Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultra-Short-Term Wind Power Forecasting Model Based on Time-Section Fusion and Pattern Classification

Accurate wind power prediction can not only effectively improve wind power consumption capacity, but also improve the stability and economy of power grid operation. However, due to the fluctuation and randomness of wind speed, it is very difficult to predict accurately wind power at present. In addition, each single algorithm model has its own inherent limitations. Therefore, none of the above algorithms can always maintain the best prediction effect in different months. For the above reasons, it is very difficult for the wind farm to choose the optimal prediction model under different prediction scenarios. Finally, for the problem that a single algorithm can’t adapt to the forecasting scenarios of different months in the whole year, a time-section fusion pattern classification based ultra-short-term wind power forecasting model is proposed, which includes multiple forecasting models based on different machine learning theories, fusion mode classification model, and fusion model corresponding to each fusion mode. Firstly, different machine learning models such as artificial neural network (ANN), support vector machine (SVM), long and short-term memory neural network (LSTM) and autoregressive integrated moving average (ARIMA) are used to predict wind power with a single model. Secondly, the model of fusion pattern classification and recognition is trained and applied to fusion pattern recognition in different time periods. Finally, the final forecasting results are obtained by using the fusion model corresponding to the data fusion model. The simulation results of real data show that compared with the standard ANN model, SVM model, LSTM model and ARIMA model, the time-section fusion based ultra-short-term wind power ensemble forecasting model proposed in this paper has higher accuracy.
Impacts of System Transparency and System Failure on Driver Trust During Partially Automated Driving

The objective of this study is to explore changes of trust by a situation where drivers need to intervene. Trust in automation is a key determinant for appropriate interaction between drivers and the system. System transparency and types of system failure influence shaping trust in a supervisory control. Subjective ratings of trust were collected to examine the impact of two factors: system transparency (Detailed vs. Less) and system failure (by Limits vs. Malfunction) in a driving simulator study in which drivers experienced a partially automated vehicle. We examined trust ratings at three points: before and after driver intervention in the automated vehicle, and after subsequent experience of flawless automated driving. Our result found that system transparency did not have significant impacts on trust change from before to after the intervention. System-malfunction led trust reduction compared to those of before the intervention, whilst system-limits did not influence trust. The subsequent experience recovered decreased trust, in addition, when the system-limit occurred to drivers who have detailed information about the system, trust prompted in spite of the intervention. The present finding has implications for automation design to achieve the appropriate level of trust.
Proof-of-Work cryptocurrency mining: a statistical approach to fairness

In Proof-of-Work Blockchain-based systems, the ledger is kept consistent through some participants solving cryptopuzzles, usually referred to as block mining. Conventional wisdom asserts that the mining protocol is incentive-compatible. However, whether some strategic mining behaviors occur in practice or not, has been the subject of extensive debate. In this paper, we target this question by detecting anomalies in the statistics of consecutive blocks among several popular cryptocurrency systems. Firstly, we measure the inequality of mining revenue distribution in each system. Secondly, we propose a statistical method to identify the selfish mining (SM) behavior, a mining attack strategy posited by Eyal and Sirer in 2014. Our method is based on abnormal (statistically significant) high probability of continuously mining blocks. Finally, we extend our method to detect the mining cartels, in which miners secretly get together and share information about newly mined blocks. Our analysis will contribute to the research of fairness in cryptocurrency mining by providing evidence that the aforementioned strategic mining behaviors do take place in practice.
Observer-Based Fixed-Time Consensus Control for Nonlinear Multi-Agent Systems Subjected to Measurement Noises

This article investigates the fixed-time consensus control problem for nonlinear second-order multi-agent systems subjected to measurement noises and in absence of the second-order state measurements of the leader and the followers under directed communication topology graphs. For each follower, an innovative augmented fixed-time disagreement error observer (AFTDEO) is first designed to reconstruct the disagreement error within fixed time. On one hand, the proposed AFTDEO can not only operate well only depending on the noisy measurements of the first-order states of the leader and the followers, but also suppress the measurement noises of the leader and the followers. On the other hand, the proposed AFTDEO can guarantee that the observation errors are convergent to the origin in fixed time independent of the initial observation errors. Based on the proposed AFTDEO, a novel fixed-time consensus control scheme under a directed graph having a spanning tree is constructed to address the consensus problem without the second-order measurements of the leader and the followers by utilizing fixed-time nonsmooth backstepping technique. The fixed-time convergence of the tracking error is guaranteed through Lyapunov stability analysis. Finally, simulation results demonstrate the effectiveness of the proposed consensus control scheme.
Mycorrhizas in Zambian trees in relation to host taxonomy, vegetation type and successional patterns

(1) Thirteen species of common Zambian trees were found to be ectomycorrhizal and ten endomycorrhizal. (2) Observations of ectomycorrhizas in Brachystegia, Julbernardia (both Caesalpiniaceae), Monotes (Dipterocarpaceae) and Uapaca (Euphorbiaceae) confirmed earlier reports, while ectomycorrhizas in Faurea (Proteaceae), Isoberlinia (Caesalpiniaceae), Marquesia (Dipterocarpaceae) and Pericopsis (Papilionaceae) are reported for the first time. (3) These genera constituted about 70% of the basal area in a representative miombo (Brachystegia-Isoberlinia-Julbernardia) woodland stand-a high proportion for a diverse tropical ecosystem. (4) Associated species in the miombo were endomycorrhizal as were principal species of the Zambian Kalahari sand vegetation, namely species of Baikiaea, Erythrophleum, Guibourtia (all Caesalpiniaceae) and Pterocarpus (Papilionaceae). Colophospermum mopane (Caesalpiniaceae), a species often forming pure stands, was also endomycorrhizal. (5) The ecological groups of species suggested by Lawton (1978) as typical of the successional stages from open fire-degraded chipya woodland to closed dry evergreen mateshi forest via miombo woodland, show differences in the occurrence of mycorrhizas. In the chipya group, endomycorrhizal tree species predominate and ectomycorrhizal species are sparse. Ectomycorrhizal Uapaca spp. invade chipya woodland, and later provide cover for the regeneration of ectomycorrhizal genera forming the miombo group-Brachystegia, Isoberlinia, Julbernardia and Monotes-and for ectomycorrhizal Marquesia of the mateshi group. In the mateshi forest group, there is a mixture of ectoand endomycorrhizal tree species.
Theoretical and Empirical Analysis of the Relationship Between Monetary Policy and Stock Market Indices

This paper focuses on investigating the relationship between monetary policy and stock market indices based on system theoretical analysis and empirical testing using vector autoregressive models. Our study reveals peculiarities of the relationship between monetary and stock market indicators in developed, successfully developing and moderately developing countries with high political and economic risks. First, we described theoretical aspects of the monetary policy mechanism that influences stock market development. Second, we systematized key economic variables and mathematical models that are widely used in the literature for similar studies. Third, we empirically demonstrated that stock market has a significant impact on monetary indicators in most countries, whereas monetary policy with focus on stock market stimulation is only effective in developed economies. We concluded that the stock market in developing countries demonstrates weak reaction to short-term changes in monetary policy of central banks and mainly depends on their long-term relationship.
On the Forecast of the Voltage Sags: First Stages of Analysis on Real Systems

This paper refers to the voltage sags, which are among the most critical Power Quality disturbances. Thanks to the availability of measured voltage sags for four years in the same regional electric system, the work is our first attempt of using the registered voltage sags in three years for forecasting the expected voltage sags in the fourth year. The approach of the paper follows that proposed very recently by the professor M.T. Correia de Barros. Several intermediate results are similar to those obtained by her researches. The final results in terms of forecasts reveal that some improvements are needed for obtaining predictions with increased precision. The paper also presents the first improved results.
Tolerance of time delay to the sense of agency for operating electric prosthetic hands: a preliminary experiment with the appearance and gripping action

Rehabilitation in patients with phantom limb pain using an electric prosthetic hand is a promising way to mitigate their pain. This method is thought to reconstruct the patient’s sensory-motor loop that connects a command from the brain for a particular movement with the perception that the electric prosthetic hand is actually being driven by this command. A sense of agency (SoA), offered by the feeling that the prosthetic hand is being operated by the patient’s own intentions, is important for this rehabilitation. However, the relationship between characteristics of the prosthetic hand, especially the degree of reproducibility of its appearance and of gripping actions, and the associated SoA is not clear. This study therefore investigated the influence of the reproducibility of gripping action of an electric prosthetic hand and its appearance on participants’ (n = 60) SoA. In advance of adopting patient with phantom limb pain, this study focused on the difference of SoA scores of healthy participants. Significant differences were observed in SoA scores due to the changes in reproducibility of the appearance and gripping action. It is suggested that SoA is more strongly perceived when the prosthetic hand does not strictly resemble a human hand.
Investigating and Suggesting the Evaluation Dataset for Image Classification Model

Image processing systems are widespread with the digital transformation of artificial intelligence. Many researchers developed and tested several image classification models using machine learning and statistical techniques. Nevertheless, the current research seldom focuses on the quality assurance of these models. The existing methods lack to verify the quality assurance, with the lack of test cases to prepare the evaluation dataset to test the model, which can cause critical drawbacks in the nuclear field and defense system. In this article, we discuss and suggest the preparation of the evaluation dataset using improved test cases through Cause-Effect Graphing. The proposed method can generate the evaluation dataset with automated test cases through the quantification method, which consists of 1) image characteristic selection 2) creating the Cause-Effect graphing approach of the image with the feature, and 3) generate all possible test coverage. The testing is performed with the COCO dataset, which shows the declining prediction accuracy with the adjusted brightness and sharpness ranging between −75 to 75%, which indicates the negligence of the important characteristics in the existing test dataset. The experiment shows the prediction fails while sharpness is less than the 0%, and the brightness fails at −75% with less number of detection object between −50% and 75%. This indicates that characteristic changes affects the prediction accuracy and the number of detected objects in an image. Our approach proves the importance of the characteristic selection process for the overall image to generate a more efficient model and increase the accuracy of object detection.
Deadbeat Current Controller for Bidirectional Dual-Active-Bridge Converter Using an Enhanced SPS Modulation Method

This letter proposes a deadbeat current controller for isolated bidirectional dual-active-bridge dc–dc converter. The controller uses an enhanced single phase shift modulation method by exploiting pulse width as an extra control variable besides phase shift ratio, the only control variable of the conventional single phase shift modulation. The current reference can be tracked within one switching cycle during the bidirectional power transmission and transition of the two directions. Deadtime effect is studied and the compensation method for the controller is proposed. Parameter robustness is analyzed by mathematical derivation and the online autotuning method for the inductance is presented. The proposed deadbeat current controller is simple and easy for implementation. Moreover, it shows superior dynamic performance, which is verified by experimental results during the transient processes of the converter. The effectiveness of the autotuning method is also validated by the prototype experiment.
High fatty acid environments result in preferential sequestration of palmitic acid into plasma membrane triacylglycerol

Human neutrophils incubated for 1 h in vitro with 10% commercial pooled, human serum containing high levels of free fatty acids (1141 mm) displayed a distinct lipid signal, typical of triacylglycerol, in the 1 H NMR spectrum. Concurrently their plasma membrane triacylglycerol mass increased 4.6-fold with a selective rise in the content of palmitic and linoleic acids. Although qualitatively similar, these effects were much greater than those observed after incubating neutrophils with 50 mg·mL 21 of lipopolysaccharide in the presence of 10% AB serum with normal free fatty acid content (345 mm, LPS/S). Incubation of neutrophils with an artificial mixture of free fatty acids at concentrations found in commercial serum, or with the fatty acid fraction isolated from commercial serum increased the 1 H NMR-detectable triacylglycerol. The signal intensity of the 1 H NMRdetectable triacylglycerol depended on the triacylglycerol composition, and correlated with increased membrane triacylglycerol mass. Cellular uptake of 3 H-labelled palmitic or oleic acids increased in the presence of commercial serum but not with LPS/S, with little contribution in either case to the triacylglycerol pool that increased in mass. Pulse-chase experiments demonstrated that with LPS/S and commercial serum, radiolabelled palmitic acid was preferentially incorporated into triacylglycerol located in the plasma membrane. This process could occur at the plasma membrane, as cytoplasts efficiently convert exogenous fatty acids into triacylglycerol. We propose that LPS/S and serum containing high levels of free fatty acid, important in conditions of sepsis and inflammation, may facilitate the sequestration of palmitic acid into triacylglycerol by different pathways. This triacylglycerol originates from exogenous and endogenous free fatty acids, is 1 H NMR-visible, and may have a
Predictive Torque and Stator Flux Control for N*3-Phase PMSM Drives With Parameter Robustness Improvement

In this article, a novel predictive torque and stator flux control (PTSF) method is proposed for N-segment three-phase permanent magnet synchronous motor (N*3-phase PMSM), which can effectively eliminate the influence of parameter mismatch on stator flux and torque. The proposed PTSF method has two loops, stator flux loop and torque loop, both implemented with robust predictive control algorithm. First, the stator flux predictive controller with parameter robustness is designed, and the sensitivity of its parameters is analyzed. In the dq stator flux space, the robust stator flux predictive control with one-step delay compensation is then proposed, which can effectively enhance robustness against parameters mismatch. Moreover, the robust torque predictive control based on unknown torque disturbance observer is developed, which can effectively strengthen the robustness against the load torque disturbance and parameter mismatch. Finally, the validity and feasibility of the proposed PTSF method are verified by simulation and experiment. Excellent simulation and experimental results were achieved with respect to the stator flux tracking error, torque /flux ripple reduction, and stator current distortion over conventional predictive torque control.
Design and Control of Power Fluctuation Delivery for Cell Capacitance Optimization in Multiport Modular Solid-State Transformers

Modular multilevel converter (MMC)-based solid-state transformers (SSTs) have gained increasing interest lately. Topologies consisting of cells made of directly coupled MMC submodules (SMs) and dual-active-bridge (DAB) modules have been introduced to form multiport SSTs to interface different grid entities. Designated as modular SSTs (M-SSTs), such devices can interconnect hybrid ac–dc distribution systems and enable flexible power flow control among participating grids with different voltage forms and levels. However, the large capacitors needed to suppress power fluctuations in the power cells are a major contributor to the SST's poor power density. This article first proposes a power fluctuation delivery (PFD) control strategy for M-SST cell capacitance optimization. Through a modified phase-shift control, the low-frequency fluctuating power in the MMC SMs is transferred to the DAB's secondary side and is automatically canceled there. As a result, the cell capacitance requirement is significantly reduced. Considering the modified ripple power path, both power semiconductors and the high-frequency transformers in DABs are affected. Therefore, the design considerations of a single M-SST cell (1 MMC SM + 1 DAB) with/without PFD control are elaborated for a 2-MVA M-SST example. It is shown that the cell capacitance can be reduced to 10% of its original value, and the power density of the whole cell is increased by 50% with only 0.22% drop in the overall efficiency. The feasibility and effectiveness of the proposed method are verified by simulation results in this 2-MVA case and experimental results obtained from a 4.8-kVA scaled-down M-SST prototype.
Broiler Performance at Different Stocking Densities

Abstract In three experiments, Peterson x Hubbard broilers were reared at different stocking densities (SD) in an environmentally controlled (cool cells) house in Yucatan, Mexico. Males were housed until 7 wk of age at: 10, 12, and 14 birds/m2 in Experiment 1: 14,15, and 16 birds/m2 in Experiment 2; and 17, 17.5, and 18 birds/m2 in Experiment 3. For females the SD were 11, 13, and 15 birds/m2, 15, 16, and 17 birds/m2, and 18, 19 and 20 birds/m2. The highest SD of 18 males/m2 resulted in only a 3% decrease in 7-wk body weight compared with the lowest density of 10 males/m2. For females, the difference between the highest and lowest SD was only 1.5%. The reductions in feed consumption at the highest SD compared with the lowest were 3.7 and 3.9% for males and females, respectively. SD did not affect feed conversion or mortality in any of the experiments. The kg of broiler/m2 and profit margins increased with SD up to 17 males/m2 and 19 females/m2.
PATCH DYNAMICS OF PLANT SUCCESSION ON ABANDONED SURFACE COAL MINES: A CASE HISTORY APPROACH

SUMMARY revegetation in Missouri, U.S.A. , is described. (2) The six sites have undulate surfaces and slightly to very acidic spoils. (3) The size and frequency of vegetation patches were measured by computer image-analysis of digital maps prepared from aerial photographs taken at various intervals after the sites were abandoned. (4) Despite differences in the rates of revegetation, the six sites were rapidly colonized by plants which grew to form patches. These patches grew at a uniform rate, while new patches were also formed. Eventually, the patches coalesced and dominated the surface. The degree of non-circularity of patches was at its maximum when fusion of patches became widespread. (5) Grazing of one site reduced total cover and produced a transient fission of patches so that, for a short time, their density increased and non-circularity decreased.
Machine Learning-Based Automatic Detection of Central Sleep Apnea Events From a Pressure Sensitive Mat

Polysomnography (PSG) is the standard test for diagnosing sleep apnea. However, the approach is obtrusive, time-consuming, and with limited access for patients in need of sleep apnea diagnosis. In recent years, there have been many attempts to search for an alternative device or approach that avoids the limitations of PSG. Pressure-sensitive mats (PSM) have proven to be able to detect central sleep apneas (CSA) and be a potential alternative for PSG. In the current study, we combine advanced machine learning approaches with a practical unobtrusive home monitoring device (PSM) to detect CSA events from data collected nocturnally and unattended. Two deep learning methods are implemented for the automatic detection of CSA events: a temporal convolutional network (TCN) and a bidirectional long short-term memory (BiLSTM) network. The deep learning models are compared to a classical machine learning approach (linear support vector machine, SVM) and a simple threshold-based algorithm. Considering the characteristics of each method, we choose strategies, including resampling and weighted cost-functions, to optimize the methods and to perform CSA detection as anomaly detection in an imbalanced data set. We evaluate the performance of all models on a database containing 7 days of data from 9 elderly patients. From the resulting 63 days, data from 7 patients (49 days) are devoted to training for optimizing hyperparameters, and data from 2 patients (14 days) are devoted to testing. Experimental results indicate that the best-performing model achieves an accuracy of 95.1% through training an BiLSTM network. Overall, the implemented deep learning methods achieve better performance than the conventional classification approach (SVM) and the simple threshold-based method, and show good potential for the use of PSM for practical unobtrusive monitoring of CSA.