Miracle-dz/UniFAR-FTUs huggingface dataset:
Observer performance evaluation of the feasibility of a deep learning model to detect cardiomegaly on chest radiographs

Background Cardiothoracic ratio (CTR) is the ratio of the diameter of the heart to the diameter of the thorax. An abnormal CTR (>0.55) is often an indicator of an underlying pathological condition. The accurate prediction of an abnormal CTR chest X-rays (CXRs) aids in the early diagnosis of clinical conditions. Purpose We propose a deep learning (DL)-based model for automatic CTR calculation to assist radiologists with rapid diagnosis of cardiomegaly and thus optimise the radiology flow. Material and Methods The study population included 1012 posteroanterior CXRs from a single institution. The Attention U-Net DL architecture was used for the automatic calculation of CTR. An observer performance test was conducted to assess the radiologist’s performance in diagnosing cardiomegaly with and without artificial intelligence assistance. Results U-Net model exhibited a sensitivity of 0.80 [95% CI: 0.75, 0.85], specificity >99%, precision of 0.99 [95% CI: 0.98, 1], and a F1 score of 0.88 [95% CI: 0.85, 0.91]. Furthermore, the sensitivity of the reviewing radiologist in identifying cardiomegaly increased from 40.50% to 88.4% when aided by the AI-generated CTR. Conclusion Our segmentation-based AI model demonstrated high specificity (>99%) and sensitivity (80%) for CTR calculation. The performance of the radiologist on the observer performance test improved significantly with provision of AI assistance. A DL-based segmentation model for rapid quantification of CTR can therefore have significant potential to be used in clinical workflows by reducing radiologists’ burden and alerting to an abnormal enlarged heart early on.
Dynamic fuel cell model improvement based on macroscopic energy representation

In this paper, a new dynamic model is presented of the proton exchange membrane fuel cell (PEMFC), using energetic macroscopic representation (EMR). This model is developed for electric automotive applications, powered by a hybrid energy storage system (HESS). The PEMFC can be used as a primary source, the supercapacitors (SC) and/or batteries are the secondary sources. The model design enables the optimization of energy use and the reduction of equipment costs, by involving the fuel cell (FC) in the transient regimes. This model takes into consideration dynamic phenomena, such as double layer capacitance, charge transfer, energy losses caused by the propagation fuel and oxidant delay. The new developed model has been tested and validated using BAHIA bench of HELION/AREVA.
Analysis of slamming loads on a catamaran section with a centre-bow appended by spray rail

ABSTRACT Wave-piercing catamarans have center-bow to diminish slamming pressure and acceleration in sea waves. Bow section modification of marine craft towards low slamming loads is an on-going research topic. This study suggests insertion of a flat-bar, called spray rail, to center-bow to reduce both slamming pressure and acceleration. A section which is called J1-section from previous research is adopted for further modification. Modification is made via appending the spray rail to its center-bow, and the new section is called J1-section with spray rail. Evaluation of the slamming pressure and acceleration of both sections are experimentally accomplished using the water entry test facilities of Amirkabir Laboratory of Hydrodynamics. J1-section with spray rail in comparison with J1-section has shown peak acceleration and pressure drop of about 60% and 70%, respectively. This study suggests that the insertion of appendages to the catamaran sections is a superior solution for the slamming impact load reduction.
Synthesis of Thulium-yttria Nanoparticles with EPR Response

Approaches to form new materials for radiation dosimetry are essential to enhance quality assurance and quality improvement practices based on radiation protection concept. The present work reports a hydrothermal synthesis based on a relative low temperature and pressure to form thulium-yttria nanoparticles with electron paramagnetic resonance response. Thulium-yttria nanoparticles were prepared and characterized by XRD, SEM, PCS, and EPR. According to results, the hydrothermal method provided thulium-yttria nanoparticles with cubic C-type structure, mean particle size (d50) less than 160nm, and EPR response. The EPR spectra of powders exhibited two resonance peaks p1 and p2 recorded at 350 and 160mT, respectively. The enhancement of the EPR response of yttria by the use of thulium as a dopant provide meaningful parameters to advance in the formation of new rare earth based materials for radiation dosimetry.
Advancement in wound healing treatment using functional nanocarriers

Abstract In recent years, due to extraordinary advances in molecular biology, the emergence of new biomolecular targets offers a better understanding of the etiology of acute and chronic wounds resulting in more innovative therapies. The nanomaterial-based approach with numerous advantages over conventional approaches shows the range of applications for wound care owing to its multi-functionality. The current study focuses on the novel nano-structural formulations for the healing of infectious wounds for prevention based on nanomaterials, such as nano-biomaterials, nanoparticles, hydrogels, and scaffolds. This review discusses the ligand internalization (polymeric, aptameric, protein, and antibody molecules) and surface function enhancers that determine the fate of nanocarrier functionalization. Graphical Abstract
Design of the laser beam transfer system for the new Gemini North adaptive optics laser guide star

In 2018 the Gemini Observatory was awarded by the NSF a large grant for the Gemini in the Era of Multi Messenger Astronomy (GEMMA) project to build the next generation laser AO system from Gemini North. This new system is tentatively called Gemini North Adaptive Optics (GNAO) and will be a flexible AO system that can run multiple types of AO correction and serve as a facility capable of feeding multiple science cameras. As part of this new system the Laser Guide Star Facility (LGSF) of Gemini North is being completely redesigned from a single laser single Laser Guide Star (LGS) beacon facility into a multi-laser multi LGS beacon facility. It will be able to generate from 0-4 LGS on sky to feed not only GNAO but potential future systems as well. The LGSF consists of 3 main components, 589 nm sodium lasers, a set of free space beam transfer optics, and Laser Launch Telescopes (LLT). The sodium lasers and the LLT are contracted out to external vendors while the beam transfer optics are being designed and built in house. The new beam transfer optics are called the Beam Expander and Control Node (BEaCoN) and function as the link between the two contracted products. Within the BEaCoN the laser beam is split, expanded, and steered before being injected into the LLT. This report covers the design and analysis of the BEaCoN optical bench.
Usefulness of Computed Tomography Parameters in Predicting the Clinical Outcomes of Acute Aortic Dissection

Background : Acute aortic dissection is a potentially life-threatening condition among patients presenting with chest pain to emergency department. Without a prompt diagnosis and treatment, it carries high mortality and morbidity. Computed tomography (CT) of thorax and abdomen is one of the commonly used non-invasive investigation modalities for diagnosis of acute aortic dissection. Apart from making a diagnosis, there are a number of parameters in the CT images that may be helpful in providing prognostic information. In this study, prognostic values of these parameters in predicting both short term and intermediate term clinical outcomes of acute aortic dissection will be evaluated. Methods : This is a retrospective observational study involving 70 patients with diagnosis of acute aortic dissection recruited between January 2004 and December 2009 in North District Hospital in Hong Kong. Results : The mean age of these patients was 61 years old and 87% of them were male. The in-hospital mortality was 18.6% (13/70). The 30-day mortality was 24.3% (17/70). The mean maximal aortic diameter of all patients was 4.60 cm ± 1 cm. The mean maximal aortic diameter of the survived group was 4.49 cm ± 0.93 cm, whereas it was 5.22 cm ± 1.22 cm in the deceased group (p ¼ 0.032). Furthermore, the presence of patent false lumen (p ¼ 0.011) in the initial scan was also more prevalent in the deceased group. By univariate logistic regression analysis, the type of aortic dissection (OR 11.0, p ¼ 0.003), the larger maximal aortic diameter (OR 2.0, p ¼ 0.041), and also patent false lumen (OR 6.6, p ¼ 0.021) in the initial imaging were adverse prognostic indicators for in-hospital mortality. However, they were not found to be independent predictors with multivariable analysis. Conclusion : In addition to establishing the diagnosis of acute aortic dissection, the type of aortic dissection, the maximal aortic diameter, and also the patency of false lumen derived from contrast CT thorax and abdomen may also provide prognostic information with regards to in-hospital and 30-day mortality. Whether this information will lead to better clinical outcomes by earlier intervention requires further studies for con ﬁ rmation.
Estimators for Structural Damage Detection Using Principal Component Analysis

Structural damage detection is an important issue in conservation. In this research, principal component analysis (PCA) has been applied to the temporal variation of modal frequencies obtained from a dynamic test of a scaled steel structure subjected to different damages and different temperatures. PCA has been applied in order to reduce, as much as possible, the number of variables involved in the problem of structural damage detection. The aim of the PCA study is to determine the minimum number of principal components necessary to explain all the modal frequency variation. Three estimators have been studied: T2 (the square of the vector norm of the projection in the principal component plan), Q (the square of the norm of the residual vector), and the variance explained. In the study, the results related to the undamaged structure needed one principal component to explain the modal frequency variation. However, the high damage configurations need five principal components to explain the modal frequency. The T2 and Q estimators have been arranged in order of increasing damage for all the performed experimental tests. The results indicate that these estimators could be useful to detect damage and to distinguish among a range of intensities of structural damage.
Bioinspired Drone Actuated Using Wing and Aileron Motion for Extended Flight Capabilities

Since the advent of flying machines, engineers have strived to develop aircraft that can fly as flexibly as birds. From the original traditional control surface to the controllable morphing wing design, they all aim to improve the flight performance of drones, including the load capacity, maneuverability and stability. We believe that a single control method has certain limitations. For example, for roll control, the aileron rudder effect will be significantly reduced when flying at a high angle of attack (AOA), and the asymmetric deformation control of a wing is almost useless under the conditions of low-speed and small AOA. Until now, solutions to the above problems have remained largely unexplored. Therefore, the combination of wing deformation and ailerons is proposed to improve the roll control ability of drones. In this research, a new bionic deformable wing drone is designed, which demonstrates the advantages of the combination of ailerons and wing morphing in roll control and verifies that the symmetrical folding of a wing can change the flight envelope. The aerodynamic characteristics of the drone were tested in a numerical simulation, and its flight capabilities for different configurations were characterized in flight experiments.
Factors Affecting Borrowers’ Intention in Peer-To-Peer Lending Platform in Mongolia

The current paper aims to examine factors that influence borrowers’ intention to borrow from Peer-to-Peer lending platform in Mongolia. This topic has been attracting researchers’ interest especially since 2008 when the 3rd round of fintech development starts over the developed countries. The most of those studies focus on investigating the factors that explain borrowers’ intention to borrow, and a few of them research this perspective from the investors’ side. It is hardly to find the study that explain intentions of parties to participate in a P2P lending platform in Mongolia. Our research tries to fill this gap by studying factors influencing borrowers’ intention in Mongolian fintech market. We have used the TAM model as a theoretical framework and have collected survey questionnaires from investors and borrowers from all fintech companies in Mongolia. The result shows that borrowers’ intention is mostly driven by initial trust and perceived risk. Other variables, perceived ease of use and perceived security, have no impact on borrowers’ decisions to participate in P2P platform.
Population characteristics and stock assessment of European anchovy (Engraulis encrasicolus L., 1758) in coastal waters of the south-eastern Black Sea of Turkey

Abstract This study was carried out throughout 10 fishing seasons between 2002 and 2018 to monitor the population and stock variations of Engraulis encrasicolus (L., 1758) on the south-eastern Black Sea coast of Turkey. Asymptotic length (L∞), growth constant (K) and growth performance indexes (φ) were calculated to be between 12.86 and 15.79 cm, 0.69 and 0.99, 2.10 and 2.29, respectively. Theoretical birth ages were determined to range between (to) −0.15 and −0.27, the maximum ages (tmax) ranged between 3.03 and 4.35, total mortality rates (Z) between 2.19 and 2.66, natural mortality rates (M) between 0.93 and 1.26, the fishing mortality rate (F) between 0.93 and 1.47, and the optimum fishing mortality rate (Fopt.) between 0.37 and 0.62. Fishing mortality rates (F) were estimated to be higher than the optimum fishing mortality rates (Fopt). Z/K ratios were determined to range between 1.91 and 3.43. Current exploitation rates (Ecurr.) were calculated to range between 0.42 and 0.62. The first recruit lengths (Lr) and first capture lengths (Lc50) were estimated to range between from 5.25–7.75 and from 7.66–8.74 cm, respectively. The first maturity lengths (Lm50) of E. encrasicolus ranged between 8.57 and 10.53 cm. The maximum sustainable exploitation levels (Emax) were determined to range between 0.72 and 0.83. These data indicate that if current levels of fishing pressure continue, anchovy stocks will collapse in the near future.
Deep‐Learning‐Based Detection of Vertebral Fracture and Osteoporosis Using Lateral Spine X‐Ray Radiography

Osteoporosis and vertebral fractures (VFs) remain underdiagnosed. The addition of deep learning methods to lateral spine radiography (a simple, widely available, low‐cost test) can potentially solve this problem. In this study, we develop deep learning scores to detect osteoporosis and VF based on lateral spine radiography and investigate whether their use can improve referral of high‐risk individuals to bone‐density testing. The derivation cohort consisted of patients aged 50 years or older who underwent lateral spine radiography in Severance Hospital, Korea, from January 2007 to December 2018, providing a total of 26,299 lateral spine plain X‐rays for 9276 patients (VF prevalence, 18.6%; osteoporosis prevalence, 40.3%). Two individual deep convolutional neural network scores to detect prevalent VF (VERTE‐X pVF score) and osteoporosis (VERTE‐X osteo score) were tested on an internal test set (20% hold‐out set) and external test set (another hospital cohort [Yongin], 395 patients). VERTE‐X pVF, osteo scores, and clinical models to detect prevalent VF or osteoporosis were compared in terms of the areas under the receiver‐operating‐characteristics curves (AUROCs). Net reclassification improvement (NRI) was calculated when using deep‐learning scores to supplement clinical indications for classification of high‐risk individuals to dual‐energy X‐ray absorptiometry (DXA) testing. VERTE‐X pVF and osteo scores outperformed clinical models in both the internal (AUROC: VF, 0.93 versus 0.78; osteoporosis, 0.85 versus 0.79) and external (VF, 0.92 versus 0.79; osteoporosis, 0.83 versus 0.65; p < 0.01 for all) test sets. VERTE‐X pVF and osteo scores improved the reclassification of individuals with osteoporosis to the DXA testing group when applied together with the clinical indications for DXA testing in both the internal (NRI 0.10) and external (NRI 0.14, p < 0.001 for all) test sets. The proposed method could detect prevalent VFs and osteoporosis, and it improved referral of individuals at high risk of fracture to DXA testing more than clinical indications alone. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
Detecting Runtime Exceptions by Deep Code Representation Learning with Attention-Based Graph Neural Networks

Uncaught runtime exceptions have been recognized as one of the commonest root causes of real-life exception bugs in Java applications. However, existing runtime exception detection techniques rely on symbolic execution or random testing, which may suffer the scalability or coverage problem. Rule-based bug detectors (e.g., SpotBugs) provide limited rule support for runtime exceptions. Inspired by the recent successes in applying deep learning to bug detection, we propose a deep learning-based technique, named Drex, to identify not only the types of runtime exceptions that a method might signal but also the statement scopes that might signal the detected runtime exceptions. It is realized by graph-based code representation learning with (i) a lightweight analysis to construct a joint graph of CFG, DFG and AST for each method without requiring a build environment so as to comprehensively characterize statement syntax and semantics and (ii) an attention-based graph neural network to learn statement embeddings in order to distinguish different types of potentially signaled runtime exceptions with interpretability. Our evaluation on 54,255 methods with caught runtime exceptions and 54,255 methods without caught runtime exceptions from 5,996 GitHub Java projects has indicated that Drex improves baseline approaches by up to 18.2% in exact accuracy and 41.6% in F1-score. Drex detects 20 new uncaught runtime exceptions in 13 real-life pro-jects, 7 of them have been fixed, while none of them is detected by rule-based bug detectors (i.e., SpotBugs and PMD).
The not-so-dead of winter: underwater light climate and primary productivity under snow and ice cover in inland lakes

ABSTRACT As global surface temperatures continue to rise as a result of anthropogenic climate change, effects in temperate lakes are likely to be more pronounced than in other ecosystems. Decreases in snow and ice cover extent and duration and extended periods of summer stratification have been observed in temperate lake systems throughout the Anthropocene. However, the effects of changing snow and ice cover on lacustrine communities remain largely uninvestigated. We examined underwater light climate and associated primary productivity patterns under snow-covered and clear-lake ice in 6 inland lakes in Minnesota, USA, spanning gradients of water column optical properties (blue, green, brown) associated with trophic status and organic material content. In all lakes, snow cover influenced not only the intensity, but also the spectral signature of light penetrating into the water column. Specifically, the wavelength of maximum penetration was shifted towards longer wavelengths under snow cover in green (eutrophic) lakes but was shifted towards shorter wavelengths in blue and brown lakes. Volumetric primary productivity was often higher than anticipated (e.g., ∼1200 mg m−3 d−1; Lake Minnetonka, snow-covered ice). Carbon assimilation rates were lower under snow-covered ice throughout the water column in all lake types except immediately under cleared ice in eutrophic lakes, where phytoplankton were likely photoinhibited because of the penetration of intense, short-wavelength light. These findings suggest that changes to snow and ice cover under ongoing climate change scenarios can affect patterns of phytoplankton primary productivity in sensitive aquatic ecosystems.
Emergence of Dengue Virus Serotype 2 Cosmopolitan Genotype, Brazil

We used nanopore sequencing and phylogenetic analyses to identify a cosmopolitan genotype of dengue virus serotype 2 that was isolated from a 56-year-old male patient from the state of Goiás in Brazil. The emergence of a cosmopolitan genotype in Brazil will require risk assessment and surveillance to reduce epidemic potential.
Financial Literacy and Financial Inclusion: A positivist view of Proprietors of Small and Medium Enterprises in Fort Portal City, Western Uganda

Purpose: The study aimed at examining the relationship between financial literacy and financial inclusion among proprietors of SMEs in Fort portal City, Western Uganda. Methodology: The study was anchored on a positivist paradigm, adopting cross-sectional and correlational designs. One hundred sixty-five proprietors of small and medium enterprises (SMEs) were purposively selected from 297 proprietors of registered SMEs operating in all Divisions of Fort Portal City in Western Uganda. Data were collected using a structured self-administered questionnaire from the proprietors who formed the unit of inquiry per SME. Pearson correlation and standard regression were used as the primary data analysis techniques. Findings: The study established that financial literacy is significantly positively associated with financial inclusion. Financial literacy accounted for 28% of the variance in financial inclusion among proprietors of SMEs in Fort Portal City. Unique contribution to theory, practice, and policy: The study uniquely contributes to financial inclusion literature by providing further evidence on the positive relationship between financial literacy and financial inclusion among proprietors of SMEs in Fort Portal city, Western Uganda. The study findings have supported the financial literacy theory of financial inclusion, which assumes that “financial literacy increases people’s willingness to participate in the formal financial sector. ” It was recommended that proprietors of small and medium enterprises orient their strategic decisions towards increasing their financial literacy to improve their financial inclusion.
3-D Inter-Robot Relative Localization via Semidefinite Optimization

In this letter, the 3-D inter-robot relative localization problem is addressed using noise-corrupted odometric and distance measurements. Unlike the existing solutions, we are devoted to providing a relative localization method that has an “overall best performance,” which means that the tradeoffs between the estimation accuracy (EA), the number of measurements (NoMs), and the computation efficiency (CE) are considered. We demonstrate that an existing formulation of the 3-D relative localization problem, the square distances weighted least square (SD-WLS), can be equivalently reformulated as a non-convex quadratic constrained quadratic programming (QCQP) problem. Further, to handle the non-convex nature of the QCQP problem, we adopt the semidefinite programming (SDP) relaxation approach, which drops the rank constraint and recovers the solution of the QCQP via an eigenvalue decomposition strategy. Finally, a refinement step is introduced to solve the problem that the quadratic constraints might not be satisfied due to the SDP relaxation. The simulation and experiment results show that, compared to existing methods, our method has the best overall performance when the three factors, i.e., EA, NoMs, and CE, are important for a relative localization application.