Miracle-dz/UniFAR-FTUs huggingface dataset:
Rv0233 is not essential for the survival of Mycobacterium tuberculosis in stress conditions

Mycobacterium tuberculosis is an important global pathogen. We were interested in understanding the role of Rv0233, a proposed subunit of the class IB ribonucleotide reductase. We constructed an in-frame, unmarked deletion strain of M. tuberculosis and characterized its growth and survival under replicating and non-replicating conditions. We confirmed previous studies that Rv0233 is not essential for aerobic growth or survival in the presence of nitrite. We demonstrate that the deletion of Rv0233 does not affect susceptibility to frontline tuberculosis drugs or hydrogen peroxide. The deletion strain survived equally well under nutrient starvation or in hypoxia and was not attenuated for growth in macrophages.
Fine and high-aspect-ratio screen printing combined with an imprinting technique

The development of screen-printing techniques in order to obtain fine patterns with a high aspect ratio is an important task in the advancement of printed electronics. To this end, we propose a new printing concept in this study that consists of a combination of the screen-printing process with an imprinting technique. We show that fine and high-aspect-ratio patterns are realized by the capillary force of parallel-walled structures (PWSs) on the material to be printed. The PWS is fabricated by an imprinting method using an ultra-violet curable resin. In order to obtain finer patterns with a higher aspect ratio, printed patterns according to the pitch and the height of the PWSs were assessed. A printed pattern with a line width of 6.3 μm was obtained at a PWS, with a pitch of 20 μm and at a height of 110 μm, when a screen mask with a 100 μm-wide resist opening was used. The line width of the printed patterns was well controlled by the pitch of the PWSs. Moreover, an aspect ratio of up to 7.4 was achieved. Furthermore, we expect this screen-printing process to implement submicron patterns as well as more complex patterns, including curves and rings, through well-designed microstructures.
An Empirical Analysis of Machine Learning Techniques for Brain Tumor Detection

The human brain is made up of a variety of tissues that are essential for the normal functioning of the brain. Tumors of the brain is one of the most dangerous diseases that can harm the brain and cause death. Any genetic mutation in these tissues has the potential to alter the functioning of the brain, and this is referred to as a brain tumor. Finding a tumor at a preliminary phase gets critical in order that could save a person’s life. On the basis of medical imaging, one of the procedures utilised for the detection of brain tumors has been developed. The detection and segmentation of brain tumors is one of the most difficult and time-consuming tasks in computer - aided diagnosis. Medical imaging using magnetic resonance is known as MRI (Magnetic Resonance Imaging). This technique is mostly utilised by radiologists to visualize the inside human anatomy without the need for surgery. Image processing techniques are now playing a key role in the field of medical imaging. There are several different approaches for detecting and segmenting brain tumors. Methods for detecting and segmenting the brain tumor from MRI pictures are used to identify and segment the tumor from the images. A reduction in inaccuracy in conventional clinical diagnosis of disease has been demonstrated by using machine learning techniques. Brain tumor diagnosis, in particular, necessitates high precision because even minor errors in diagnosis might result in serious consequences. Brain tumor detection and disclosure continues to be a difficult task in medical image processing. The methods of brain tumor segmentation and classification are covered in detail in this work, which is well-structured and well-mannered throughout. This contributes to the provision of potential study directions in the classification of brain tumors in the future.
Development of an Intra-Operative Active Navigation System for Robot-Assisted Surgery

Optical tracking system (OTS), an essential component of robot-assisted surgery, measures the position and orientation of the surgical tools during surgery operations. OTS has proven to be a reliable intra-operative navigation method in clinical applications. However, OTS suffers from the vision occlusion where obstacles block the vision of the position sensors of the OTS. To tackle with this problem, we propose an active navigation system which can automatically moves the OTS so as to avoid the occlusion and meanwhile maintain the tracking accuracy. The active navigation system is composed of a multi-DOF robot, an optical tracker and an RGB-D camera. The accuracy of OTS is influenced by several factors, such as working distance and relative orientation of the tracked tool. Since in the active navigation system, the OTS moves during navigation, the impact of the movement of OTS on its accuracy is investigated. The corresponding experiments are conducted, and the results are analyzed. Subsequently, an occlusion avoidance strategy is developed to control the OTS to not only avoid the obstacles but also keep the accuracy degradation acceptable. Ultimately, the occlusion avoidance experiment is conducted to verify the proposed strategy. The experimental results demonstrate the effectiveness of the proposed active navigation system.
Design and force analysis based an integration of soft pneumatic ankle and toe actuating

A new flexible foot based on airtight corrugated pipe and pneumatic finger composite structure is presented in this paper. The structure is mainly composed of two parts: the ankle composed of corrugated pipe and the flexible toe. The corrugated pipe are connected with the air chamber inside the toe to form a complete internal gas circuit of the foot. When the foot end acts on the ground, the corrugated pipe are deformed to absorb energy and reduce the impact on the robot. At the same time, the deformation of the corrugated pipe causes a change in the air pressure of the internal gas circuit, which pushes the gas towards the toes, thus forcing the toes to bend downward. Under the action of ground reaction force, the three toes undergo reverse deformation to form the touchdown plane, thus increasing the grip of the structure.By modeling the flexible foot of the composite structure, the deformation and force characteristics of the composite structure are analyzed and simulated. Simulation results show that the inherent characteristics of the composite structure are similar to a single degree of freedom system with variable stiffness and linear damping. It is proved that the designed integrated flexible foot can be applied to multi-legged robot, which has good cushioning effect on landing impact, increases the performance of grip, and has strong adaptability to the environment.
Hybrid Integrated Low Residual Noise Optical Phase Locked Loop

In order to low down the residual phase noise and reduce the loop time delay, a hybrid integrated optical phase locked loop is designed based on unltra-low linewidth frequency tunable self-injected semiconductor laser, which achieves a loop delay of 30 ps, the residual phase noise of -80 dBc/Hz at 100 Hz offset and phase noise variance of 0.0018 rad2.
An Empirical Study on the Critical Factors in Enterprise Informationalization Based on Rough Set Theory

Making an overview of the research on primary factors in enterprise informationalization,this paper puts forward twenty primary factors that may impact information systems performance,and determines key factors and weight coefficient by the Informationalization data of 153 enterprises based on rough set theory.Then,the paper sets up a criterion of relations between the key factors and the success of enterprise informationalization.
Manipulability and Robustness Optimization of the Cable-Driven Redundant Soft Manipulator

Redundancy makes soft manipulators manage to complete a variety of tasks in complex environments. However, a pseudo-inverse kinematics controller for redundant soft manipulators simply maximizes the manipulability while ignoring some material constraints, e.g., actuation saturation and control noise. As manipulability is the indicator for the dexterity of motion in all directions of the redundant soft manipulators, this work proposes an optimization based controller, where the manipulability and robustness are optimized for a cable-driven redundant soft manipulator to adapt actuation saturation and control noise. Experiments are conducted to demonstrate the effectiveness of the proposed controller.
The Icosahedron Marker for Robots 6-Dof Pose Estimation

ArUco is an efficient and convenient method to estimate pose of the robot or the object by detecting corner points. However, ArUco cannot be used in some cases due to its lack of detection stability and insufficient accuracy. In this paper, an improved monocular 6D pose estimation method based on the spatial icosahedron marked by ArUco is proposed. By identifying the marked points on the surface of the icosahedron in the image, matching the 3D points in the database and solving with the Perspective-n-points(PnP) method, the accurate 6D pose estimation of the robot can be obtained. Compared with the original ArUco, the method proposed in this paper can cope with larger pose changes, provide higher accuracy, and improve the detection stability, while the time complexity is not significantly increased. In most cases, the method proposed in this paper can achieve translation accuracy of sub-millimeter level and rotation accuracy of less than 1°. Besides, it realized the ±180° large-scale rotation detection of three axes, and the real-time indicators is also satisfied.
A Development of Object Detection System based on Deep Learning Approach to Support the Laparoscope Manipulating Robot (LMR)

This paper presents the development of an object detection system based on the deep learning approach of computer vision to support the laparoscopic surgical robotic position control system. The system comprises two main phases, the training phase, and the real-time operating phase. In the training phase, a modified YOLOv4 algorithm is proposed to be adopted within the Darknet framework for training the weights of detection models. The best detection model is then selected to be assigned to the object detector in the real-time operating phase. The validation result of the detection model across the experiment datasets is 90% accuracy on average during the training phase. To validate the viability of real-time object detection, the proposed system was implemented in two test-cases of gynecologic surgery with the soft-body cadavers.
A stairway detection algorithm based on vision for UGV stair climbing

In the paper, we present a vision based algorithm used to guide the Unmanned Ground Vehicles (UGV) for autonomous stairways climbing and implement it on UGV successfully. The reliability of guiding UGV to climb stairs requires evaluating two offset parameters: the position of vehicle on stairs and the orientation angle to stairs. The intention of our algorithm is to estimate these two parameters through extracting the stair edges robustly. To achieve this goal, we apply the Gabor filter to eliminate the influence of the illumination and keep edges, and propose a fast method to remove small lines. Finally we link stair edges, and estimate the offset parameters used to steer the vehicle by RANSAC algorithm. Experiments on various stairways including indoor and outdoor are given in various light conditions. The results validate our algorithm.
Wavelet Multi-Scale Analysis of Wind Turbines Smoothing Effect and Power Fluctuations

With the increased interest for co-location of wind and solar PV plants, the issues related to power fluctuations and their impact on production curtailment losses due to grid constraints gained additional importance. This paper investigates power fluctuations and the smoothing effect in wind farms utilising wavelet multi-scale analysis based on 1-second wind speed and wind power data measurements. Maximal overlap discrete wavelet transform (MODWT) is applied to decompose the wind power time series into several scales, each representing particular frequency band. Analysis of variance across multiple scales is provided for wind speed and power time series of a single and multiple wind turbine generators (WTGs). Additionally, multi-scale correlations are captured between a pair of WTGs on two sites. Smoothing effect has been analysed across different time scales in three operational wind farms of different sizes. The results imply that power fluctuations in large-scale wind farms can be accurately captured using 1-minute time resolution.
Real-Time Flame Segmentation based on RGB-Thermal Fusion

The conflagration would cause great damage to our lives and property. It is challenging and meaningful to identify dangerous fires accurately, and flame segmentation is an important part of this task. In this work, a new network model is proposed to segment the wild flame based on the RGB and thermal camera data. In particular, two encoders are used to extract features from RGB and thermal information. Afterward, their high-dimensional features are combined to get the segmented flame image through the decoder. To improve the performance of the network model, the SkipInception module is designed to extract features, which turn out to be effective. Besides, the skip connection is used to fuse features in a sandwich structure model called SFSeg. Furthermore, the model is evaluated on flame and urban scene segmentation. The experimental results have confirmed that our proposed approach is more effective and robust than other state-of-the-art approaches.
Deforestation Detection using Geographical Information System

This paper proposes an approach for deforestation detection using Geographical information system to manipulate and analyze geographical data. Here, the rate of deforestation is detected using a vegetation index measure and hence can estimate and control the extent of deforestation. An NDVI based algorithm is applied on the raster data and the final output is obtained in the form of a map in which the area of deforestation is highlighted in different colors. Visualization of the data helps analysis in a much easier and efficient manner and hence NGO’s can do faster and accurate analysis helping them to take preventive measures and reduce rate of deforestation.
An Optical Power Divider Based on Two-dimensional Photonic Crystal Structure

Abstract In this paper, an optical power divider with one input and four outputs has been proposed in a two-dimensional photonic crystal with triangular lattice and simulated using dielectric holes in an air substrate. The dividing properties of the power divider have been numerically simulated and analyzed using the plane wave expansion and finite difference time domain methods. The results show that the transmittance of this divider can be as high as 94.22 % for λ=1.55 µm; thus, the proposed structure is suitable for wavelength division multiplexing communication systems. Also, due to the small footprint of the proposed structure, this optical power divider is applicable for optical-integrated circuit design.
Joint Limit Optimal Inverse Kinematics of the 7-DoF Manipulator with Link Offset based on Semi-analytical Solution

The 7-DoF redundant manipulator with offset links and symmetrical structure is applied in the space station. The analytical solution of the inverse kinematics(IK) is difficult when considering the joint limits. This study proposes a joint limit optimal IK solution based on the optimization of the fixed joint angle parameters. The analytical method of fixed joint inverse solution is combined with the numerical method of joint limit optimization. This semi-analytical method converges to a definite solution with a closed form and have symmetry to avoid the non-conservative of the pseudo-inverse solution. Simulation results verify the effectiveness of the IK algorithm.
Blockchain-Based Transparency and Data Provenance in the Wine Value Chain

The food market is changing dramatically in the last century as the world population is growing with the unprecedented pace. The wine industry is recognized as part of both agriculture and food industry, but also as a commodity. Developments in information technology and digitalization are playing a major role in the introduction of new solutions in agriculture and food production. The idea is to improve productivity of farms and vineyards, improve quality of agriculture products by optimizing irrigation, pesticide usage, and overall efficiency of the process. Furthermore, the consumer awareness about food products, its quality and origin, is on the constant rise. The information about the product throughout the whole “farm-to-fork”, or in this case “vineyard-to-glass”, value chain needs to be collected and utilized by all the participating stakeholders, in order to get a better, healthier, and more affordable product. This paper address the considerations related to implementation of a blockchain-based transparency and data provenance in the food value chain, more specifically with a focus on the wine industry.
A Framework for the Determination of realistic Usage Profiles for Automated Shuttle Pods

Realistic testing and lifetime simulation of automated vehicles and all related components is a huge challenge during the development and testing phase of these components. In order to keep expenses as small as possible and hence also reduce the cost for the end-user, efforts for these tests should be reduced as well. For this purpose, realistic usage profiles are required, either for simulation or for testbed installations. Such usage profiles need to comprehensively represent the daily operation of an automated vehicle under different environmental conditions. This paper presents a novel approach for usage profile generation, including drive cycles for automated shuttle pods and weather profiles.
A Learning from Demonstration Method for Generating Human-like Actions on Redundant Manipulators

Achieving human-like actions on robots can significantly improve the quality of human-robot collaboration (HRC). However, it is not easy to control the general-purpose robotic manipulators to work in a human-like style when the kinematic discrepancy exists between humans and robots. In this paper, we propose a human-in-the-loop learning framework to enable human-like properties on a general-purpose redundant manipulator. We collect the human-like demonstration dataset via teleoperation. We then use the behavioral cloning method to train a neural network policy to generate human-like constraints for the redundant manipulator automatically. Furthermore, we introduce an online relabeling method to accelerate learning and relieve workload in the demonstration. Experimental results showed that our proposed framework could successfully train a human-like constraint generation policy with a demonstration dataset collected within a few minutes. Appearance similarity could be seen between the human arm posture and the configuration of the manipulator when performing pose tracking tasks.
Progressive Outcomes: is it a handy approach to support agile methods process improvement?

While software engineering relies on reference models, such as CMMI-DEV, to improve its processes and gain maturity; agile teams that wish to sustain agility demand other guidelines to drive improvements. To address this need, in a previous study we have proposed the Progressive Outcomes Framework as a result of a research about how real agile teams evolve in agility. Although we could describe agile maturing mechanisms, we lacked a perception of how handy out approach would be to guide improvements. The objective of this study was, thus, to identify agile practitioners’ perception about the framework. We performed a survey with agile practitioners in Brazilian conferences and analyzed collected data using descriptive statistics. Results show that agile practitioners feel comfortable in following nonprescriptive guidelines, but they still lack guidance on how to improve in agility.
Assessing the impact of Scrum in airborne software quality assurance

In this paper, we present an evaluation of Scrum's impacts over the software quality assurance process in the embedded aerospace software domain. We present basic concepts of the DO-178C and Scrum, then we assess related work and introduce the case study used as reference for our study, as well as the perceived results of using Scrum in both the development teams and in quality assurance. Finally, we summarize good practices for using Scrum in software quality assurance teams. Resumo. Este artigo apresenta uma avaliação dos impactos do Scrum no processo de garantia de qualidade de software no domínio de software embarcado aeronáutico. Após uma introdução ao documento DO-178C e ao Scrum, trabalhos correlatos são avaliados e o caso de estudo utilizado neste artigo é apresentado, bem como os resultados do uso de Scrum tanto nos times de desenvolvimento quanto na garantia de qualidade. Por fim, sugerem-se boas práticas para o uso de Scrum em times de garantia de qualidade.
Highly-selective pseudoelliptic filters based on dual-mode substrate integrated waveguide resonators

A non-degenerate dual-mode resonator derived from circular substrate integrated waveguide is proposed, and two pseudoelliptic bandpass filters with non-resonant mode based on the dual-mode resonators are presented. Two resonant modes (higher order modes) are employed to degenerate a passband, while a non-resonant mode (fundamental mode) is designed to provide a direct input-to-output coupling. Owning to an additional path provided by non-resonant mode, the filters have good selectivity and very compact size. The presented simulation and measurement results are in good agreement.
A Systematic Review of Innovative Software Project Management

Nowadays, innovation is one of the keys to success in organization and project management has become an important way to improve it. Innovative Software Projects (ISP) have a high level of uncertainty and complexity, so we need a specific approach to manage those threats. This paper presents a systematic literature review of Innovative Software Project Management (ISPM), helping to identify the factors that affect ISP and their management such as tools, techniques, processes, practices, organizational capabilities and IT assets; and how managers can prepare themselves for the challenges of their innovative projects. This paper aims to contribute to the improvement and success of project management in organizations.
A modular control and power isolation platform for power electronics research and development

Power and control isolation is necessary for many applications including isolated gate drive supply, device characterisation and the testing of power electronics converters. Commonly the isolation stage is integrated directly into the converter and therefore cannot easily be repurposed for another project, serviced in case of failure or easily adjusted for other power supply requirements. This paper proposes a modular, flexible, isolation stage that can easily be connected to the prototype under test, has a variety of power supply options, digital channel flexibility and can be easily replaced in case of failure. The load regulation of the module outputs have been experimentally measured and are presented. The proposed isolation stage has been utilised for the control and gate drive supply isolation in a 1MHz switching frequency Capacitor-Inductor-Inductor-Capacitor (CLLC) Resonant Dual Active Bridge (RDAB) converter to demonstrate the isolation stages usefulness in high switching frequency applications.