Miracle-dz/UniFAR-FTUs huggingface dataset:
Romanian Guidelines for the Diagnosis and Treatment of GERD-induced Respiratory Manifestations.

BACKGROUND AND AIMS Gastroesophageal reflux disease (GERD) is a common condition present in daily practice with a wide range of clinical phenotypes. In this line, respiratory conditions may be associated with GERD. The Romanian Societies of Gastroenterology and Neurogastroenterology, in association with the Romanian Society of Pneumology, aimed to create a guideline regarding the epidemiology, diagnosis and treatment of respiratory conditions associated with GERD. METHODS Delphi methodology was used and eleven common working groups of experts were created. The experts reviewed the literature according to GRADE criteria and formulated 34 statements and recommendations. Consensus (>80% agreement) was reached for some of the statements after all participants voted. RESULTS All the statements and the literature review are presented in the paper, together with their correspondent grade of evidence and the voting results. Based on >80% voting agreement, a number of 22 recommendations were postulated regarding the diagnosis and treatment of GERD-induced respiratory symptoms. The experts considered that GERD may cause bronchial asthma and chronic cough in an important number of patients through micro-aspiration and vagal-mediated tracheobronchial reflex. GERD should be suspected in patients with asthma with suboptimal controlled or after exclusion of other causes, also in nocturnal refractory cough which needs gastroenterological investigations to confirm the diagnosis. Therapeutic test with double dose proton pump inhibitors (PPI) for 3 months is also useful. GERD induced respiratory conditions are difficult to treat; however,proton pump inhibitors and laparoscopic Nissen fundoplication are endorsed for therapy. CONCLUSIONS This guideline could be useful for the multidisciplinary management of GERD with respiratory symptoms in current practice.
Performance Enhancements of Fully Inkjet-Printing Technology for Antenna-in-Package and Substrate Integrated Waveguides

A fully inkjet-printing technology is applied to antenna-in-package (AiP) and substrate integrated waveguide (SIW) to enhance the performance of three components, including via holes, wire bonding, and flexible antenna arrays. First of all, earlier studies utilize shorting pins for the conductive pathway in high-density AiP and SIW, but this requires an additional procedure to plate the conductor. We propose a mechanical approach to form a cylindrical hole, plating the surface with silver nanoparticles and realizing the equivalent circuit model of the shorting pin. The proposed approach does not require high alignment sensitivity or the precise control of laser power level. Second, fully inkjet-printed wire bonding is proposed for the system on the package. The proposed technique not only reduces the discontinuity but also enables a fabrication without additional assembly. Third, the proposed technique is implemented for antenna development, which shows desirable performance with reduced fabrication complexity. The proposed technology is validated by microstrip lines, SIWs, SIW cavity slots, and flexible 4 × 4 patch arrays fabricated on various substrates including RO 4003C, polyimide, and polyethylene naphthalate. For comparison purposes, conventional approaches using printed circuit boards are also implemented and tested. The results indicate the generality and capability of the proposed technique.
Research on wind power penetration of wind-hydro-thermal power system

Aiming at studying the relation between wind capacity and power system stability,the mathematical models of wind generator are given,and the simulation module of wind-water-fire power system is built up.Based on genetic algorithm,the optimal mathematics model of wind power penetration is discussed.After building the optimal function,the capacity of wind power in a practical power network is calculated by programming and simulation.Approximately results have been obtained although difference exists between the two results.
Analytic Hierarchy Process-Based Evaluation of Recycling Solid Waste in Shanghai

Based on the practices of recycling utilization of solid waste in Shanghai,an analytical hierarchy process is presented to evaluate the economy,society and environment,and each of the index's weight is analyzed.The results show that environment impact is the most important factor.The binary source separation is an optimal choice,the investigative keystone and the heading direction in the recycling of solid waste in future Shanghai.
Discriminating ecological processes affecting different dimensions of α‐ and β‐diversity in desert plant communities

Abstract Understanding the spatial distribution of plant diversity and its drivers are major challenges in biogeography and conservation biology. Integrating multiple facets of biodiversity (e.g., taxonomic, phylogenetic, and functional biodiversity) may advance our understanding on how community assembly processes drive the distribution of biodiversity. In this study, plant communities in 60 sampling plots in desert ecosystems were investigated. The effects of local environment and spatial factors on the species, functional, and phylogenetic α‐ and β‐diversity (including turnover and nestedness components) of desert plant communities were investigated. The results showed that functional and phylogenetic α‐diversity were negatively correlated with species richness, and were significantly positively correlated with each other. Environmental filtering mainly influenced species richness and Rao quadratic entropy; phylogenetic α‐diversity was mainly influenced by dispersal limitation. Species and phylogenetic β‐diversity were mainly consisted of turnover component. The functional β‐diversity and its turnover component were mainly influenced by environmental factors, while dispersal limitation dominantly effected species and phylogenetic β‐diversity and their turnover component of species and phylogenetic β‐diversity. Soil organic carbon and soil pH significantly influenced different dimensions of α‐diversity, and soil moisture, salinity, organic carbon, and total nitrogen significantly influenced different dimensions of α‐ and β‐diversity and their components. Overall, it appeared that the relative influence of environmental and spatial factors on taxonomic, functional, and phylogenetic diversity differed at the α and β scales. Quantifying α‐ and β‐diversity at different biodiversity dimensions can help researchers to more accurately assess patterns of diversity and community assembly.
COVID-19 Diagnosis System using SimpNet Deep Model

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings, and Pneumonia) classification tasks. Our model has achieved an accuracy value of 98.4% for binary and 93.8% for the multi-class classification. The number of parameters of our model is 11 Million parameters which are fewer than some state-of-the-art methods with achieving higher results.
Real-Time Single Laser based 3.2 Tb/s (32×100-Gb/s) PM-QPSK Transmission using Coherent Detection Over 2,080-km SSMF

In this paper, we real-time transmitted 32 × 100-Gb/s PM-QPSK modulated signal over 2,080-km standard single mode fiber (SSMF) using coherent detection based on 100G transponder with 31.78 Gbaud/s. 32 WDM channel sources are generated by a single laser source on a 32 GHz grid and yield a spectral efficiency of 3.11 b/s/Hz.
Emission characteristics, sources, and airborne fate of speciated organics in particulate matters in a Hong Kong residence.

A growing number of studies warn of the adverse health effects of indoor particulate matters (PM). However, little is known about the molecular compositions and emission characteristics of PM-bound organics (OM) indoors, a critical group of species with highest concentration and complexity in indoor PM. In a Hong Kong residence where prescribed activities were performed with normal frequency and intensity, we found that the activities significantly elevated not only the total concentration but also the fraction of OM in indoor PM. However, the concentration of the total PM-bound OM outdoors (10.3 ± 0.7 μg/m3 ) surpassed that for the indoor counterpart during the undisturbed period (8.2 ± 0.1 μg/m3 ), that is, period when there was no activity with high emission of PM but the residual effects of previous activities might remain. Emissions of indoor activities involving combustion or high-temperature processes significantly elevated the indoor-to-outdoor (I/O) ratios for a majority of organic species. In addition, gas-to-particle partitioning, secondary formation, carrying-over (residues of pollutants in the air), and re-emission also modulated the I/O ratios of some compounds. Chemically comprehensive emission profiles of speciated organics were obtained for 5 indoor activities in the residence. While the indoor contribution to PM-bound OM was estimated to be not higher than 13.1% during the undisturbed period, carrying-over and/or re-emission seemed to exist for certain compounds emitted from cigarette smoking and incense burning. This study enhances knowledge on emissions and airborne fate of speciated organics in indoor PM.
SABRE: Swarm-based Aerial Beamforming Radios: Experimentation and Emulation

We propose a novel distributed beamforming framework for UAVs, called SABRE, wherein airborne transmitters synchronize their operations for data communication with target receivers. SABRE chooses the best-suited subset of transmitters that maximizes user-defined QoS, considering relative distances from receivers, traffic characteristics, cumulative SNR desired at the receiver, and individual SNR estimated for each link. This paper makes three main contributions: (i) It shows how to achieve distributed beamforming in challenging, aerial hovering conditions by accurately synchronizing start-times and eliminating relative clock offsets. (ii) It proposes an algorithm with polynomial complexity that groups transmitters and chooses the receiver, maximizing the number of satisfied receivers in each round. (iii) It experimentally validates the concept of aerial beamforming in a testbed composed of four DJI-M100 UAVs in realistic outdoor environments. We follow this up with at-scale emulation involving beamforming with multiple candidate UAV transmitters in Colosseum, the world’s largest RF emulator. SABRE keeps the overall network frame error rate below 10% with a probability of 0.95 and manifests a 40% improvement in meeting user QoS thresholds over classical resource allocation methods. From a community viewpoint, the beamforming code, UAV interfacing designs, and the Colosseum container will be released publicly, allowing further independent investigations.
Research on Curriculum Design of Deep Learning based on the WeChat

Deep learning is an active, critical way of learning, but also the effective way of meaningful learning. The WeChat is applied to the students in the process of deep learning in the paper. Combined with the distinctive features of the WeChat and several major advantages of the WeChat public platform, the curriculum design of deep learning is studied based on the WeChat under on the guiding principles of the teaching strategies of deep learning based the mobile internet. The curriculum design of deep learning based on the WeChat is comprised of three parts, which are the construction of learning environment, the process of deep learning and the feedback of effect. The flow chart of the whole learning process is drew and the specific implementation of the three parts explained in detail.
Ensemble methods for meningitis aetiology diagnosis

In this work, we explore data‐driven techniques for the fast and early diagnosis concerning the etiological origin of meningitis, more specifically with regard to differentiating between viral and bacterial meningitis. We study how machine learning can be used to predict meningitis aetiology once a patient has been diagnosed with this disease. We have a dataset of 26,228 patients described by 19 attributes, mainly about the patient's observable symptoms and the early results of the cerebrospinal fluid analysis. Using this dataset, we have explored several techniques of dataset sampling, feature selection and classification models based both on ensemble methods and on simple techniques (mainly, decision trees). Experiments with 27 classification models (19 of them involving ensemble methods) have been conducted for this paper. Our main finding is that the combination of ensemble methods with decision trees leads to the best meningitis aetiology classifiers. The best performance indicator values (precision, recall and f‐measure of 89% and an AUC value of 95%) have been achieved by the synergy between bagging and NBTrees. Nonetheless, our results also suggest that the combination of ensemble methods with certain decision tree clearly improves the performance of diagnosis in comparison with those obtained with only the corresponding decision tree.
The Regulatory Role of Neuropeptide Gene Glucagon in Colorectal Cancer: A Comprehensive Bioinformatic Analysis

Background Colorectal cancer is highly prevalent and causes high global mortality, and glucagon axis has been implicated in colon cancer. The present study is aimed at investigating the regulating mechanisms of glucagon involvement in colorectal cancer. Methods Publicly available data from the TCGA database was utilized to explore the expression pattern and regulating role of glucagon (GCG) in colorectal cancer (COADREAD) including colon adenocarcinomas (COAD) and rectum adenocarcinomas (READ). Statistical analyses were performed using the R software packages and public web servers. The expression pattern and prognostic significance of GCG gene in pan-cancer and TCGA-COADREAD data were investigated by performing unpaired and paired sample analyses. The association of GCG expression with clinical characteristics was investigated using logistic regression analysis. Univariate cox regression analysis was performed to test the prognostic value of GCG expression for overall survival in COADREAD patients. GCG-significantly correlated genes were obtained. Biological functions and signaling pathways were identified by performing functional enrichment analysis and Gene Set Enrichment Analysis (GSEA). Additionally, the potential involvement of GCG in tumor immunity was researched by investigating the correlation between GCG expression and 24 tumor infiltrating immune cells. Results GCG was found to be significantly downregulated in COADREAD tumor samples compared with healthy control samples. GCG gene was shown to be associated with the prognostic outcomes of COADREAD, whereby its upregulation predicted improved survival outcomes. Functional enrichment analysis showed that the top 100 positively and top 100 negatively GCG-correlated genes were mainly enriched in three signaling pathways including ribosome, nitrogen metabolism, and proximal tubule bicarbonate reclamation. The GSEA showed that GCG-significantly correlated genes were mainly enriched in cell cycle-related pathways (reactome cell cycle, reactome cell cycle mitotic, reactome cell cycle checkpoints, reactome M phase, Reactome G2 M DNA damage checkpoint, and Reactome G2 M checkpoints), neuropeptide ligand receptor interaction, RHO GTPases signaling, WNT signaling, RUNX1 signaling, NOTCH signaling, ESR signaling, HCMV infection, and oxidative stress-related signaling. GCG was positively correlated with Th17 cells, pDC, macrophages, TFH cells, iDC, Tem, B cells, dendritic cells, neutrophils, mast cells, and eosinophils and was negatively associated with NK cells. Conclusions GCG dysregulation with high prognostic value in COADREAD was noted. Several tumor progression-related pathways and tumor immune-modulatory cells were linked to GCG expression in COADREAD. Therefore, GCG may be regarded as a potential therapeutic target for treating colorectal cancer.
Power and Area Efficient Cascaded Effectless GDI Approximate Adder for Accelerating Multimedia Applications Using Deep Learning Model

Approximate computing is an upsurging technique to accelerate the process through less computational effort while keeping admissible accuracy of error-tolerant applications such as multimedia and deep learning. Inheritance properties of the deep learning process aid the designer to abridge the circuitry and also to increase the computation speed at the cost of the accuracy of results. High computational complexity and low-power requirement of portable devices in the dark silicon era sought suitable alternate for Complementary Metal Oxide Semiconductor (CMOS) technology. Gate Diffusion Input (GDI) logic is one of the prompting alternatives to CMOS logic to reduce transistors and low-power design. In this work, a novel energy and area efficient 1-bit GDI-based full swing Energy and Area efficient Full Adder (EAFA) with minimum error distance is proposed. The proposed architecture was constructed to mitigate the cascaded effect problem in GDI-based circuits. It is proved by extending the proposed 1-bit GDI-based adder for different 16-bit Energy and Area Efficient High-Speed Error-Tolerant Adders (EAHSETA) segmented as accurate and inaccurate adder circuits. The proposed adder's design metrics in terms of delay, area, and power dissipation are verified through simulation using the Cadence tool. The proposed logic is deployed to accelerate the convolution process in the Low-Weight Digit Detector neural network for real-time handwritten digit classification application as a case study in the Intel Cyclone IV Field Programmable Gate Array (FPGA). The results confirm that our proposed EAHSETA occupies fewer logic elements and improves operation speed with the speed-up factor of 1.29 than other similar techniques while producing 95% of classification accuracy.
Efficiency improvement of a cantilever-type energy harvester using torsional vibration

In this paper, a piezoelectric vibrational energy harvester utilizing coupled bending and torsional vibrations is investigated. The proposed system consists of a cantilever-type substrate covered by the piezoelectric ceramic and a proof mass which is perpendicularly connected to the free end of the cantilever beam by a rigid bar. While the natural frequency and output voltage of the conventional system are affected by bending deformation of the piezoelectric plate, the proposed system makes use of its twisting deformation. The natural frequency of the device can be significantly decreased by manipulating the location of the proof mass on the rigid bar. In order to validate the performance of the proposed energy harvester, numerical simulations and vertical shaker tests are carried out. It is demonstrated that the proposed energy harvester can shift down its resonant frequency considerably and generate much higher output power than the conventional system. It is, therefore, concluded that the proposed energy harvester utilizing the coupled bending and torsional vibrations can be effectively applied to low-frequency vibration situations.
Neural-Observer-Based Terminal Sliding Mode Control: Design and Application

In view of the fact that most devices are nonlinear systems, this article proposes a recurrent probabilistic compensation fuzzy neural network (RPCFNN) control scheme based on global fast terminal sliding mode control (GFTSMC) for a class of nonlinear systems with uncertainties. First, GFTSMC is developed to impose a finite-time convergence feature for the considered systems. Second, a novel RPCFNN framework is designed to further enhance the ability to deal with uncertainty. Due to the added probabilistic estimation and dynamic fuzzy operator, the developed RPCFNN controller possesses superior nonlinearity handling capability. Meanwhile, the stability of RPCFNN is proved by the Lyapunov theory. Finally, active power filter is taken as the representative of nonlinear systems to verify the effectiveness and feasibility of the proposed method. Simulation and experimental results show that the novel RPCFNN controller has good steady-state and dynamic performance and has excellent robustness to deal with uncertain disturbances.
A Duty-Cycled Integrated-Fluxgate Magnetometer for Current Sensing

This article presents a duty-cycled integrated-fluxgate (IFG) magnetic-to-digital converter for contactless current sensing. While previous works have proven high linearity, low noise, and low offset capabilities of fluxgate sensing, this work focuses on low energy operation. The mixed-signal front-end design enables energy-efficient duty cycling with a 13-bit digital integrator to retain the last converged output during sleep mode and resume searching in its vicinity upon wake up. The system achieves a ±2.4 mT measured range of magnetic fields for indirect current measurement. The hierarchical search modes achieve $10\times $ faster convergence than analog implementations. The faster readout with a peak sampling rate of 770 kS/s translates to lower compensation energy per sample. The 3-dB bandwidth of 125 kHz is 67% higher than prior IFG art, with competitive 500-nT rms noise. The test chip fabricated in 250-nm CMOS technology consumes the lowest power of known IFG works. It can be duty-cycled to save energy in low-bandwidth applications or run at peak bandwidth for low latency.
Progress of Statin Therapy in the Treatment of Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is a type of interstitial lung disease (ILD) characterized by the proliferation of fibroblasts and aberrant accumulation of extracellular matrix. These changes are accompanied by structural destruction of the lung tissue and the progressive decline of pulmonary function. In the past few decades, researchers have investigated the pathogenesis of IPF and sought a therapeutic approach for its treatment. Some studies have shown that the occurrence of IPF is related to pulmonary inflammatory injury; however, its specific etiology and pathogenesis remain unknown, and no effective treatment, with the exception of lung transplantation, has been identified yet. Several basic science and clinical studies in recent years have shown that statins, the traditional lipid-lowering drugs, exert significant antifibrotic effects, which can delay the progression of IPF and impairment of pulmonary function. This article is aimed at summarizing the current understanding of the pathogenesis of IPF, the progress of research on the use of statins in IPF models and clinical trials, and its main molecular targets.