bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing transfusion management of multiple myeloma patients receiving daratumumab‐based regimens
Authors: Phou Samantha , Costello Caitlin , Kopko Patricia M. , Allen Elizabeth S.
Published: May 2021

Background

Daratumumab, a human anti‐CD38 monoclonal antibody used to treat multiple myeloma, interferes with pretransfusion testing and can mask alloantibodies. Incidence of alloimmunization in patients on daratumumab has not been well characterized, and optimal transfusion guidelines regarding prophylactic antigen matching, accounting for both patient safety and efficiency, have not been well established for these patients.

Methods

Records of patients who received daratumumab between January 1, 2014 and July 2, 2019 were reviewed. Daratumumab interference with pretransfusion testing was managed by testing with reagent red blood cells (RBCs) treated with 0.2 M dithiothreitol. When daratumumab was present during antibody testing, patients were transfused with RBC units prophylactically matched for D, C, c, E, e, and K antigens per hospital policy.

Results

Out of 90 patients identified, 52 received a total of 638 RBC transfusions (average of 12.3 units per patient, SD 17.2, range 1–105, median 5 among those transfused). Alloantibodies existing before daratumumab initiation were identified in seven patients. No new alloantibodies were detected in any patients after starting daratumumab treatment.

Conclusions

The incidence of alloimmunization in patients receiving daratumumab is low. Whether this is due to the effect of daratumumab, underlying pathophysiology, or other factors, is unknown. Because these patients require a large number of RBC transfusions overall and have little observed alloimmunization, phenotype matching (beyond RhD) may be unnecessary. Since the use of dithiothreitol cannot rule out the presence of anti‐K, we recommend transfusion of ABO‐compatible units, prophylactically matched for the D and K antigens only.
Familial pseudohyperkalemia induces significantly higher levels of extracellular potassium in early storage of red cell concentrates without affecting other standard measures of quality: A case control and allele frequency study
Authors: Meli Athinoula , McAndrew Margaret , Frary Amy , Rehnstrom Karola , Stevens‐Hernandez Christian J. , Flatt Joanna F. , Griffiths Alexandra , Stefanucci Luca , Astle William , Anand Rekha , New Helen V. , Bruce Lesley J. , Cardigan Rebecca
Published: May 2021

Background

Familial pseudohyperkalemia (FP) is characterized by an increased rate of potassium leakage in refrigerated red cells and is associated with the minor allele of the single nucleotide polymorphism rs148211042 (R723Q) in the ABCB6 gene. The study aims were to obtain the minor allele frequencies of ABCB6 variants and to measure supernatant potassium accumulation, and other red cell storage parameters, in red cell concentrates (RCC) from carriers of variant rs148211042 under standard blood bank conditions.

Study Design

Whole blood units were collected from 6 FP individuals and 11 controls and processed into RCC in additive solution. RCC were sampled and tested over cold storage for full blood count, extracellular potassium, glucose, lactate, microvesicle release, deformability, hemolysis, pH, adenosine triphosphate, and 2,3‐diphosphoglycerate.

Results

Screening of genotyped cohorts identified that variant rs148211042 is present in 1 in 394 British citizens of European ancestry. FP RCC had significantly higher supernatant potassium at all time points from day 3 onwards (p < .001) and higher mean cell volume (p = .032) than controls. The initial rate of potassium release was higher in FP RCC; supernatant potassium reached 46.0 (23.8–57.6) mmol/L (mean [range]) by day 5, increasing to 68.9 (58.8–73.7) mmol/L by day 35. Other quality parameters were not significantly different between FP RCC and controls.

Conclusion

These data suggest that if a blood donor has FP, reducing the RCC shelf‐life to 5 days may be insufficient to reduce the risk of hyperkalemia in clinical scenarios such as neonatal large volume transfusion.
Retrospective investigation of the neutrophil‐to‐lymphocyte ratio in dogs with pneumonia: 49 cases (2011–2016)
Authors: Conway Elizabeth A. , Pizarro Del Valle Carlos , Waugh Elspeth M. , French Anne , Ridyard Alison E.
Published: May 2021

Objective

To assess the utility of the neutrophil‐to‐lymphocyte ratio (NLR) in predicting outcome in canine pneumonia compared with routine hematological parameters and systemic inflammatory response syndrome (SIRS) scores.

Design

Retrospective study.

Setting

University teaching hospital.

Animals

Forty‐nine client‐owned dogs.

Interventions

None

Measurements and Main Results

Medical records were reviewed to identify dogs with a diagnosis of pneumonia from July 2011 to December 2016. Signalment, clinical findings, laboratory characteristics, and outcome were recorded. Inclusion criteria were a clinical and radiographic diagnosis of pneumonia, plus reference laboratory hematology at diagnosis. Cases that received steroids were excluded. Euthanized dogs were only included in statistical analysis if euthanized solely due to pneumonia severity. The NLR, total WBC count, neutrophil count, lymphocyte count, band neutrophil percent of total WBC count (%‐bands), and percentage of cases diagnosed with SIRS were compared between survivors and nonsurvivors. Receiver operating characteristic curves were generated to identify optimal sensitivity and specificity cutoffs for nonsurvival to discharge.

Two hundred records were retrieved; 49 cases fulfilled the inclusion criteria. Of these, 33 (67%) survived to discharge. The NLR did not differ significantly between the survivors and nonsurvivors, nor did total WBC count or neutrophil count. Survivors had a significantly lower %‐bands than nonsurvivors (P < 0.001) and higher lymphocyte count (P = 0.004). The mortality rate did not differ significantly between dogs with and without SIRS. Receiver operating characteristic analysis identified a %‐bands cutoff of 2.5% or higher had an 83% sensitivity and 79% specificity for nonsurvival.

Conclusions

Unlike in human medicine, neither NLR nor SIRS scores predicted outcome in this cohort of dogs with pneumonia. However, survivors had a lower %‐bands and higher lymphocyte count than nonsurvivors, which may be helpful prognostically in clinical cases.
Autoresuscitation and pheochromocytoma multisystem crisis in a dog
Authors: Kato Michael T. , Mallard John M.
Published: May 2021

Objective

To describe the clinical course of a dog with a pheochromocytoma multisystem crisis that exhibited autoresuscitation after cardiac arrest.

Case Summary

An approximately 10‐year‐old male neutered terrier mix dog presented for collapse. Abdominal imaging was suggestive of a pheochromocytoma, and clinical pathology data and thoracic imaging suggested the presence of a multisystem crisis. The dog developed cardiopulmonary arrest that was followed by autoresuscitation.

New or Unique Information Provided

To the authors’ knowledge, this is the first report of autoresuscitation in a dog. The phenomenon may have been associated with a pheochromocytoma multisystem crisis.
V-Shaped Holes for Full Coverage Film Cooling of a High-Pressure Nozzle Guide Vane
Authors: Barigozzi G. , Abdeh H. , Rouina S. , Abba L. , Iannone M. , Odemondo V.
Published: May 2021

This paper describes an experimental activity carried out to investigate the potential of V-shaped holes for film cooling a high-pressure nozzle guide vane. The newly designed V-shaped scheme was compared with standard laidback fan-shaped holes. The influence of showerhead cooling was also assessed. Different injection conditions were examined under the same cascade operating condition using CO2 as a coolant. The quality of holes geometry and their discharging behavior was first characterized. Then, dual-luminophore pressure-sensitive paint (PSP) was used for measuring the adiabatic film cooling effectiveness all over the vane surface. Results of the current work showed that using a V-shaped hole configuration would give nearly the same surface protection as standard shaped holes with a reduced number of holes and, thus, at lower coolant flow consumption.
Numerical Modeling of the Interaction of Moving Fish Nets and Fluid
Authors: Martin Tobias , Wang Gang , Bihs Hans
Published: May 2021

The significant difference in length scales between the flow around a moving fish net and the flow around each twine of the net prevents the resolution of the complete structure within a discrete fluid domain. In this article, this issue is overcome by calculating the net and fluid dynamics separately and incorporate their interaction implicitly. The forces on the net are approximated using a screen force model, and the motion of the net is computed with a lumped mass method. Here, a linear system of equations is derived from the dynamic equilibria and kinematic relations. The net model is coupled to the computational fluid dynamics (CFD) solver reef3d, which solves the incompressible Navier–Stokes equations using high-order finite differences in space and time. Several numerical calculations are provided, and the comparison of loads and velocity reduction with available measurements indicates the good performance of the proposed model.
A Modular Mechanism for Downhole Weight-on-Bit and Torque Reaction in Small Diameter Boreholes
Authors: Mazumdar Anirban , Buerger Stephen P. , Foris Adam J. , Su Jiann-Cherng
Published: May 2021

Drilling systems that use downhole rotation must react torque either through the drill-string or near the motor to achieve effective drilling performance. Problems with drill-string loading such as buckling, friction, and twist become more severe as hole diameter decreases. Therefore, for small holes, reacting torque downhole without interfering with the application of weight-on-bit, is preferred. In this paper, we present a novel mechanism that enables effective and controllable downhole weight on bit transmission and torque reaction. This scalable design achieves its unique performance through four key features: (1) mechanical advantage based on geometry, (2) direction dependent behavior using rolling and sliding contact, (3) modular scalability by combining modules in series, and (4) torque reaction and weight on bit that are proportional to applied axial force. As a result, simple mechanical devices can be used to react large torques while allowing controlled force to be transmitted to the drill bit. We outline our design, provide theoretical predictions of performance, and validate the results using full-scale testing. The experimental results include laboratory studies as well as limited field testing using a percussive hammer. These results demonstrate effective torque reaction, axial force transmission, favorable scaling with multiple modules, and predictable performance that is proportional to applied force.
Efficient Global Sensitivity Analysis of Model-Based Ultrasonic Nondestructive Testing Systems Using Machine Learning and Sobol’ Indices
Authors: Nagawkar Jethro , Leifsson Leifur
Published: May 2021

The objective of this work is to reduce the cost of performing model-based sensitivity analysis for ultrasonic nondestructive testing systems by replacing the accurate physics-based model with machine learning (ML) algorithms and quickly compute Sobol’ indices. The ML algorithms considered in this work are neural networks (NNs), convolutional NN (CNNs), and deep Gaussian processes (DGPs). The performance of these algorithms is measured by the root mean-squared error on a fixed number of testing points and by the number of high-fidelity samples required to reach a target accuracy. The algorithms are compared on three ultrasonic testing benchmark cases with three uncertainty parameters, namely, spherically void defect under a focused and a planar transducer and spherical-inclusion defect under a focused transducer. The results show that NNs required 35, 100, and 35 samples for the three cases, respectively. CNNs required 35, 100, and 56, respectively, while DGPs required 84, 84, and 56, respectively.
Experimental and Numerical Analysis of the Transient Behavior of the Oil Decongealing Process in an Aero Fuel-Cooled Oil Cooler Under Low-Temperature Conditions
Authors: Kholi Foster Kwame , Park Jaehyun , Lee Kyeongho , Ha Man Yeong , Klingsporn Michael , Chetwynd-Chatwin Jason , Yoon Sang Youl , Min June Kee
Published: May 2021

The fuel-cooled oil cooler (FCOC) in the lubrication circuit plays a critical role in the aero gas-turbine engine's aerothermal management. However, the low temperature of the operating environment can congeal the oil and reduce the FCOC efficiency. The oil bypass valve (OBV) installed on the FCOC prevents pressure loss. Its failure may cause overheating, requiring preemptive performance prediction. Experimental and numerical analyses were used to evaluate the cooler's decongealing performance under typical boundary conditions of pressure and temperature, OBV configurations, and rerouting of feed oil and fuel flow paths. The temporal variation of oil and fuel mass flow rates, temperature, and pressure of the feed oil and fuel provided an insight into the decongealing process and duration. The experimental data were used to develop a one-dimensional (1D) flow and thermal network analysis model based on the effectiveness (ε)-number of thermal units (NTU) method to predict the transient oil decongealing performance of the FCOC. The customized commercial code predicted the decongealing phenomena using empirical correlations with property correction schemes, showing good agreement with the experiment. The findings revealed various ways to enhance the decongealing performance of the FCOC. The study results showed that the operating boundary conditions, OBV location and status, and flow arrangements affect decongealing behavior and time. The present numerical model provides results quickly and can effectively predict experimentally costly and complicated cases. The attempted estimates of steady heat rejection and detailed methodology could guide future studies and practical applications.
Leakage and Rotordynamic Characteristics for Three Types of Annular Gas Seals Operating in Supercritical CO2 Turbomachinery
Authors: Li Zhigang , Li Zhuocong , Li Jun , Feng Zhenping
Published: May 2021

The balance piston seal in multiple-stage centrifugal compressors and axial turbines sustains the largest pressure drop through the machines and therefore plays an important role in successful full load operation at high rotational speed. This is especially true for power dense turbomachines in supercritical CO2 power cycles that generate or expend higher fluid pressures (above the critical value 7.3 MPa) and density (close to water 1000 kg/m3), because the fluid forces generated by the balance piston seals are directly proportional to the fluid density and the pressure drop across the seal. This paper presents a comprehensive assessment and comparison on the leakage and rotordynamic performance of three types of annular gas seals for application in a 14 MW supercritical CO2 turbine. These three seals represent the main seal types used in high-speed rotating machines at the balance piston location in efforts to limit internal leakage flow and achieve rotordynamic stability, including a labyrinth seal (LABY), a fully partitioned pocket damper seal (FPDS), and a hole-pattern damper seal (HPS). These three seals were designed to have the same sealing clearance and similar axial lengths. To enhance the seal net damping capability at high inlet preswirl condition, a straight swirl brake was also designed and employed at seal entrance for each type seal to reduce the seal inlet preswirl velocity. Numerical results of leakage flow rates, rotordynamic force coefficients, cavity dynamic pressure, and swirl velocity developments were analyzed and compared for three seal designs at high positive inlet preswirl (in the direction of shaft rotation), using a proposed transient computational fluid dynamic (CFD)-based perturbation method based on the multiple-frequency elliptical-orbit rotor whirling model and the mesh deformation technique. To take into account of real gas effect with high accuracy, a table look-up procedure based on the National Institute of Standards and Technology reference fluid properties database was implemented, using an in-house code, for the fluid properties of CO2 in both supercritical and subcritical conditions. Results show that the inlet swirl brake can significantly reduce the preswirl velocity at seal entrance, lowering the effective damping crossover frequency fco (or even fco = 0) to maximize the full operational frequency range of the machines. In stability analysis phase of a MW-scale supercritical CO2 turbine/compressor, the seal stiffness effects on the rotor mode shape must be evaluated carefully, where the seal stiffness is sufficiently large (comparable to the bearing stiffness). From a rotordynamic viewpoint, the HPS seal with entrance swirl brake is a better seal concept for the balance piston seal in supercritical CO2 turbomachinery, which possesses the largest positive effective damping throughout the entire subsynchronous frequency range.
Geomorphic expression and slip rate of the Fairweather fault, southeast Alaska, and evidence for predecessors of the 1958 rupture
Authors: Witter Robert C. , Bender Adrian M. , Scharer Katherine M. , DuRoss Christopher B. , Haeussler Peter J. , Lease Richard O.
Published: May 2021

Active traces of the southern Fairweather fault were revealed by light detection and ranging (lidar) and show evidence for transpressional deformation between North America and the Yakutat block in southeast Alaska. We map the Holocene geomorphic expression of tectonic deformation along the southern 30 km of the Fairweather fault, which ruptured in the 1958 moment magnitude 7.8 earthquake. Digital maps of surficial geology, geomorphology, and active faults illustrate both strike-slip and dip-slip deformation styles within a 10°–30° double restraining bend where the southern Fairweather fault steps offshore to the Queen Charlotte fault. We measure offset landforms along the fault and calibrate legacy 14C data to reassess the rate of Holocene strike-slip motion (≥49 mm/yr), which corroborates published estimates that place most of the plate boundary motion on the Fairweather fault. Our slip-rate estimates allow a component of oblique-reverse motion to be accommodated by contractional structures west of the Fairweather fault consistent with geodetic block models. Stratigraphic and structural relations in hand-dug excavations across two active fault strands provide an incomplete paleoseismic record including evidence for up to six surface ruptures in the past 5600 years, and at least two to four events in the past 810 years. The incomplete record suggests an earthquake recurrence interval of ≥270 years—much longer than intervals <100 years implied by published slip rates and expected earthquake displacements. Our paleoseismic observations and map of active traces of the southern Fairweather fault illustrate the complexity of transpressional deformation and seismic potential along one of Earth's fastest strike-slip plate boundaries.
Detrital zircon record of Phanerozoic magmatism in the southern Central Andes
Authors: Capaldi T.N. , McKenzie N.R. , Horton B.K. , Mackaman-Lofland C. , Colleps C.L. , Stockli D.F.
Published: May 2021

The spatial and temporal distribution of arc magmatism and associated isotopic variations provide insights into the Phanerozoic history of the western margin of South America during major shifts in Andean and pre-Andean plate interactions. We integrated detrital zircon U-Th-Pb and Hf isotopic results across continental magmatic arc systems of Chile and western Argentina (28°S–33°S) with igneous bedrock geochronologic and zircon Hf isotope results to define isotopic signatures linked to changes in continental margin processes. Key tectonic phases included: Paleozoic terrane accretion and Carboniferous subduction initiation during Gondwanide orogenesis, Permian–Triassic extensional collapse, Jurassic–Paleogene continental arc magmatism, and Neogene flat slab subduction during Andean shortening. The ~550 m.y. record of magmatic activity records spatial trends in magma composition associated with terrane boundaries. East of 69°W, radiogenic isotopic signatures indicate reworked continental lithosphere with enriched (evolved) εHf values and low (<0.65) zircon Th/U ratios during phases of early Paleozoic and Miocene shortening and lithospheric thickening. In contrast, the magmatic record west of 69°W displays depleted (juvenile) εHf values and high (>0.7) zircon Th/U values consistent with increased asthenospheric contributions during lithospheric thinning. Spatial constraints on Mesozoic to Cenozoic arc width provide a rough approximation of relative subduction angle, such that an increase in arc width reflects shallower slab dip. Comparisons among slab dip calculations with time-averaged εHf and Th/U zircon results exhibit a clear trend of decreasing (enriched) magma compositions with increasing arc width and decreasing slab dip. Collectively, these data sets demonstrate the influence of subduction angle on the position of upper-plate magmatism (including inboard arc advance and outboard arc retreat), changes in isotopic signatures, and overall composition of crustal and mantle material along the western edge of South America.
Inferring psychological traits from spending categories and dynamic consumption patterns
Authors: Tovanich Natkamon , Centellegher Simone , Bennacer Seghouani Nacéra , Gladstone Joe , Matz Sandra , Lepri Bruno
Published: May 2021

In recent years there has been a growing interest in analyzing human behavioral data generated by new technologies. One type of digital footprint that is universal across the world, but that has received relatively little attention to date, is spending behavior.

In this paper, using the transaction records of 1306 bank customers, we investigated the extent to which individual-level psychological characteristics can be inferred from bank transaction data. Specifically, we developed a more comprehensive feature space using: (1) overall spending behavior (i.e. total number and total amount of transaction), (2) temporal spending behavior (i.e. variability, persistence, and burstiness), (3) category-related spending behavior (i.e. diversity, persistence, and turnover), (4) customer category profile, and (5) socio-demographic information. Using these features, we first explore their association with individual psychological characteristics, we then analyze the performances of the different feature families and finally, we try to understand to what extent psychological characteristics from spending records can be inferred.

Our results show that inferring the psychological traits of an individual is a challenging task, even when using a comprehensive set of features that take temporal aspects of spending into account. We found that Materialism and Self-Control could be inferred with relatively high levels of accuracy, while the accuracy obtained for the Big Five traits was lower, with only Extraversion and Neuroticism reaching reasonable classification performances.

Hence, for traits like Materialism, Self-control, Extraversion, and Neuroticism our findings could be used to improve psychologically-informed advertising strategies for specific products as well as personality-based spending management apps and credit scoring approaches.
Network Embedding on Hierarchical Community Structure Network
Authors: Song Guojie , Wang Yun , Du Lun , Li Yi , Wang Junshan
Published: May 2021

Network embedding is a method of learning a low-dimensional vector representation of network vertices under the condition of preserving different types of network properties. Previous studies mainly focus on preserving structural information of vertices at a particular scale, like neighbor information or community information, but cannot preserve the hierarchical community structure, which would enable the network to be easily analyzed at various scales. Inspired by the hierarchical structure of galaxies, we propose the Galaxy Network Embedding (GNE) model, which formulates an optimization problem with spherical constraints to describe the hierarchical community structure preserving network embedding. More specifically, we present an approach of embedding communities into a low-dimensional spherical surface, the center of which represents the parent community they belong to. Our experiments reveal that the representations from GNE preserve the hierarchical community structure and show advantages in several applications such as vertex multi-class classification, network visualization, and link prediction. The source code of GNE is available online.
Variable-lag Granger Causality and Transfer Entropy for Time Series Analysis
Authors: Amornbunchornvej Chainarong , Zheleva Elena , Berger-Wolf Tanya
Published: May 2021

Granger causality is a fundamental technique for causal inference in time series data, commonly used in the social and biological sciences. Typical operationalizations of Granger causality make a strong assumption that every time point of the effect time series is influenced by a combination of other time series with a fixed time delay. The assumption of fixed time delay also exists in Transfer Entropy, which is considered to be a non-linear version of Granger causality. However, the assumption of the fixed time delay does not hold in many applications, such as collective behavior, financial markets, and many natural phenomena. To address this issue, we develop Variable-lag Granger causality and Variable-lag Transfer Entropy, generalizations of both Granger causality and Transfer Entropy that relax the assumption of the fixed time delay and allow causes to influence effects with arbitrary time delays. In addition, we propose methods for inferring both Variable-lag Granger causality and Transfer Entropy relations. In our approaches, we utilize an optimal warping path of Dynamic Time Warping to infer variable-lag causal relations. We demonstrate our approaches on an application for studying coordinated collective behavior and other real-world casual-inference datasets and show that our proposed approaches perform better than several existing methods in both simulated and real-world datasets. Our approaches can be applied in any domain of time series analysis. The software of this work is available in the R-CRAN package: VLTimeCausality.
IntDroid
Authors: Zou Deqing , Wu Yueming , Yang Siru , Chauhan Anki , Yang Wei , Zhong Jiangying , Dou Shihan , Jin Hai
Published: May 2021

Android, the most popular mobile operating system, has attracted millions of users around the world. Meanwhile, the number of new Android malware instances has grown exponentially in recent years. On the one hand, existing Android malware detection systems have shown that distilling the program semantics into a graph representation and detecting malicious programs by conducting graph matching are able to achieve high accuracy on detecting Android malware. However, these traditional graph-based approaches always perform expensive program analysis and suffer from low scalability on malware detection. On the other hand, because of the high scalability of social network analysis, it has been applied to complete large-scale malware detection. However, the social-network-analysis-based method only considers simple semantic information (i.e., centrality) for achieving market-wide mobile malware scanning, which may limit the detection effectiveness when benign apps show some similar behaviors as malware.

In this article, we aim to combine the high accuracy of traditional graph-based method with the high scalability of social-network-analysis--based method for Android malware detection. Instead of using traditional heavyweight static analysis, we treat function call graphs of apps as complex social networks and apply social-network--based centrality analysis to unearth the central nodes within call graphs. After obtaining the central nodes, the average intimacies between sensitive API calls and central nodes are computed to represent the semantic features of the graphs. We implement our approach in a tool called
IntDroid
and evaluate it on a dataset of 3,988 benign samples and 4,265 malicious samples. Experimental results show that
IntDroid
is capable of detecting Android malware with an F-measure of 97.1% while maintaining a True-positive Rate of 99.1%. Although the scalability is not as fast as a social-network-analysis--based method (i.e.,
MalScan
), compared to a traditional graph-based method,
IntDroid
is more than six times faster than
MaMaDroid
. Moreover, in a corpus of apps collected from GooglePlay market,
IntDroid
is able to identify 28 zero-day malware that can evade detection of existing tools, one of which has been downloaded and installed by more than ten million users. This app has also been flagged as malware by six anti-virus scanners in VirusTotal, one of which is
Symantec Mobile Insight
.
Generative Adversarial Networks (GANs)
Authors: Saxena Divya , Cao Jiannong
Published: May 2021

Generative Adversarial Networks (GANs) is a novel class of deep generative models that has recently gained significant attention. GANs learn complex and high-dimensional distributions implicitly over images, audio, and data. However, there exist major challenges in training of GANs, i.e., mode collapse, non-convergence, and instability, due to inappropriate design of network architectre, use of objective function, and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions, and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on the broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present promising research directions in this rapidly growing field.
The Electrochemical Behavior of Tellurium Tetrachloride in LiCl-KCl Eutectic Molten Salt at 450 ºC
Authors: Day Brandon C , Zillinger James , Utgikar Vivek , Raja Krishnan S
Published: May 2021

Cyclic voltammetry CV), square wave voltammetry (SWV), and electrochemical impedance spectroscopy measurements were carried out in 1 and 5 wt% TeCl4 containing LiCl-KCl eutectic molten salt at 450 °C using tungsten and glassy carbon as working electrodes. Reduction of Te(IV) to Te(0) occurred in three steps at potentials more negative than −0.4 VAg/AgCl. The Te(0) was further reduced to Te2− at more negative potentials. Formation of Te2− was observed at potentials more negative than −2.2 VAg/AgCl. Diffusivities of Te4+, Te2+, and Te2− and their formal potentials were estimated from the CV data. The diffusion coefficients of Te4+, Te2+ and Te2− were 0.9 × 10−5, 3.8 × 10−5, and 1.5 × 10−5 cm2 s−1, respectively in the 1 wt% TeCl4 containing LiCl-KCl molten salt. The diffusivity of Te species generally decreased with increase in the concentration of TeCl4.
Interfacial Influence of Strontium Niobium Engulfed Reduced Graphene Oxide Composite for Sulfamethazine Detection: Employing an Electrochemical Route in Real Samples
Authors: Nataraj Nandini , Chen Shen-Ming
Published: May 2021

Sulfonamides are a broad assortment of drugs utilized in treating bacterial diseases in veterinary and human existence as anti-infection agents. The escalating usage of such medications requires explicit recognition. This work describes the electrochemical detection of sulfamethazine (SMZ) (a subgroup of sulfonamides) using strontium niobium combined with reduced graphene oxide (SN/rGO) as composite material. The structural and topological criteria illustrate the surface immaculateness and elemental presence of the crystalline material SN/rGO with XRD and FE-SEM analysis. Additional examinations of chemical states and the functional group were identified with XPS and FT-IR measurements. The feasibility of the sensor was inspected in the record of electrochemical studies. Electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) studies have demonstrated the effective implementation of the fabricated sensor with facilitating higher electroactive sites and enhanced conductance with SN/rGO. The amalgamation of SN and rGO has substantial influence with a lower limit of detection 6 nM with linearity from 0.009 μM to 128 μM. The limit of quantification was about 0.0224 μM. The specific detection acquired over anti-interference studies and real-world actualize over milk, honey, and human blood serum tests mirror SN/rGO modified platform’s viable capability. The construction of SN/rGO is envisioned as a promising dais for improved SMZ sensing in real samples.
Multiphase and Pore Scale Modeling on Catalyst Layer of High-Temperature Polymer Electrolyte Membrane Fuel Cell
Authors: Duan Kangjun , Zhu Lijun , Li Min , Xiao Liusheng , Bevilacqua Nico , Eifert László , Manke Ingo , Markötter Henning , Zhang Ruiming , Zeis Roswitha , Sui Pang-Chieh
Published: May 2021

Phosphoric acid as the electrolyte in high-temperature polymer electrolyte membrane fuel cell plays an essential role in its performance and lifetime. Maldistribution of phosphoric acid in the catalyst layer (CL) may result in performance degradation. In the present study, pore-scale simulations were carried out to investigate phosphoric acid’s multiphase flow in a cathode CL. A reconstructed CL model was built using focused ion beam-SEM images, where distributions of pore, carbon support, binder, and catalyst particles can be identified. The multi-relaxation time lattice Boltzmann method was employed to simulate phosphoric acid invading and leaching from the membrane into the CL during the membrane electrode assembly fabrication process. The predicted redistribution of phosphoric acid indicates that phosphoric acid of low viscosity or low wettability is prone to leaching into the CL. The effective transport properties and the active electrochemical active surface area (ECSA) were computed using a pore-scale model. They were subsequently used in a macroscopic model to evaluate the cell performance. A parametric study shows that cell performance first increases with increasing phosphoric acid content due to the increase of ECSA. However, further increasing phosphoric acid content results in performance degradation due to mass transfer limitation caused by acid flooding.
The Role of Anesthetic Selection in Perioperative Bleeding
Authors: Yuki Koichi , DiNardo James A. , Koutsogiannaki Sophia
Published: May 2021

Perioperative bleeding is one of the major comorbidities associated with surgery. While anesthesia is a critical component to perform surgery, a number of clinical studies supported the contribution of anesthetic drugs to perioperative bleeding. Here, we reviewed the literature on this topic including the underlying mechanism and discussed the future direction on coagulation research in anesthesia.
Carica papaya L. Leaf: A Systematic Scoping Review on Biological Safety and Herb-Drug Interactions
Authors: Lim X. Y. , Chan J. S. W. , Japri N. , Lee J. C. , Tan T. Y. C.
Published: May 2021

Introduction. The Carica papaya L. leaf is gaining interest as a potential therapeutic agent for alleviating dengue- and non-dengue-associated thrombocytopaenia. In that regard, safety considerations are as important as efficacy potential. The safety evaluation of botanical products for human use is complicated by variable formulations, complex phytochemical composition, and extrinsic toxicants. This review aimed to systematically collate related safety clinical and preclinical data, as well as reports on herb-drug interactions of C. papaya leaf consumption. Methods. A systematic search using predetermined keywords on electronic databases (MEDLINE, Cochrane Library Central, LILACS, and Web of Science) and grey literature was conducted. Relevant clinical and preclinical studies were identified, screened, and analysed to present an overall safety profile of C. papaya leaf consumption. Results. A total of 41 articles were included (23 clinical, 5 ongoing trials, and 13 preclinical) for descriptive analysis on study characteristics, adverse reactions, toxicity findings, and herb-drug interactions, from which 13 randomised controlled and quasiexperimental trials were further assessed for risk of bias and reporting quality. Overall, C. papaya leaf consumption (in the form of juice and standardised aqueous extract) was well tolerated by adult humans for short durations (<five days) while one randomised controlled trial reported safe consumption of C. papaya leaf standardised aqueous extract in children (aged 1–12 years). Minor gastrointestinal side effects were most commonly reported. There are concerns about hepatotoxicity and reproductive toxicity in long-term use, supported by animal studies. Unfavourable herb-drug interactions with metformin, glimepiride, digoxin, ciprofloxacin, and artemisinin were accounted. Conclusion. C. papaya leaf consumption in adults is generally safe for short-term use though cautioned in pregnancy and people with liver impairment. It has potential herb-drug interactions with oral hypoglycaemic agents, p-glycoprotein substrates, and antibiotics with cation chelating properties.
Using Recursive Feature Selection with Random Forest to Improve Protein Structural Class Prediction for Low-Similarity Sequences
Authors: Wang Yaoxin , Xu Yingjie , Yang Zhenyu , Liu Xiaoqing , Dai Qi
Published: May 2021

Many combinations of protein features are used to improve protein structural class prediction, but the information redundancy is often ignored. In order to select the important features with strong classification ability, we proposed a recursive feature selection with random forest to improve protein structural class prediction. We evaluated the proposed method with four experiments and compared it with the available competing prediction methods. The results indicate that the proposed feature selection method effectively improves the efficiency of protein structural class prediction. Only less than 5% features are used, but the prediction accuracy is improved by 4.6-13.3%. We further compared different protein features and found that the predicted secondary structural features achieve the best performance. This understanding can be used to design more powerful prediction methods for the protein structural class.
Influence of Concrete Sludge Addition in the Properties of Alkali‐Activated and Non‐Alkali‐Activated Fly Ash‐Based Mortars
Authors: Kesikidou Fotini , Konopisi Stavroula , Anastasiou Eleftherios K.
Published: May 2021

This study investigated the use of concrete sludge, a by‐product of the ready‐mix concrete industry, in combination with high‐calcium fly ash in binary cementless binders. Concrete sludge was used in substitution rates ranging from 0% to 60% in test fly ash‐based mortars to determine potential synergy. The mortars were tested for fresh and hardened properties; workability, viscosity, strength development, open porosity, early‐age shrinkage, and analytical tests were carried out. A mortar with 50% fly ash and 50% limestone filler as binders was used for comparison purposes. Furthermore, a series of mortars with fly ash and concrete sludge were alkali‐activated in order to determine potential strength gain. In the activated mortars, two fractions of concrete sludge were used, under 75 μm and 200 μm, due to different silicon oxide contents, while one mortar was cured at 40°C to investigate the effect of heating on alkali activation. Results show that sludge contributes to the formation of C‐S‐H and strength development when used in combination with high‐calcium fly ash even at high replacement rates. The alkali activation of fly ash‐concrete sludge system contributed to early‐age strength development and to early‐age shrinkage reduction.
Evaluation of Sentiment Analysis via Word Embedding and RNN Variants for Amazon Online Reviews
Authors: Alharbi Najla M. , Alghamdi Norah S. , Alkhammash Eman H. , Al Amri Jehad F.
Published: May 2021

Consumer feedback is highly valuable in business to assess their performance and is also beneficial to customers as it gives them an idea of what to expect from new products. In this research, the aim is to evaluate different deep learning approaches to accurately predict the opinion of customers based on mobile phone reviews obtained from Amazon.com. The prediction is based on analysing these reviews and categorizing them as positive, negative, or neutral. Different deep learning algorithms have been implemented and evaluated such as simple RNN with its four variants, namely, Long Short-Term Memory Networks (LRNN), Group Long Short-Term Memory Networks (GLRNN), gated recurrent unit (GRNN), and update recurrent unit (UGRNN). All evaluated algorithms are combined with word embedding as feature extraction approach for sentiment analysis including Glove, word2vec, and FastText by Skip-grams. The five different algorithms with the three feature extraction methods are evaluated based on accuracy, recall, precision, and F1-score for both balanced and unbalanced datasets. For the unbalanced dataset, it was found that the GLRNN algorithms with FastText feature extraction scored the highest accuracy of 93.75%. This result achieved the highest accuracy on this dataset when compared with other methods mentioned in the literature. For the balanced dataset, the highest achieved accuracy was 88.39% by the LRNN algorithm.
The Application of an Adaptive Genetic Algorithm Based on Collision Detection in Path Planning of Mobile Robots
Authors: Hao Kun , Zhao Jiale , Wang Beibei , Liu Yonglei , Wang Chuanqi
Published: May 2021

An adaptive genetic algorithm based on collision detection (AGACD) is proposed to solve the problems of the basic genetic algorithm in the field of path planning, such as low convergence path quality, many iterations required for convergence, and easily falling into the local optimal solution. First, this paper introduces the Delphi weight method to evaluate the weight of path length, path smoothness, and path safety in the fitness function, and a collision detection method is proposed to detect whether the planned path collides with obstacles. Then, the population initialization process is improved to reduce the program running time. After comprehensively considering the population diversity and the number of algorithm iterations, the traditional crossover operator and mutation operator are improved, and the adaptive crossover operator and adaptive mutation operator are proposed to avoid the local optimal solution. Finally, an optimization operator is proposed to improve the quality of convergent individuals through the second optimization of convergent individuals. The simulation results show that the adaptive genetic algorithm based on collision detection is not only suitable for simulation maps with various sizes and obstacle distributions but also has excellent performance, such as greatly reducing the running time of the algorithm program, and the adaptive genetic algorithm based on collision detection can effectively solve the problems of the basic genetic algorithm.
Analysis and Optimization of Low‐Speed Road Noise in Electric Vehicles
Authors: Yu Wentao
Published: May 2021

When a certain electric vehicle is driving at a constant speed of 40 km/h on the rough asphalt road, the rear passenger can obviously feel the ear pressure, which seriously affects the comfort. Through the analysis of objective data, it was found that the problem was caused by the road excitation, which leads to the coupling between the mode of the backup door and the mode of the acoustic cavity, and causes the resonance of the car cavity, thus causing the ear pressure sensation. To solve this problem, this paper optimizes the backup door by means of experiment and simulation, increases the dynamic vibration absorber, makes its modal frequency avoid the acoustic cavity modal frequency, and achieves the purpose of reducing the interior noise. After optimization, the vehicle noise is reduced by 8 dBA at 42 Hz under 40 km/h working condition of rough road surface, and the ear pressure sensation is reduced at the same time, thus improving the NVH (noise, vibration, and harshness) performance of the vehicle.
Decision Tree Ensembles to Predict Coronavirus Disease 2019 Infection: A Comparative Study
Authors: Ahmad Amir , Safi Ourooj , Malebary Sharaf , Alesawi Sami , Alkayal Entisar
Published: May 2021

The coronavirus disease 2019 (Covid‐19) pandemic has affected most countries of the world. The detection of Covid‐19 positive cases is an important step to fight the pandemic and save human lives. The polymerase chain reaction test is the most used method to detect Covid‐19 positive cases. Various molecular methods and serological methods have also been explored to detect Covid‐19 positive cases. Machine learning algorithms have been applied to various kinds of datasets to predict Covid‐19 positive cases. The machine learning algorithms were applied on a Covid‐19 dataset based on commonly taken laboratory tests to predict Covid‐19 positive cases. These types of datasets are easy to collect. The paper investigates the application of decision tree ensembles which are accurate and robust to the selection of parameters. As there is an imbalance between the number of positive cases and the number of negative cases, decision tree ensembles developed for imbalanced datasets are applied. F‐measure, precision, recall, area under the precision‐recall curve, and area under the receiver operating characteristic curve are used to compare different decision tree ensembles. Different performance measures suggest that decision tree ensembles developed for imbalanced datasets perform better. Results also suggest that including age as a variable can improve the performance of various ensembles of decision trees.
Forecasting the Effects of the New SARS-CoV-2 Variant in Europe
Authors: Area Iván , Nieto Juan J.
Published: May 2021

Due to the emergence of a new SARS-CoV-2 variant, we use a previous model to simulate the behaviour of this new SARS-CoV-2 variant. The analysis and simulations are performed for Europe, in order to provide a global analysis of the pandemic. In this context, numerical results are obtained in the first 100 days of the pandemic assuming an infectivity of 70%, 56%, and 35%, respectively, higher for the new SAR-CoV-2 variant, as compared with the real data.
Local Stereo Matching Using Adaptive Cross-Region-Based Guided Image Filtering with Orthogonal Weights
Authors: Kong Lingyin , Zhu Jiangping , Ying Sancong
Published: May 2021

Adaptive cross-region-based guided image filtering (ACR-GIF) is a commonly used cost aggregation method. However, the weights of points in the adaptive cross-region (ACR) are generally not considered, which affects the accuracy of disparity results. In this study, we propose an improved cost aggregation method to address this issue. First, the orthogonal weight is proposed according to the structural feature of the ACR, and then the orthogonal weight of each point in the ACR is computed. Second, the matching cost volume is filtered using ACR-GIF with orthogonal weights (ACR-GIF-OW). In order to reduce the computing time of the proposed method, an efficient weighted aggregation computing method based on orthogonal weights is proposed. Additionally, by combining ACR-GIF-OW with our recently proposed matching cost computation method and disparity refinement method, a local stereo matching algorithm is proposed as well. The results of Middlebury evaluation platform show that, compared with ACR-GIF, the proposed cost aggregation method can significantly improve the disparity accuracy with less additional time overhead, and the performance of the proposed stereo matching algorithm outperforms other state-of-the-art local and nonlocal algorithms.