Large Speed Research Articles

Multifunctional optoelectronic devices based on two-dimensional tellurium/MoS2 heterojunction

Two-dimensional van der Waals heterojunctions consisted of p–n-type semiconductors have been rapidly developed owing to their built-in electric field which can facilitate the separation of photogenerated electron–hole pairs and properties like current rectification and negative differential transconductance. Benefitting from these advantages, we have prepared an air-stable multifunctional p-tellurium (Te)/n-MoS2 heterostructure working both as a self-driven broadband photodetector and as an optically switchable complementary metal-oxide-semiconductor inverter. For photodetection, this device exhibits wavelength-modulated positive/negative optical response with large responsivity (1.51 A/W at 520 nm and 642.92 mA/W at 1550 nm, Vds = 0 V) and fast response speed, showcasing its prospects for optical encoding communication. Moreover, the device has been demonstrated to function as an inverter that will be shut down by illumination. Our multifunctional device possesses the compactness of integrated modules, widens the application scope of Te-based heterojunctions, and provides a reference for the application of Te-based devices in the field of integrated circuits.

ADRC Control of Ultra-High-Speed Electric Air Compressor Considering Excitation Observation

With the increasing power of fuel cells, ultra-high-speed electric air compressors (UHSEACs) have been widely used. However, due to the ultra-high speeds involved, UHSEACs face problems such as long speed adjustment times and large speed fluctuations. Compared to other control methods, Active Disturbance Rejection Control (ADRC) is well-suited for highly nonlinear systems like UHSEACs. The Extended State Observer (ESO), a key component of the ADRC, struggles to accurately observe high-frequency excitations. To address this, the first step is to add a cascaded structure to the ESO and design a Current State Extended State Observer (CS-ESO) to better observe the electromagnetic and load excitations in the UHSEAC. The second step involves designing the ADRC based on the CS-ESO and performing speed adjustment simulations. The third step is to build a UHSEAC experimental platform and a conduct speed adjustment experiment. The findings indicate that, compared to the Proportional Integral Derivative (PID) control, the ADRC with the ESO, and the Sliding Mode Control (SMC), the use of the ADRC with the CS-ESO results in a significant reduction in overshoot—by at least 760 RPM under load-increasing conditions and 140 RPM under load-reducing conditions. Furthermore, the speed regulation time is notably decreased by at least 0.2 s and 0.1 s under these respective conditions.

Application of model predictive control scheme for piezoelectric ceramic micro-displacement

Abstract Currently, in the domain of micro-control technology, many operations need to meet the standard of nanometer-level positioning. As a high-precision micro-displacement element, piezoelectric ceramics are widely used in the field of micro-control due to their characteristics such as no need for transmission devices, high resolution, large output force, small size, and fast response speed. However, the inherent hysteresis nonlinearity of piezoelectric ceramics can lead to decreased displacement accuracy, oscillation, and even system instability. Therefore, eliminating the influence of nonlinear characteristics and improving displacement accuracy possess significant practical importance. In this paper, a model predictive control scheme is designed, which achieves faster response speed and higher accuracy compared to traditional control methods such as Proportional Integral Derivative control. Relevant simulations and tests have been conducted. In this scheme, hysteresis nonlinearity is first modeled and then compensated through feedforward, thereby simplifying the displacement control of piezoelectric ceramics into a more manageable linear problem. Subsequently, a linear model predictive control method is adopted to control the entire system. Both simulation and experimental results have verified the effectiveness of this control method.

Research on Acoustic Monitoring of Escape of Large Yellow Croaker Cultured in Cages

It is an important problem for large yellow croaker culture to escape due to the damage of net coat. In this study, a horizontal moving fish finder was installed in a cage to monitor large yellow croaker groups with different numbers and body lengths by acoustic means, and the behavioral characteristics of fish groups were observed after the experimental net was damaged by using the nesting method. Through the identification of large yellow croaker group individual, swimming speed, size, direction and other group behavior characteristics, in order to prevent the escape of fish, set green (normal), yellow (ordinary alarm), red (strong alarm) three state display. This study will provide technical support for the more scientific and safe development of large yellow croaker cage culture.

Data Mining of Telematics Data: Unveiling the Hidden Patterns in Driving Behavior

With the advancements in technology, telematics data that capture vehicle movement information are becoming available to more insurers. Because these data capture the actual driving behavior, they are expected to improve our understanding of driving risk and facilitate more accurate auto insurance ratemaking. In this article, we analyze an auto insurance dataset with telematics data collected from a major European insurer. Through a detailed discussion of the telematics data structure and related data quality issues, we elaborate on practical challenges in processing and incorporating telematics information in loss modeling and ratemaking. Then, with an exploratory data analysis, we demonstrate the existence of heterogeneity in individual driving behavior, even within the groups of policyholders with and without claims, which supports the study of telematics data. Our regression analysis reiterates the importance of telematics data in claims modeling; in particular, we propose a speed transition matrix that describes discretely recorded speed time series and produces statistically significant predictors for claim counts. We conclude that large speed transitions, together with higher maximum speed attained, nighttime driving, and increased harsh braking, are associated with increased claim counts. Moreover, we empirically illustrate the learning effects in driving behavior: we show that both severe harsh events detected at a high threshold and expected claim counts are not directly proportional with driving time or distance but they increase at a decreasing rate.

Graphene electrode-enhanced InSe/WSe2 van der Waals heterostructure for high-performance broadband photodetector with imaging capabilities

2D vdWs heterostructure is realized as a powerful technique for tuning the optoelectronic properties and thus developing the future generation optoelectronic devices, especially for photodetectors. However, photodetectors based on 2D vdWs heterostructure suffer from responsivity, detectivity, and large photoresponse speed inhibits their applications in further diverse areas. Here, a graphene electrode-based InSe/WSe2 vdWs heterostructure is proposed aiming to develop a high-performance photodetector. Owing to the graphene electrode, the heterostructure devices build a strong electronic field at the surface of the InSe/WSe2 vdWs heterostructure. As a result, the device shows excellent optoelectronic characteristics such as broad band photoresponse ranging from 532 nm (visible) to 1100 nm (near infrared) including a high responsivity of 829.7 AW−1, a detectivity of 2.81×1014 Jones and a rapid photoresponse of 10 µs. Significantly, as presented photodetector device is applied in an imaging system, showcasing its capability for high-contrast photodetection. These findings highlight that the potential of graphene electrode integration in 2D vdWs heterostructure provides an effective roadmap for developing the advancing next generation of optoelectronic devices.

Numerical Study of Skipping Motion of Blended-Wing–Body Aircraft Ditching on Calm/Wavy Water

The blended-wing–body (BWB) is hoped to be the main configuration of next-generation transport aircraft. However, its large flat belly may cause a violent skipping motion during the ditching on water. In this paper, the skipping motions of a BWB aircraft ditching on calm and wavy water are numerically investigated. The finite volume method with the volume-of-fluid approach is employed to solve the unsteady Reynolds-averaged Navier–Stokes equations. A coupled method between global moving mesh and overset mesh is proposed to avoid a large background domain and improve the prediction accuracy of the free surface, and its accuracy is well validated by predicting the motions of a skipping stone, the hydrodynamic load and pressure on a flat plate in high-speed ditching, and the fifth-order Stokes wave in a dynamic wave tank. At the beginning of every surfing stage, the flat belly of BWB aircraft impacts heavily on the water surface at a smaller pitch angle and larger speed, resulting in the local vertical overload peak, which is much larger than the peak in the porpoising motion. The wavy water surface intensifies the heave and pitch motions and increases the risk of the cockpit diving into the water.

Coupled Fire–Atmosphere Simulations of the Split, Croatia, Wildfire

Abstract The Weather Research and Forecasting Spread FIRE (WRF-SFIRE) model was used to simulate the evolution of the fire perimeter for the Split wildfire in Croatia, evaluated in our previous detailed reconstruction of the event. A four-domain configuration was applied, with the innermost domain having a resolution of 500 m with a planetary boundary layer parameterization. The coupled fire grid was further downscaled to 33.3-m resolution, using the best available estimates of topography and fuel load data. WRF-SFIRE simulated slightly smaller fire areas in the four observed fire perimeter stages of the Split wildfire event, especially the northwestward spread in the last stage along the Adriatic coast. Additional experiments with multiple ignitions in the WRF-SFIRE model, representing the spotting mechanism, successfully simulated the northwestward spread in the event. The study also included sensitivity experiments that turned off the heat and moisture flux coupling between WRF and the fire grid. It was found that the simulated fire area is larger than the model run that included heat and moisture feedback from the fire grid to the atmosphere, which is contrary to some previous studies. The large wind speed and convergence along the fire line in our feedback-on simulation, which constrain the fire when there are feedback processes, were likely the cause of the smaller fire area. Last, areas of pine that burnt in the Split fire event favored crown fire spread, which is not represented in the current modeling framework of WRF-SFIRE.

Three-dimensional positioning and trajectory tracking of pigeons in large indoor spaces

Animal localization and trajectory tracking are of great value for the study of brain spatial cognition and navigation neural mechanisms. However, traditional optical lens video positioning techniques are limited in their scope due to factors such as camera perspective. For pigeons with excellent spatial cognition and navigation abilities, based on the beacon positioning technology, a three-dimensional (3D) trajectory positioning and tracking method suitable for large indoor spaces was proposed, and the corresponding positioning principle and hardware structure were provided. The results of in vitro and in vivo experiments showed that the system could achieve centimeter-level positioning and trajectory tracking of pigeons in a space of 360 cm × 200 cm × 245 cm. Compared with traditional optical lens video positioning techniques, this system has the advantages of large space, high precision, and high response speed. It not only helps to study the neural mechanisms of pigeon 3D spatial cognition and navigation, but also has high reference value for trajectory tracking of other animals.

Deep residual fully connected network for GNSS-R wind speed retrieval and its interpretation

Global navigation satellite system reflectometry (GNSS-R) has emerged as a new technique to provide L-band bistatic measurements for ocean wind speed retrieval, in which traditional geophysical model functions (GMFs) or shallow neural networks (NNs) are normally used. However, it is still challenging to identify and consider all relevant parameters in the GMF. Meanwhile, NN models face limitations due to the degradation problem, which restricts their depth and consequently their performance. Furthermore, the interpretation of NN models for GNSS-R wind retrieval is another issue. To this end, we propose a residual fully connected network (RFCN) fusing auxiliary information such as geometry, receiver gain, significant wave height, and current speed with track-wise corrected σ0. Referred to the European Centre for Medium-Range Weather Forecast (ECMWF) ERA5 wind product, the root mean square error (RMSE) and bias of RFCN winds are 1.031 m/s and -0.0003 m/s, respectively, with a 6% improvement in RMSE compared to debiased NOAA Cyclone Global Navigation Satellite System (CYGNSS) Version 1.2 (V1.2) wind speed retrieval. Moreover, in an intertropical convergence zone (ITCZ) area with large current speeds, the RMSE and bias are 1.006 m/s and -0.022 m/s: an improvement of 11.6% and 87.9% compared to debiased NOAA CYGNSS V1.2 winds. The bias ‘strips’ in these areas are nearly eliminated. Daily averaged error analyses also demonstrate that RFCN winds are more robust and consistent with ECMWF winds. For wind speeds larger than 20 m/s, referred to Soil Moisture Active Passive (SMAP) Level 3 final wind products, the RMSE and bias of fine-tuning RFCN (FT_RFCN) winds are reduced by 25.7% and 91.5% compared to NOAA winds. Finally, the RMSE and bias of retrievals in tropical cyclones, measured by Stepped Frequency Microwave Radiometer (SFMR) during 2021-2022, reveal an improvement of 3.5% and 21.2% compared to NOAA winds. Through SHapley Additive exPlanations (SHAP) models developed for RFCN and FT_RFCN, the contribution of each feature is quantitatively evaluated, while providing insights into their interactions within the ‘black-box’ NN models with clear physical meanings.

ReadCurrent: a VDCNN-based tool for fast and accurate nanopore selective sequencing.

Nanopore selective sequencing allows the targeted sequencing of DNA of interest using computational approaches rather than experimental methods such as targeted multiplex polymerase chain reaction or hybridization capture. Compared to sequence-alignment strategies, deep learning (DL) models for classifying target and nontarget DNA provide large speed advantages. However, the relatively low accuracy of these DL-based tools hinders their application in nanopore selective sequencing. Here, we present a DL-based tool named ReadCurrent for nanopore selective sequencing, which takes electric currents as inputs. ReadCurrent employs a modified very deep convolutional neural network (VDCNN) architecture, enabling significantly lower computational costs for training and quicker inference compared to conventional VDCNN. We evaluated the performance of ReadCurrent across 10 nanopore sequencing datasets spanning human, yeasts, bacteria, and viruses. We observed that ReadCurrent achieved a mean accuracy of 98.57% for classification, outperforming four other DL-based selective sequencing methods. In experimental validation that selectively sequenced microbial DNA from human DNA, ReadCurrent achieved an enrichment ratio of 2.85, which was higher than the 2.7 ratio achieved by MinKNOW using the sequence-alignment strategy. In summary, ReadCurrent can rapidly classify target and nontarget DNA with high accuracy, providing an alternative in the toolbox for nanopore selective sequencing. ReadCurrent is available at https://github.com/Ming-Ni-Group/ReadCurrent.

Physical quantity characteristics of severe aircraft turbulence near convective clouds over Australia

Using FY–2G satellite data, Aircraft Meteorological Data Relay (AMDAR) downlink data, and the fifth generation European Centre for Medium–Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5) dataset, we analyzed the circulation, thermal, and dynamic characteristics of the convective aircraft turbulence over Australia in the Southern Hemisphere. The results show that the near-convective clouds turbulence (NCCT) in the Southern Hemisphere mostly occurred in front of deep warm high–pressure ridges in mid–latitude regions and on the left side of the axis of the subtropical westerly jet stream. The isotherms in this area were relatively dense (i.e., large gradients), and the wind speed was high, with strong horizontal and/or vertical cyclonic wind shear. In addition, the NCCT usually occurred near the zero–divergence or zero–vorticity line and in areas with large vertical wind speed gradients. There were also strong vertical and horizontal wind shears in this area, which could easily trigger severe turbulence. Furthermore, the NCCT in the Southern Hemisphere mostly occurred at the intersection of cold and warm temperature advections (i.e., near the zero–temperature advection line), and the turbulence point was located near the high–altitude frontal zone where there was a strong gradient of cold and warm advections. There was temperature inversion with pseudo–equivalent potential temperatures in the middle and lower troposphere on the warmer side of the turbulence point. The unstable stratification of cold air at the top and warm air at the bottom was conducive to triggering convection from the ground, forming strong convective clouds, and causing severe turbulence.

A novel higher rotational speed maintaining control for wind power generation systems under unstable wind conditions

This study proposes the Higher Rotational Speed Maintaining (HRSM) control algorithm that is suitable for large wind speed fluctuations. One of the challenges faced by wind generators is operating with significant wind speed oscillations. A wind generator with a large moment of inertia increases the time constant in rotational speed control and delays the tracking of the optimal operating point. This delay could reduce generated power by up to 40% or more. There are numerous regions in Japan’s inland areas where average wind speeds exceed 6.5 m/s. However, frequent fluctuations in wind speeds make wind power generation challenging. Therefore, this paper proposes a control algorithm suitable for wind turbines operating under unstable wind conditions. Higher rotational speeds are required to convert sudden high wind speeds into higher power output, especially when wind speed oscillations are large. Hence, the proposed algorithm focuses on maintaining the rotational speed during periods of unstable wind conditions to improve generation efficiency. The effectiveness of the proposed control method for wind turbines is verified through numerical simulation results. The novel control algorithm aims to promote the spread of wind turbines in areas with unstable wind conditions by overcoming these disadvantages.

Kinh tế số nhìn từ thành công của Đan Mạch và bài học kinh nghiệm cho Việt Nam

Digital economic development is an inevitable trend of modern society based on the application of information and communications technology to socio-economic development. With large data scale and fast processing speed, the digital economy has given birth to new business models and highly digitized goods and services. Based on the synthesis of general theoretical issues related to the digital economy, the article focuses on researching Denmark's experience in developing the digital economy and drawing some practical lessons to suggest applications for Vietnam in the process of promoting the implementation of the Digital Economy and Digital Society Development Strategy to 2025, with a vision to 2030. At the same time, to accomplish the goal of the article, the author used the research method of synthesizing and analyzing secondary data from documents and scientific works published domestically and internationally related to the topic research problem and the scope of the study in terms of time is from the period 2016- 2024 and the Danish government's orientation to 2025.

THE INFLUENCE OF BIG DATA ANALYSIS ON THE TIMING OF FINANCIAL REPORTS ITS EFFECTS ON INVESTOR DECISIONS

The digital revolution has led to the proliferation of big data, which poses distinct obstacles to data analysis due to its large quantity, diverse nature, and high speed. These challenges are a direct consequence of the digital revolution. Hence, our article examines the notable impact of big data technology on investors' decision-making. This is seen in the choices made by investors to divest from their investments or reallocate their funds to other assets in response to perceived dangers in the Iraq market and by considering the indirect influence of the timing of financial reporting on gathering the necessary data. A survey was created based on the topics discussed in the studies. Our analysis focuses on the period from January to March 2024. A total of 157 respondents participated in this research, comprising academics from universities, financial experts, and investors in companies listed on the Iraq Stock Exchange. This study used the statistical program SPSS and the course analysis method. The study discovered a statistically significant impact of big data, including its attributes, such as quantity, diversity, and speed, on the timing of financial reporting. The effect of big data on investors' decision-making is statistically significant. The time of issuing information has a significant impact on investors' judgments. Furthermore, investing decisions in Iraqi companies listed on the Iraqi Stock Exchange are marginally impacted by the preparation of financial reports inside the big data framework.

Trajectory Tracking Control of Fixed-Wing Hybrid Aerial Underwater Vehicle Subject to Wind and Wave Disturbances

The hybrid aerial underwater vehicle (HAUV) could operate in the air and underwater might provide a great convenience for aerial and underwater exploration, and the fixed-wing HAUV (FHAUV) has time, space and cost advantages in future large-scale applications, while the large difference between the aerial and underwater environments is a challenge to control, especially in the air/water transition. However, for FHAUV control, there is a lack of research on phenomena or problems caused by large changes in the air/water transition. In addition, the effects of wind, wave, other factors and conditions on motion control are not investigated. This paper presents the first control study on the above issues. The motion model of FHAUV is developed, with the effects of wind and wave disturbances. Then, this paper improves a cascade gain scheduling (CGS) PID for different media environments (air and water) and proposes a cascade state feedback (CSF) control strategy to address the convergence problem of FHAUV control caused by large speed change in the air/water transition. In the comparisons of the two control schemes in various tracking cases including trajectory slopes, reference speeds, wind and wave disturbances, CSF has a better control effect, convergence rate and robustness; the key factors and conditions of the air/water transition are investigated, the critical relations and feasible domains of the trajectory slopes and reference speeds that the FHAUV must meet to successfully exit the water and enter the air are obtained, the critical slope decreases as the reference speed increases, and the feasible domain of CSF is larger than that of CGS, revealing that CSF is superior than CGS for exiting the water.

Diffusion of noiseless active particles in a planar convection array.

We investigated, both analytically and numerically, the dynamics of a noiseless overdamped active particle in a square lattice of planar counter-rotating convection rolls. Below a first threshold of the self-propulsion speed, a fraction of the simulated particle's trajectories spatially diffuse around the convection rolls, whereas the remaining trajectories remain trapped inside the injection roll. We detected two chaotic diffusion regimes: (i) below a second, higher threshold of the self-propulsion speed, the particle performs a random motion characterized by asymptotic normal diffusion. Long superdiffusive transients were observed for vanishing small self-propulsion speeds. (ii) above that threshold, the particle follows chaotic running trajectories with speed and orientation close to those of the self-propulsion vector at injection and its dynamics is superdiffusive. Chaotic diffusion disappears in the ballistic limit of extremely large self-propulsion speeds.

ADAPTATION OF THE ALGORITHM FOR DETECTING ANOMALIES IN TIME SERIES FOR NON-STATIONARY STREAMING DATA

Detection of anomalies in streaming time series data has become an important research topic due to the wide range of possible applications, including the detection of extreme weather conditions, malicious attacks on protected facilities, monitoring unauthorised gas and oil leaks, illegal pipeline connections, power cable faults, and water and other environmental pollution. Rapid detection of abnormal conditions and identifying these critical events is essential to protect lives and assets. Therefore, developing appropriate systems and detection methods is an urgent task. Anomaly detection in streaming data is challenging due to its large volume and high speed, presence of noise, and non-stationarity of the signal (or ‘concept drift’). The latter significantly complicates the identification of differences between new ‘typical’ behaviour and abnormal events. Solving this problem requires the algorithm for processing such data to learn and adapt to changing conditions. The paper proposes a modification of the algorithm for detecting anomalies in time series. This algorithm provides early detection of abnormal series in a massive collection of non-stationary streaming time series data. Anomalies are observations that are highly improbable given the previous time series values. The proposed approach is based on the primary detection of the predictive limit for the typical system behaviour using the theory of extreme values, followed by checking for the abnormality of the following series using the sliding window technique. The time series parameters are used as input data and compared by density distribution to detect any significant changes in the distribution of the characteristics. It allows the decision-making model to automatically adapt to the changing environment according to detected changes. Since anomalies are, by definition, exceptions to the typical behaviour of a system, most of the available stored data should reflect that typical behaviour of the system in question. It is unnecessary to have representative samples of all possible types of the standard behaviour of a given system for the algorithm to work well. The basic idea is to have a warm-up data set to obtain initial values for the decision model parameters. It makes it possible to determine if there is any significant difference between the last typical behaviour and the new typical behaviour. The proposed algorithm demonstrates its performance under conditions of noisy non-stationary data in several time series classes. Keywords: concept drift, extreme value theory, multivariate time series, outlier detection.

A Common Source 3D FeFET with Disturb Inhibition Program and Erase Method

A common source p-type single-crystal channel three-dimensional ferroelectric field-effect transistor (3D FeFET) in a 2 × 2 × 3 array is proposed. Two programming and erasing conditions are introduced. A large memory window (>1.2 V), good retention (>10 years), and high speed (<100 ns) was presented under high voltage (±6 V) conditions. The endurance, >103, was observed under relatively low voltage (±3 V) conditions. Based on these two conditions, a novel asymmetric bias program and erase method is proposed to obtain good disturb inhibition. A more than 0.5 V threshold voltage shift in target cell was achieved while threshold voltage shift in unselected cell was limited, and analysis of long term disturb in novel method is proposed, showing good disturb inhibition. Additional investigation in word line disturbance shows causation and efficiency of disturb. Building upon the proposed structure of the 3D FeFET array, a vector matrix multiplication able to calculate 2-bit weights was designed and demonstrated. This work provides a potential solution for increasing integration density with 3D FeFET array.

FSI simulation of a high-head pump-turbine during load rejection process

Abstract High-head pump-turbine suffers large centrifugal and hydraulic forces due to large rotational speed and severe pressure pulsations during transient processes, and the strong vibration may increase the risk of runner failure. Load rejection transient process is crucial for pumped-storage hydropower stations, and the evolution laws of the runner dynamic stress during this process need profound research. In this study, the evolution of the runner dynamic stress of a prototype pump-turbine during a load rejection transient process was simulated using the one-dimensional and three-dimensional coupling (1D-3D) method and the fluid-structure interaction (FSI) method. The results of this dynamic transient process were compared to the results of the static working points. It is shown that the rotational speed dominates the mean value of stress while the fluctuating amplitude of stress is dominated by pressure fluctuations, which are larger in the S-shaped region of characteristics. Compared with the static working point solutions, the transient process solutions can better reflect the stress fluctuations and should be used for safety assessment.