estimation

The state of in situ stress is a crucial parameter in subsurface engineering,especially for critical projects like nuclear waste repository.As one of the two ISRM suggested methods,the overcoring(OC)method is widely used to estimate the full stress tensors in rocks by independent regression analysis of the data from each OC test.However,such customary independent analysis of individual OC tests,known as no pooling,is liable to yield unreliable test-specific stress estimates due to various uncertainty sources involved in the OC method.To address this problem,a practical and no-cost solution is considered by incorporating into OC data analysis additional information implied within adjacent OC tests,which are usually available in OC measurement campaigns.Hence,this paper presents a Bayesian partial pooling(hierarchical)model for combined analysis of adjacent OC tests.We performed five case studies using OC test data made at a nuclear waste repository research site of Sweden.The results demonstrate that partial pooling of adjacent OC tests indeed allows borrowing of information across adjacent tests,and yields improved stress tensor estimates with reduced uncertainties simultaneously for all individual tests than they are independently analysed as no pooling,particularly for those unreliable no pooling stress estimates.A further model comparison shows that the partial pooling model also gives better predictive performance,and thus confirms that the information borrowed across adjacent OC tests is relevant and effective.

Tackling the proton limit under industrial electrochemical CO_(2)reduction by a local proton shuttle

Industrial CO_(2)electroreduction has received tremendous attentions for resolution of the current energy and environmental crisis,but its performance is greatly limited by mass transport at high current density.In this work,an ion‐polymer‐modified gas‐diffusion electrode is used to tackle this proton limit.It is found that gas diffusion electrode‐Nafion shows an impressive performance of 75.2%Faradaic efficiency in multicarbon products at an industrial current density of 1.16 A/cm^(2).Significantly,in‐depth electrochemical characterizations combined with in situ Raman have been used to determine the full workflow of protons,and it is found that HCO_(3)^(−)acts as a proton pool near the reaction environment,and HCO_(3)^(−)and H_(3)O^(+)are local proton donors that interact with the proton shuttle−SO_(3)^(−)from Nafion.With rich proton hopping sites that decrease the activation energy,a“Grotthuss”mechanism for proton transport in the above system has been identified rather than the“Vehicle”mechanism with a higher energy barrier.Therefore,this work could be very useful in terms of the achievement of industrial CO_(2)reduction fundamentally and practically.

An Intelligent Framework for Resilience Recovery of FANETs with Spatio-Temporal Aggregation and Multi-Head Attention Mechanism

Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanned Aerial Vehicle(UAV)swarms in harsh environments.This paper proposes an intelligent framework to quickly recover the cooperative coveragemission by aggregating the historical spatio-temporal network with the attention mechanism.The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model.A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction by capturing the temporal network structure.Combined with the corresponding Laplacian matrix as the hyperparameter,a recovery algorithm based on the multi-head attention graph network is designed to achieve rapid recovery.Simulation results showed that the proposed framework can facilitate rapid recovery of the connectivity and coverage more effectively compared to the existing studies.The results demonstrate that the average connectivity and coverage results is improved by 17.92%and 16.96%,respectively compared with the state-of-the-art model.Furthermore,by the ablation study,the contributions of each different improvement are compared.The proposed model can be used to support resilient network design for real-time mission execution.

NFHP-RN:AMethod of Few-Shot Network Attack Detection Based on the Network Flow Holographic Picture-ResNet

Due to the rapid evolution of Advanced Persistent Threats(APTs)attacks,the emergence of new and rare attack samples,and even those never seen before,make it challenging for traditional rule-based detection methods to extract universal rules for effective detection.With the progress in techniques such as transfer learning and meta-learning,few-shot network attack detection has progressed.However,challenges in few-shot network attack detection arise from the inability of time sequence flow features to adapt to the fixed length input requirement of deep learning,difficulties in capturing rich information from original flow in the case of insufficient samples,and the challenge of high-level abstract representation.To address these challenges,a few-shot network attack detection based on NFHP(Network Flow Holographic Picture)-RN(ResNet)is proposed.Specifically,leveraging inherent properties of images such as translation invariance,rotation invariance,scale invariance,and illumination invariance,network attack traffic features and contextual relationships are intuitively represented in NFHP.In addition,an improved RN network model is employed for high-level abstract feature extraction,ensuring that the extracted high-level abstract features maintain the detailed characteristics of the original traffic behavior,regardless of changes in background traffic.Finally,a meta-learning model based on the self-attention mechanism is constructed,achieving the detection of novel APT few-shot network attacks through the empirical generalization of high-level abstract feature representations of known-class network attack behaviors.Experimental results demonstrate that the proposed method can learn high-level abstract features of network attacks across different traffic detail granularities.Comparedwith state-of-the-artmethods,it achieves favorable accuracy,precision,recall,and F1 scores for the identification of unknown-class network attacks through cross-validation onmultiple datasets.

A Pooling Method Developed for Use in Convolutional Neural Networks

In convolutional neural networks,pooling methods are used to reduce both the size of the data and the number of parameters after the convolution of the models.These methods reduce the computational amount of convolutional neural networks,making the neural network more efficient.Maximum pooling,average pooling,and minimum pooling methods are generally used in convolutional neural networks.However,these pooling methods are not suitable for all datasets used in neural network applications.In this study,a new pooling approach to the literature is proposed to increase the efficiency and success rates of convolutional neural networks.This method,which we call MAM(Maximum Average Minimum)pooling,is more interactive than other traditional maximum pooling,average pooling,and minimum pooling methods and reduces data loss by calculating the more appropriate pixel value.The proposed MAM pooling method increases the performance of the neural network by calculating the optimal value during the training of convolutional neural networks.To determine the success accuracy of the proposed MAM pooling method and compare it with other traditional pooling methods,training was carried out on the LeNet-5 model using CIFAR-10,CIFAR-100,and MNIST datasets.According to the results obtained,the proposed MAM pooling method performed better than the maximum pooling,average pooling,and minimum pooling methods in all pool sizes on three different datasets.

Towards implementation of alloy-specific thermo-fluid modelling for laser powder-bed fusion of Mg alloys

Multi-physics thermo-fluid modeling has been extensively used as an approach to understand melt pool dynamics and defect formation as well as optimizing the process-related parameters of laser powder-bed fusion(L-PBF).However,its capabilities for being implemented as a reliable tool for material design,where minor changes in material-related parameters must be accurately captured,is still in question.In the present research,first,a thermo-fluid computational fluid dynamics(CFD)model is developed and validated against experimental data.Considering the predicted material properties of the pure Mg and commercial ZK60 and WE43 Mg alloys,parametric studies are done attempting to elucidate how the difference in some of the material properties,i.e.,saturated vapor pressure,viscosity,and solidification range,can influence the melt pool dynamics.It is found that a higher saturated vapor pressure,associated with the ZK60 alloy,leads to a deeper unstable keyhole,increasing the keyhole-induced porosity and evaporation mass loss.Higher viscosity and wider solidification range can increase the non-uniformity of temperature and velocity distribution on the keyhole walls,resulting in increased keyhole instability and formation of defects.Finally,the WE43 alloy showed the best behavior in terms of defect formation and evaporation mass loss,providing theoretical support to the extensive use of this alloy in L-PBF.In summary,this study suggests an approach to investigate the effect of materials-related parameters on L-PBF melting and solidification,which can be extremely helpful for future design of new alloys suitable for L-PBF.

Evolution Mechanism of a Severe Squall Line Triggered by the Coupling of a Sea Breeze Front and a Gust Front

In the present study, a severe squall line(SL) was analyzed by using intensive observational surface data and radar monitoring products. In this process, mesoscale convergence lines, such as the sea breeze front(SBF), gust front and dry line, served as the main triggering and strengthening factors. The transition from convection triggering to the formation of the initial shape was mainly affected by the convergence line of the SBF, which combined with thermal convection to form the main parts of the SL. In the later stage, the convergence line of the gust front merged with other convergence lines to form a series of strong convective cells. The SBF had good indicative significance in terms of severe convective weather warnings. The suitable conditions of heat, water vapor and vertical wind shear on the Shandong Peninsula were beneficial to the maintenance of the SL. Before SL occurrence, tropopause folding strengthened, which consequently enhanced the baroclinic property in the middle and upper troposphere. The high sensible heat flux at the surface easily produced a positive potential vorticity anomaly in the low layer, resulting in convective instability, which was conducive to the maintenance of these processes. In the system, when precipitation particles passed through the unsaturated air layer, they underwent strong evaporation, melting or sublimation, and the cooling effect formed negative buoyancy, which accelerated the sinking of the air and promoted the sustained development of the surface gale. Together with the development of lowlevel mesocyclones, the air pressure decreased rapidly, which was conducive to gale initiation.

Mesoscale and Microphysical Characteristics of a Double Rain Belt Event in South China on May 10–13,2022

A second rain belt sometimes occurs ahead of a frontal rain belt in the warm sector over coastal South China,leading to heavy precipitation.We examined the differences in the mesoscale characteristics and microphysics of the frontal and warm sector rain belts that occurred in South China on May 10–13,2022.The southern rain belt occurred in an environment with favorable mesoscale conditions but weak large-scale forcing.In contrast,the northern rain belt was related to low-level horizontal shear and the surface-level front.The interaction between the enhanced southeasterly winds and the rainfall-induced cold pool promoted the persistent growth of convection along the southern rain belt.The convective cell propagated east over the coastal area,where there was a large temperature gradient.The bow-shaped echo in this region may be closely related to the rear-inflow jet.By contrast,the initial convection of the northern rain belt was triggered along the front and the region of low-level horizontal shear,with mesoscale interactions between the enhanced warm-moist southeasterly airflow and the cold dome associated with the earlier rain.The terrain blocked the movement of the cold pool,resulting in the stagnation of the frontal convective cell at an early stage.Subsequently,a meso-γ-scale vortex formed during the rapid movement of the convective cell,corresponding to an enhancement of precipitation.The representative raindrop spectra for the southern rain belt were characterized by a greater number and higher density of raindrops than the northern rain belt,even though both resulted in comparable hourly rainfalls.These results help us better understand the characteristics of double rain belts over South China.

The Importance of Setting Treatment Goals for Cardiovascular Diseases

Background: Guidelines are issued by most major organizations that focus on a specific disease entity. Guidelines should be a significant help to the practicing physician who may not be up-to-date with the recent medical literature. Unfortunately, when conflicting guidelines for a specific disease are published, confusion results. Purpose: This article provides a suggested guideline outcome measure that would benefit the physician and patient. Methods: A review of 19 different guidelines for cardiovascular disease treatment is one example of the lack of specific outcomes that currently exist. The basic problem with most guidelines is that they do not state the expected end result (i.e., the benefit to the patient) if that guideline is followed. When guidelines use cardiovascular disease risk factors to dictate therapy, the end benefit is never stated so that the patient can make an appropriate choice of which (if any) guideline to follow. Results: A good example is guidelines published by the American Heart Association for reducing cardiovascular disease. These guidelines are risk factor based and only indicate that cardiovascular disease would be reduced if followed. No specific percentage in the reduction of the incidence of disease is given. In contrast, when elimination of the disease is the stated goal of the guideline, the end result is clear. To date, this goal has been stated by only one organization devoted to eliminating cardiovascular disease. Conclusion: Guidelines need to be written to provide the physician and the patient with a specific end point that is expected when the guideline is followed. Patient acceptance and compliance will be much improved if the patient knows the risk/benefit of following the guideline’s recommendations.

Expanding the liver donor pool worldwide with hepatitis C infected livers, is it the time?

Liver transplantation(LT)provides a life-saving option for cirrhotic patients with complications and hepatocellular carcinoma.Despite the increasing number of liver transplants performed each year,the number of LT candidates on the waitlist remains unchanged due to an imbalance between donor organ supply and the demand which increases the waitlist time and mortality.Living donor liver transplant had a great role in increasing the donor pool and shortened waitlist time for LT candidates.Nevertheless,further strategies can be implemented to increase the pool of potential donors in deceased donor LT,such as reducing the rate of organ discards.Utilizing hepatitis C virus(HCV)seropositive liver grafts is one of the expanded donor organ criteria.A yearly increase of hundreds of transplants is anticipated as a result of maximizing the utilization of HCV-positive organs for HCV-negative recipients.Direct-acting antiviral therapy's efficacy has revolutionized the treatment of HCV infection and the use of HCV-seropositive donors in transplantation.The American Society of Transplantation advises against performing transplants from HCV-infected liver donors(D+)into HCV-negative recipient(R-)unless under Institutional Review Board-approved study rules and with full informed consent of the knowledge gaps associated with such transplants.Proper selection of patients to be transplanted with HCV-infected grafts and confirming their access to direct-acting antivirals if needed is im-portant.National and international consensuses are needed to regulate this process to ensure the maximum benefit and the least adverse events.

U-Net Based Dual-Pooling Segmentation of Bone Metastases in Thoracic SPECT Bone Scintigrams

In order to enhance the performance of the CNN-based segmentation models for bone metastases, this study proposes a segmentation method that integrates dual-pooling, DAC, and RMP modules. The network consists of distinct feature encoding and decoding stages, with dual-pooling modules employed in encoding stages to maintain the background information needed for bone scintigrams diagnosis. Both the DAC and RMP modules are utilized in the bottleneck layer to address the multi-scale problem of metastatic lesions. Experimental evaluations on 306 clinical SPECT data have demonstrated that the proposed method showcases a substantial improvement in both DSC and Recall scores by 3.28% and 6.55% compared the baseline. Exhaustive case studies illustrate the superiority of the methodology.

基于CSSE模型的公路路面裂缝检测方法研究

为解决目前现有的裂缝识别方法存在的识别效果不佳、识别精度低等弊端,提出一种基于CSSE模型的路面裂缝检测识别方法。该方法以CNN卷积网络为基础,通过融合SPPFCSPC空间金字塔池化结构与SE注意力机制,从而实现裂缝的准确快速的定位。首先,并通过lableimg图像标注软件对裂缝图像进行标注,建立本文的裂缝图像数据集,然后使用CSSE模型以及Yolov5-s和Yolov5-mobileone目标检测模型对裂缝数据集进行训练和测试对比,检测结果表明,CSSE模型识别效果优于yolov5裂缝检测模型。该模型能够满足裂缝数据准确、快速的检测需求,实现高阈值检测的裂缝精准定位。为高质量道路裂缝数据集的构建以及复杂裂缝损害智能识别奠定基础。