Miracle-dz/UniFAR-FTUs huggingface dataset:
Structured Reporting of Computed Tomography and Magnetic Resonance in the Staging of Pancreatic Adenocarcinoma: A Delphi Consensus Proposal

Background: Structured reporting (SR) in radiology has been recognized recently by major scientific societies. This study aims to build structured computed tomography (CT) and magnetic resonance (MR)-based reports in pancreatic adenocarcinoma during the staging phase in order to improve communication between the radiologist and members of multidisciplinary teams. Materials and Methods: A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A modified Delphi process was used to develop the CT-SR and MRI-SR, assessing a level of agreement for all report sections. Cronbach’s alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to measure quality analysis according to the average inter-item correlation. Results: The final CT-SR version was built by including n = 16 items in the “Patient Clinical Data” section, n = 11 items in the “Clinical Evaluation” section, n = 7 items in the “Imaging Protocol” section, and n = 18 items in the “Report” section. Overall, 52 items were included in the final version of the CT-SR. The final MRI-SR version was built by including n = 16 items in the “Patient Clinical Data” section, n = 11 items in the “Clinical Evaluation” section, n = 8 items in the “Imaging Protocol” section, and n = 14 items in the “Report” section. Overall, 49 items were included in the final version of the MRI-SR. In the first round for CT-SR, all sections received more than a good rating. The overall mean score of the experts was 4.85. The Cα correlation coefficient was 0.85. In the second round, the overall mean score of the experts was 4.87, and the Cα correlation coefficient was 0.94. In the first round, for MRI-SR, all sections received more than a good rating. The overall mean score of the experts was 4.73. The Cα correlation coefficient was 0.82. In the second round, the overall mean score of the experts was 4.91, and the Cα correlation coefficient was 0.93. Conclusions: The CT-SR and MRI-SR are based on a multi-round consensus-building Delphi exercise derived from the multidisciplinary agreement of expert radiologists in order to obtain more appropriate communication tools for referring physicians.
A STRATEGY TO PROMOTE THE COMPETITIVENESS OF MICRO, SMALL AND MEDIUM ENTERPRISES (MSME) DURING THE CONDITION OF COVID 19 PANDEMICS

The research is aimed to review competitiveness strategies of Micro, Small, and Medium Enterprises (MSME) during the pandemic of Covid 19 to the MSME creative industries of Kp. Pelintahan notably. This research used primary and secondary data and used a descriptive qualitative approach for criticism to analyze the latest problems of MSME creative industries in this pandemic of Covid 19, which used semi-structured interviews, participative observation, and questionnaires for data accumulation technic. Then used AHP (Analytical Hierarchy Process) method and continued with SWOT analysis to formulate a strategy for solving it. The respondence of this research is about ten employees of MSME creative industries Kp. Pelintahan. The result of SWOT analysis is about 0,49 for IFAS and -1,54 for EFAS. Based on SWOT Matrix, the MSME Creative Industries position in Kp. Pelintahan is on 2nd Quadran for rating, which MSME Creative Industries employees of Kp. Pelintahan can take advantage of even a little strength to minimize a terrifying threat. The primary strategy is the Diversification Strategy with their salespeople to market their products MSME Creative Industries Kp. Pelintahan, which the market is reached out of Sumatera except for Lampung.
Prediction of Nitrate and Phosphorus Concentrations Using Machine Learning Algorithms in Watersheds with Different Landuse

Rapid industrialization and population growth have elevated the concerns over water quality. Excessive nitrates and phosphates in the water system have an adverse effect on the aquatic ecosystem. In recent years, machine learning (ML) algorithms have been extensively employed to estimate water quality over traditional methods. In this study, the performance of nine different ML algorithms is evaluated to predict nitrate and phosphorus concentration for five different watersheds with different land-use practices. The land-use distribution affects the model performance for all methods. In urban watersheds, the regular and predictable nature of nitrate concentration from wastewater treatment plants results in more accurate estimates. For the nitrate prediction, ANN outperforms other ML models for the urban and agricultural watersheds, while RT-BO performs well for the forested Grand watershed. For the total phosphorus prediction, ensemble-BO and M-SVM outperform other ML models for the agricultural and forested watershed, while the ANN performs better than other ML models for the urban Cuyahoga watershed. In predicting phosphorus concentration, the model predictability is better for agricultural and forested watersheds. Regarding consistency, Bayesian optimized RT, ensemble, and GPR consistently yielded good performance for all watersheds. The methodology and results outlined in this study will assist policymakers in accurately predicting nitrate and phosphorus concentration which will be instrumental in drafting a proper plan to deal with the problem of water pollution.
Elastoplastic Cross-Sectional Behavior of Composite Beams with High-Strength Steel: Analytical Modeling

AbstractComposite steel-concrete beams that use high-strength (HS) steel sections are more sustainable from an environmental low-carbon perspective than those that use mild steel because less steel is needed. Quantifying the elastoplastic cross-sectional behavior of composite beams using HS steel sections is important because HS steel has significant differences in its plastic properties when compared with conventional mild steel. It is also important to establish this behavior in order to apply the principles of rigid plastic design. This paper develops a new analytical model for the cross-sectional analysis of HS steel-concrete composite beams to elucidate this elastoplastic response, so as to ascertain the validity of applying rigid plastic design. Both material nonlinearity and partial shear connection are incorporated in the analysis. The constitutive model for the steel is specified by using a multilinear stress-strain relationship, and that of the concrete is based on a model in terms of its axial .
Geological and Geochemical Characteristics of the Zhiluo Formation in the Bayinqinggeli Uranium Deposit, Northern Ordos Basin: Significance for Uranium Mineralization

The Bayinqinggeli deposit in the northern Ordos Basin, northwestern of China, is a recently discovered sandstone‐type uranium deposit. The uranium (U) orebodies are generally hosted in the lower member of the Jurassic Zhiluo Formation (Fm.), and are primarily tabular or irregular in shape. In the study area, 23 sandstone samples were collected from the Zhiluo Fm. and analyzed for major, trace, and rare earth elements (REEs). The geochemical characteristics of these sandstones are used to evaluate the factors controlling U mineralization. The source rocks of the Zhiluo Fm. sandstones are mainly volcanic and felsic magmatic rocks formed in continental arc and active continental‐marginal arc environments, and they provided the material required for the mineralization. The index of compositional variability ranges from 1.02 to 3.29 (average1.38), indicating that the Zhiluo Fm. sandstones are immature and composed of first‐cycle sediments. The corrected chemical index of alteration averages 56, suggesting that the source rocks underwent weak chemical weathering. The ore host rocks are loose, providing favorable conditions for epigenetic oxidation and U precipitation and enrichment. Ferrous iron in minerals such as chlorite, biotite, ilmenite, and pyrite might have played a role either in adsorbing or reducing the uranium.
Indigenous trauma and resilience: pathways to ‘bridging the river’ in social work education

ABSTRACT The traumatic effects of colonization on generations of Indigenous peoples and communities are referred to as intergenerational trauma. Alongside intergenerational effects of trauma experienced by Indigenous peoples and cultures across the globe is the capacity for individual and collective resilience, whereby an individual has good life outcomes despite having been subjected to situations with a high risk of emotional and/or physical distress. In North America and globally there have been calls to action for social work to find pathways toward reconciliation with Indigenous peoples. The purpose of this paper is to share the conceptual foundations of an innovative Master of Social Work program, currently in its sixth year. The program was designed to bridge Indigenous worldviews and social work by: creating links in the curriculum between neuroscience research in Western treatment modalities and Indigenous/traditional healing practices throughout the globe; fostering communication among all age groups; developing respect, kindness, and communication across all races; uncovering resiliency in understanding intergenerational trauma; understanding attachment difficulties created through colonization and rebuilding support systems; and creating learning objectives that address wellness. The objective is to prepare social workers to work with individuals, families and communities across the globe affected by intergenerational trauma.
Carrot AOX2a Transcript Profile Responds to Growth and Chilling Exposure

Alternative oxidase (AOX) is a key enzyme of the alternative respiration, known to be involved in plant development and in response to various stresses. To verify the role of DcAOX1 and DcAOX2a genes in carrot tap root growth and in response to cold stress, their expression was analyzed in two experiments: during root growth for 13 weeks and in response to a cold challenge trial of 7 days, in both cases using different carrot cultivars. Carrot root growth is initially characterized by an increase in length, followed by a strong increase in weight. DcAOX2a presented the highest expression levels during the initial stages of root growth for all cultivars, but DcAOX1 showed no particular trend in expression. Cold stress had a negative impact on root growth, and generally up-regulated DcAOX2a with no consistent effect on DcAOX1. The identification of cis-acting regulatory elements (CAREs) located at the promoters of both genes showed putative sequences involved in cold stress responsiveness, as well as growth. However, DcAOX2a promoter presented more CAREs related to hormonal pathways, including abscisic acid and gibberellins synthesis, than DcAOX1. These results point to a dual role of DcAOX2a on carrot tap root secondary growth and cold stress response.
SENSORLESS CONTROL OF PERMANENT MAGNET SYNCHRONOUS MOTOR BASED ON T-S FUZZY INFERENCE ALGORITHM FRACTIONAL ORDER SLIDING MODE

In order to improve the robustness of the fractional order sliding mode controller (FSMC) for permanent magnet synchronous motor (PMSM) sensorless control, a fractional order sliding mode controller based on T-S fuzzy inference algorithm (FFSMC) is proposed to observe the rotor speed and position information. Based on the mathematical model of PMSM and sliding mode controller, a fractional order sliding mode controller is designed, and its stability is proved. The T-S fuzzy inference algorithm is used to tune the reaching law parameters of the FSMC, so that the reaching law parameters are no longer fixed values, but change with the state of the system. The correctness of the proposed method is verified by MATLAB simulation software. The effectiveness of the simulation results is verified by building a PMSM sensorless control experimental platform. The results show that the PMSM sensorless control based on FFSMC achieves parameter self-tuning and improves the observation accuracy. And the robustness of the control system is enhanced.
Quantification and Topographical Distribution of Terminal and Linked Fucose Residues in Human Spermatozoa by Using Field Emission Scanning Electron Microscopy (FE-SEM)

The modification of sperm glycocalyx is an essential process during sperm capacitation. The presence and redistribution of terminal and linked fucose have been described during in vitro capacitation in humans. However, the influence of the capacitation time on the quantification and localization of terminal and linked fucose is still unknown. In this study, the quantitative and qualitative changes in fucosyl residues during different in vitro capacitation times (1 and 4 h), are simultaneously characterized by using Aleuria aurantia (AAA) lectin–gold labelling and high-resolution field emission scanning electron microscopy (FE-SEM) in human sperm. A significant decrease was found in the number of terminal fucose registered in the whole sperm head during the in vitro capacitation. Nevertheless, the quantification of fucose residues after 1 h of in vitro capacitation was very similar to those found after 4 h. Therefore, the changes observed in terminal and linked fucose during capacitation were not time-dependent. Furthermore, the comprehensive analysis of the topographic distribution showed the preferential fucosyl location in the acrosomal region and the presence of distinct clusters distributed over the head in all the studied conditions. Overall, these findings corroborate the validity of FE-SEM combined with gold labelling to register changes in surface molecules during in vitro sperm capacitation.
Current position and associated challenges of BIM education in UK higher education

In May 2011 the Cabinet Office published the Government Construction Strategy, announcing the Government’s intention to require collaborative 3D BIM (with all project and asset information, documentation and data being digital) on its projects by 2016. Together with industry the UK Government has set out on a four year programme to reduce capital expenditure and the carbon burden from the construction and operation of the built environment by 20% through a modernisation of the sector. Such proposed transformation of the construction sector has significant implications for Built Environment education providers in ensuring they meet the demands required of future professionals. To this end, this study assessed the current position and associated challenges along with perspectives of BIM education in UK HE through surveys targeted at BIM-related Academic networks across the UK. Key areas focused on included: - The definition of staff resource - State of BIM in HEI (Higher Education Institutions). - BIM adoption Strategy - BIM awareness and associated issues - Generation implications Findings from the study indicated a nuanced appraisal of BIM readiness in UK HEIs. There are clear distinctions between the category of top performers and low performers concerning investigated concepts with clear defining parameters. However the disconnect between these two tiers exists in a state of inertia that is counterproductive to the overall strategic role/contributions of HEIs to the ongoing BIM digital revolution. A BIM assessment matrix was developed from the key investigated issues. This represents an update to the optimum requirements for BIM teaching among HEIs in the UK. This update mapped additional crucial strategic considerations concerning BIM in HEIs.
Gingival crevicular fluid levels of cytokine, chemokine, and growth factors in patients with periodontitis or gingivitis and periodontally healthy subjects: a cross-sectional multiplex study

Introduction Gingival crevicular fluid (GCF) is an important resource for detecting inflammatory biomarkers related to periodontal disease. The purpose of this research was to identify the possible relation between cytokine levels and periodontal status. Material and methods GCF samples collected from 25 periodontally healthy individuals, 24 with gingivitis, and 24 with periodontitis were analyzed using the MAGPIX system with a Bio-Plex Pro Human Cytokine 27-plex kit. Gingival index (GI), periodontal pocket depth (PPD), plaque index (PI), and clinical attachment level (CAL) were measured from each tooth to determine each patient’s periodontal status. Results All clinical parameters showed statistically significant differences between groups. While interleukin (IL)-1β, IL-6, macrophage chemotactic protein 1 (MCP-1), interferon γ-induced protein 10 (IP-10) and vascular endothelial growth factor (VEGF) levels were statistically significantly higher in patients with periodontitis compared to periodontally healthy subjects, IL-15 levels were found to be statistically significantly higher in periodontally healthy individuals compared to periodontitis (p < 0.05). Also, IL-1β and IP-10 showed positive correlations with PPD, CAL, GI and bleeding on probing (BOP). Conclusions It is considered that IL-1β, IL-6, IL-15, MCP-1, VEGF and IP-10 could be related to periodontal disease and health and can be used as an adjunct to clinical examination in future research design. Scanning cytokines in GCF with a multiplex immunoassay technique is consequential, especially because many cytokine types are found in one sample.
COVID-19 Outcomes and Genomic Characterization of SARS-CoV-2 Isolated From Veterans in New England States: Retrospective Analysis

Background Clinical and virologic characteristics of COVID-19 infections in veterans in New England have not been described. The average US veteran is a male older than the general US population. SARS-CoV-2 infection is known to cause poorer outcomes among men and older adults, making the veteran population an especially vulnerable group for COVID-19. Objective This study aims to evaluate clinical and virologic factors impacting COVID-19 outcomes. Methods This retrospective chart review included 476 veterans in six New England states with confirmed SARS-CoV-2 infection between April and September 2020. Whole genome sequencing was performed on SARS-CoV-2 RNA isolated from these veterans, and the correlation of genomic data to clinical outcomes was evaluated. Clinical and demographic variables were collected by manual chart review and were correlated to the end points of peak disease severity (based on oxygenation requirements), hospitalization, and mortality using multivariate regression analyses. Results Of 476 veterans, 274 had complete and accessible charts. Of the 274 veterans, 92.7% (n=254) were men and 83.2% (n=228) were White, and the mean age was 63 years. In the multivariate regression, significant predictors of hospitalization (C statistic 0.75) were age (odds ratio [OR] 1.05, 95% CI 1.03-1.08) and non-White race (OR 2.39, 95% CI 1.13-5.01). Peak severity (C statistic 0.70) also varied by age (OR 1.07, 95% CI 1.03-1.11) and O2 requirement on admission (OR 45.7, 95% CI 18.79-111). Mortality (C statistic 0.87) was predicted by age (OR 1.06, 95% CI 1.01-1.11), dementia (OR 3.44, 95% CI 1.07-11.1), and O2 requirement on admission (OR 6.74, 95% CI 1.74-26.1). Most (291/299, 97.3%) of our samples were dominated by the spike protein D614G substitution and were from SARS-CoV-2 B.1 lineage or one of 37 different B.1 sublineages, with none representing more than 8.7% (26/299) of the cases. Conclusions In a cohort of veterans from the six New England states with a mean age of 63 years and a high comorbidity burden, age was the largest predictor of hospitalization, peak disease severity, and mortality. Non-White veterans were more likely to be hospitalized, and patients who required oxygen on admission were more likely to have severe disease and higher rates of mortality. Multiple SARS-CoV-2 lineages were distributed in patients in New England early in the COVID-19 era, mostly related to viruses from New York State with D614G mutation.
GPS signal availability in an urban area-receiver performance analysis

GPS signal availability and DGPS positioning accuracy for two-dimensional navigation in two types of urban area, namely a downtown type with buildings up to 50 stories, and a residential area with two-story housing and tree-lined streets, is investigated using three multi-channel C/A code receiver types, including a fast-reacquisition narrow correlator spacing receiver. Signal availability, defined as the percentage of time during which HDOP/spl les/5, is shown to be strongly dependent on the receiver signal tracking performance. Signal availability variations between receivers exceed 25% in some cases. The DGPS positions obtained with various receivers are intercompared and analysed as a function of satellite geometry and of the multipath environment. The narrow correlator spacing receiver is shown to produce superior positioning results, in terms of repeatability, as compared to the standard wide correlator spacing receivers used. Performance statistics based on repeated test runs are presented for the various scenarios described above. <<ETX>>
Wave Propagation Modeling Analysis of Earthquake Records for Buildings

This paper introduces an application of wave propagation modeling to building response investigation. We have analyzed the computed responses of analytical building models and the obtained strong-motion records for forty-one actual buildings during five different earthquakes by using the normalized input output minimization (NIOM) method. This method can model wave propagation in multiple linear systems by considering the statistical correlation of the earthquake motions at different observation locations, and can reveal the arrival times of incident and reflected waves as well as their relative amplitudes. From these values, the fundamental period and the damping ratio of the building could be simply estimated. The estimated values were then compared with the values for the analytical building models, the values estimated in the previous studies for the actual buildings, and the building code formula.
Reconstruction Error-Based Decomposition Feature Selection for PolSAR Image

Target decomposition features are the cornerstone of subsequent analyses for PolSAR images. Generally, adopting single or several decomposition algorithms limits the representation ability for original terrain characteristics. Using all the existing decomposition features, however, will definitely increase computational complexity. Besides, some features even have a negative effect on the following tasks. To address these problems, a sparse variational autoencoder feature selection framework (SVAE-FS) is proposed in this article. In detail, the encoder transforms the original feature set into latent space and then decoder reconstructs the corresponding pseudo set on this latent space. Similarly, a pseudo subset is subsequently obtained by the SVAE. The discrepancy, namely reconstruction error, between the pseudo set and the pseudo subset is taken as an evaluation criterion which reflects the feature representation ability of pseudo subset. Sparse constraint in the encoder makes the representative features stand out. Meanwhile, the linear feature transformation layer of the encoder enables the SVAE to evaluate different scale subsets without repeated training. Finally, a greedy selection approach with search scale $K$ is proposed to find the suboptimal subset. This procedure not only reduces time consumption, but also ensures the performance of the subset. The selected features are analyzed on four real PolSAR datasets according to the terrain scattering characteristics. Furthermore, these features have achieved competitive performance on three PolSAR image tasks.