Miracle-dz/UniFAR-FTUs huggingface dataset:
Megaplasmids on the Rise: Combining Sequencing Approaches to Fully Resolve a Carbapenemase-Encoding Plasmid in a Proposed Novel Pseudomonas Species

Horizontal transfer of plasmids plays a pivotal role in the dissemination of antibiotic resistance genes and emergence of multidrug-resistant bacteria. Sequencing of plasmids is thus paramount for the success of accurate epidemiological tracking strategies in the hospital setting and routine surveillance. Here, we combine Nanopore and Illumina sequencing to fully assemble a carbapenemase-encoding megaplasmid carried by a clinical isolate belonging to a putative novel Pseudomonas species. FFUP_PS_41 has a multidrug resistance phenotype and was initially identified as Pseudomonas putida, but an average nucleotide identity below the cut-off for species delineation suggests a new species related to the P. putida phylogenetic group. FFUP_PS_41 harbors a 498,516-bp untypable megaplasmid (pJBCL41) with low similarity compared with publicly available plasmids. pJBCL41 contains a full set of genes for self-transmission and genes predicted to be responsible for plasmid replication, partitioning, maintenance and heavy metal resistance. pJBCL41 carries a class 1 integron with the |aacA7|blaVIM-2|aacA4| cassette array (In103) located within a defective Tn402-like transposon that forms part of a 50,273-bp mosaic region bound by 38-bp inverted repeats typical of the Tn3 family and flanked by 5-bp direct repeats. This region is composed of different elements, including additional transposon fragments, five insertion sequences and a Tn3-Derived Inverted-Repeat Miniature Element. The hybrid Nanopore/Illumina approach resulted in contiguous assemblies and allowed us to fully resolve a carbapenemase-encoding megaplasmid from Pseudomonas spp. The identification of novel megaplasmids will shed a new light on the evolutionary effects of gene transfer and the selective forces driving AR.
Reputation-based DNMPC Design for a Heterogeneous Platoon under False Data Injection Attack

This paper presents a novel approach, the Reputation-Based Distributed Nonlinear Model Predictive Controller, designed to manage heterogeneous vehicular platoons under the threat of false data attacks. Using an adapted reputation algorithm, the DNMPC enhances cooperative behavior and system stability while effectively countering potential cyber-attacks. In the simulation results section, the cost function values for individual vehicles are depicted to demonstrate the achieved stability in the presence of an attack. This innovation holds the potential to significantly enhance the security and performance of vehicular platooning systems, ensuring safe and reliable urban mobility in the face of evolving cybersecurity challenges.
A Hierarchical Architecture for Power Management in Interconnected DC Microgrids

Enhancing the storage capacity to improve the reliability of standalone DC systems caused by the intermittency of Renewable Energy Sources (RESs), raises the cost and maintenance requirements. Interconnections of DC MicroGrids (DC MGs) can address this issue and it also has various benefits such as higher efficiency, resilient operation, no synchronisation and reactive power requirements. This work introduces a novel coordinated hierarchical power management strategy of interconnected DC MG. With DC bus voltage regulation, SoC balancing and optimal power sharing via Interlinking Bidirectional Converters (IBCs), the proposed power management strategy provides efficient load dispatch by optimal utilisation of resources. The results are validated in MATLAB/Simulink for different modes of operation.
Constant Common-Mode Voltage Modulation Analysis and Implementation for a Dual Three-Phase Machine

This paper presents the simulation and implementation of a modified constant CMV SVPWM technique. In the literature, SVPWM techniques to reduce the CMV are proposed. However, these modulations present a complex implementation. This paper proposes a modulation with a much simpler implementation. The proposed modulation is compared in simulation with SVPWM-4L and Carrier-Based PWM SVPWM modulations regarding low-order harmonics, $\text{THD}_{i}$ and CMV. The proposed hardware implementation is validated experimentally with dSPACE.
Optimal tracking control for a nonlinear system with constrained-input via model-free reinforcement learning

This paper uses a model-free adaptive control algorithm to look into the optimal tracking control policy for multi-agent unknown nonlinear systems with constrained input. By building a new augmented multi-agent nonlinear system to represent the original system, we can obtain the optimal control policy by seeking the solution of the coupled Hamilton-Jacobi-Bellman (HJB) equation of the new system. Firstly, we adopted the traditional model-based technique to prove the feasibility of the technique of solving the HJB equation. Then, for the sake of avoid the difficult problem of solving HJB equation, we adopted the model-free method and used an actor-critic architecture to get the approximate solution of HJB equation. Meanwhile, we also affirm the stability of the system with the optimal control policy by employing the Lyapunov approach. The effectiveness of the algorithm is verified through a simulation example.
Design and Manufacturing of an Intelligent Ping-Pong Robot (RoboPing)

Robotics and artificial intelligence (AI) are rapidly transforming many industries and aspects of our lives, and sport activities are no exception. Robots and AI are already being used in a variety of sports, including training and performance analysis. This paper introduces a new assistant ping-pong robot (RoboPing) that shoots ping-pong balls with different speeds and spins, besides using AI to analyze training sessions and providing useful insights into the game. RoboPing represents a significant advancement in mechanical design over previous versions, offering enhanced functionality. Being equipped with a precise vision system makes RoboPing more advanced compared to existing versions.
Research on Prediction of Wind Power Generation Based on K-means Clustering and Machine Learning

In order to further study the relationship between wind power generation and multiple influencing factors, this paper proposes a combined regression model for wind power generation based on K-means clustering, BP neural network and random forest algorithm. Firstly, this paper uses K-means clustering algorithm to divide the data set into two categories. Then, BP neural network and random forest were used to establish regression models for the two types of data, and grid search-K-fold cross-validation method was used to optimize the relevant parameters in the models. Finally, the average value of correlation indicators such as the determination coefficient $(R^{2})$ of BP neural network and random forest is calculated as the result of model evaluation. In this paper, 18,995 sets of wind power generation data from China’s Hulun Lake Wind Farm, Ulanqab Wind Farm and Fengshagang Wind Farm in the past three years are taken as examples to verify that the combined regression model proposed in this paper can provide more accurate and reliable prediction, and offer valuable perspectives for future research and practical applications in the realm of wind power generation.
Brain Patterns Generated while Using a Tongue Control Interface: A Preliminary Study with Two Individuals with ALS

Individuals suffering from a progressive neurodegenerative disease, such as amyotrophic lateral sclerosis (ALS), will lose muscle function over time and become completely paralysed. For some time, people with ALS may retain functional tongue movement, despite losing mobility below the neck. These individuals can benefit from using an inductive tongue control interface (ITCI) to control computers or assistive robotic devices to gain independence in their daily lives. Eventually, when the individual can no longer use their tongue, they can rely on a brain computer interface (BCI). However, these require a lot of data to calibrate and function properly. Recording this data while the individual can still use the ITCI can potentially speed up the training process, allowing for an easier transition between interface technologies. This study investigates whether it is possible to create a background data collector for a BCI based on attempted tongue movement by analyzing brain patterns of two individuals with ALS while using an ITCI. The participants used an inductive ITCI in simple cued movement trials while electroencephalogram (EEG) was collected from the motor cortex. The EEG signal indicated that movement-related cortical potentials (MRCP) were generated after the cued movements. After synchronising the signal to the activations recorded on the ITCI, the MRCP became even more apparent. Therefore, it is concluded that it is possible to record MRCPs from individuals with ALS performing tongue movements, that the ITCI can assist in better extracting synchronized MRCP epochs, and that a background data collector for a tongue movement intention-based BCI is very feasible.
Control of NPC Three Phase Three Level Inverter Using a Switching Logic-based Control Algorithm With Constant Switching Frequency

This paper proposes a control strategy for Neutral point clamped(NPC) three phase three level inverter which employs a hysteresis based frequency limiting switching algorithm to decide the switching instants. The low pass filtered inverter output voltage is used as the feedback signal. Depending on the slope of the feedback voltage, the control algorithm varies slightly for different regions of reference signal to provide constant switching frequency. The reference signal modification is another key feature that improves the quality of the output waveforms. This method ensures a fast transient response comparable with conventional hysteresis control method along with well defined harmonic spectrum and improved THD. Simulation results are included to verify the proposed concept.
Design and Development of Visual System for Intelligent Management of Transmission Lines

Transmission lines play a crucial role in electricity, managing and monitoring it can increase its security and reliability. This study aimed to design and develop an intelligent management visualization system for transmission lines, to achieve monitoring and management of transmission lines. This study adopted data mining, machine learning, and visualization technologies to develop an intelligent management system for transmission lines. This system mainly consists of data collection, visual display, and other parts, which achieves real-time monitoring and data analysis of transmission lines, and achieves functions such as line status prediction and anomaly detection. The research results showed that all indicators of this system have been greatly improved, with the inspection cycle shortened by about 50 times, the number of failures significantly reduced, and management efficiency improved from 80% to 90%. This system can achieve real-time monitoring and management of transmission lines, and can predict and detect abnormal conditions of the lines. At the same time, the system also has scalability and flexibility, which can adapt to transmission lines of different scales and complexities. In practical applications, this system can improve the practicality of transmission lines, laying the foundation for the safe operation of the power grid.
Ultra Low Power-Low Noise GNRFET Wideband Amplifier for Cryogenic Applications

Cryogenics amplifiers feature very low noise injection and low power consumption, and therefore, they are of highly demand in RF imaging, space communications, medical applications, and quantum computing. Unconditional stability with high gain also feature these applications. The band gaps of these devices are independent from temperature, suggesting their suitability for cryogenics applications. Furthermore, the nanometer scale lengths of these devices lead to ballistic transport with no scattering or diffusion, resulting in high-speed, low-power, and low noise capabilities within Extremely High Frequency (EHF) operating range that may extend into the THz. This study addresses these parameter issues within the range of 90K to 300K, assuming liquid nitrogen coolant medium, and further shows its stability above the room temperature as well. An appropriate model for the device that incorporates complex parasitics was investigated within this temperature range. The device with its estimated parameters, has been integrated into a simulated three-stage amplifier system. A low-noise gain between 16.9-18.9 dB with estimated noise figure of 2.97 dB and a bandwidth of 269 GHz bandwidth were predicted, with a high Stern stability factor suggesting unconditional stability over the bandwidth.
Image-Guided Point Cloud Completion with Multi-modal Fusion Transformers

The task of image-guided point cloud completion aims to leverage information from images to address uncertainty issues in the completion inference of point clouds. The key challenge in this setting lies in how to effectively combine features extracted from both modalities. Due to the large domain discrepancy between the image and point cloud, existing methods that use cross-modal attention to directly fuse features have increased attention on redundant information and noise from different modalities, resulting in poor feature fusion performance. Hence, by introducing multi-modal fusion transformers that use bottleneck tokens, we enabled point cloud feature to learn image feature through information bridges, leading to improved point cloud completion performance. Our method can not only benefit from RGB images, but also from sketches with less feature information but more emphasis on edge information. Extensive experiments demonstrate that our proposed method enhances the quality of point cloud completion and outperforms other state-of-the-art methods.
Ultra Wideband Frequency Selective Surface for Microwave Band Applications

This paper is about creating and testing a special surface that can block specific frequencies of electromagnetic waves, and it's designed to work well regardless of the direction and polarization of the incoming waves. The unit cell of this FSS is composed of a square loop element combined with a crossed dipole featuring a circular groove element. Especially, the unit cell's size has been significantly reduced to 0.047 times the free-space wavelength (λ) corresponding to the lowest UWB frequency. The FSS in this work performs exceptionally well, as it has a bandwidth with a signal loss of -3dB that ranges from 3.05 GHz to 10.73 GHz. This bandwidth effectively covers the entire Ultra-Wideband (UWB) spectrum and the proposed FSS unit cell consists of a square-loop and a ring printed on opposite sides of a low-cost metamaterial substrate. Additionally, because of the small size of the individual unit cells in the FSS, it maintains its performance very well even when the electromagnetic waves hit it at angles as wide as 60 degrees, whether the waves have a perpendicular (TE) or parallel (TM) polarization.
A High Speed 32-bit Approximate Adder with Improved Accuracy

Approximate computing has gained quite some research attention in the last few years. In the recent techniques developed in this domain, a trade-off between accuracy and design metrics, such as delay, power and area has been observed. Our prime purpose is to reduce the value of the design metrics without sacrificing excessive accuracy of the concerned adder. In this paper, firstly two novel 32-bit approximate adders are proposed, then a comparative study is performed with some already existing adders. We portray a wide comparison based on both conventional and approximate design metrics. The proposed adder provides almost a 57.6% decrease in time delay than the conventional Ripple Carry Adder with a small trade-off of accuracy. This adder makes use of already existing approximate adders to optimise its metrics. The designs have been verified and tested on ASIC using UMC 65 nm technology on Synopsys and on Digilent Basys3 FPGA board.
Taguchi-based Optimal Dry Electrode Design

Surface Electromyography (sEMG) electrodes, including both wet and dry classes, play a significant role in the EMG signals analysis and recording of muscle activation area. Users of prosthesis hands must occasionally manipulate and move their limbs for several hours during daily tasks. Hence, high-quality and durable muscle activation is crucial. Although the designs of electrodes with different probe shapes, sizes, metals, and roughness are presented in previous studies, finding the optimal electrode design is an open problem. Hence, this paper proposes a different application of the Taguchi method to specify a reduced set of examinations for determining the optimal electrode design. This study considers the length, width, and distance of probes as controllable parameters as well as subject dominant hand, skin humidity, and muscle fatigue as data recording noises. The design of the experiment process (DOE) selects reasonable orthogonal arrays for the inner and outer arrays of the DOE table. The probes of the electrodes are made of silver due to their high conductivity, less oxidation, and fewer skin allergies in the presented study. In this investigation, the primary focus lies in electrode optimization through the decrease in the error of grasp force prediction. The Support Vector Machine (SVM) method is used to evaluate the error of grasp force. For this purpose, the signal-to-noise ratio (SNR) assessment is performed, identifying each parameter’s optimum level and significance. Results indicate that the electrodes with probes of 1 cm in length, 1 cm in width, and 2 cm apart lead to better sEMG signals. These results offer a design for significantly improved muscle activation signal based on the FUMCARE sEMG setup to use in hand prosthesis applications.
Octree-Based Temporal-Spatial Context Entropy Model for LiDAR Point Cloud Compression

It’s difficult to effectively remove redundancy in Li-DAR point clouds due to their extremely sparse and nonuniform distribution. Taking advantage of both octree-based methods and voxel-based schemes, we propose to design an effective temporal-spatial context to compress the sequence octree-structured point cloud data into a more compact bitstream. In this paper, we first build a temporal-spatial multiscale context for the deep learning entropy model. It further utilize the correlation of sequential point cloud data from both the spatial domain and temporal domain. In terms of spatial context, we design a hierarchical dependency in an octree to encode the occupancy information of each non-leaf octree node into a bitstream. We propose to further group the nodes according to their octant which effectively expands the context receptive field. In terms of temporal context, the KNN algorithm is applied to explore the most relative context with the strongest dependency in the temporal domain. Finally, we design a voxel re-localization network to convert the discrete voxels into refined 3D points, which makes up for the coordinate loss in the process of generating an octree. The quantitative evaluation shows that our method outperforms state-of-the-art baselines with saving most bitrate on KITTI Odometry dataset, and achieving the best reconstreuction benefit by the designed refinement module.
Optimization of BiLSTM for PV Output Prediction Based on Hybrid Bat Algorithm

Photovoltaic (PV) power generation is affected by the climatic environment and has a large degree of uncertainty. Accurate PV output prediction can reduce its uncertainty and ensure the safe operation of the power system. The paper introduces a predictive model for photovoltaic (PV) output that leverages a bidirectional long-short-term memory neural network (BiLSTM) optimized using the Hybrid Bat Algorithm (HBA). Firstly, the correlation analysis between environmental factors and PV output data is carried out using gray correlation analysis, and the environ mental factors with higher correlation coefficients are taken as the input features of the predictive model; secondly, the HBA is employed to optimize the optimal parameters within the BiLSTM network, resulting in the creation of the HBA-LSTM prediction model. Finally, the prediction analysis is carried out with the actual data of a certain region, and the findings demonstrate that the predictive precision of the approach introduced in this article is higher compared with the BiLSTM prediction method.
Research on neural network method for online temperature measurement of FSW

The friction stir welding process is used for the fuel tank of heavy launch vehicles. Due to the increase in the size of the fuel tank and the large temperature difference between the long and thick welding seams, it is difficult to achieve the stability of the welding process, resulting in a significant decrease in the strength and performance of the welds. This paper studies the temperature signals in the core region of friction stir welding process, establishes a real-time temperature detection system for friction stir welding, and further studies and constructs a BP neural network model to predict welding temperature, laying a foundation for temperature control and improving welding stability in friction stir welding. The results show that the established real-time temperature detection system for friction stir welding can meet the requirements of engineering applications while effectively detecting the welding temperature; The BP neural network model established based on the temperature measurement system can predict the welding temperature using the spindle rotation speed, feed speed, and pressing amount.
Experimental Analysis of Immersion & Invariance Adaptive Control for an Interleaved DC/DC Boost Converter with Unknown Load Type

The retrofit of the energy system leads to new challenges for the control of power electronic devices. To account for parametric uncertainties of passive elements, or unknown ambient conditions in real-world applications, the use of proportional-integral (PI) controllers in cascaded schemes is common. Observer based adaptive control approaches can be leveraged to accommodate such uncertainties and at the same time provide parameter estimates and enable model based control approaches. From an application perspective, the question arises about the consequences of wrong modelling assumptions, for example regarding the often unknown load type. In this work an interleaved DC/DC boost converter is considered, that supplies a DC-bus to which generally unknown loads can be connected, such as a grid-tied three phase inverter. This paper illustrates the design of an observer based adaptive controller using the Immersion & Invariance framework with an energy shaping plus damping injection passivity based controller. A highly simplified, large signal plant model as well as the assumption of a purely resistive load are used for the design. The provided experimental results show that the adaptive controller is able to cope with violations of the modeling assumptions concerning the load type in a real-world application scenario.
Design and Fabrication of an Inflatable Soft Robot for Enhancing Foot Drop Rehabilitation

Foot drop is characterized by the inability to raise the front of the foot, resulting from a weakening of the dorsiflexor muscles. This condition can cause an unsteady and painful walking gait, increasing the risk of tripping and potential falls. The traditional method of managing foot drop involves using an ankle-foot orthosis. However, wearable robots provide a promising alternative for muscle injury rehabilitation. This paper presents a soft wearable rehabilitation robot equipped with pneumatic fiber actuators designed to assist dorsiflexor muscles. Unlike conventional medical orthoses, this soft ankle orthosis preserves natural ankle motion, facilitating a more natural gait recovery for patients. With a maximum force output of 150 Newtons and a lightweight design of just 82 grams, it offers comfortable wear, similar to regular clothing.