Miracle-dz/UniFAR-FTUs huggingface dataset:
A new chapter of the Japanese beetle invasion saga: predicting suitability from long-infested areas to inform surveillance strategies in Europe

The Japanese beetle (Popillia japonica) is a polyphagous pest that spreads rapidly and is estimated to cost more than 460 M$/year in damage and control in the USA alone. This study provides risk maps to inform surveillance strategies in Continental Europe, following the beetle’s introduction and successive spread in the last decade. We developed a species distribution model using a machine-learning algorithm, considering factors relevant to the beetle’s biology, climate, land use and human-related variables. This analysis was performed using presence-only data from native and invaded ranges (Japan, North America, Azores archipelago - Portugal). We gathered more than 30 000 presence data from citizen science platforms and standardized surveys, and generated pseudo-absences using the target-group method. We used the environmental structure of data to randomly sample pseudo-absences, and evaluate model performance via a block cross-validation strategy. Our results show that climate, in particular seasonal trends, and human-related variables, are major drivers of the Japanese beetle distribution at the global scale. Risk maps show that Central Europe can be considered as suitable, whereas Southern and Northern European countries are at lower risk. The region currently occupied is among the most suitable according to our predictions, and represents less than 1% of the highest suitable area in Europe. A major cluster of high suitability areas is located near the currently infested zone, whereas others are scattered across the continent. This highlights the importance of designing surveillance strategies considering both active insect dispersal and the possibility of hitchhiking to reach distant areas.
Relationship between thyroid hormones and associated metabolites and gene expression bioindicators in the serum of Rana [Lithobates] catesbeiana tadpoles and frogs during metamorphosis

Anuran metamorphosis is characterized by profound morphological changes including remodeling of 14 tissues and organs. This transition is initiated by thyroid hormones (THs). However, the current 15 knowledge of baseline levels of THs during metamorphosis relies on pooled samples using methods 16 known for high variability with little reporting of measured variation. Moreover, establishing a clear linkage between key gene expression bioindicators and TH levels throughout the metamorphic 18 process is needed. Using state-of-the-art ultra-high performance liquid chromatography isotope- 19 dilution tandem mass spectrometry, we target 12 THs -and metabolites in the serum of Rana 20 [Lithobates] catesbeiana (n=5-10) across seven distinct postembryonic stages beginning with 21 premetamorphic tadpoles (Gosner stage 31-33) and continuing through metamorphosis to a juvenile 22 frog (Gosner stage 46). TH levels are then related to TH-relevant gene transcripts ( thra , thrb , and 23 thibz ) in back skin of the same individual animals. Significant increases from basal levels were 24 observed for thyroxine (T4) and 3,3’,5-triiodothyronine
The perceptual nature of illusory object recognition

We occasionally misinterpret ambiguous sensory input or report a stimulus when none is presented. It is unknown whether such errors have a sensory origin and reflect true perceptual illusions, or whether they have a more cognitive origin (e.g., are due to guessing), or both. When participants performed an error-prone and challenging face/house discrimination task, multivariate EEG analyses revealed that during decision errors (mistaking a face for a house), sensory stages of visual information processing initially represent the presented stimulus category. Crucially however, when participants were confident in their erroneous decision, so when the illusion was strongest, this neural representation flipped later in time and reflected the incorrectly reported percept. This flip in neural pattern was absent for decisions that were made with low confidence. This work demonstrates that decision confidence arbitrates between perceptual decision errors, which reflect true illusions of perception, and cognitive decision errors, which do not.
Cortical neuronal assemblies coordinate with EEG microstate dynamics during resting wakefulness

The disruption of cortical assembly activity has been associated with anesthesia-induced loss of consciousness. However, information about the relationship between cortical assembly activity and the variations in consciousness associated with natural vigilance states is currently lacking. To address this, we performed vigilance state-specific clustering analysis on 2-photon calcium imaging data from sensorimotor cortex in combination with global EEG microstate analysis derived from multi-EEG signals obtained from widespread cortical locations. Our analysis revealed no difference in the structure of assembly activity during quiet wakefulness (QW), NREMs, or REMs, despite the latter two vigilance states being associated with significantly reduced levels of consciousness relative to QW. However, we found a significant coordination between global EEG microstate dynamics and local cortical assembly activity during periods of QW, but not sleep. These results suggest that the coordination of cortical assembly activity with global brain dynamics could be a key factor of sustained conscious experience.
Myonuclear alterations associated with exercise are independent of age in humans

Age-related decline in skeletal muscle structure and function can be mitigated by regular exercise. However, the precise mechanisms that govern this are not fully understood. The nucleus plays an active role in translating forces into biochemical signals (mechanotransduction), with nuclear lamina protein Lamin A regulating nuclear shape, nuclear mechanics, and ultimately gene expression. Defective Lamin A expression causes muscle pathologies and premature ageing syndromes, but the roles of nuclear structure and function in physiological ageing and in exercise adaptations remain obscure. Here, we isolated single muscle fibres and carried out detailed morphological and functional analyses on myonuclei from young and older exercise-trained individuals. Strikingly, myonuclei from trained individuals were more spherical, less deformable, and contained a thicker nuclear lamina than untrained individuals. Complementary to this, exercise resulted in increased levels of Lamin A and increased myonuclear stiffness in mice. We conclude that exercise is associated with myonuclear remodelling, independently of age, which may contribute to the preservative effects of exercise on muscle function throughout the lifespan. Key points The nucleus plays an active role in translating forces into biochemical signals Myonuclear aberrations in a group of muscular dystrophies called laminopathies suggest that the shape and mechanical properties of myonuclei are important for maintaining muscle function. Here, we present striking differences in myonuclear shape and mechanics associated with exercise, in both young and old humans. Myonuclei from trained individuals were more spherical, less deformable, and contained a thicker nuclear lamina than untrained individuals. We conclude that exercise is associated with age-independent myonuclear remodelling, which may help to maintain muscle function throughout the lifespan.
14-pixel, multiplexed array of gamma-ray microcalorimeters with 47 eV energy resolution at 103 keV

The authors present a prototype for a high-energy-resolution, high-count-rate, gamma-ray spectrometer intended for nuclear forensics and international nuclear safeguards. The prototype spectrometer is an array of 14 transition-edge-sensor microcalorimeters with an average energy resolution of 47eV (full width at half maximum) at 103keV. The resolution of the best pixel is 25eV. A cryogenic, time-division multiplexer reads out the array. Several important topics related to microcalorimeter arrays are discussed, including cross-talk, the uniformity of detector bias conditions, fabrication of the arrays, and the multiplexed readout. The measurements and calculations demonstrate that a kilopixel array of high-resolution microcalorimeters is feasible.
The Andromeda Study: A Femto-Spacecraft Mission to Alpha Centauri

This paper discusses the physics, engineering and mission architecture relating to a gram-sized interstellar probe propelled by a laser beam. The objectives are to design a fly-by mission to Alpha Centauri with a total mission duration of 50 years travelling at a cruise speed of 0.1c. Furthermore, optical data from the target star system is to be obtained and sent back to the Solar system. The main challenges of such a mission are presented and possible solutions proposed. The results show that by extrapolating from currently existing technology, such a mission would be feasible. The total mass of the proposed spacecraft is 23g and the space-based laser infrastructure has a beam power output of 15GW. Rurther exploration of the laser - spacecraft tradespace and associated technologies are necessary.
Lightning Stroke Transient Identification Method for HVDC Transmission Line based on DBN

For the transient protection of HVDC transmission lines, the transient interference signal of lightning stroke may cause the protection malfunction. Research on accurate and fast identification methods for lightning transients of HVDC transmission lines is of great significance to improve the reliability of power systems. This paper proposes a lightning stroke transient identification method based on deep belief network (DBN) and wavelet energy moment, using wavelet energy moment theory to extract the characteristics of three transient time-domain waveforms to generate sample sets, building a training unit based on the DBN algorithm to construct the basic reliability assignment of a transient signal identification framework. Finally, the transient signal discrimination result can be obtained through the decision rule. Through the example analysis of building a simulation model in PSCAD, the results show that DBN has a higher recognition accuracy rate than the shallow machine learning algorithms in transient identification, and at the same time verifies that the method has a certain practicability.
Challenges and Peculiarities of Attack Detection in Virtual Power Plants : Towards an Advanced Persistent Threat Detection System

Currently, there are no mission-capable systems that can successfully detect advanced persistent threats (APTs). These types of threats are hazardous in critical infrastructures (CIs). Due to the integration of operational technology (OT) and information communication technology (ICT), CI systems are particularly vulnerable to cyberattacks. In addition, power systems, in particular, are an attractive target for attackers, as they are responsible for the operation of modern infrastructures and are thus of great importance for modern warfare or even for strategic purposes of other criminal activities. Virtual power plants (VPPs) are a new implementation of power plants for energy management. The protection of virtual power plants against APTs is not yet sufficiently researched. This circumstance raises the research question - What might an APT detection system architecture for VPPs look like? Our methodology is based on intensive literature research to bundle knowledge from different sub-areas to solve a superordinate problem. After the literature review and domain analysis, a synthesis of new knowledge is provided in the presentation of a possible architecture. The in-depth proposal for a potential system architecture relies on the study of VPPs, APTs, and previous prevention mechanisms. The architecture is then evaluated for its effectiveness based on the challenges identified. The proposed architecture combines concepts such as defense-in-depth and breath with situation awareness, and the observe, orient, decide, and act loop. Furthermore, a combination of traditional detection methods with graph analysis in the architecture is targeted to meet the challenges and peculiarities of VPPs and APTs.
A Differential Relaxation Oscillator for Inductive Power Transfer

We present a novel differential relaxation oscillator for inductive power transfer. The proposed oscillator offers an automatic adaption of the self-oscillation period to a change in the power link impedance, which ensures a high efficient operation for variable loads and coupling factors. A detailed circuit model of the self-oscillation system is provided with analytical design equations. We find a large self-oscillation range for variable coupling factors and loads. The theoretical results are confirmed by circuit simulation and measurements carried out on a low-power demonstrator system. We demonstrate non-resonant and robust self-adaption of the relaxation oscillator supplying an inductive power link with coupling factors ranging from k=0.6-0.9.
Identification and Control of Linear Systems with Piece-wise Constant Parameters

The paper deals with the adaptive control of linear systems whose parameters can vary in a piece-wise constant fashion. The principal aim of the paper is to discuss the questions that arise when dealing with such systems and describe the methods used to identify and control them. These include the use of many models, the choice of their location, and how they are to be activated. Second level adaptation, which incorporates many of these features, is found to be the best method for tracking piece-wise constant systems from the point of view of speed, accuracy, and stability. Simulation results are included to indicate the improvement in performance at every stage.
Hetero-doped Silicon Modulator with Bandwidth Expansion

In order to improve the phase shift efficiency of all silicon modulator, we designed an L-type pn junction doping structure, but the huge junction capacitance limis the bandwidth of the modulator. So we use feedback circuit to compensate it. The phase shift efficiency of the optimized modulator is 1.2~2.2V • cm at 1~4 V bias. And the bandwidth can be increased from 20GHz to 30GHz, which is expanded by nearly 50%.
A Comparison of Two-and Three-level Converter Systems in PMSG Grid Connection

The paper analyses the performance of the two- and three-level back to back converters in the permanent magnets synchronous generator (PMSG) grid connection. The proposed system is combined with a variable speed wind turbine, nonsalient PMSG, back to back converter, intermediate capacitor bank, LC filter, and grid model. Instead of the neutral point clamped (NPC) converter circuit, the T-type converter circuit is applied as the three-level converter. The T-type converter has fewer components, a more straightforward control sequence, and a shorter commutation path than the NPC converter circuit. The control system includes maximum power point tracking (MPPT) control, machine-side control and grid-side control, all achieved by controlling the switching signals of converters. Two types of converter topologies are simulated through Matlab/Simulink. Extensive experimental results verify that both converter topologies have a fast dynamic response and efficiently transfer power to the grid. By comparison, the three-level converter system has a better total harmonic distortion (THD) performance than the two-level converter system. The THD of the three-level converter system can be significantly improved at low wind speeds. However, although the three-level converter introduces neutral point control, the DC link voltage and reactive power ripples are higher than the two-level converter system.
Vacuum polarization in two-dimensional static spacetimes and dimensional reduction

We obtain an analytic approximation for the effective action of a quantum scalar field in a general static two-dimensional spacetime. We apply this to the dilaton gravity model resulting from the spherical reduction of a massive, non-minimally coupled scalar field in the four-dimensional Schwarzschild geometry. Careful analysis near the event horizon shows the resulting two-dimensional system to be regular in the Hartle-Hawking state for general values of the field mass, coupling, and angular momentum, while at spatial infinity it reduces to a thermal gas at the black-hole temperature.
Analysis and Investigation of a Simple and Extendable DC-DC Converter to Obtain High Voltage-Gain for Renewable Energy Applications

In this article, an extendable high step-up DC-DC converter is proposed and investigated. The topology of the proposed converter is simple, and despite other similar structures, this converter does not implement switched-capacitor (SC) cells, switched-inductor (SI) cells, diode-capacitor-inductor (DCL) cells, or coupled inductors to enhance the voltage gain. In addition to the simple topology of the proposed converter, its high voltage-gain and reduced voltage stresses across the elements can be listed as the main advantages of this structure. In this paper, the proposed extendable high step-up converter is explained, and its operational modes are investigated comprehensively. The voltage stress across different elements and the voltage gain of the proposed converter are obtained. In addition, a 2-stage model of the proposed converter is designed for efficiency investigation and compared to other similar topologies. Finally, the designed 2-stage (25-400V, 500W) model of the proposed converter is simulated in PSCAD/EMTC environment to verify the theoretical analysis.
An Integrated Multi-resource Clustering Method Based on a High Proportion of Renewable Power Systems

The increasing proportion of renewable energy sources such as wind power and photovoltaics in the power system poses a challenge to the operational planning of the power system due to its volatility and the dramatic increase in the amount of data due to the increasing complexity of the system structure. How to form representative typical scenarios to replace the previous day- by-day time-series simulation method for production simulation, in order to achieve results that balance efficiency and accuracy, is a problem worth solving. The scenario reduction method is one of the effective methods, and the k-means method is the more commonly used clustering method. Unlike previous scenario reduction methods that only cluster a single node and a single resource, this paper proposes an integrated clustering method based on the k-means clustering algorithm that clusters multiple resources simultaneously in a complex power system network that considers multiple renewable energy sources, and compares it with a typical scenario clustering method that clusters multiple resources separately and then combines them, using year-round time-series simulation as a reference basis. The integrated clustering has a small error in terms of computational accuracy and computational efficiency, and can effectively reflect the annual characteristics of various resources, which can provide a more reliable basis for the operation planning of power systems.
Logistic Regression Analysis to Determine Factors Affecting Asset Growth Based on Financial Performance In LQ45 Indexed Companies

Asset growth describes how the company invests its own funds for operations and investments. Asset growth is a change (increase or decrease) in the total assets owned by a company. This study aims to examine total assets, total liabilities, total equity, total revenues, dividends, Earnings Per Share (EPS), Book Value (BV), Debt to Asset Ratio (DAR), Debt to Equity Ratio (DER), Return On Asset (ROA), Return On Equity (ROE), Gross Profit Mergin (GPM), Operating Profit Margin (OPM), Net Profit Margin (NPM), payout ratio, and yield which has a significant effect on asset growth based on financial performance in LQ45 indexed companies in 2016–2019. The population used in this study is LQ45 indexed companies listed on the Indonesia Stock Exchange in 2016–2019, the data used in this study is secondary data. The total sample is 56 enterprises. We analyse the data with binary logistic regression analysis. The results showed that total assets, total liabilities, total revenues, dividends, Earnings Per Share (EPS), Book Value (BV), Debt to Asset Ratio (DAR), Debt to Equity Ratio (DER), Return on Asset (ROA), Return on Equity (ROE), Gross Profit Mergin (GPM), Net Profit Margin (NPM), and payout ratio did not have a significant effect on asset growth. Financial performance that has a significant effect on asset growth obtained 3 free variables, namely total equity, Operating Profit Margin (OPM), and yield. Other variables, although they have an influence, do not have a significant effect on asset growth. The result of reseach from the sixteen variables, three variables were found that had an effect on the response.
Electromagnetic wave scattering by assemblies of particles in the Rayleigh approximation

A system of 3N linear simultaneous equations is derived from which the scattering properties of an assembly of N spheres can be obtained. Except that the individual spheres must satisfy the criteria for the Rayleigh approximation, they may be of arbitrary volume and complex refractive index. The results of numerical calculations are presented illustrating the properties of the scattered light, and comparisons are made with previous work. A diagnostic technique is proposed which should be capable of determining the size and complex refractive index of the spheres and the number of particles in a long chain agglomerate.
Efficient tuning of $k$ NN hyperparameters for indoor positioning with N-TBEA

Machine Learning is a very popular approach for indoor positioning. However, most of models rely on a set of hyperparameters, which need to be properly set. When the number of hyperparameters is large, exploring all the combinations of values (what is known as brute force) can be computationally prohibitive, especially in those cases where the training or operational time is high, such as in the kNN algorithm in fingerprint-based indoor positioning. This paper introduces $\boldsymbol{N}$-Tuple Bandit Evolutionary Algorithm (N-TBEA) to find the hyperparameters in this last case. N-TBEA is an efficient exploration technique which evaluates the feasibility of similar combinations of parameters. The results show that N-TBEA can provide a solution with an accuracy similar to the best combination of parameters retrieved using brute force, which shows the potential of N-TBEA to be used in other advanced machine learning models.
Multi-indicator Water Time Series Imputation with Autoregressive Generative Adversarial Networks

The water quality data has missing values and lacks integrity because water environment monitoring equipments are easily damaged by environmental influences, thereby affecting the analysis accuracy of downstream tasks. Traditional data imputation methods include mea/Mast filling, K-nearest neighbor, matrix factorization, Lahrangian interpolation, etc., do not consider time dependence or fail to use complex relations among multiple features. Inspired by successful applications of various variants of Generative Adversarial Networks (GANs) on time series data, this work proposes a time series data imputation method called GEDA, which integrates -GAN, an Encoder-Decoder structure, and an Autoregressive network. GEDA adopts GAN to learn the probability distribution of multi-feature time series, and imputes the missing values with the generated data. Then, GEDA combines feature extraction of the encoder-decoder structure, and time dependence capturing of the autoregressive network. Real-life dataset-based experimental results demonstrate GEDA outperforms several state-of-the-art data imputation methods in terms of accuracy.