Miracle-dz/UniFAR-FTUs huggingface dataset:
Lyapunov-Based Fast Finite-State Model Predictive Control for Sensorless Three-Phase Four-Arm MMC

This article investigates a novel finite control set model predictive control (FCS-MPC) for sensorless three-phase four-arm modular multilevel converter (MMC). The main objectives of the proposed solution are to avoid the cumbersome online tuning of weighting factors and to achieve the balance of multiple control objectives, allowing a lower control complexity and associated costs. To be specific, a computationally efficient FCS-MPC with respect to a Lyapunov-based cost function formula, which has a good potential to flexibly achieve the balance of multiple control objectives and to avoid the selection of weighting factors, is first proposed. Next, an improved adaptive linear-neuron-based submodule voltage estimation together with arm-current sensorless sorting technique is incorporated into this proposal. The main contribution of the proposed methodology is to provide a simple and user-friendly solution to complex multiple control problems while allowing an improvement in computationally efficient optimization. Finally, comprehensive simulation and experimental results are provided to illustrate the efficacy of the proposal for three-phase four-arm MMC.
Estimation of the Environmental Flow Regime to Deter-mine the Minimum Flow for Pollution Reduction: Wadi Zomar - Palestine as a Case Study

Possible factors that play a role in restoring a flow regime that supports a healthy environment were identified for Wadi Zomar, Palestine. The hydrograph shows that threshold discharges for bed drying are less than 0.14 m3/s, bar formation is less than 0.4 ms/s and floodplain inundation events are over 3 m3/s. The HEC-RAS model suggests a continuous discharge throughout the year that maintains the base flow in the range of 0.5 m3/s. The model suggests that the restoration process is controlled by two main factors: role of sufficient flow and flood inundation frequency
Joint Motion Estimation and Compensation for Four-Dimensional Cone-Beam Computed Tomography Image Reconstruction

Four-Dimensional Cone-Beam Computed Tomography (4D-CBCT) imaging is important in upper abdomen tumor radiation treatment as it can obtain the tumor motion information immediately before treatment. However, the number of projections at a single phase for 4D-CBCT imaging is very small, and the CT images reconstructed by using conventional algorithms will be contaminated by view aliasing artifacts and noises. To address this problem, we propose a framework to jointly estimate and compensate the inter-phase motion for 4D-CBCT image reconstruction. Specifically, by introducing the intensity-based optical flow (OF) constraint into the reconstruction framework, the model can deal with the inter-frame displacements and improve the image quality simultaneously. The primal-dual algorithm method was used to optimize the cost function of the proposed model. Experiments on physical phantoms and patient data show that the proposed approach can effectively reduce the noise and artifacts in 4D–CBCT images and achieve the inter-phase motion vector fields (MVFs).
RSS-Based Selective Clustering Technique Using Master Node for WSN

: Wireless sensor networks (WSN) are designed to monitor the physical properties of the target area. The received signal strength (RSS) plays a significant role in reducing sensor node power consumption during data transmission. Proper utilization of RSS values with clustering is required to harvest the energy of each network node to prolong the network life span. This paper introduces the RSS-based energy-efficient selective clustering technique using a master node (RESCM) to improve energy utilization using a master node. The master node positioned at the center of the network area and base station (BS) is placed outside the network area. During cluster head (CH) selection, the node with a high RSS value is more likely to become CH. The network is divided into segments according to the distance from the master node. All nodes near BS or master node transmit their data using direct transmission without the clustering process. The simulation results showed that the RESCM method improves the total network lifespan effectively.
MIXED METHODS APPRAISAL TOOL: STRENGTHENING THE METHODOLOGICAL RIGOR OF MIXED METHODS RESEARCH STUDIES IN NURSING

ABSTRACT Objective: to describe the use of the Mixed Methods Appraisal Tool to appraise and to strengthen the methodological rigor of mixed methods studies in nursing. Method: a theoretical essay was used to describe the application of Mixed Methods Appraisal Tool to support the development and assessment of mixed methods research in nursing. Four mixed methods articles in nursing were purposely chosen and evaluated based on the Mixed Methods Appraisal Tool criteria. Results: Mixed Methods Appraisal Tool is a tool for evaluating primary mixed methods studies based on five evaluation criteria: 1) justification for adopting mixed methods methodology; 2) integration between the quantitative and qualitative components; 3) interpretation of integrated findings of quantitative and qualitative data; 4) presentation of divergences between quantitative and qualitative results; and 5) compliance with the methodological rigor of each individual approach in mixed methods research. Conclusion: Mixed Methods Appraisal Tool is an instrumental resource that can be used to appraise mixed methods research and strengthen the methodological rigor in planning and conducting future mixed studies in nursing research.
IT-Enabled Sustainable Innovation and the Global Digital Divides

This article investigates the impact of information and communication technologies (ICTs), human capital, institutional settings, socio-economic, and environmental parameters on sustainable innovation (SI) using archival data for 127 economies from 2008 to 2017. We developed an econometrics research framework for investigating factors influencing SI on a global scale. We found that ICT variables, such as ICT access and ICT broadband network, positively influence sustainable innovation in conjunction with the socio-economic and political parameters. Despite differences among economies in terms of ICTs, socio-economic development, and educational attainment, ICTs are the significant drivers of sustainable innovation and economic growth. We observed a growing digital divide among nations within the context of the knowledge-based economy and the expansion of digital commerce, particularly in the least developed countries and Africa, a phenomenon impeding sustainable innovation growth. To the best of our knowledge, this is the first study that empirically investigates the global digital divide from sustainable innovation perspectives. The results of this study suggest that to tackle the digital divide issues, policymakers and educational institutes need to perform constructive educational reform in higher education curricula, particularly concerning STEM programs, which should reflect the necessary skills and competencies for deploying emergent technologies. In addition, ICT should be considered part of a country’s critical infrastructure, particularly investment in the broadband networks regarded as the backbone of today’s innovation.
Prevalence and Predictors of Caregiver Burden in a Memory Clinic Population

Background: Although caregiver burden is common among carers of people with dementia, little is known about its prevalence and predictors among caregivers of patients attending memory clinics. Objective: To examine carer and patient-specific characteristics associated with caregiver burden across the cognitive spectrum in a memory clinic population. Methods: Consecutive patients referred to a university hospital geriatric memory clinic were included. Caregiver burden was scored using the Caregiver Burden Score (CBS), (modified Zarit), with scores≥15/30 suggesting burden. BPSD were measured with the dysfunctional behaviour rating instrument (DBRI). Cognition was screened using the Montreal Cognitive Assessment (MoCA) and Quick Mild Cognitive Impairment (Qmci) screen. Results: In all, 351 patients were included, median age 77 (±11) years; 65.5% were female. The prevalence of caregiver burden was 33.6% overall, increasing from 10.8% in subjective cognitive decline (SCD), to 15% in mild cognitive impairment (MCI) and 43% in dementia; CBS scores were significantly higher in dementia (p < 0.001). Caregivers with burden were significantly younger (p = 0.045) and were more likely to be adult children (p = 0.007). The CBS weakly correlated with the stage of cognitive impairment (r = 0.16) but had moderate correlation with MoCA (r = –0.54) and Qmci scores (r = –0.60). After adjustment for co-variates, DBRI scores alone independently predicted burden (odds ratio 1.23;1.11–1.35, p < 0.001). Conclusion: Caregiver burden is associated with the stage of cognitive impairment, with higher prevalence proportions in those with dementia compared with MCI and SCD. Only the severity of neuropsychiatric symptoms independently predicted caregiver burden in this population and its presence should prompt assessment for burden.
A special prognostic indicator: tumor mutation burden combined with immune infiltrates in lung adenocarcinoma with TP53 mutation

Background TP53 mutation (TP53mut) is significantly associated with immunotherapy response in lung adenocarcinoma (LUAD), but not an ideal independent prognostic predictor for it. Here, we investigated a novel potential biomarker and constructed a model for prognostic prediction in LUAD TP53mut patients. Methods 469 LUAD samples retrieved from The Cancer Genome Atlas database were divided into TP53wt (wild-type TP53) and TP53mut groups. TMB values were calculated based on the number of variants/exon lengths, and high- and low-TMB groups were divided by the median value. Differentially expressed genes (DEGs) between the two TMB groups were identified using “limma” package, and functional analyses were performed by Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and Gene Set Enrichment Analysis. The infiltration ratio of 22 immune cells were calculated with the CIBERSORT algorithm. Survival analyses were estimated by Kaplan-Meier with the log-rank test. Finally a TMB prognostic index (TMBPI) with receiver operating characteristic (ROC) curve was constructed and calculated to evaluate the predictive value in TP53mut LUAD. Results There were diverse mutation types in 100% of TP53 mutants, while mutations were present in 86.5% of cases with TP53wt. TP53mut patients had higher TMB levels than TP53wt patients. Overall survival in TP53mut patients with low-TMB levels was significantly shorter than that in high-TMB TP53mut patients. High-TMB patients had higher levels of CD8 T cell and effector B cell, while lower levels of resting memory CD4 T cells, monocytes, activated dendritic cells, etc. than low-TMB patients. Poor survival outcome in TP53mut patients was correlated with lower effector B cell infiltration and higher activated dendritic cell. Survival risk analyses of 121 DEGs showed that good survival outcomes correlated positively with FBXO36 and KLHL35 expression levels, but correlated negatively with that of LINC0054. TMBPI analysis of the TP53mut patients showed that high-TMBPI patients had worse survival outcomes than low-TMBPI patients. Conclusions Our findings suggest that the TMB value with immune infiltrates is a novel potential biomarker for prognostic prediction of TP53mut patients. The TMBPI combined with detection of TP53 mutation can be used as a better predictor of prognosis in LUAD.
Activity and Rotation of Nearby Field M Dwarfs in the TESS Southern Continuous Viewing Zone

The evolution of magnetism in late-type dwarfs remains murky, as we can only weakly predict levels of activity for M dwarfs of a given mass and age. We report results from our spectroscopic survey of M dwarfs in the Southern Continuous Viewing Zone (CVZ) of the Transiting Exoplanet Survey Satellite (TESS). As the TESS CVZs overlap with those of the James Webb Space Telescope, our targets constitute a legacy sample for studies of nearby M dwarfs. For 122 stars, we obtained at least one R ≈ 2000 optical spectrum with which we measure chromospheric Hα emission, a proxy for magnetic field strength. The fraction of active stars is consistent with what is expected for field M dwarfs; as in previous studies, we find that late-type M dwarfs remain active for longer than their early-type counterparts. While the TESS light curves for ≈20% of our targets show modulations consistent with rotation, TESS systematics are not well enough understood for confident measurements of rotation periods (P rot) longer than half the length of an observing sector. We report periods for 12 stars for which we measure P rot ≲ 15 days or find confirmation for the TESS-derived P rot in the literature. Our sample of 21 P rot, which includes periods from the literature, is consistent with our targets being spun-down field stars. Finally, we examine the Hα-to-bolometric luminosity distribution for our sample. Two stars are rotating fast enough to be magnetically saturated, but are not, hinting at the possibility that fast rotators may appear inactive in Hα.
Equipment selection for an e-commerce company using Entropy-based TOPSIS, EDAS and CODAS methods during the COVID-19

Background: The importance and market share of e-commerce has been increasing with the COVID-19 pandemic in recent days. Employees sometimes cannot go to the workplace due to epidemics such as COVID-19 that is spreading rapidly around the world, natural disasters and accidents. Companies can continue to serve their customers with the internet infrastructure and computer technologies they will provide to their employees. Thus, e-commerce companies can provide a sustainable competitive advantage in the sector. Working with the right suppliers is one of the important decisions that will improve the service quality of the firms and affect the sustainability of the enterprise. Methods: This study aims to select the best laptop for a company in the online trade industry using Entropy-based EDAS, CODAS and TOPSIS methods. In the study, 6 alternative laptops have been evaluated according to hard disk capacity, ram, battery power, processor speed, weight, price criteria. The Entropy method has been used to identify the weights of the criteria in the study. These criteria weights have been used in EDAS, CODAS and TOPSIS methods. TOPSIS, EDAS and CODAS methods have been used to determine the best alternative. Also, the correlation between the results of the TOPSIS, EDAS and CODAS methods has been examined with the Spearman Correlation approach. Results: As a result of the Entropy method, it has been determined that the most important criterion is the hard disk capacity criterion. TOPSIS, EDAS and CODAS method results have been compared and the most suitable alternative has been selected. According to the results of the study, the best alternative has been selected as A5. Spearman Correlation analysis results show that there was a strong positive relationship between the methods used and the results obtained. Conclusions: The study differs from existing studies in the literature in that it is the first study in which laptop selection was made using TOPSIS, EDAS and CODAS methods together. The results of this study can be compared with the results of future studies that will be carried out using different MCDM methods and different data.
Automatic Detection of COVID-19 Using a Stacked Denoising Convolutional Autoencoder

The exponential increase in new coronavirus disease 2019 (COVID-19) cases and deaths has made COVID-19 the leading cause of death in many countries. Thus, in this study, we propose an efficient technique for the automatic detection of COVID-19 and pneumonia based on X-ray images. A stacked denoising convolutional autoencoder (SDCA) model was proposed to classify X-ray images into three classes: normal, pneumonia, and COVID-19. The SDCA model was used to obtain a good representation of the input data and extract the relevant features from noisy images. The proposed model's architecture mainly composed of eight autoencoders, which were fed to two dense layers and SoftMax classifiers. The proposed model was evaluated with 6356 images from the datasets from different sources. The experiments and evaluation of the proposed model were applied to an 80/20 training/validation split and for five cross-validation data splitting, respectively. The metrics used for the SDCA model were the classification accuracy, precision, sensitivity, and specificity for both schemes. Our results demonstrated the superiority of the proposed model in classifying X-ray images with high accuracy of 96.8%. Therefore, this model can help physicians accelerate COVID-19 diagnosis.
Synthesis of Graphene Oxide Functionalized with Amio Methyl Phosphonic Acid (AMPA) and its Structural Characterization

In this paper, graphene oxide (GO) and its functionalization with “Amio Methyl Phosphonic Acid” (AMPA) are synthesized using modified Hummers method. Crystal structure of the compounds is investigated by X-ray diffraction pattern (XRD). Fourier transform infrared spectroscopy (FTIR) clearly shows that the AMPA agent does really enter into the GO. Transmission electron microscope (TEM) images of the compounds reveal that they are in the form of nano-sheets. High resolution TEM (HRTEM) microscope is also used to observe and study the nanoscopic morphology of the structures. In addition, the samples are element-analyzed by energy dispersive X-ray spectroscope (EDS), and X-ray photoelectron spectroscopy (XPS) to get more information. Finally, the functionalization mechanism of GO with AMPA is studied and the mechanisms of “nucleophilic displacement” as well as “condensation reaction” are suggested andare experimentally confirmed. Due to the favorable properties of the synthesized material AMPA-GO, its use was suggested for water treatment and removal of heavy metals such as lead and strontium.
Screening of COVID-19 Patients Using Deep Learning and IoT Framework

In March 2020, the World Health Organization declared the coronavirus disease (COVID-19) outbreak as a pandemic due to its uncontrolled global spread. Reverse transcription polymerase chain reaction is a laboratory test that is widely used for the diagnosis of this deadly disease. However, the limited availability of testing kits and qualified staff and the drastically increasing number of cases have hampered massive testing. To handle COVID19 testing problems, we apply the Internet of Things and artificial intelligence to achieve self-adaptive, secure, and fast resource allocation, real-time tracking, remote screening, and patient monitoring. In addition, we implement a cloud platform for efficient spectrum utilization. Thus, we propose a cloud based intelligent system for remote COVID-19 screening using cognitive radio-based Internet of Things and deep learning. Specifically, a deep learning technique recognizes radiographic patterns in chest computed tomography (CT) scans. To this end, contrast-limited adaptive histogram equalization is applied to an input CT scan followed by bilateral filtering to enhance the spatial quality. The image quality assessment of the CT scan is performed using the blind/referenceless image spatial quality evaluator. Then, a deep transfer learning model, VGG-16, is trained to diagnose a suspected CT scan as either COVID-19 positive or negative. Experimental results demonstrate that the proposed VGG-16 model outperforms existing COVID-19 screening models regarding accuracy, sensitivity, and specificity. The results obtained from the proposed system can be verified by doctors and sent to remote places through the Internet.
Frequency of homologous recombination-related (HRR) genes mutations in gastric cancer.

4031 Background: Homologous recombination deficiency (HRD) might contribute to predict response of immunotherapy and Chemotherapy response and PARP inhibitor (PARPi) therapy. The HRD phenotype has been defined as the presence of a non-silent somatic mutation in homologous recombination-related (HRR) genes. We aimed to analyze the mutational pattern of HRR genes and the relationship between HRR-related genes and clinical outcomes in gastric cancer. Methods: We prospectively sequenced 386 Chinese patients with gastric cancer using next-generation sequencing techniques with 808 cancer-related genes. 440 patients with WES data from the TCGA project were included. The 15 HRR genes were selected on clinical evidence of sensitivity to PARP inhibitors and immunotherapy. Correlations between HRR genes and clinical outcomes were identified via bioinformatic analysis using TCGA datasets. Results: In the Chinese cohort, 10.4% (40/386) patients exhibited genomic alterations in HRR genes. The frequently mutated genes were BRCA2 (2.3%), ATM (1.8%), CDK12 (1.8%), BRCA1 (1.0%), CHEK2(1.0%), PALB2(0.8%), BARD1 (0.5%), RAD51B (0.5%), and RAD54L (0.3%). 8.3% patients had at least one somatic HRR genes mutation. Pathogenic germline variants were identified in 2.1% (8/386) patients. No participants carried both germline and somatic HRR gene mutations. In TCGA cohort, 19.1% (841/440) patients had at least one somatic mutation in an HRR gene. The frequently mutated genes were BRCA2 (6.4%), ATM (6.4%), CDK12 (3.2%), BRCA1 (3.2%), BARD1 (2.3%), PALB2 (2.3%), HEK2 (1.6%), CHEK1 (1.4%), BROP1 (1.1%), RAD54L (1.1%), FANCL (0.9%), RAD51D (0.9%), RAD51C (0.7%), PPP2R2A (0.5%), and RAD51B (0.5%). Chinese cohort had lower frequently somatic mutation of HRR genes compared to that in TCGA cohort (8.3% vs 19.1%, p < 0.001). Interestingly, in the TCGA cohort, patients with HRR-mut had significantly elevated tumor mutational burden, microsatellite instability status and enhanced immune activity than patients with HRR-wt. In the MSK-IMPACT cohort comprising 49 patients treated with ICIs, patients with HRR-mut had significantly better overall survival compared to those in patients with HRR-wt (p = 0.027). Conclusions: Our data suggest that patients with altered HRR genes may be rational candidates for precision oncology treatment and provide new opportunities to predict the tumor response to multiple treatments, such as immunotherapeutic combine PARP inhibitor (PARPi) therapy. Exploring other biomarkers of HRD to predict the response to PARPi and immunotherapeutic in GC is necessary.
Multiscale Deep Learning Network With Self-Calibrated Convolution for Hyperspectral and LiDAR Data Collaborative Classification

In this article, we propose a novel multiscale deep learning network with self-calibrated convolution (MSNetSC) for hyperspectral and light detection and ranging (LiDAR) data collaborative classification. Conventional deep learning methods have limitations in extracting multiscale features at a granular level from multimodality data and fusing these features in a context-awareness way, which will severely restrict the performance of hyperspectral and LiDAR data joint classification. The proposed multiscale deep learning network utilizes a hierarchical residual structure combined with self-calibrated convolution to extract features with different receptive fields, and this can enhance the model’s capability to represent the multimodality data. Besides, we employ spectral and spatial self-attention modules to adaptively calibrate weights of features with different scales, thereby enhancing the discriminative ability of extracted multiscale features. Furthermore, the attentional feature fusion module can dynamically and adaptively fuse the features from multimodality data in a contextual scale-aware way, and this attention-based feature fusion method will further improve the collaborative classification performance of hyperspectral and LiDAR data. Four benchmark multimodality data (i.e., hyperspectral and LiDAR data) sets collected by different sensors and at different acquisition times are employed for joint classification experiments. These comparative classification results and ablation study sufficiently certify the superiority of the proposed model in terms of collaborative classification accuracy when compared with other state-of-the-art methods.