新型智能交通系统防撞车 可根据司机习惯发警告

据悉，美国麻省理工学院的机械工程师正在开发一种新型防撞车智能安全系统，其与众不同之处在于，其智能算法可根据司机的驾驶习惯向司机发出撞车警告，并能最终采取相应措施来控制车辆，以防止车祸的发生。

Machine learning molecular dynamics simulations of liquid methanol

As the simplest hydrogen-bonded alcohol,liquid methanol has attracted intensive experimental and theoretical interest.However,theoretical investigations on this system have primarily relied on empirical intermolecular force fields or ab initio molecular dynamics with semilocal density functionals.Inspired by recent studies on bulk water using increasingly accurate machine learning force fields,we report a new machine learning force field for liquid methanol with a hybrid functional revPBE0 plus dispersion correction.Molecular dynamics simulations on this machine learning force field are orders of magnitude faster than ab initio molecular dynamics simulations,yielding the radial distribution functions,selfdiffusion coefficients,and hydrogen bond network properties with very small statistical errors.The resulting structural and dynamical properties are compared well with the experimental data,demonstrating the superior accuracy of this machine learning force field.This work represents a successful step toward a first-principles description of this benchmark system and showcases the general applicability of the machine learning force field in studying liquid systems.

Wafer Defect Map Pattern Recognition Based on Improved ResNet

The defect detection of wafers is an important part of semiconductor manufacturing.The wafer defect map formed from the defects can be used to trace back the problems in the production process and make improvements in the yield of wafer manufacturing.Therefore,for the pattern recognition of wafer defects,this paper uses an improved ResNet convolutional neural network for automatic pattern recognition of seven common wafer defects.On the basis of the original ResNet,the squeeze-and-excitation(SE)attention mechanism is embedded into the network,through which the feature extraction ability of the network can be improved,key features can be found,and useless features can be suppressed.In addition,the residual structure is improved,and the depth separable convolution is added to replace the traditional convolution to reduce the computational and parametric quantities of the network.In addition,the network structure is improved and the activation function is changed.Comprehensive experiments show that the precision of the improved ResNet in this paper reaches 98.5%,while the number of parameters is greatly reduced compared with the original model,and has well results compared with the common convolutional neural network.Comprehensively,the method in this paper can be very good for pattern recognition of common wafer defect types,and has certain application value.

Modeling and characterization on electroplastic effect during dynamic deformation of 5182-O aluminum alloy

The coupling effects of electrical pulse,temperature,strain rate,and strain on the flow behavior and plasticity of 5182-O aluminum alloy were investigated and characterized.The isothermal tensile test and electrically-assisted isothermal tensile test were performed at the same temperature,and three typical models were further embedded in ABAQUS/Explicit for numerical simulation to illustrate the electroplastic effect.The results show that electric pulse reduces the deformation resistance but enhances the elongation greatly.The calibration accuracy of the proposed modified Lim−Huh model for highly nonlinear and coupled dynamic hardening behavior is not much improved compared to the modified Kocks−Mecking model.Moreover,the artificial neural network model is very suitable to describe the macromechenical response of materials under the coupling effect of different variables.

Predictive maintenance and its applications in civil engineering structures:A review

Structural health monitoring and performance prediction are crucial for smart disaster mitigation and intelligent management of structures throughout their lifespan.Recent advancements in predictive maintenance strategies within the industrial manufacturing industry have inspired similar innovations in civil engineering,aiming to improve structural performance evaluation,damage diagnosis,and capacity prediction.This review delves into the framework of predictive maintenance and examines various existing solutions,focusing on critical areas such as data acquisition,condition monitoring,damage prognosis,and maintenance planning.Results from real-world applications of predictive maintenance in civil engineering,covering high-rise structures,deep foundation pits,and other infrastructure,are presented.The challenges of implementing predictive maintenance in civil engineering structures under current technology,such as model interpretability of data-driven methods and standards for predictive maintenance,are explored.Future research prospects within this area are also discussed.

Dynamic evaluation of digital and green development policies based on text mining of the PMC framework

Aiming to identify policy topics and their evolutionary logic that enhance the digital and green development(dual development)of traditional manufacturing enterprises,address weaknesses in current policies,and provide resources for refining dual development policies,a total of 15954 dual development-related policies issued by national and various departmental authorities in China from January 2000 to August 2023 were analyzed.Based on topic modeling techniques and the policy modeling consistency(PMC)framework,the evolution of policy topics was visualized,and a dynamic assessment of the policies was conducted.The results show that the digital and green development policy framework is progressively refined,and the governance philosophy shifts from a“regulatory government”paradigm to a“service-oriented government”.The support pattern evolves from“dispersed matching”to“integrated symbiosis”.However,there are still significant deficiencies in departmental cooperation,balanced measures,coordinated links,and multi-stakeholder participation.Future policy improvements should,therefore,focus on guiding multi-stakeholder participation,enhancing public demand orientation,and addressing the entire value chain.These steps aim to create an open and shared digital industry ecosystem to promote the coordinated dual development of traditional manufacturing enterprises.

Rapid detection and risk assessment of soil contamination at lead smelting site based on machine learning

A general prediction model for seven heavy metals was established using the heavy metal contents of 207soil samples measured by a portable X-ray fluorescence spectrometer(XRF)and six environmental factors as model correction coefficients.The eXtreme Gradient Boosting(XGBoost)model was used to fit the relationship between the content of heavy metals and environment characteristics to evaluate the soil ecological risk of the smelting site.The results demonstrated that the generalized prediction model developed for Pb,Cd,and As was highly accurate with fitted coefficients(R^(2))values of 0.911,0.950,and 0.835,respectively.Topsoil presented the highest ecological risk,and there existed high potential ecological risk at some positions with different depths due to high mobility of Cd.Generally,the application of machine learning significantly increased the accuracy of pXRF measurements,and identified key environmental factors.The adapted potential ecological risk assessment emphasized the need to focus on Pb,Cd,and As in future site remediation efforts.

Single-atom catalysts based on polarization switching of ferroelectric In_(2)Se_(3) for N_(2) reduction

The polarization switching plays a crucial role in controlling the final products in the catalytic pro-cess.The effect of polarization orientation on nitrogen reduction was investigated by anchoring transition metal atoms to form active centers on ferroelectric material In_(2)Se_(3).During the polariza-tion switching process,the difference in surface electrostatic potential leads to a redistribution of electronic states.This affects the interaction strength between the adsorbed small molecules and the catalyst substrate,thereby altering the reaction barrier.In addition,the surface states must be considered to prevent the adsorption of other small molecules(such as *O,*OH,and *H).Further-more,the V@↓-In_(2)Se_(3) possesses excellent catalytic properties,high electrochemical and thermody-namic stability,which facilitates the catalytic process.Machine learning also helps us further ex-plore the underlying mechanisms.The systematic investigation provides novel insights into the design and application of two-dimensional switchable ferroelectric catalysts for various chemical processes.

温馨提示：失眠请关掉手机

现在很多青少年在晚上关灯后．仍继续使用手机打电话、传送简讯，在对这些人进行一年的追踪调查后发明，与普通学生相比。这些睡前有摆弄手机习惯的人，有明显的身体疲惫感，使用手机越频繁．人就会感到越劳累。

Deep learning model based on PET/CT and combination with Cox proportional hazard model for predicting progression of lung invasive adenocarcinoma after surgery

Objective To observe the efficacy of deep learning(DL)model based on PET/CT and its combination with Cox proportional hazard model for predicting progressive disease(PD)of lung invasive adenocarcinoma within 5 years after surgery.Methods The clinical,PET/CT and 5-year follow-up data of 250 patients with lung invasive adenocarcinoma were retrospectively analyzed.According to PD or not,the patients were divided into the PD group(n=71)and non-PD group(n=179).The basic data and PET/CT findings were compared between groups,among which the quantitative variables being significant different between groups were transformed to categorical variables using receiver operating characteristic(ROC)curve and corresponding cut-off value.Multivariant Cox proportional hazard model was used to select independent predicting factors of PD of lung invasive adenocarcinoma within 5 years after surgery.The patients were divided into training,validation and test sets at the ratio of 6∶2∶2,and PET/CT data in training set and validation set were used to train model and tuning parameters to build the PET/CT DL model,and the combination model was built in serial connection of DL model and the predictive factors.In test set,the efficacy of each model for predicting PD of lung invasive adenocarcinoma within 5 years after surgery was assessed and compared using the area under the curve(AUC).Results Patients'gender and smoking status,as well as the long diameter,SUV max and SUV mean of lesions measured on PET images,the long diameter,short diameter and type of lesions showed on CT were statistically different between groups(all P<0.05).Smoking(HR=1.787[1.053,3.031],P=0.031)and lesion SUV max>4.15(HR=5.249[1.062,25.945],P=0.042)were both predictors of PD of lung invasive adenocarcinoma within 5 years after surgery.In test set,the AUC of PET/CT DL model for predicting PD was 0.847,of the combination model was 0.890,of the latter was higher than of the former(P=0.036).Conclusion DL model based on PET/CT had high efficacy for predicting PD of lung invasive adenocarcinoma within 5 years after surgery.Combining with Cox proportional hazard model could further improve its predicting efficacy.

新鲜应用引领NFC起航

智能手机的普及速度超出了曾垒的预期，也因此，许多与之相关的应用得以更加迅速的实现。通过手机习台，依托专业的手机应用，生活变得更加便捷，而许多技术也得以进一当拓展自身的价值空间，凸显出巨大舒潜力。比如，我们所熟知的NFC。为了更好地了解NFC技术的发居趋势，记者专门采访了HID公司大中耷区营销总监赵建邦。

Whisper intelligibility enhancement based on noise robust feature and SVM

A machine learning based speech enhancement method is proposed to improve the intelligibility of whispered speech. A binary mask estimated by a two-class support vector machine （SVM） classifier is used to synthesize the enhanced whisper. A novel noise robust feature called Gammatone feature cosine coefficients （GFCCs） extracted by an auditory periphery model is derived and used for the binary mask estimation. The intelligibility performance of the proposed method is evaluated and compared with the traditional speech enhancement methods. Objective and subjective evaluation results indicate that the proposed method can effectively improve the intelligibility of whispered speech which is contaminated by noise. Compared with the power subtract algorithm and the log-MMSE algorithm, both of which do not improve the intelligibility in lower signal-to-noise ratio （SNR） environments, the proposed method has good performance in improving the intelligibility of noisy whisper. Additionally, the intelligibility of the enhanced whispered speech using the proposed method also outperforms that of the corresponding unprocessed noisy whispered speech.

Intrusion detection model based on deep belief nets

This paper focuses on the intrusion classification of huge amounts of data in a network intrusion detection system. An intrusion detection model based on deep belief nets （DBN） is proposed to conduct intrusion detection,and the principles regarding DBN are discussed.The DBN is composed of a multiple unsupervised restricted Boltzmann machine （RBM） and a supervised back propagation （BP）network.First,the DBN in the proposed model is pre-trained in a fast and greedy way,and each RBM is trained by the contrastive divergence algorithm.Secondly,the whole network is fine-tuned by the supervised BP algorithm,which is employed for classifying the low-dimensional features of the intrusion data generated by the last RBM layer simultaneously.The experimental results on the KDD CUP 1999 dataset demonstrate that the DBN using the RBM network with three or more layers outperforms the self-organizing maps （SOM）and neural network （NN）in intrusion classification.Therefore,the DBN is an efficient approach for intrusion detection in high-dimensional space.

Phishing detection method based on URL features

In order to effectively detect malicious phishing behaviors, a phishing detection method based on the uniform resource locator （URL） features is proposed. First, the method compares the phishing URLs with legal ones to extract the features of phishing URLs. Then a machine learning algorithm is applied to obtain the URL classification model from the sample data set training. In order to adapt to the change of a phishing URL, the classification model should be constantly updated according to the new samples. So, an incremental learning algorithm based on the feedback of the original sample data set is designed. The experiments verify that the combination of the URL features extracted in this paper and the support vector machine （SVM） classification algorithm can achieve a high phishing detection accuracy, and the incremental learning algorithm is also effective.

企业财务预警系统的理论与系统建立

以工业企业为对象,探讨建立企业财务预警系统的一般性理论及系统的结构形式,给出建立系统的基本思想和理论依据,并将“神经网络技术”应用于微观预警系统,给出普遍意义上的“财务预警指标体系”。

New rank learning algorithm

To overcome the limitation that complex data types with noun attributes cannot be processed by rank learning algorithms, a new rank learning algorithm is designed. In the learning algorithm based on the decision tree, the splitting rule of the decision tree is revised with a new definition of rank impurity. A new rank learning algorithm, which can be intuitively explained, is obtained and its theoretical basis is provided. The experimental results show that in the aspect of average rank loss, the ranking tree algorithm outperforms perception ranking and ordinal regression algorithms and it also has a faster convergence speed. The rank learning algorithm based on the decision tree is able to process categorical data and select relative features.

ON HYBRID POSITION/FORCE COORDINATED LEARNING CONTROL OF MULTIPLE MANIPULATORS

In this paper, coordinated control of multiple robot manipulators holding a rigid object is discussed. In consideration of inaccuracy of the dynamic model of a multiple manipulator system, the error equations on object position and internal force are derived. Then a hybrid position/force coordinated learning control scheme is presented and its convergence is proved. The scheme can improve the system performance by modifying the control input of the system after each iterative learning. Simulation results of two planar robot manipulators holding an object show the effectiveness of this control scheme.

On-road vehicle verification based on VS-HOG and ELM

A solution is proposed for the real-time vehicle verification which is an important problem for numerous on- road vehicle applications. First, based on the vertical symmetry characteristics of vehicle images, a vertical symmetrical histograms of oriented gradients （VS-HOG） descriptor is proposed for extracting the image features. In the classification stage, an extreme learning machine （ELM） is used to improve the real-time performance. Experimental data demonstrate that, compared with other classical methods, the vehicle verification algorithm based on VS-HOG and ELM achieves a better trade-off between cost and performance. The computational cost is reduced by using the algorithm, while keeping the performance loss as low as possible. Furthermore, experimental results further show that the proposed vehicle verification method is suitable for on-road vehicle applications due to its better performance both in efficiency and accuracy.