Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Servo Positioning System Based on PID Variable Structure

The direct-drive servo motor is servo product with high performance widely applied in the fields of robotics and manufacturing due to its special construction and high-precision design. Considering the working principles of laser engraving machines, as well as the impact on the dynamic and static performance of the control system exerted by PID controllers frequently integral correction link in the process of start and stop, this article describes in detail how to achieve accurate positioning of direct drive servo motor by adjusting PID parameters. Key Words Servo motor,PID variable structure,Servo positioning,Laser engraving machine
Optimized design of a complete three-phase PWM-VS inverter

This paper defines optimized design of power electronic circuits and uses the concept of a complete three-phase inverter. The optimization is done in order to obtain maximum efficiency by changing the gate-drive conditions like gate-resistances and gate-drive supply. Another optimization is done where the thermal behaviour of the diode and the IGBT is taken into account. A loss model of a complete three-phase inverter is developed, and simulation results show that different optimization points can be calculated and used for a design. Based on known data for the IGBTs and the diodes, the inverter circuit, the outputs of the optimizer are two gate-drive resistances, a gate-drive supply, and a load current which give the inverter the highest efficiency. It is concluded the optimization points of the inverter design are different when the optimization criteria change. The highest load current is found for the inverter when the thermal effects are included.
Switching Surges on Northern States Power Company's 345-kV Circuits

This paper reports the results of field tests conducted on the Northern States Power Company's 345-kV transmission system which investigated the effects of various system switching operations on transient overvoltages. Following the field tests a transient network analyzer was used to duplicate the field tests and then explore the maximum switching transients which system parameters could produce.
microRNA-378b regulates ethanol-induced hepatic steatosis by targeting CaMKK2 to mediate lipid metabolism

ABSTRACT Alcoholic liver disease (ALD) has seriously harmed the health of people worldwide, but its underlying mechanisms remain unclear. This study aims to clarify the biological function of microRNA-378b (miR-378b) in ethanol (EtOH)-induced hepatic lipid accumulation. Here, we report miR-378b is over-expressed in EtOH-induced cells and EtOH-fed mice and finally accelerates lipid accumulation. MiR-378b directly targets Ca2+/calmodulin-dependent protein kinase kinase 2 (CaMKK2), a kinase of AMP-activated protein kinase (AMPK), and mediates the protein level of CaMKK2. Over-expression of miR-378b exacerbated the lipid accumulation induced by EtOH and inhibited CaMKK2 and the AMPK cascade while inhibition of miR-378b ameliorated lipid metabolism dysfunction in vivo and in vitro. In brief, our results show that miR-378b plays an important role in the regulation of lipid metabolism by directly targeting CaMKK2.
Use of continuous glucose monitoring trend arrows in the younger population with type 1 diabetes

Early control of glycaemia is key to reduce vascular complications in individuals with Type 1 diabetes. Therefore, encouraging children and adolescents with T1DM to take responsibility for controlling glucose levels is an important yet a challenging task. The rapid expansion of continuous glucose monitoring (CGM) systems has allowed for more comprehensive analysis of glycaemia in T1D. Moreover, CGM devices have the ability to calculate rate of change in glucose levels and display the information as trend arrows. In turn, this can help to take evasive actions to return glucose levels to near physiological glycaemia, which can be highly motivating for young people with T1DM. In the absence of standardised, evidence-based guidance, this consensus document, generated by experts from the Arab Society of Paediatric Endocrinology and Diabetes and international advisors, summarises recent literature on the use of trend arrows in young people with T1DM. The use of trend arrows in different CGM systems is reviewed and their clinical significance is highlighted. Adjusting insulin doses according to trend arrows is discussed while also addressing special situations, such as exercise, fasting, nocturnal hypoglycaemia and menstruation. Adequate understanding of trend arrows should facilitate optimisation of glycaemic control in the T1D population.
Genetic Algorithm-Based Multi-Hop Routing to Improve the Lifetime of Wireless Sensor Networks

Wireless sensor networks are known for their monitoring and tracking application-specific operations. These operations diversely demand improvement in existing strategies and their parameters. One key parameter is energy usage during operations. Energy plays a vital role in each application, as the wireless sensor networks lack battery lifetime and energy resources. So, there is a need for an optimized and efficient routing method with regard to energy consumption in wireless sensor networks. For multi-hop routing, the genetic algorithm serves as a robust algorithm with diverse optimized routing plans to improve the lifespan for large-scale wireless sensor networks. In this paper, the genetic algorithm provides the optimized routes for data operations and improves the lifetime of wireless sensor networks by saving energy. The performance of the genetic algorithm is compared with the TEEN algorithm.
Feasibility on the Use of Radiomics Features of 11[C]-MET PET/CT in Central Nervous System Tumours: Preliminary Results on Potential Grading Discrimination Using a Machine Learning Model

Background/Aim: Nowadays, Machine Learning (ML) algorithms have demonstrated remarkable progress in image-recognition tasks and could be useful for the new concept of precision medicine in order to help physicians in the choice of therapeutic strategies for brain tumours. Previous data suggest that, in the central nervous system (CNS) tumours, amino acid PET may more accurately demarcate the active disease than paramagnetic enhanced MRI, which is currently the standard method of evaluation in brain tumours and helps in the assessment of disease grading, as a fundamental basis for proper clinical patient management. The aim of this study is to evaluate the feasibility of ML on 11[C]-MET PET/CT scan images and to propose a radiomics workflow using a machine-learning method to create a predictive model capable of discriminating between low-grade and high-grade CNS tumours. Materials and Methods: In this retrospective study, fifty-six patients affected by a primary brain tumour who underwent 11[C]-MET PET/CT were selected from January 2016 to December 2019. Pathological examination was available in all patients to confirm the diagnosis and grading of disease. PET/CT acquisition was performed after 10 min from the administration of 11C-Methionine (401–610 MBq) for a time acquisition of 15 min. 11[C]-MET PET/CT images were acquired using two scanners (24 patients on a Siemens scan and 32 patients on a GE scan). Then, LIFEx software was used to delineate brain tumours using two different semi-automatic and user-independent segmentation approaches and to extract 44 radiomics features for each segmentation. A novel mixed descriptive-inferential sequential approach was used to identify a subset of relevant features that correlate with the grading of disease confirmed by pathological examination and clinical outcome. Finally, a machine learning model based on discriminant analysis was used in the evaluation of grading prediction (low grade CNS vs. high-grade CNS) of 11[C]-MET PET/CT. Results: The proposed machine learning model based on (i) two semi-automatic and user-independent segmentation processes, (ii) an innovative feature selection and reduction process, and (iii) the discriminant analysis, showed good performance in the prediction of tumour grade when the volumetric segmentation was used for feature extraction. In this case, the proposed model obtained an accuracy of ~85% (AUC ~79%) in the subgroup of patients who underwent Siemens tomography scans, of 80.51% (AUC 65.73%) in patients who underwent GE tomography scans, and of 70.31% (AUC 64.13%) in the whole patients’ dataset (Siemens and GE scans). Conclusions: This preliminary study on the use of an ML model demonstrated to be feasible and able to select radiomics features of 11[C]-MET PET with potential value in prediction of grading of disease. Further studies are needed to improve radiomics algorithms to personalize predictive and prognostic models and potentially support the medical decision process.
An optimal inspection policy for hidden-function systems

An economic inspection scheduling model for a hidden-function system is presented. The probabilities of the hidden-function system failure as well as the failure of the object the system intends to protect are taken into account to develop this model. Through balancing the cost of the multiple failure against the cost of inspection, an optimal inspection period that minimizes the average total cost is determined. As an example, the calculation results for a hidden-function system with a Gamma failure distribution are presented.
Outcome of intraoperative dexmedetomidine infusion in laparoscopic cholecystectomy

ABSTRACT Background and Aims Laparoscopic cholecystectomy (LC) is favored by less postoperative pain compared with the open approach; nevertheless, pain is still a frequent complaint. Dexmedetomidine is a centrally acting α2 agonist that has sedative, sympatholytic, and analgesic properties. We aimed primarily to study the effect of different doses of dexmedetomidine on the quality of anesthesia in the patients undergoing LC. The secondary aims were to detect the implications of these different doses on the postoperative outcome (postoperative pain, nausea, and vomiting. Methods Sixty patients of the American Society of Anesthesiologists (ASA) physical grades I and II scheduled for elective LC were randomly divided into three equal groups. Group Dex 0.2, Group Dex 0.4, and Group Dex 0.6 (patients received dexmedetomidine infusion at 0.2 mcg/kg/h, 0.4 mcg/kg/h, and 0.6 mcg/kg/h, respectively) 15 min before induction and throughout the surgical procedure. Hemodynamic parameters, spontaneous respiratory recovery time, extubation time, incidence of cough, postoperative pain, and postoperative nausea and vomiting (PONV) were recorded. Results Significantly attenuated hemodynamic stress response was observed in Dex 0.4 and Dex 0.6 groups. The incidence of cough, PONV, was significantly less and postoperative analgesic requirements were fewer in Dex 0.6 group compared to the other two groups. Nevertheless, the time of spontaneous respiratory recovery and extubation in Dex 0.6 group was insignificantly longer compared to the other two groups. Conclusion Intravenous infusion of 0.6 μg/kg/h dexmedetomidine before induction can attenuate hemodynamic stress response, reduce cough incidence, PONV, and postoperative analgesic requirements in patients undergoing LC without significant prolongation of spontaneous respiratory recovery time.
Evaluation of Biometric Technologies for Access Control at Transportation Facilities and Border Crossings

To ensure that only authorized individuals–-legitimate workers, travelers, and visitors–-enter a transportation facility or border crossing, their identities must be ascertained. Because manual procedures are time-consuming, resource intensive, and vulnerable to human error and manipulation, the use of biometric technologies should be considered. This paper discusses several biometric technologies–-fingerprint recognition, iris recognition, facial recognition, and hand geometry–-and assesses their feasibility for use in access control at transportation facilities and border crossings. The advantages and disadvantages of the technologies are provided, as are cost, accuracy, and other performance data. Potential privacy and data issues are also discussed.
Impact of human immunodeficiency virus on 2-year revision rates following lumbar fusion for degenerative spinal conditions: a retrospective cohort study.

Background To our knowledge, no studies exist that evaluate the impact of human immunodeficiency virus (HIV) on long-term revision rates following lumbar fusion. This study aims to understand how HIV impacts 2-year revision rates and 90-day postoperative complication rates following primary lumbar fusion for degenerative spine conditions. Methods Data collection was done using PearlDiver Patient Records Database, a national insurance database from 2010-2019. Patients who underwent primary lumbar fusion for degenerative diseases were divided into a cohort of patients that are HIV positive (HIV), have asymptomatic HIV (AHIV), acquired immune deficiency syndrome (AIDS), and HIV-negative at the time of their procedure. Univariate and multivariable regression analyses were performed to determine rates of revision surgery, surgical site infection (SSI), neurological complications, sensory deficits, bowel/bladder dysfunction and myelopathies within two years of surgery as well as 90-day postoperative complications. Results In total, 216,587 patients were included in this study. Of these, 631 patients (0.29%) had HIV including 502 patients (0.23%) with asymptomatic HIV and 129 patients (0.06%) with AIDS. Relative to the control cohort, patients with HIV had no difference in odds of two-year revision surgery, SSI, neurological complications, sensory deficits, bowel/bladder dysfunction and myelopathies. Patients with HIV did have increased odds of postoperative pneumonia (OR 1.592; 95% CI: 1.048-2.314; P=0.021). AIDS patients had greater odds of respiratory complications (OR 2.585; 95% CI: 1.075-5.264; P=0.017) and septic complications (OR 2.702; 95% CI: 1.122-5.514; P=0.013) 90-day postoperatively. Conclusions Our study found that HIV positive status did not significantly affect two-year revision rates compared to the control group. However, there is a higher rate of pneumonia and respiratory complications among HIV positive patients. Before deciding to proceed with surgical intervention in HIV positive individuals, surgeons must carefully consider each patient's specific diagnosis, comorbidities, and risk factors.
IN VITRO PROPAGATION OF Balanites aegyptiaca (L) DEL., AN ENDANGERED MEDICINAL PLANT.

Young vigorous shoot tips of wild Balanites aegyptiaca (L) Del. were collected from Paris Oasis, Alwadi Algadeed, Egypt and used as the source of explants for micro-propagation. Shoots were established on MS basal solid medium supplemented with 6-benzyladenine (BA) at different levels combined with αnaphthalene acetic acid (NAA). MS medium without any growth regulators enhanced shoot elongation, while shoots response and axillary branching were affected by BA levels. Shoots productivity were significantly affected by the presence of BA. Shoots number and length were significantly achieved on MS medium contained 2.0 or 2.5 mg/l of BA combined with 0.2 mg/l of NAA. In vitro rooting, shoots were rooted on MS basal solid medium contained either IBA singly or mixture of IBA and NAA. Shoots were rooted well on MS basal solid medium supplemented with 2.0 mg/l of IBA . Rooted shoots were planted in pots filled with peat moss and sand ( 1 : 1 ) v/v mixture. Survival percentage for adapted plantlet were 90 % after 4 weeks. The plantlets were transferred to polyethylene bags and replanted successfully under field conditions.
The LEGA-C of Nature and Nurture in Stellar Populations at z ∼ 0.6–1.0: D n 4000 and Hδ Reveal Different Assembly Histories for Quiescent Galaxies in Different Environments

Galaxy evolution is driven by a variety of physical processes that are predicted to proceed at different rates for different dark matter haloes and environments across cosmic times. A record of this evolution is preserved in galaxy stellar populations, which we can access using absorption-line spectroscopy. Here we explore the large LEGA-C survey (DR3) to investigate the role of the environment and stellar mass on stellar populations at z ∼ 0.6–1 in the COSMOS field. Leveraging the statistical power and depth of LEGA-C, we reveal significant gradients in D n 4000 and Hδ equivalent widths (EWs) distributions over the stellar mass versus environment 2D spaces for the massive galaxy population (M > 1010 M ⊙) at z ∼ 0.6–1.0. D n 4000 and Hδ EWs primarily depend on stellar mass, but they also depend on environment at fixed stellar mass. By splitting the sample into centrals and satellites, and in terms of star-forming galaxies and quiescent galaxies, we reveal that the significant environmental trends of D n 4000 and Hδ EW, when controlling for stellar mass, are driven by quiescent galaxies. Regardless of being centrals or satellites, star-forming galaxies reveal D n 4000 and Hδ EWs, which depend strongly on their stellar mass and are completely independent of the environment at 0.6 < z < 1.0. The environmental trends seen for satellite galaxies are fully driven by the trends that hold only for quiescent galaxies, combined with the strong environmental dependency of the quiescent fraction at fixed stellar mass. Our results are consistent with recent predictions from simulations that point toward massive galaxies forming first in overdensities or the most compact dark matter haloes.
APTSHIELD: A Stable, Efficient and Real-Time APT Detection System for Linux Hosts

Advanced Persistent Threat (APT) attacks have caused massive financial loss worldwide. Researchers thereby have proposed a series of solutions to detect APT attacks, such as dynamic/static code analysis, traffic detection, sandbox technology, endpoint detection and response (EDR), etc. However, existing defenses are failed to accurately and effectively defend against the current APT attacks that exhibit strong persistent, stealthy, diverse and dynamic characteristics due to the weak data source integrity, large data processing overhead and poor real-time performance in the process of real-world scenarios. To overcome these difficulties, in this paper we propose APTSHIELD, a stable, efficient and real-time APT detection system for Linux hosts. In the aspect of data collection, audit is selected to stably collect kernel data of the operating system so as to carry out a complete portrait of the attack based on comprehensive analysis and comparison of existing logging tools; In the aspect of data processing, redundant semantics skipping and non-viable node pruning are adopted to reduce the amount of data, so as to reduce the overhead of the detection system; In the aspect of attack detection, an APT attack detection framework based on ATT&CK model is designed to carry out real-time attack response and alarm through the transfer and aggregation of labels. Experimental results on both laboratory and Darpa Engagement show that our system can effectively detect web vulnerability attacks, file-less attacks and remote access trojan attacks, and has a low false positive rate, which adds far more value than the existing frontier work.
A comparative study of paired versus unpaired deep learning methods for physically enhancing digital rock image resolution

X-ray micro-computed tomography (micro-CT) has been widely leveraged to characterise pore-scale geometry in subsurface porous rock. Recent developments in super resolution (SR) methods using deep learning allow the digital enhancement of low resolution (LR) images over large spatial scales, creating SR images comparable to the high resolution (HR) ground truth. This circumvents traditional resolution and field-of-view trade-offs. An outstanding issue is the use of paired (registered) LR and HR data, which is often required in the training step of such methods but is difficult to obtain. In this work, we rigorously compare two different state-of-the-art SR deep learning techniques, using both paired and unpaired data, with like-for-like ground truth data. The first approach requires paired images to train a convolutional neural network (CNN) while the second approach uses unpaired images to train a generative adversarial network (GAN). The two approaches are compared using a micro-CT carbonate rock sample with complicated micro-porous textures. We implemented various image based and numerical verifications and experimental validation to quantitatively evaluate the physical accuracy and sensitivities of the two methods. Our quantitative results show that unpaired GAN approach can reconstruct super-resolution images as precise as paired CNN method, with comparable training times and dataset requirement. This unlocks new applications for micro-CT image enhancement using unpaired deep learning methods; image registration is no longer needed during the data processing stage. Decoupled images from data storage platforms can be exploited more efficiently to train networks for SR digital rock applications. This opens up a new pathway for various applications of multi-scale flow simulation in heterogeneous porous media.
Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation Learning

Effective exploration continues to be a significant challenge that prevents the deployment of reinforcement learning for many physical systems. This is particularly true for systems with continuous and high-dimensional state and action spaces, such as robotic manipulators. The challenge is accentuated in the sparse rewards setting, where the low-level state information required for the design of dense rewards is unavailable. Adversarial imitation learning (AIL) can partially overcome this barrier by leveraging expert-generated demonstrations of optimal behaviour and providing, essentially, a replacement for dense reward information. Unfortunately, the availability of expert demonstrations does not necessarily improve an agent's capability to explore effectively and, as we empirically show, can lead to inefficient or stagnated learning. We present Learning from Guided Play (LfGP), a framework in which we leverage expert demonstrations of, in addition to a main task, multiple auxiliary tasks. Subsequently, a hierarchical model is used to learn each task reward and policy through a modified AIL procedure, in which exploration of all tasks is enforced via a scheduler composing different tasks together. This affords many benefits: learning efficiency is improved for main tasks with challenging bottleneck transitions, expert data becomes reusable between tasks, and transfer learning through the reuse of learned auxiliary task models becomes possible. Our experimental results in a challenging multitask robotic manipulation domain indicate that our method compares favourably to supervised imitation learning and to a state-of-the-art AIL method. Code is available at https://github.com/utiasSTARS/lfgp.
Hadron Spin Structure from Lattice QCD

We present results on the spin carried by quarks and gluons in the nucleon using lattice QCD simulations with physical values of the light, strange and charm quark masses. We also discuss selective results on the x-dependence of parton distribution functions computed in lattice QCD employing the quasi-parton distribution approach and the large momentum effective theory.