Miracle-dz/UniFAR-FTUs huggingface dataset:
BERTE: High-precision hierarchical classification of transposable elements by a transfer learning method with BERT pre-trained model and convolutional neural network

Transposable Elements (TEs) are abundant repeat sequences found in living organisms. They play a pivotal role in biological evolution and gene regulation and are intimately linked to human diseases. Existing TE classification tools can classify classes, orders, and superfamilies concurrently, but they often struggle to effectively extract sequence features. This limitation frequently results in subpar classification results, especially in hierarchical classification. To tackle this problem, we introduced BERTE, a tool for TE hierarchical classification. BERTE encoded TE sequences into distinctive features that consisted of both attentional and cumulative k-mer frequency information. By leveraging the multi-head self-attention mechanism of the pre-trained BERT model, BERTE transformed sequences into attentional features. Additionally, we calculated multiple k-mer frequency vectors and concatenate them to form cumulative features. Following feature extraction, a parallel Convolutional Neural Network (CNN) model was employed as an efficient sequence classifier, capitalizing on its capability for high-dimensional feature transformation. We evaluated BERTE’s performance on filtered datasets collected from 12 eukaryotic databases. Experimental results demonstrated that BERTE could improve the F1-score at different levels by up to 21% compared to current state-of-the-art methods. Furthermore, the results indicated that not only could BERT better characterize TE sequences in feature extraction, but also that CNN was more efficient than other popular deep learning classifiers. In general, BERTE classifies TE sequences with greater precision. BERTE is available at https://github.com/yiqichen-2000/BERTE.
The HSFA1a is stabilized by the co-chaperone HSP70-HSP90 organizing protein HOP in Arabidopsis

Living organisms have the capacity to sense and respond to environmental stimuli, including warm conditions. Upon the increase of temperature, eukaryotic cells promote the onset of a universal transcriptional response called the heat stress response (HSR). HSR is triggered by the activation of the heat shock factor 1 (HSF1), whose activity is highly regulated in all eukaryotes. A common process of HSFA1 regulation involves its binding to HSP70 and HSP90 chaperones. However, the effect of this binding differs among eukaryotes and is not fully elucidated in plants. Moreover, the role of chaperone auxiliary proteins, such as HOP, in HSF1 regulation remains unknown. Here, we show that HOP is involved in the folding and stabilization of the HSFA1a and is required for the onset of the HSR during thermomorphogenesis in Arabidopsis. Our results demonstrate that the members of the AtHOP family bind in vivo to the HSFA1a and that the expression of multiple genes involved in the HSR, including numerous HSFA1a-responsive genes, is altered in the hop1 hop2 hop3 mutant under warm temperature. HOP is not involved in the subcellular localization of the AtHSFA1a. Instead, HSFA1a is accumulated at lower levels in the hop1 hop2 hop3 mutant while control levels are recovered in the presence of the proteasome inhibitor MG132 or the synthetic chaperone TUDCA. This uncovers the HSFA1a as a new client of HOP in plants and reveals the participation of HOP in HSFA1a stability, expanding the notorious function of the co-chaperone HOP in thermomorphogenesis.
Recurrent drought increases grassland community seasonal synchrony

Climate change leads to an increase in the frequency and severity of drought events with strong repercussions on grassland ecosystems. While the effects of single drought events on ecosystem structure and functioning are well understood, it is largely unknown whether and how drought frequency modifies ecosystem responses to drought. Here, we assessed how the increase in frequency of severe, annual summer drought affected grassland communities. We examined these effects in a species-rich sub-alpine mountain meadow with a drought frequency of one, three, and 13 years, as well as ambient conditions. We found that high drought frequency increased seasonal plant community synchrony through a reduction in species richness, a shift of plant functional groups, a loss of early-seasonal plant species, and the constrained establishment of new species and individuals throughout the growing season. These changes are associated with a decreased fraction of biomass as drought frequency increased. We conclude that with the ongoing increase in drought frequency due to climate change, grassland plant communities will increase in seasonal synchrony. We suggest that this will leave ecosystems highly vulnerable to future disturbances, as asynchrony is a critical component of stability. Furthermore, we show that negative drought effects were enhanced with an increasing drought frequency. We therefore conclude that single and low-frequency drought studies may not adequately predict longer-term changes in our rapidly shifting climate. Moreover, we found that negative drought effects on plant communities outweighed the weak adaptive effects of species. We therefore conclude that plant communities are unlikely to be able to adapt to recurrent drought.
Characterizing substructure via mixture modeling of genetic similarity in large-scale summary statistics

Genetic summary data are both broadly accessible and highly useful including for risk prediction, causal inference, fine mapping, and the incorporation of external controls. Nevertheless, collapsing individual-level data into groups masks intra- and inter-sample heterogeneity (e.g., population structure), leading to confounding, reduced power, and bias. Unaccounted for substructure limits summary data usability, especially for understudied or admixed populations. Here, we present Summix2, a comprehensive set of methods and software based on a computationally efficient mixture model of genetic similarity to estimate and adjust for substructure in genetic summary data. In extensive simulations and application to public data, Summix2 provides accurate and precise estimates detecting regions of selection, identifying fine-scale population structure, and disentangling latent genetic disease risk. Summix2 increases the robust use of diverse publicly available summary data resulting in improved and more equitable research to address population structure, disease etiology, and risk prediction.
Imaging Aβ aggregation by liquid-phase transmission electron microscopy

The amyloid beta peptide (Aβ) readily aggregates into amyloid fibrils. This process has been the subject of intense investigations since it is associated with Alzheimer’s disease. However, it has been highly challenging to observe the microscopic steps in the aggregation reaction directly and to characterize the oligomeric assemblies formed as intermediates. To achieve this goal, we apply liquid-phase transmission electron microscopy (LTEM) in combination with all-atom molecular dynamics simulations. Our results offer an initial visualization of the dynamics of Aβ oligomers, the formation of Aβ protofibrils, and the presence of Aβ oligomers on the surface of Aβ fibrils. This work illustrates how the application of LTEM to the study of protein aggregation in solution enables the imaging of key molecular events in the aggregation process of Aβ.
Weak Target Detection Based on Keystone Transform

The improvement of the capability to detect moving target with very small radar signature is usually realized by long term accumulation.According to the design principle of conventional PD(Pulse Doppler) radar,target's range migration can't exceed half of the range resolution cell in coherent integration time,which means that the accumulation time is restrained by target's motion.For high range resolution radar or the radar system focusing on the detection of fast moving targets,such a restriction is difficult to satisfy.In this paper,a novel motion compensation method based on keystone transform is presented,which can correct range migration without the exact value of target velocity,and as a result,make the accumulation time free of the restriction due to target motion.
Forecasting Pink Salmon Harvest in Southeast Alaska from Juvenile Salmon Abundance and Associated Biophysical Parameters : 2012 Returns and 2013 Forecast by

The Southeast Alaska Coastal Monitoring (SECM) project has been sampling juvenile salmon (Oncorhynchus spp.) and associated biophysical parameters in northern Southeast Alaska (SEAK) annually since 1997 to better understand effects of environmental change on salmon production. A pragmatic application of the annual sampling effort is to forecast the abundance of adult salmon returns in subsequent years. Since 2004, peak juvenile pink salmon catch-per-unit-effort (CPUE), adjusted for highly-correlated biophysical parameters, has been used to forecast adult pink salmon harvest (O. gorbuscha) in SEAK. The 2012 forecast of 18.8 M fish was 12% lower than the actual harvest of 21.3 M fish. Eight of nine forecasts produced over the period 2004-2012 have been within 17% of the actual harvest, with an average forecast deviation of 7%. The forecast for 2006 was the exception; while the simple CPUE model indicated a downturn in harvest, the prediction substantially overestimated the harvest. These results show that the CPUE information has great utility for forecasting year class strength of SEAK pink salmon, but additional information may be needed to avoid forecast “misses. ” For the 2013 forecast, model selection included a review of ecosystem indicator variables and considered additional biophysical parameters to improve the simple single-parameter juvenile CPUE forecast model. The “best” forecast model for 2013 included two parameters, the Icy Strait Temperature Index (ISTI) and juvenile CPUE. The 2013 forecast of 53.8 M fish from this model, using juvenile salmon data collected in 2012, had an 80% bootstrap confidence interval of 48-60 M fish.
Improving Enterprise Architecture Mining Methods

Enterprise architecture takes a step in the path of digital transformation by providing a comprehensive system with an integrated view of the organization. In traditional methods, architects manually prepare enterprise architecture products. The manual methods of producing enterprise architecture products have many challenges. One of the main challenges is the production of volumes of documentation, the complexity and difficulty of making enterprise architecture artifacts, and their costly and error-prone nature. In response to these challenges, enterprise architecture mining is proposed, automatically producing outputs and artifacts of enterprise architecture instead of manual modeling. Surveys show that enterprise architecture analysis still needs to be mature, and full automation is still far away. Some methods may be successful in some layers, but reaching a method that automatically provides an integrated enterprise architecture model with sufficient accuracy remains the challenge.This research will use model analysis methods to improve enterprise architecture mining. At first, the presented method extracts enterprise architecture models automatically. Detecting architecture smells reveals enterprise architecture debts in the next step. Ultimately, it improves the extracted models using refactoring methods. The proposed method can improve the accuracy and quality of the extracted models.
A Persuasive Approach for Urging Construction Workers to Behave Safely

On-site workers' safety behavior has been one of the most important issues for construction safety management. To this end, this study put forward a persuasive approach to prompt the workers' safe behaviors. It considers the worker's ability from a cognitive perspective and uses the psychological needs to promote the worker's safety motivation based on the FOGG behavior model. It shows that the principles of persuasive technology can be applied in the safety intervention design; a comprehensive profile including the worker's personality traits, cognitive-based competence, psychological needs, and safety motivation can be used to determine the frequency, content, and media of the behavioral triggers. The approach guides the design of personalized safety interventions to improve construction workers' occupational health and safety levels.
Orderly Charging Strategy for Electric Vehicles Based on the Edge Controller and Its Application

With the wide application of electric vehicles, it has become crucial to ensure their orderly charging to maintain grid stability and efficient utilization. Therefore, a new solution was proposed in this paper, which not only prevents the wastage of cloud server resources but also helps to enhance the real-time responsiveness of the charging control system. First, the optimization objective and corresponding constraints were determined after a comprehensive analysis on the charging demands of both the power grid and users in this paper. Secondly, a high-precision and fast convergence hybrid algorithm was adopted to formulate an orderly charging optimization strategy, which not only successfully realizes peak load shifting of the power grid and optimization of user charging costs, but also ensures power grid stability and brought users economic benefits. In addition, the software architecture of the edge controller was designed, with successful deployment of an orderly charging control strategy. At the same time, a practical visualization interface was established to facilitate real-time monitoring of charging status.
Towards Asynchronously Triggered Spiking Neural Network on FPGA for Event-based Vision

In this paper, the design and implementation of an asynchronously-triggered spiking neuron tailored for FPGA implementations is presented. The asynchronous triggering mechanism is designed to effectively preserve the spatial-temporal information captured by event-based vision sensors. Neurons of the target network model are implemented using the configurable logic blocks of an FPGA spatially and communicate through an asynchronous handshaking protocol. Without using a global clock to synchronize operations of the neurons, energy consumption and inference latency are minimized. The proposed design is composable and scalable for applications with different spiking neural network (SNN) configurations. Experimental results show that the proposed design is able to achieve 95.76% accuracy on the N-MNIST dataset while consuming less than 1 mW dynamic power on an Xillinx ZCU102 board.
Research on Accurate Grasping Method of Steel Shaft Parts Based on Depth Camera

Aiming at the precise grasping problem of steel shaft parts in the automatic loading and unloading process of the composite mobile robot for the guide roller production line, a part grasping pose estimation method based on depth camera is proposed. Firstly, the transformation relationship between the camera coordinate system and the robotic arm end coordinate system is established through camera calibration, depth map registration, and hand-eye calibration. Secondly, based on OpenCV, the RGB image and depth image are preprocessed, and the maximum contour information of the steel shaft part is extracted by combining the Canny operator and morphological processing. A gripping pose estimation method based on contour geometric moments and PCA is proposed. Finally, combined with the loading and unloading process of the steel shaft part, the automatic loading and unloading operation tasks of the parts are completed. Experimental results show that the overall success rate of the gripping operation of the compound robot visual grasping system can reach over 90%, and its accuracy and stability meet the requirements of the loading and unloading gripping tasks.
Dynamic Response of Grid-following and Grid-forming Inverters when encountering disturbances

The integration of renewable energy sources leads to the increase of inverter-based generators, causing a decrease in grid strength and stability issues. To deal with this problem, researchers proposed different control structures of the grid-forming (GFM) control strategy, which can automatically provide active and reactive energy support when the power network encounters disturbances during operation. This paper focuses on the capability of different types of inverters to recover from disturbances when encountering balanced load disturbance and different depths of voltage fault. The theoretical assessment and PSCAD simulations of different control strategies have been conducted. The results indicate that when encountering a fault under weak grid conditions, large frequency and voltage variations will occur in the power network connected with the grid-following (GFL) inverter, while these variations are much smaller in the power network connected with the grid-forming inverter, which demonstrates better dynamic response to disturbance.
The Effect of Performance Features of Telepresence Robots on the Personality Perception of Their Users

The Media Equation Hypothesis suggests that people attribute features of the medium to the content. In line with this hypothesis, in this work we investigate the effects of speed and shakiness of movement of a telepresence robot on the perception of the robot pilot's personality. To this end, we designed a between-subject study with three conditions. In the first condition, participants watched a video in which a telepresence robot was being driven at a speed of 1.5 km/h. In condition two, the robot moved with the same speed, but this time in a shaky manner. In the third condition, the robot was driven with double speed. 100 participants judged the robot pilot's personality in a questionnaire. The analysis of our data shows that the telepresence robot pilot is considered significantly more depressed, tense, shy, moody, and less self-confident when the robot is driven with low speed and more tense and irritable when it shakes while being driven. Therefore, the personality of the telepresence robot pilot is perceived differently depending on the speed or the quirkiness of the robot's movement, in line with the media equation hypothesis.
Induction Motor Fault Detection through External Magnetic Field Aided by Sigma-Delta Microcontroller: Rotor Broken Bar Study

Induction motors are widely used in different industrial applications, and although they are robust, they are still susceptible to faults. Based on this, non-invasive monitoring systems have become appropriate devices for monitoring electric motors. This work aims to develop a non-invasive fault detection based on the information provided by the harmonics content of the external magnetic field of the induction motor, through the application of microcontrolled sigma-delta converter to continuously monitor the motor. It has the advantage of signal sampling has no analog filters between the transducer and the microcontroller.
Detection and Classification of False Data Injection Attacks in Power Grids Using Machine Learning and Hyperparameter Optimization Methods

False Data Injection Attacks (FDIA) are a significant threat to power systems, compromising system integrity through data corruption and false information injection, leading to severe impact on system state estimation and potential extensive damage. This paper proposes multiple machine learning models for stealthy FDIA detection, accounting for various attack types that a system may encounter. Hyperparameter optimization is introduced to mitigate the impact of imbalanced training data on model performance. The proposed models are comprehensively evaluated against different FDIA types, considering the heterogeneity of these attacks. Experimental results demonstrate the effectiveness of hyperparameter optimization in improving FDIA detection and preventing potential undetected attacks. Additionally, an analysis is conducted to assess the likelihood of different attack types (ramp, step, poison, and freeze), evading detection by the models. This analysis aids in understanding the magnitude of threat posed by each attack type and identifying the models that excel in detecting specific FDIA types.
Hybrid Stepping Motion Generation for Wheeled-Bipedal Robots Without Roll Joints on Legs

Wheeled-bipedal robots without roll joints on legs, such as Handle by Boston Dynamics and Ascento by ETH, have drawn increasing attention due to their superior motion agility but pose unique challenges to motion generation. So far, there is little to no research on how to enable these robots to step forward with their legs. In this study, we will explore hybrid stepping locomotion strategies via a two-phase design procedure. During the single-leg support phase, a two-mass variable height inverted pendulum model will be used for stepping locomotion generation and control. As for the double-leg support phase, given the difficulty of modeling contact sliding, friction, and collision, a model-free reinforcement learning approach is employed to leverage the rich data for reliable motion generation. Experiments on our own developed wheeled-bipedal robot Ollie demonstrate that the robot is capable of stepping forward with varied stepping frequencies. Stepping with yaw rotation and tests in different scenarios show the efficacy and robustness of the hybrid stepping motion generation method.
Study on Space Charge Accumulation in Silicone Rubber Encapsulant Under DC Voltage

Silicone rubber is a kind of polymeric insulating material commonly used in power electronic device encapsulation. The insulation performance of encapsulant directly determines the operating reliability of power electronic devices. Under DC voltages, space charge accumulation will directly affect short-time breakdown characteristics and long-term aging properties of the insulation. This paper carried out experimental measurement on space charge based on pulsed electro-acoustic (PEA) method for revealing space charge characteristics in silicone rubber encapsulant. The experimental samples were prepared using two curing processes. And further comparative experiments were also conducted to explore the effect of post-curing on space charge characteristics in silicone rubber encapsulant.
Social Network Rumor Spread Based on Adaptive Threshold Change

The spread of rumors caused by malicious nodes on social networks destroys the normal environment of public opinion and has a lot of adverse effects on society, so it is of great significance to restrain the spread of rumors. Considering that users will develop resistance after multiple rounds of rumors, there is a lack of relevant research. Using this characteristic, this paper introduces an authoritative mechanism, which measures the authority of neighbors when rational individuals make decisions. after each round of rumors spread, the threshold changes adaptively, so the threshold adaptive change function is established. Based on the linear threshold model, a rumor propagation model based on adaptive threshold change is proposed. The traditional rumor propagation model of this model does not take into account the dynamic change of individual threshold with time, and can not effectively block the spread of rumors. In this paper, a rumor propagation model based on adaptive threshold change is proposed. based on the traditional linear threshold rumor propagation model, this model introduces an authoritative mechanism to measure the authority of neighbors when making rational individual decisions. and after each round of rumor spread, the threshold changes adaptively. The experiment was carried out on the synthetic benchmark network and the real data set, and compared with the benchmark model. The results show that the threshold adaptive model can effectively reduce the negative impact of rumor spread in different networks. And the infection rate of the whole network is restrained to less than 5%, and the average threshold value of the network as a whole is increased, and the resistance to rumors is increased.