Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Active Magnetic Shielding for Electric Vehicle Wireless Power Transfer System

To mitigate the magnetic induction intensity surrounding the human body during the Wireless Power Transfer (WPT) of electric vehicles, this paper introduces an active coil shielding technique. Firstly, the circuit model of the WPT system is established, and a set of finite element simulation models of active magnetic shielding for 3.3 kW WPT system is designed, and the validity of the active magnetic shielding method is verified through simulation, and active magnetic shielding of the WPT system of the electric vehicle is successfully realized, and the best active magnetic shielding effect is achieved through further optimization of the current amplitude and phase in the active magnetic shielding coil,. The effectiveness of the active magnetic shielding theory and design method is proved.
Attributed Network Embedding with Data Distribution Adaptation

Network embedding aims to learn the low-dimensional representations of nodes in networks and preserve the features of networks simultaneously. In this paper, we propose a novel approach of network embedding, named Attributed Network Embedding with Data Distribution Adaptation (ANEDDA). In our model, we consider network structure and node attributes as two kinds of data which have different probability distributions. This is because that they come from different aspects or views and capture different kinds of features. However, because they come from the same network, there must exist some latent features which are related to both two kinds of information, and these features are significant to detect the community structure and analyze the network. We utilize a domain adaptation model named Transfer Component Analysis (TCA) to identify the latent common features. The proposed method is designed for unweighted networks (graphs), and it integrates the structure information and nodes attributes.
Comparative Analysis of CMOS Schmitt Triggers

This paper presents the performance analysis of various CMOS Schmitt triggers which are used in the VLSI industry for noise reduction. A comparative analysis of different CMOS Schmitt trigger designs has been provided. The design along with the working principle for each Schmitt trigger architecture is discussed. At last comparison between all the architectures with respect to parameters like hysteresis width, power and delay is shown. It is shown that a single architecture may not be able to meet all the specification needs. So, a comparative study has been done for some of the widely used architectures in GPIO buffers. So that depending on the requirement, designer can decide which design architecture to proceed with. All the simulations were implemented using CADENCE Virtuoso in Spectre simulator using 500nm CMOS technology.
Data-Dependent Tasks Scheduling and Analysis of Examples

An algorithm of task scheduling with data-dependent is proposed. The algorithm consists of two steps: determining the priority of tasks and selecting the resources for tasks scheduling. In the step of prioritizing tasks, a new definition of task rank is introduced, which improves the traditional definition by taking the sum instead of the maximum of the upper and the lower ranks, therefore better representing the residual load of tasks in the workflow. In the step of selecting resources, a new method based on the fastest computation time is designed. In addition, the selection of critical path used in task scheduling depends on the new definition of task ranks, and comparison is given with the traditional methods. Finally, we analysis and compare the results of several task scheduling methods and prove that our algorithm outperforms better than the existing algorithms.
A 2-AXIS SI/AL Bimorph-Based Electrothermal Micromirror Integrated with Piezoresistors for High Resolution Position Sensing

In this work, single-crystal silicon (SCS) is employed to replace fragile SiO2 to construct robust 2-axis electrothermal micromirrors with piezoresistors for position sensing. A lateral shift-free (LSF) SCS/Al bimorph electrothermal bimorph actuator is proposed, and a two-axis LSF SCS/Al electrothermal micromirror with piezoresistors was designed, fabricated and characterized. Each actuator was composed of six segments of bimorph and a thin silicon layer with the thickness of 2 μm. The dimensions of the square mirror plate are 1800 μm × 1800 μm × 30 μm. The fabricated micromirror achieved a tip-tilt angle of 7.2 ° and piston displacement of 240 μm under a voltage of 5 V. The performance of the angular shift of the proposed micromirror was improved about one order of magnitude with the value decreasing from 0.035 ° under open-loop control to 0.0062 ° under closed-loop control. Compared with SiO2-based bimorphs, the fabrication process is not only simplified, but also provides a method for integrating piezoresistive sensors on the bimorph actuator and realizing feedback control.
An Open Science Approach to Artificial Intelligence in Healthcare

Summary Objectives: Artificial Intelligence (AI) offers significant potential for improving healthcare. This paper discusses how an “open science” approach to AI tool development, data sharing, education, and research can support the clinical adoption of AI systems. Method: In response to the call for participation for the 2019 International Medical Informatics Association (IMIA) Yearbook theme issue on AI in healthcare, the IMIA Open Source Working Group conducted a rapid review of recent literature relating to open science and AI in healthcare and discussed how an open science approach could help overcome concerns about the adoption of new AI technology in healthcare settings. Results: The recent literature reveals that open science approaches to AI system development are well established. The ecosystem of software development, data sharing, education, and research in the AI community has, in general, adopted an open science ethos that has driven much of the recent innovation and adoption of new AI techniques. However, within the healthcare domain, adoption may be inhibited by the use of “black-box” AI systems, where only the inputs and outputs of those systems are understood, and clinical effectiveness and implementation studies are missing. Conclusions: As AI-based data analysis and clinical decision support systems begin to be implemented in healthcare systems around the world, further openness of clinical effectiveness and mechanisms of action may be required by safety-conscious healthcare policy-makers to ensure they are clinically effective in real world use.
Positioning biologics and new therapies in the management of inflammatory bowel disease.

PURPOSE OF REVIEW Since the advent of anti-tumour necrosis factor agents, knowledge of their optimal use and their pitfalls has grown exponentially. However, the range of therapeutic agents available to clinicians and patients is expanding, creating challenging decisions about which drugs to use at any given time point. The present review aims to provide a framework within which positioning decisions can be made in the context of limited comparative effectiveness data. RECENT FINDINGS The present review will summarize the current literature emphasizing how best to use biologic agents, and will provide suggestions as to how they should be positioned. Recent trials comparing efficacy and safety will be considered as will their strengths and weaknesses. SUMMARY On reading this review, clinicians should have an understanding of the optimal use of currently approved biologic agents and tofacitinib and how they might be positioned in relation to one another.
SDN Controller Deployment Strategy based on Electric Power Communication Network Community Partitioning

The current electric power communication services encompass a wide variety of services with significant differences in their business requirements. Traditional electric power communication networks struggle to meet the diverse needs of these services. Software-Defined Networking (SDN) has introduced a more convenient and efficient approach to network management and upgrades for electric power communication networks. However, the traffic in electric power communication networks is characterized by spatiotemporal dynamics, leading to an uneven distribution of SDN controllers, which severely impacts network performance. To address the controller deployment issue in SDN networks, this paper establishes a quantification model for SDN network controller power, divides the SDN network into communities based on community modularity, and proposes a controller deployment strategy tailored to electric power communication network community division, optimizing the power for each controller within each community effectively. This resolves the controller deployment issue in SDN networks. Finally, a controller deployment algorithm based on power optimization is implemented through simulation experiments, and the experiments demonstrate that the algorithm proposed in this paper, in comparison with the baseline algorithm, exhibits lower total controller power consumption and average response time. Furthermore, it reduces controller power fluctuation and latency jitter, thereby enhancing the quality of network service.
Frame Interval Control Technique for High-Frequency Electric Field Imaging by Equivalent-Time Sampling

We propose an equivalent-time sampling method with controlled frame spacing to improve the intermediate frequency of field imaging systems. Simple equivalent-time sampling with a constant frame interval detects frequency components other than those to be observed. We present a method that cancels frequency components other than the frequency to be observed by lengthening the frame interval by 1/4 cycle every four frames instead of keeping it constant. We show that only the signal of the frequency to be observed can be detected by an image sensor. We also apply this method to electric field imaging.
Conformational Dynamics of the RNA G-Quadruplex and its Effect on Translation Efficiency

During translation, intracellular mRNA folds co-transcriptionally and must refold following the passage of ribosome. The mRNAs can be entrapped in metastable structures during these folding events. In the present study, we evaluated the conformational dynamics of the kinetically favored, metastable, and hairpin-like structure, which disturbs the thermodynamically favored G-quadruplex structure, and its effect on co-transcriptional translation in prokaryotic cells. We found that nascent mRNA forms a metastable hairpin-like structure during co-transcriptional folding instead of the G-quadruplex structure. When the translation progressed co-transcriptionally before the metastable hairpin-like structure transition to the G-quadruplex, function of the G-quadruplex as a roadblock of the ribosome was sequestered. This suggested that kinetically formed RNA structures had a dominant effect on gene expression in prokaryotes. The results of this study indicate that it is critical to consider the conformational dynamics of RNA-folding to understand the contributions of the mRNA structures in controlling gene expression.
Quantifying changes on susceptibility weighted images in amyotrophic lateral sclerosis using MRI texture analysis

Abstract Objective: Susceptibility-weighted imaging (SWI) has been used to identify neurodegeneration in amyotrophic lateral sclerosis (ALS) through qualitative gross visual comparison of signal intensity. The aim of this study was to quantitatively identify cerebral degeneration in ALS on SWI using texture analysis. Methods: SW images were acquired from 17 ALS patients (58.4 ± 10.3 years, 13M/4F, ALSFRS-R 41.2 ± 4.1) and 18 healthy controls (56.3 ± 17.6 years, 9M/9F) at 4.7 tesla. Textures were computed within the precentral gyrus and basal ganglia and compared between patients and controls using ANCOVA with age and gender as covariates. Texture features were correlated with clinical measures in patients. Texture features found to be significantly different between patients and controls in the precentral gyrus were then used in a whole-brain 3D texture analysis. Results: The texture feature autocorrelation was significantly higher in ALS patients compared to healthy controls in the precentral gyrus and basal ganglia (p < 0.05). Autocorrelation correlated significantly with clinical measures such as disease progression rate and finger tapping speed (p < 0.05). Whole brain 3D texture analysis using autocorrelation revealed differences between ALS patients and controls within the precentral gyrus on SWI images (p < 0.001). Conclusion: Texture analysis on SWI can quantitatively identify cerebral differences between ALS patients and controls.
Phase Modulation Based TX Channel Calibration for MIMO Radar Systems

Since the demand for high resolution automotive sensors is continuously growing, aperture sizes of mmW radars are rising as well. This is especially challenging for calibration purposes in lab environments since the far-field distances are increasing with the aperture squared. Therefore, new calibration techniques need to be developed to allow compact-range calibration. In this paper a new phase modulation based method is presented to calculate the contribution of the transmit channels for the calibration coefficients. The method is validated with a measurement and shows equal results with the established farfield procedure while improving the previously presented time division multiple access approach in robustness and speed.
A Monolithic On-chip 15 GHz Phase-Shifter

This paper presents a monolithic on-chip phase-shifter designed to work at 15 GHz on a p-type silicon substrate. Using a simple process with only two photolithography masks, the active elements are some $\mathrm{N}^{+} \mathrm{P P}^{+}$junctions integrated in the substrate and optimized at the same time as the transmission lines with a co-design approach. These junctions allow to commute the signal transmission between two paths and the difference in the paths length offers a phase difference which can be chosen depending on the application. In the proposed demonstrator, the measured phase difference is 160°.
Long-term effectiveness of cinacalcet in non-dialysis patients with chronic kidney disease and secondary hyperparathyroidism

Background Secondary hyperparathyroidism (SHPT) is a common complication of chronic kidney disease (CKD). Cinacalcet use is controversial in non-dialysis patients. Methods This retrospective observational study recruited patients receiving cinacalcet (off-label use) in 2010 and 2011. Patients were followed for three years from the beginning of treatment using an intention-to-treat approach. Results Forty-one patients were studied: 14 CKD stage 3 (34.1%), 21 CKD stage 4 (51.2%), and 6 CKD stage 5 (14.6%). Median baseline parathyroid hormone (PTH) was 396 (101–1,300) pg/mL. Upon cinacalcet treatment (22 ± 12 months), PTH levels decreased by ≥ 30% in 73.2% of patients (P < 0.001; 95% confidence interval [CI], 59–87%), with a mean time for response of 18.7 months (95% CI, 15.4–22.1). Sixteen patients were followed for 36 months and treated for 32 ± 9 months. Mean reduction in their PTH levels was 50.1% (P < 0.001; 95% CI, 33.8–66.4%) at 36 months, with 62.5% of patients (P < 0.001; 95% CI, 35.9–89.1%) presenting reductions of ≥ 30%. Serum calcium levels decreased from 9.95 ± 0.62 mg/dL to 9.21 ± 0.83 and 9.12 ± 0.78 mg/dL at 12 and 36 months, respectively (P < 0.001). Serum phosphorus levels increased from 3.59 ± 0.43 to 3.82 ± 0.84 at 12 months (P = 0.180), remaining so at 36 months (P = 0.324). At 12 and 36 months, 2 (12.5%) patients experienced hypocalcemia. Meanwhile, 1 (6.3%) and 4 (25.0%) patients reported hyperphosphatemia at 12 and 36 months, respectively. Conclusion Cinacalcet remained effective for at least 36 months in non-dialysis patients with SHPT. Electrolytic disturbances were managed with concurrent use of vitamin D and its analogs or phosphate binders.
Dangerous Farm Insects Detection Using Transfer Learning and CNN

In the ever-evolving landscape of machine learning, transfer learning has emerged as a powerful technique that leverages knowledge gained from one task to enhance the performance of another. This research paper delves into the realm of agricultural applications by employing the transfer learning technique on the “Dangerous Farm Insects Image Dataset. ” This curated repository contains a diverse array of images depicting 15 distinct types of insects commonly found in agricultural environments, serving as a vital resource for understanding and studying these potentially destructive creatures. The focus of this study revolves around harnessing the pretrained knowledge of deep learning models and applying it to the task of classifying and identifying the various insect species within the dataset. By building upon the foundations laid by these models, we aim to optimize classification accuracy, reduce training time, and pave the way for real-world applications in pest management, crop protection, and beyond.
A Comparative Analysis of Word Embeddings Techniques for Italian News Categorization

Text categorization remains a formidable challenge in information retrieval, requiring effective strategies, especially when applied to low-resource languages such as Italian. This paper delves into the intricacies of categorizing Italian news articles, addressing the complexities arising from the language’s unique structure and writing style. The implemented methodology involves preprocessing the text, generating word embeddings, conducting feature engineering to extract meaningful representations, and training a classifier using the document vectors. The evaluation of the model’s performance is done on a partitioned dataset with a training set for model training and a test set for categorization, allowing assessment of its efficacy on unseen data. Within this paper, we assessed fifteen classifiers for the categorization of Italian news articles, scrutinizing eight models and three approaches for combining word embeddings to derive document vectors. We conducted a comparative analysis between established models such as Word2Vec and FastText and six novel Italian models pre-trained on native datasets. A significant highlight of our work is the introduction of an Italian GloVe model, previously absent for the Italian language. The datasets selected for testing the models’ performances are DICE, a dataset of 10,395 crime news articles extracted from an Italian newspaper, and RCV2-it, a collection of 28,405 Italian news stories released by the multinational media company Reuters Ltd. The tests conducted achieved as the best F-scores 84% and 93%. The results underscore the efficacy of the Support Vector Classification algorithm, while also revealing the inefficacy of Gaussian Naive Bayes, Bernoulli Naive Bayes, and Decision Tree models within the domain of text categorization. The comparison of the word embedding models revealed the better performance of Word2Vec and GloVe concerning FastText. The broader impact of this paper lies not only in advancing text categorization methodologies for Italian documents but also in enriching the linguistic landscape by releasing six novel Italian word embedding models.
High-Density Iridium Oxide Microneedle Electrode Based on Self-Constriction Purple Clay Mold for Eeg Recording

This paper reports a high-density flexible iridium oxide (IrOx)-modified microneedle dry electrode based on a self-constriction purple clay mold. Benefiting from the shrinkage of the purple clay mold after heating, the microneedle electrode is fabricated through replica molding and sputtering technology realizing high density without the restriction of expensive sophisticated equipment. This paper provides a method of applying purple clay mold to accomplish the high density of microneedles, which contributes to low skin-electrode contact impedance with the coordination of the modification of IrOx. The proposed electrode achieves good electrical performance making it applicable to wearable electroencephalogram (EEG) recording.
A 28 GHz 8-channel BFIC for 5G in 28 nm CMOS

This paper presents a 28 GHz 8-channel (in 2-polarization) BFIC for 5G band n257, integrated in a standard 28 nm CMOS technology and packaged in a compact 4.6 by 5.1 mm2 FCBGA. Each channel achieves 5.6 deg phase resolution and 0.5 dB gain resolution with high accuracy and orthogonality, while offering more than 10 dBm average TX output power and 4.5 dB RX NF per channel.
Design Optimization of Metasurface Filter Integrated With Base Station Antenna for Sub-6 GHz 5G Communications

This paper proposes a metasurface filter integrated with a base station antenna for sub-6 GHz 5G communications. The metasurface filter, composed of a pixel-based geometric structure, is optimized using the conformational space annealing (CSA) algorithm. The proposed metasurface filter exhibits bandpass characteristics around the 3.5 GHz frequency, commonly used in sub-6 GHz 5G networks. The performance of the metasurface filter combined with a 4×1 patch antenna array was measured in a fully anechoic chamber. The measured gain pattern shows that the metasurface filter passes the signal with a small insertion loss in the passband and effectively suppresses out-of-band frequencies, providing frequency-selective performance.
Classification of Satellite Images and Predicting Field Areas after Fine- Tuning Using Sequential CNN Model

The categorization of satellite images is a discipline within the domains of remote sensing and computer vision. It is the process of assigning pixels or areas within satellite imagery to predetermined classifications or categories related to land cover or land use. The aforementioned procedure is of utmost importance in the extraction of meaningful information from international Earth observation data for the research development. The categorization of satellite images is dependent upon the use of data obtained from Earth-observing satellites. The satellites in question are designed to acquire electromagnetic radiation, including various wavelengths such as visible, infrared, and microwave, which is either reflected or emitted by the Earth's surface. Multispectral or hyperspectral sensors provide the acquisition of pictures including several spectral bands, hence enabling the retrieval of intricate data pertaining to the Earth's surface. The present research employs a suggested Sequential Convolutional Neural Network (CNN) model to classify the Satellite Image. The graphics processing unit (GPU) is used for the purpose of doing data pre-processing. The Deep Learning model is then visualised via a study of the Loss and Accuracy curves. The suggested model is used to forecast the geographical coverage in four distinct categories, namely cloudy, desert, green area, and water respectively which is essential for ecosystem management.