Miracle-dz/UniFAR-FTUs huggingface dataset:
Ship Recognition Method Based on Spiking Neural Networks in Synthetic Aperture Radar Images

Ship recognition is a hot issue for the intelligent interpretation in the synthetic aperture radar (SAR) images. A modified ship recognition approach based on the spiking neural networks (SNNs) in the SAR images has been proposed. Firstly, a Poisson encoder on the basis of the visual attention mechanism is used to encode the input SAR image in a pulse sequence, to remove the background noise interference during the encoding process and retain the visual saliency of the image as much as possible. Then, based on the neuron model of the leaky integrate and fire (LIF), combined with the convolutional neural network, an end-to-end SNNs model was constructed, and the LIF neuron pulse emission frequency has been used to perform the target recognition in the SAR images. Finally, to resolve the issue that the SNNs model is very difficult to train, an arctangent function is used as a substitute function of the pulse emission function during backpropagation for the gradient calculation, and the backpropagation algorithm is applied to the training process of the ship target recognition. The experimental results prove that the presented approach can accurately identify the ship targets and has the advantages of fewer parameters and high efficiency.
Fault Diagnosis Based on Deep Residual Shrinkage Network and Maximum Mean Discrepancy

The working environment of rolling bearings is complex, and it is often influenced by noise When the condition changes, a large number of samples with known fault types cannot be provided in the previous condition. These two reasons lead to the decrease of the accuracy of fault diagnosis on testing samples. This paper proposes a transfer learning way based on deep residual shrinkage network and maximum mean discrepancy (DRSN-MMD), which can be used in bearing fault diagnosis under complex and changing conditions. Deep residual shrinkage network (DRSN) has the capacity to eliminate redundant information from vibration signals because soft thresholding is inserted. As a classical criterion to measure the distance between source and target domain, maximum mean discrepancy (MMD) can be used in transfer learning. In our paper, DRSN is used to extract fault feature, and MMD is applied as loss function on model training. In contrast with other methods, the proposed DRSN-MMD approach has higher diagnosis accuracy on the well-known Case Western Reserve University bearing dataset.
Using equity crowdfunding to build a loyal brand community: The case of Brewdog

This case study provides a critical understanding of the connection between start-up investment and the development of a loyal brand community. Learners develop an appreciation of how engagement in crowdfunding campaigns can lead to the creation of engaged partners. This is explored through applying the Business Model Canvas to the case of BrewDog, a company that has expanded beyond the niche market of craft brewing to become an international brand. The use of crowdfunding has not only enabled Brewdog to raise the capital to finance expansion but also to develop a special relationship with some of their customers, who through investment and engagement can become partners in the product development process.
Efficient Violence Detection in Surveillance

Intelligent video surveillance systems are rapidly being introduced to public places. The adoption of computer vision and machine learning techniques enables various applications for collected video features; one of the major is safety monitoring. The efficacy of violent event detection is measured by the efficiency and accuracy of violent event detection. In this paper, we present a novel architecture for violence detection from video surveillance cameras. Our proposed model is a spatial feature extracting a U-Net-like network that uses MobileNet V2 as an encoder followed by LSTM for temporal feature extraction and classification. The proposed model is computationally light and still achieves good results—experiments showed that an average accuracy is 0.82 ± 2% and average precision is 0.81 ± 3% using a complex real-world security camera footage dataset based on RWF-2000.
The Battle for Homestead, 1880–1892: Politics, Culture, and Steel by Paul Krause (review)

Paul Krause calls upon the methods and insights of labor history, intellectual history, anthropology, and the history of technology to situate the events of the lockout and their significance in the broad context of America s Guilded Age. Utilizing extensive archival material, much of it heretofore unknown, he reconstructs the social, intellectual, and political climate of the burgeoning post-Civil War steel industry. "
Growth curve in elephant grass genotypes based on morpho-agronomic traits for energy production

The elephant grass ( Cenchrus purpureus (Schumach.) Morrone) is one of the species with great prominence for energy production because of its favorable traits, such as high yield and short cycle, combined with several biomass quality characteristics. Therefore, this study intended to evaluate the morpho-agronomic traits of eight elephant grass genotypes under different cutting ages. A randomized block design with three replicates was applied to the experiment, and plots were composed of a 12 m long line divided into six subplots for each of the bimonthly cuts. Eight genotypes were evaluated in the plots, and the cutting ages (2, 4, 6, 8, 10, and 12-mo) were analyzed in the subplots. The following traits were assessed: DM yield, (DMY) percentage of DM, and average plant height (HEI). All genetic-statistical analyses were conducted using the Genes Program. The evaluated genotypes showed nonsignificant differences, indicating that, for the evaluated traits, these genotypes are genetically similar. Most genotypes presented linear effect of 2 nd degree in the two assessment cuts, pointing they did not reach their maximum capacity of DMY in the time interval of the evaluated cuts. The maximum %DM achieved for the most genotypes occurred at about 9-mo age, while genotypes Vruckwona and BAG-86 at about 10-mo age. The maximum points for HEI trait were between 8 and 10-mo age. Increasing the cutting interval leads to increases in DM yield, with the 10-mo cutting age being the most efficient, resulting in higher yields.
Design of Integral Sliding Mode Controller with Fuzzy Switching Gain Adjustment for Morphing Aircraft

The morphing aircraft can obtain the best aerodynamic characteristics by changing its shape in order to adapt to the current mission and environment. In this paper, a non-affine parameter-dependent linear variable parameter (LPV) system model of a morphing aircraft whose wingspan and sweep angle can be changed is used as the object. First, the linear fractional form (LFR) is used to design an integral sliding mode that converges in a finite time. Then, the fuzzy switching gain adjustment method is introduced to eliminate the interference items and uncertain items in the nonlinear system. The simulation results show that the method can ensure the stability of the variant process in the presence of large disturbances and uncertainties. This controller design method has universal adaptability to LPV system.
A Mobile Robotic System for Data Center Thermal Environment Measurement and Reconstruction

Data centers are the key infrastructure for information services. The monitoring of the thermal environment of the data center computer room is an important work for its safe operation. This paper proposed a mobile robotic system for data center thermal environment measurement, which can freely sample the temperature and humidity data around the facilities within the computer room. On one hand, measuring the temperature and humidity in a mobile way can obtain far more environmental data than fixed-point sensors. On the other hand, the mobile robot can be used instead of manual inspection. In addition, a temperature field reconstruction method is provided based on the sampled temperature data. Theoretically, the temperature of locations that have not been measured can be evaluated by interpolating sample temperature around them. The reconstruction of the temperature field can present the variation of the temperature more clearly and help find the hot spots or locations with low cooling efficiency during the operation. Lastly, experiments are carried out to study the measurement error of the mobile robotic system and an error correction method is proposed. After that, the relationship between the temperature reconstruction error and the layout of sampling points is investigated.
Long Short-Term Memory Recurrent Neural Network for Automatic Speech Recognition

Automatic speech recognition (ASR) is one of the most demanding tasks in natural language processing owing to its complexity. Recently, deep learning approaches have been deployed for this task and have been proven to outperform traditional machine learning approaches such as Artificial Neural Network (ANN). In particular, deep-learning methods such as long short-term memory (LSTM) have achieved improved ASR performance. However, this method is limited to processing continuous input streams. Traditional LSTM requires four (4) linear layers (multilayer perceptron (MLP) layer) per cell with a large memory bandwidth for each sequence time step. LSTM cannot accommodate the many computational units required for processing continuous input streams because the system does not have sufficient memory bandwidth to feed the computational units. In this study, an enhanced deep learning LSTM recurrent neural network (RNN) model was proposed to resolve this shortcoming. In the proposed model, the RNN is incorporated as a “forget gate” to the memory block to allow the resetting of cell states at the beginning of the sub-sequences. This enables the system to process continuous input streams efficiently without necessarily increasing the required bandwidths. In the proposed model, the standard architecture of the LSTM network is modified to effectively use the model parameters. Some CNN-based and sequential models were used on the same dataset, and the models were compared with the proposed model. LSTM-RNN outperformed the other deep learning models with an accuracy of 99.36% on the well-established public benchmark spoken English digit dataset.
Structural, Magnetic, and AC Measurements of Nanoferrites/Graphene Composites

As a contribution to the graphene-based nanoferrite composites, this article is intended to present Mn, Co, and Co-Mn nanoferrites for the preparation and investigation of such samples. Nanoparticles of Co ferrite, Mn ferrite, and Co-Mn ferrite were chemically synthesized by the coprecipitation method. The composites of ferrite/graphene were made by incorporating weight ratios of 25% graphene to 75% ferrite. Various structural and characterizing investigations of ferrite samples and ferrite/graphene composites were performed, including XRD, EDX, SEM, VSM hysteresis loops, AC conductivity, and dielectric behavior. The investigations ensured the formation of the intended nanoferrite powders, each having a single-phase crystal structure with no undesired phases or elements. All samples exhibit a soft magnetic behavior. They show a semiconducting behavior of AC electrical conductivity as well. This was proved by the temperature dependence of the AC’s electrical conductivity. Whereas the dielectric function and loss tangent show an expected, well-explained behavior, the ferrite/graphene composite samples have lower saturation magnetization values, lower AC conductivity, and dielectric constant values than the pure ferrites but still have the same behavior trends as those of the pure ferrites. The values obtained may represent steps on developing new materials for expected applications, such as manufacturing supercapacitors and/or improved battery electrodes.
3D Distance Filter for the Autonomous Navigation of UAVs in Agricultural Scenarios

In precision agriculture, remote sensing is an essential phase in assessing crop status and variability when considering both the spatial and the temporal dimensions. To this aim, the use of unmanned aerial vehicles (UAVs) is growing in popularity, allowing for the autonomous performance of a variety of in-field tasks which are not limited to scouting or monitoring. To enable autonomous navigation, however, a crucial capability lies in accurately locating the vehicle within the surrounding environment. This task becomes challenging in agricultural scenarios where the crops and/or the adopted trellis systems can negatively affect GPS signal reception and localisation reliability. A viable solution to this problem can be the exploitation of high-accuracy 3D maps, which provide important data regarding crop morphology, as an additional input of the UAVs’ localisation system. However, the management of such big data may be difficult in real-time applications. In this paper, an innovative 3D sensor fusion approach is proposed, which combines the data provided by onboard proprioceptive (i.e., GPS and IMU) and exteroceptive (i.e., ultrasound) sensors with the information provided by a georeferenced 3D low-complexity map. In particular, the parallel-cuts ellipsoid method is used to merge the data from the distance sensors and the 3D map. Then, the improved estimation of the UAV location is fused with the data provided by the GPS and IMU sensors, using a Kalman-based filtering scheme. The simulation results prove the efficacy of the proposed navigation approach when applied to a quadrotor that autonomously navigates between vine rows.
Object Rearrangement Through Planar Pushing: A Theoretical Analysis and Validation

In this article, we focus on rearranging an object by pushing it to any target planar pose. We identify the essential elements to guarantee that the target pose can be reached at an acceptable precision. We present a simple rearrangement algorithm that relies on only a few known straight-line pushes for some novel object and requires no analytical models, force sensors, or large training datasets. We derive the step upper bound, which relates the initial pose of the object, stopping criterion, and quality of the set of pushes, to facilitate the estimation of the maximum number of required steps without the need to perform a task. We experimentally verified the performance of our algorithm at different noise levels, stopping criteria, and task difficulties on datasets containing several types of objects. By applying combinations of only nine known pushes, our simple algorithm performed successfully in real-world experiments with challenging objects, including partially deformable objects that are difficult to model analytically; it achieved the precise stopping criterion (7.5 mm, 5$^\circ$) in various rearrangement tasks.
A Study on the Antioxidant Activity of Rosmarinic Acid Against Carbon Tetrachloride-Induced Liver Toxicity in Adult Male Albino Rats

study on the antioxidant activity of rosmarinic acid against carbon tetrachloride-induced liver toxicity in adult male albino rats. :157-167, SUMMARY: Carbon (CCl4) is a manufactured chemical does environment. CCl4 is a clear liquid that evaporates very easily. It has a sweet odor. CCl4 is toxic to the mammalian liver and is hepatocarcinogenic in both rats and mice. Rosemary (Rosmarinus Officinalis) is commonly used as a spice and flavoring agent in food processing. It is known for its antioxidant properties. The present study aims to investigate the antioxidant activity of rosmarinic acid (RA) on CCl4-induced liver toxicity in adult male albino rats. Forty adult male albino rats were divided into 4 groups with 10 rats in each group. Group I (control group). Group II animals received RA at a dose of 50 mg/kg/day by oral gavage for 4 weeks. Group III animals received CCl4 intraperitoneally at a dose of 3ml/kg twice weekly for 4 weeks. Group IV animals received CCl4 Plus RA. At the end of the experiment, liver specimens are processed for histological, immunohistochemical, EM and biochemical studies. Administration of RA deceased the elevated serum liver enzymes (AST, ALT, and ALP), elevated MDA level and immunoexpression of the proapoptotic protein (Bax) induced by CCl4. It increased reduced glutathione (GSH), glutathione peroxidase (GSH-Px), superoxide dismutase (SOD), and immunoexpression of the antiapoptotic protein (Bcl2). It also improved the histological and ultrastructural changes induced by CCl4. It appears that Rosmarinic acid has protective effects against CCl4-induced hepatotoxicity as indicated by biochemical, histological, immunohistochemical and ultrastructural results.
Multi-data source trajectory post-fusion based on sliding clustering in the Internet of Vehicles

In the Internet of Vehicles project, multiple sensors are used to detect the trajectory of the vehicle. Due to the detection error of the sensors, the detection results of each group of sensors will not be completely consistent. At the same time, since the local clock of each sensor has a time difference, the detection time of different sensors cannot be strictly consistent. This leads to the problem of temporal and spatial inconsistency of trajectory data. In order to solve the problem of temporal and spatial inconsistency in the data, it is necessary to perform post-fusion processing on the trajectory data. This paper proposes an algorithm based on sliding clustering to post-fusion of multiple data source trajectories. The experimental results prove that the fusion trajectory data with lower error can be obtained in real time using this algorithm.
Sexual health in married women and the role of meta-cognitive belief and emotion regulation strategies in predicting that

Introduction: Sexual health is one of the main aspects of health and investigation of its relevant factors is so important for primary healthcare. Aim: The present study was conducted to determine the role of meta-cognitive belief and emotion regulation strategies in predicting Sexual health of married women. Method: This research is a descriptive correlation study. The statistical population of the study was all the married women in Ardabil City in 2020. Out of the mentioned population, 110 people were selected as the samples. Data collection was done by Meta-Cognitive Belief Questionnaire (Wells & Cartwright-Hatton, 2004), Emotion Regulation Strategies Scale (Gross & John, 2003), and Sexual Health Scale (Manavi Pour, 2008). Research data were analyzed using Pearson correlation coefficient and multiple regression using SPSS18 software. Results: The results showed a significant relationship between meta-cognitive belief -0.62 and emotion regulation strategies (Reappraisal 0.56 and Suppression -0.43) with Sexual health in married women. Also, the results of multiple regression showed that meta-cognitive belief and emotion regulation strategies can predict the Sexual health of married women by 0.55 (p<0.05). Conclusion: Regarding the effect of meta-cognitive belief and emotion regulation strategies on sexual health in married women, it is suggested for psychologists and health experts to pay more attention the role of these variables in the improvement of sexual health in married women.
Research on Drag Control Algorithm of Cooperative Robot Based on Torque Compensation

At present, the teaching methods of collaborative robot drag based on joint current feedback mainly include integral and partial dynamics model, both of which can make robots dragged. However, there are few researches aimed at the advantages and disadvantages of the drag effect and the specific application requirements of the systematic comparative study. Based on the above problems, two zero-force control methods are used on the same 6 degree-of-freedom(DOF) cooperative robot in this paper. And method of torque compensation is improved by parameter identification. Firstly, both of the two methods need to model and analyse the robotic arm. However, processes of the method based on integral dynamics model are modeling, excitation trajectory, filtering, identification and vertification. The zero-force control based on partial dynamics model mainly focuses on the compensation of gravity torque and static friction torque. The gravity term in the dynamic equation is adopted by modeling. The difference in this part is that the parameters of gravity moment function are identified, that is, the weight moment is calculated under the condition of the mass and center of mass deviation are unknown. Finally, NaiWei robot which is a six-joints cooperative robot in China is taken as the experimental object to test and compare. The experimental results show that method of partial dynamics model needs smaller force than integral dynamics model. And the comparison of drag power can be displayed by sensor.
An Improved VM Obstacle Identification Method for Reflection Road

An obstacle detection method based on VM (VIDAR and machine learning joint detection model) is proposed to improve the monocular vision system's identification accuracy. When VIDAR (Vision-IMU-based detection and range method) detects unknown obstacles in a reflective environment, the reflections of the obstacles are identified as obstacles, reducing the accuracy of obstacle identification. We proposed an obstacle detection method called improved VM to avoid this situation. The experimental results demonstrated that the improved VM could identify and eliminate unknown obstacles. Compared with more advanced detection methods, the improved VM obstacle detection method is more accurate. It can detect unknown obstacles in reflection, reflective road environments.
Clinical severity of Omicron SARS-CoV-2 variant relative to Delta in British Columbia, Canada: A retrospective analysis of whole genome sequenced cases

ABSTRACT Background In late 2021, the Omicron SARS-CoV-2 variant emerged and rapidly replaced Delta as the dominant variant globally. The increased transmissibility of the variant led to surges in case rates as well as increases in hospitalizations, however, the true severity of the variant remained unclear. We aimed to provide robust estimates of Omicron severity relative to Delta. Methods This study was conducted using a retrospective cohort design with data from the British Columbia COVID-19 Cohort – a large provincial surveillance platform with linkage to administrative datasets. To capture the time of co-circulation with Omicron and Delta, December 2021 was chosen as the study period. We included individuals diagnosed with Omicron or Delta infection, as determined by whole genome sequencing (WGS). To assess the severity (hospitalization, ICU admission, length of stay), we conducted adjusted Cox proportional hazard models, weighted by inverse probability of treatment weights (IPTW), accounting for age, sex, underlying comorbidities, vaccination, sociodemographic status, and geographical variation. Results The cohort was composed of 13,128 individuals (7,729 Omicron and 5,399 Delta). There were 419 COVID-19 hospitalizations, with 118 (22%) among people diagnosed with Omicron (crude rate = 1.5% Omicron, 5.6% Delta). In multivariable IPTW analysis, Omicron was associated with a 50% lower risk of hospitalization compared to Delta (aHR = 0.50; 95%CI = 0·43-0.59), a 73% lower risk of ICU admission (aHR = 0.27; 95%CI = 0.19-0.38), and a 5 days shorter hospital stay on average (aß=-5.03; 95% CI=-8.01, -2.05). Conclusions Our analysis supports findings from other studies demonstrating lower risk of severe outcomes in Omicron-infected individuals relative to Delta.
Antifertility Efficacy of Testosterone on Male and Female Albino Mice

Maha Mostafa1, Sohail Soliman1, Reham I. Mohamed2 and Wael M. El-Sayed1* 1Department of Zoology, Faculty of Science, Ain Shams University, Abbassia 11566, Cairo, Egypt. 2Pesticide Chemistry Department, Chemical Industries Research Institute, National Research Centre, Dokki, Giza, Egypt Article Information Received 10 November 2021 Revised 26 December 2021 Accepted 19 January 2022 Available online 14 March 2022 (early access)