Miracle-dz/UniFAR-FTUs huggingface dataset:
Genome-Wide Detection of CNVs and Association With Body Weight in Sheep Based on 600K SNP Arrays

Copy number variations (CNVs) are important genomic structural variations and can give rise to significant phenotypic diversity. Herein, we used high-density 600K SNP arrays to detect CNVs in two synthetic lines of sheep (DS and SHH) and in Hu sheep (a local Chinese breed). A total of 919 CNV regions (CNVRs) were detected with a total length of 48.17 Mb, accounting for 1.96% of the sheep genome. These CNVRs consisted of 730 gains, 102 losses, and 87 complex CNVRs. These CNVRs were significantly enriched in the segmental duplication (SD) region. A CNVR-based cluster analysis of the three breeds revealed that the DS and SHH breeds share a close genetic relationship. Functional analysis revealed that some genes in these CNVRs were also significantly enriched in the olfactory transduction pathway (oas04740), including members of the OR gene family such as OR6C76, OR4Q2, and OR4K14. Using association analyses and previous gene annotations, we determined that a subset of identified genes was likely to be associated with body weight, including FOXF2, MAPK12, MAP3K11, STRBP, and C14orf132. Together, these results offer valuable information that will guide future efforts to explore the genetic basis for body weight in sheep.
SNIFU: Secure Network Interception for Firmware Updates in legacy PLCs

Attacks on Industrial Control Systems (ICS) are increasingly targeting field devices and the firmware that instruments their operation. Securing the firmware images and their update procedure has, therefore, become an important challenge. This is especially true for widely deployed legacy devices which are not equipped with the necessary security mechanisms/capabilities. In this paper, we address the problem by reverse engineering PLC firmware update tools to build a device that ensures the integrity and authenticity of firmware updates, before allowing them to be flashed onto a field device. Our tool is directly connected to field devices and consists of a firmware signing mechanism, a PLC emulation module, and a payload detection classifier – all integrated in a bump-in-the-wire device, SNIFU. SNIFU monitors serial traffic sent to the PLC for firmware update commands. When it identifies such commands, it emulates a PLC, capturing the entire firmware image and verifying it before relaying it to the PLC. We implement and evaluate a prototype of SNIFU using a Raspberry Pi, that secures the update process of a commercial PLC by Wago.
Spatial Frequency and the Performance of Image-Based Visual Complexity Metrics

There is a wide range of visual and spatial complexity measurement methods that aim to quantify perceived image complexity. While image-based calculation methods (edge detection, image compression, contrast) characterize a digital image, visual perception studies focus on fundamental visual mechanisms, such as contrast sensitivity and visual task performance. Despite the evidence from several vision studies, spatial frequency information has not been widely utilized to assess image complexity. Previous studies suggest that image-based performance metrics are limited in explaining perceived complexity due to confounding factors, such as context, memory, familiarity, and expectation. Here, a visual experiment is conducted to assess the performance of image-based metrics and spatial frequency information using 16 abstract and natural images. A new image complexity metric (<inline-formula> <tex-math notation="LaTeX">$R_{\mathrm {spt}}$ </tex-math></inline-formula>), based on detectability suprathreshold, was proposed to benchmark the performance of existing measures. Forty-four naïve participants used a 5-point Likert-type scale to judge the visual complexity of the images displayed on a tablet. Results indicate that root-mean-square error (RMSE) and <inline-formula> <tex-math notation="LaTeX">$R_{\mathrm {spt}}$ </tex-math></inline-formula> correlate statistically significantly with subjective evaluations. Biological sex did not affect perceived spatial complexity. While RMSE and <inline-formula> <tex-math notation="LaTeX">$R_{\mathrm {spt}}$ </tex-math></inline-formula> can potentially be used to estimate the spatial complexity of display images, the performance of spatial frequency information and image assessment measures in immersive viewing conditions require further research.
An Intelligent Path Planning Scheme of Autonomous Vehicles Platoon Using Deep Reinforcement Learning on Network Edge

Recent advancements in Intelligent Transportation Systems suggest that the roads will gradually be filled with autonomous vehicles that are able to drive themselves while communicating with each other and the infrastructure. As a representative driving pattern of autonomous vehicles, the platooning technology has great potential for reducing transport costs by lowering fuel consumption and increasing traffic efficiency. In this paper, to improve the driving efficiency of autonomous vehicular platoon in terms of fuel consumption, a path planning scheme is envisioned using deep reinforcement learning on the network edge node. At first, the system model of autonomous vehicles platooning is given on the common highway. Next, a joint optimization problem is developed considering the task deadline and fuel consumption of each vehicle in the platoon. After that, a path determination strategy employing deep reinforcement learning is designed for the platoon. To make the readers readily follow, a case study is also presented with instantiated parameters. Numerical results shows that our proposed model could significantly reduce the fuel consumption of vehicle platoons while ensuring their task deadlines.
System Design and Performance Evaluation of Wireless Mobile Ad Hoc Backhaul Network

Aiming at the problem of wireless networking among mobile devices and data backhaul in the IoT scenario, this paper proposes a mobile Ad Hoc network information backhaul system design. This paper mainly focuses the system design especially software design of the information backhaul network, including short-distance network communication design, mobile terminal and server interaction design and cloud display platform design. In addition, the performance of the system is evaluated. The results show that the system has a low packet loss rate and meets the system design requirements.
Effects of Salicylic Acid Pretreatment on the Seed Germination, Seedling Growth and Leaf Anatomy of Barley under Saline Conditions

In this work, the effects of salicylic acid pretreatment on the seed germination, seedling growth (coleoptile percentage, radicle length, coleoptile length, radicle number and fresh weight) and leaf anatomy of barley under saline conditions were studied. In parallel with concentration rise, salt stress inhibited the germination and seedling growth of barley seeds. The inhibitive effect of salt on seed germination and seedling growth was alleviated in varying degrees, and dramatically, by salicylic acid pretreatment. On the other hand, it was determined that the mentioned plant growth regulator affected in different degrees on the various parameters of leaf anatomy of barley seedlings, and this difference was statistically important.
Contactless Finger Knuckle Authentication under Severe Pose Deformations

Contactless biometrics identification using finger knuckle images has shown significant potential for the e-business and forensic applications. One of the key challenges in accurately matching the real-world contactless finger knuckle images is related to the knuckle pattern deformations that are involuntarily generated due to finger pose changes. Earlier work in this area therefore acquired fixed pose finger images for the authentication and therefore the performance achieved from such images cannot reflect the expected performance under the deployment scenarios. This paper adopts a new approach to accurately match such finger knuckle images and presents first attempt to authenticate finger-knuckle patterns under severe pose changes. This approach attempts to correct pose related deformations by identifying the correspondence between a fixed number of chosen points between two matched images. The match score is computed using local feature descriptors, at each of these correspondence points, and consolidated to generate average match score. The experimental results are presented in this paper, both using two-session and single-session index finger knuckle images from 221 different subjects, using publicly available database. These results are outperforming and indicate the merit of spatial-domain approach to match deformed finger knuckle images using a fixed number of correspondence points.
A Brain Computer Interface for Attention Study

Neuroscience has discussed the relationship between human behavior and brain activates. A brain computer interface (BCI) is a common device to investigate the neuroscience issue. In recent years, the researches make efforts in developing a wireless and portable brain computer interface. The aim of this paper is to develop BCI using the embedded systems and Bluetooth. The application of BCI is to attention experiment and observes the correlation between brainwave and attention.
Design of a Three-phase Isolated SEPIC-Based Off-Board Fast Charger for Electric Vehicles

The industry of Electric Vehicles (EVs) is emerging as a significant solution for the environmental issues introduced by Internal-Combustion Engine (ICE) vehicles. This paper presents the design of a unidirectional three-phase fast charger for EV, using three single-phase modules of isolated SEPIC with Power Factor Correction (PFC) capability. The converter can be designed to operate in both Continuous or Discontinuous Conduction Modes (CCM or DCM). The presented concept is validated using MATLAB/Simulink platform, where the simulation results show an input current THD of less than 3% and an almost unity input power factor. Also, the effect of the source impedance on the input current THD is studied considering an insignificant SEPIC's input inductance.
A Batch Variable Learning Rate Gradient Descent Algorithm With the Smoothing L1/2 Regularization for Takagi-Sugeno Models

A batch variable learning rate gradient descent algorithm is proposed to efficiently train a neuro-fuzzy network of zero-order Takagi-Sugeno inference systems. By using the advantages of regularization, the smoothing $L_{1/2}$ regularization is utilized to find more appropriate sparse network. Combining the second-order information of the smoothing error function, a variable learning rate is chosen along the steep descent direction, which avoids line search procedure and may reduce the cost of computation. In order to appropriately adjust the Lipschitz constant of the smoothing error function in the learning rate, a new scheme is proposed by introducing a hyper-parameter. Also the article applies the modified secant equation for estimating the Lipschitz constant, which makes the algorithm greatly reduce the oscillating phenomenon and improve the robustness. Under appropriate assumptions, a convergent result of the proposed algorithm is also given. Simulation results for two identification and classification problems show that the proposed algorithm has better numerical performance and promotes the sparsity capability of the network, compared with the common batch gradient descent algorithm and a variable learning rate gradient-based algorithm.
Pixel-Level Bird View Image Generation from Front View by Using a Generative Adversarial Network

Understanding traffic scenes robustly is a cornerstone for autonomous driving, where bird view is an essential component to create panoramas of surroundings. However, since a large gap of two domains in terms of color, types of cars, landmarks, and occlusions, the task of synthesizing the associated bird view from a front view is quite challenging. Therefore, we propose a different framework for a bird view generation, where our approach employs a network that contains one generator and two discriminators. The generator consists of an encoder and a decoder, the real/fake discriminator inspired by the original GAN, and the identification discriminator has been designed to improve relevance between the source and the target domains. When compared with other previous methods, our approach utilizes neither geometry-based transformation nor an intermediate view approach. Our proposed network successfully synthesizes the associated bird view from a front view with sharper details and higher accuracy.
Financial Awards and Their Effect on Football Players’ Anxiety and Coping Skills

Objective Financial awards can be an important factor affecting athletes’ mental preparation and various skills to manage stress. Since such a link has not yet been studied, the study has been designed to evaluate the moderation effect of financial awards in relation to football players’ anxiety and coping skills. Methods The study consists of 110 male football players aged 18–32 years old (mean ± SD: 23.98 ± 3.01 years) who were divided into two groups: financial awarded (n = 48) and financial unawarded for sports performance (n = 62). The anxiety of football players was measured by the Sport Anxiety Scale SAS-2. Coping strategies to manage stress were assessed by the Athletic Coping Skills Inventory ACSI-28. The effect of financial awards in relation to football players’ anxiety and coping skills was evaluated by the mediators’ model using the PROCESS software (Hayes, 2018). Results The results suggest that financial awards are important factors that influence football players’ anxiety and coping skills. The financial awards increase the motivation of football players to better prepare for sports performance, which has been proven, through better setting of performance goals and more careful mental preparation. Financially awarded football players seem to respect the coach and follow his instructions to a greater extent than unawarded football players, which may be due to the financial benefits and the commitment they have confirmed by signing to the football club. In another aspect, the financial awards are likely to increase the cognitive trait of the anxiety of football players. It seems that financial players are more concerned about the failure of the match, which increases their anxiety, especially since it is a cognitive part and affects their sports performance. Conclusion For this reason, we encourage sports organizations to focus more on the mental preparation of football players. It is important to provide football players the opportunity to graduate from short- or long-term mental training conducted by a trained sports psychologist not only at the time of the athlete’s failure but also as a preventive measure against increasing cognitive anxiety. We recommend sports organizations to train coaches in the field of mental training, preferably through annual short training sessions with a sports psychologist, to influence the development of desirable athletes’ coping skills.
Seasonal and Annual Body Mass Changes in Breeding and Prospecting Black-Legged Kittiwakes Rissa tridactyla: Adaptation or Food Shortage?

Abstract. Body masses of over 1,500 adult and sub-adult Black-legged Kittiwakes Rissa tridactyla were measured during breeding seasons from 1954 to 1995 at colonies in NE England. There was little change between years in the mean mass of each sex, except for a 2% increase in the 1980s and a decrease of 4–7% in 1995. The relative constancy of mass, together with high breeding success throughout, suggested that periods of food shortage in the breeding season were absent throughout the study period. Breeding males averaged 394g and were about 13% heavier than females (340g). Breeding females showed a brief and temporary increase in mass prior to egg laying, and both sexes showed a small but non-significant increase in mass during incubation. There was an abrupt loss of about 6% of mass in adults of both sexes at the time the eggs hatched, and mass remained at this lower level through the nestling period. Masses of prospecting and breeding females were similar and both showed the abrupt decrease in mass in late May and in June. In contrast, prospecting males had a consistently lower mass (377g) than breeding males throughout. The pattern and timing of the loss of mass in breeding kittiwakes did not support the hypothesis that it is induced by stress arising from the need to obtain extra food for the brood, but that it can be regarded as an adaptation for more energetically efficient flight during a period when increased flying activity is needed to obtain additional food for the brood. The assumption that a higher mass in an individual is always a useful measure of quality is probably unjustified.
Implementation of Designed PV Integrated Controlled Converter System

Photovoltaic (PV) energy has increased drastically at an average annual rate of 60% in the last few years. It quickly became an integral part of the power system networks. This increase, in turn, has triggered the evolution of PV power converters with more efficiency and reliability. In this paper, a prototype of the PV integrated controlled converter system is designed, which is applicable in water pumping for irrigation. Main components of this system are floating dual boost converter (FDBC), three-phase voltage source inverter (VSI) and three-phase squirrel cage induction motor (pump). For address the issue of PV voltage fluctuations, a voltage mode controller is designed for the FDBC which regulates the output voltage of the FDBC to a fixed value irrespective of any voltage fluctuations at the PV side.
Improved Salp Swarm Algorithm Based on Levy Flight and Sine Cosine Operator

The salp swarm algorithm (SSA) is a swarm intelligence optimization algorithm that simulates the chain movement behavior of salp populations in the sea. Aiming at the shortcomings of the SSA, such as low precision, low optimization dimension and slow convergence speed, an improved salp swarm algorithm based on Levy flight and sine cosine operator (LSC-SSA) was proposed. The Levy flight mechanism uses the route of short walks combined with long jumps to search the solution space, which can effectively improve the global exploration capability of the algorithm. Improved sine cosine operator use sine search for global exploration and cosine search for local exploitation. At the same time, an adaptively switching between the two function search methods can achieve a smooth transition between global exploration and local exploitation. In the simulation experiment, salp swarm algorithm (SSA), whale optimization algorithm (WOA), particle swarm algorithm (PSO), sine cosine algorithm (SCA), firefly algorithm (FA) and LSC-SSA were adopted for solving function optimization problems. Then, the feasibility of the improved algorithm for solving high-dimensional large-scale optimization problems and the effectiveness of the improvement strategy are evaluated. Finally, LSC-SSA was applied to train muti-layer perceptron neural network. Simulation results show that the introduction of Levy flight and improved sine cosine operator in LSC-SSA significantly improves optimization accuracy and convergence speed compared with other swarm optimization algorithms. In addition, the improved algorithm can effectively solve high-dimensional large-scale optimization problems. In the application of training muti-layer perceptron NN, the improved algorithm can avoid falling into the local optimal value and obtain the ideal classification accuracy.
SM-PI Control Strategy of Electric Motor-Pump for Pure Electric Construction Machinery

Although the pure electric drive system is widely applied to mobile machines, it is not easy for construction machinery (CM) to realize electrification because of the different working style. The CM load changes dramatically and continuously, and the traditional PI control cannot handle this condition. A vector control strategy based on speed sliding mode control (SMC) and torque PI control is proposed for CM with the characteristic of drastically changing loads. Based on the mathematical model of an electric motor-pump, the electric motor-pump control performance requirement for electric CM is analyzed. Combined with the permanent magnet synchronous motor (PMSM) mathematical model, the speed SMC-PI observer is designed. The controller is designed using an exponential approximation method and uses integral and differential links, which has excellent robustness, to suppress flutter in SMC-PI. Control strategies of speed loops with PI and SMC-PI were compared under different conditions. The overshoot of the motor-pump speed was small and zero static control could be achieved. When the load was variable, the fluctuation of rotation speed was approximately 0.5%~1% with SMC-PI, which is only 25%~50% of PI control. Finally, the vector control algorithm based on speed SMC-PI was tested on a hydraulic excavator driven by the electric motor. The test results show that the vector control base on speed SMC-PI achieved a maximum static difference of rotational speed of approximately 1%. When the load power was close to 50% of the rated power, the electric motor-pump speed fluctuation range was −5%~5%, and the steady-state error was only approximately 0.3%.
Evolution of reduced minimum critical size as a response to selection for rapid pre-adult development in Drosophila melanogaster

Adult body size in holometabolus insects is directly proportional to the time spent during the larval period. The larval duration can be divided into two parts: (i) pre-critical duration—time required to attain a critical size/critical weight that would result in successful completion of development and metamorphosis even under non-availability of nutrition beyond the time of attainment of critical size, and (ii) post-critical duration—the time duration from the attainment of critical size till pupation. It is of interest to decipher the relative contribution of the two larval growth phases (from the hatching of the egg to the attainment of critical size, and from the attainment of critical size to pupation) to the final adult size. Many studies using Drosophila melanogaster have shown that selecting populations for faster development results in the emergence of small adults. Some of these studies have indirectly reported the evolution of smaller critical size. Using two kinds of D. melanogaster populations, one of which is selected for faster/accelerated pre-adult development and the other their ancestral control, we demonstrate that the final adult size is determined by the time spent as larvae post the attainment of critical size despite having increased growth rate during the second larval instar. Our populations under selection for faster pre-adult development are exhibiting adaptive bailout due to intrinsic food limitation as against extrinsic food limitation in the yellow dung fly.