An integrated machine learning model for accurate and robust prediction of superconducting critical temperature

Discovering new superconductors via traditional trial-and-error experimental approaches is apparently a time-consuming process,and the correlations between the critical temperature(Tc) and material features are still obscure.The rise of machine learning(ML) technology provides new opportunities to speed up inefficient exploration processes,and could potentially uncover new hints on the unclear correlations.In this work,we utilize open-source materials data,ML models,and data mining methods to explore the correlation between the chemical features and Tcvalues of superconducting materials.To further improve the prediction accuracy,a new model is created by integrating three basic algorithms,showing an enhanced accuracy with the coefficient of determination(R2) score of 95.9 % and root mean square error(RMSE) of 6.3 K.The average marginal contributions of material features towards Tcvalues are estimated to determine the importance of various features during prediction processes.The results suggest that the range thermal conductivity plays a critical role in Tcprediction among all element features.Furthermore,the integrated ML model is utilized to screen out potential twenty superconducting materials with Tcvalues beyond 50.0 K.This study provides insights towards Tcprediction to accelerate the exploration of potential high-Tcsuperconductors.

Conformal Human–Machine Integration Using Highly Bending‑Insensitive,Unpixelated,and Waterproof Epidermal Electronics Toward Metaverse

Efficient and flexible interactions require precisely converting human intentions into computer-recognizable signals,which is critical to the breakthrough development of metaverse.Interactive electronics face common dilemmas,which realize highprecision and stable touch detection but are rigid,bulky,and thick or achieve high flexibility to wear but lose precision.Here,we construct highly bending-insensitive,unpixelated,and waterproof epidermal interfaces(BUW epidermal interfaces)and demonstrate their interactive applications of conformal human–machine integration.The BUW epidermal interface based on the addressable electrical contact structure exhibits high-precision and stable touch detection,high flexibility,rapid response time,excellent stability,and versatile“cut-and-paste”character.Regardless of whether being flat or bent,the BUW epidermal interface can be conformally attached to the human skin for real-time,comfortable,and unrestrained interactions.This research provides promising insight into the functional composite and structural design strategies for developing epidermal electronics,which offers a new technology route and may further broaden human–machine interactions toward metaverse.

An anomaly detection method for spacecraft solar arrays based on the ILS-SVM model

Solar arrays are important and indispensable parts of spacecraft and provide energy support for spacecraft to operate in orbit and complete on-orbit missions.When a spacecraft is in orbit,because the solar array is exposed to the harsh space environment,with increasing working time,the performance of its internal electronic components gradually degrade until abnormal damage occurs.This damage makes solar array power generation unable to fully meet the energy demand of a spacecraft.Therefore,timely and accurate detection of solar array anomalies is of great significance for the on-orbit operation and maintenance management of spacecraft.In this paper,we propose an anomaly detection method for spacecraft solar arrays based on the integrated least squares support vector machine(ILS-SVM)model:it selects correlated telemetry data from spacecraft solar arrays to form a training set and extracts n groups of training subsets from this set,then gets n corresponding least squares support vector machine(LS-SVM)submodels by training on these training subsets,respectively;after that,the ILS-SVM model is obtained by integrating these submodels through a weighting operation to increase the prediction accuracy and so on;finally,based on the obtained ILS-SVM model,a parameterfree and unsupervised anomaly determination method is proposed to detect the health status of solar arrays.We use the telemetry data set from a satellite in orbit to carry out experimental verification and find that the proposed method can diagnose solar array anomalies in time and can capture the signs before a solar array anomaly occurs,which reflects the applicability of the method.

Online payment fraud:from anomaly detection to risk management

Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account.Successfully preventing this requires the detection of as many fraudsters as possible,without producing too many false alarms.This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud.In addition,classical machine learning methods must be extended,minimizing expected financial losses.Finally,fraud can only be combated systematically and economically if the risks and costs in payment channels are known.We define three models that overcome these challenges:machine learning-based fraud detection,economic optimization of machine learning results,and a risk model to predict the risk of fraud while considering countermeasures.The models were tested utilizing real data.Our machine learning model alone reduces the expected and unexpected losses in the three aggregated payment channels by 15%compared to a benchmark consisting of static if-then rules.Optimizing the machine-learning model further reduces the expected losses by 52%.These results hold with a low false positive rate of 0.4%.Thus,the risk framework of the three models is viable from a business and risk perspective.

Population amount risk assessment of extreme precipitation-induced landslides based on integrated machine learning model and scenario simulation

In this study,the future landslide population amount risk(LPAR)is assessed based on integrated machine learning models(MLMs)and scenario simulation techniques in Shuicheng County,China.Firstly,multiple MLMs were selected and hyperparameters were optimized,and the generated 11 models were crossintegrated to select the best model to calculate landslide susceptibility;by calculating precipitation for different extreme precipitation recurrence periods and combining the susceptibility results to assess the landslide hazard.Using the town as the basic unit,the exposure and vulnerability of the future landslide population under different Shared Socioeconomic Pathways(SSPs)scenarios in each town were assessed,and then combined with the hazard to estimate the LPAR in 2050.The results showed that the integrated model with the optimized random forest model as the combination strategy had the best comprehensive performance in susceptibility assessment.The distribution of hazard classes is similar to susceptibility,and with an increase in precipitation,the low-hazard area and high-hazard decrease and shift to medium-hazard and very high-hazard classes.The high-risk areas for future landslide populations in Shuicheng County are mainly concentrated in the three southwestern towns with high vulnerability,whereas the northern towns of Baohua and Qinglin are at the lowest risk class.The LPAR increased with the intensity of extreme precipitation.The LPAR differs significantly among the SSPs scenarios,with the lowest in the“fossil-fueled development(SSP5)”scenario and the highest in the“regional rivalry(SSP3)”scenario.In summary,the landslide susceptibility model based on integrated machine learning proposed in this study has a high predictive capability.The results of future LPAR assessment can provide theoretical guidance for relevant departments to cope with future socioeconomic development challenges and make corresponding disaster prevention and mitigation plans to prevent landslide risks from a developmental perspective.

Supporting Seeder and Planting Technology for Spring Corn in Arid Areas

The annual rainfall is low and the fresh water resources are scarce for the rainfed farming in dry zone of northern China,which seriously affects the sowing and growth of spring corn. In order to solve this problem,the technology of ridge-mulching and side-sowing of spring corn is put forward,the supporting compound operation seeder is developed,and the effect of different speed on the quality of sowing is tested and analyzed. Under the test conditions described in this paper,the seeding operation with a high speed( up to 6 km/h) can be realized,and the quality of the seeding operation can meet the requirements of the national standards. The application of this machine can solve the problem of " drought damage at the booting stage" for spring corn,thereby realizing the deep fusion of farming machine and agronomy in dry farming of northern China,and achieving the integration of farming machine and agronomy.

Parallel-Path Power Flows in Magnetic-Geared Permanent Magnet Machines with Sandwiched Armature Stator

Due to the large torque density of magnetic gears(MGs),magnetic-geared permanent magnet machines(MGPMs)have become promising competitors in the family of direct-drive machines.According to the deployment of armature stators,MGPMs can be generally classified into three types,viz.,MGPM with inner armature stator(MGPM-IAS),MGPM with outer armature stator(MGPM-OAS),and MGPM with sandwiched armature stator(MGPM-SAS).Our investigation finds out that the MGPM-SAS can achieve parallel-path power flows better than the MGPM-OAS,while the MGPM-IAS is with serial power flow paths.Therefore,the torque capability of MGPM-IAS is limited by the torque transmission capability of its integrated MG.However,the MGPM-SAS has the possibility to offer even higher output torque than its integrated MG.This paper focuses on the MGPM-SAS.And three typical MGPMs and their power flow paths are introduced;the interaction of electromagnetic fields in MGPM-SAS is analyzed;simulation calculation and experimental verification are conducted to demonstrate the validity of the theoretical analysis.

Machinability of damage-tolerant titanium alloy in orthogonal turn-milling

The damage-tolerant titanium alloy TC21 is used extensively in important parts of advanced aircraft because of its high strength and durability. However, cutting TC21 entails problems, such as high cutting temperature, high tool tip stress, rapid tool wear, and difficulty guaranteeing processing quality. Orthogonal turn-milling can be used to solve these problems. In this study, the machinability of TC21 in orthogonal turn-milling is investigated experimentally to optimize the cutting parameters of orthogonal turn-milling and improve the machining efficiency, tool life, and machining quality of TC21. The mechanism of the effect of turn-milling parameters on tool life is discussed, the relationship between each parameter and tool life is analyzed, and the failure process of a TiAlN-coated tool in turn-milling is explored. Experiments are conducted on the integrity of the machined surface (surface roughness, metallographic structure, and work hardening) by turn-milling, and how the parameters influence such integrity is analyzed. Then, reasonable cutting parameters for TC21 in orthogonal turn-milling are recommended. This study provides strong guidance for exploring the machinability of difficult-to-cut-materials in orthogonal turn-milling and improves the applicability of orthogonal turn-milling for such materials.