bluuebunny/crossref_metadata_2025 huggingface dataset:
Diagnostic Management of Acute Pulmonary Embolism in COVID-19 and Other Special Patient Populations
Authors: Martens Emily S. L. , Huisman Menno V. , Klok Frederikus A.
Published: May 2022

Venous thromboembolism (VTE), in particular acute pulmonary embolism (PE), has been shown to be a frequent and potentially fatal complication of coronavirus disease 2019 (COVID-19). In response to the observed thrombotic complications, a large number of studies has been devoted to the understanding and management of COVID-19-associated coagulopathy. Notably, only a limited number of mostly retrospective studies has focused on the optimal diagnostic strategy for suspected PE in COVID-19 patients. As in other special populations, the accuracy of diagnostic algorithms for PE-exclusion has been debated in this specific patient subgroup as the specificity of D-dimer assays and clinical decision rules (CDRs) may be lower than normal. From this viewpoint, we discuss the current state-of-the-art diagnostic algorithms for acute PE with a focus on patients with COVID-19 in the perspective of other special patient populations. Furthermore, we summarize current knowledge regarding the natural history of PE resolution with anticoagulant treatment in patients with COVID-19.
Acute and Chronic Cardiopulmonary Effects of High Dose Interleukin-2 Therapy: An Observational Magnetic Resonance Imaging Study
Authors: Lagan Jakub , Naish Josephine H. , Fortune Christien , Campbell Christopher , Chow Shien , Pillai Manon , Bradley Joshua , Francis Lenin , Clark David , Macnab Anita , Nucifora Gaetano , Dobson Rebecca , Schelbert Erik B. , Schmitt Matthias , Hawkins Robert , Miller Christopher A.
Published: May 2022

High dose interleukin-2 (IL-2) is known to be associated with cardiopulmonary toxicity. The goal of this study was to evaluate the effects of high dose IL-2 therapy on cardiopulmonary structure and function. Combined cardiopulmonary magnetic resonance imaging (MRI) was performed in 7 patients in the acute period following IL-2 therapy and repeated in 4 patients in the chronic period. Comparison was made to 10 healthy volunteers. IL-2 therapy was associated with myocardial and pulmonary capillary leak, tissue oedema and cardiomyocyte injury, which resulted in acute significant left ventricular (LV) dilatation, a reduction in LV ejection fraction (EF), an increase in LV mass and a prolongation of QT interval. The acute effects occurred irrespective of symptoms. In the chronic period many of the effects resolved, but LV hypertrophy ensued, driven by focal replacement and diffuse interstitial myocardial fibrosis and increased cardiomyocyte mass. In conclusion, IL-2 therapy is ubiquitously associated with acute cardiopulmonary inflammation, irrespective of symptoms, which leads to acute LV dilatation and dysfunction, increased LV mass and QT interval prolongation. Most of these effects are reversible but IL-2 therapy is associated with chronic LV hypertrophy, driven by interstitial myocardial fibrosis and increased cardiomyocyte mass. The findings have important implications for the monitoring and long term impact of newer immunotherapies. Future studies are needed to improve risk stratification and develop cardiopulmonary-protective strategies.
A Fast kNN Algorithm Using Multiple Space-Filling Curves
Authors: Barkalov Konstantin , Shtanyuk Anton , Sysoyev Alexander
Published: May 2022

The paper considers a time-efficient implementation of the k nearest neighbours (kNN) algorithm. A well-known approach for accelerating the kNN algorithm is to utilise dimensionality reduction methods based on the use of space-filling curves. In this paper, we take this approach further and propose an algorithm that employs multiple space-filling curves and is faster (with comparable quality) compared with the kNN algorithm, which uses kd-trees to determine the nearest neighbours. A specific method for constructing multiple Peano curves is outlined, and statements are given about the preservation of object proximity information in the course of dimensionality reduction. An experimental comparison with known kNN implementations using kd-trees was performed using test and real-life data.
Force Tracking Control of Functional Electrical Stimulation via Hybrid Active Disturbance Rejection Control
Authors: Huo Benyan , Wang Ruishun , Qin Yunhui , Wu Zhenlong , Bian Guibin , Liu Yanhong
Published: May 2022

Stroke is a worldwide disease with a high incidence rate. After surviving a stroke, most patients are left with impaired upper or lower limb. Muscle force training is vital for stroke patients to recover limb function and improve their quality of life. This paper proposes a force tracking control method for upper limb based on functional electrical stimulation (FES), which is a promising rehabilitation approach. A modified Hammerstein model is proposed to describe the nonlinear dynamics of biceps brachii, which consists of a nonlinear mapping function, linear dynamics and time delay component to represent the biochemical process of muscle contraction. A quick model identification method is presented based on the least square algorithm. To deal with the variation of muscle dynamics, a hybrid active disturbance rejection control (ADRC) is proposed to estimate and compensate for the model uncertainty and unmeasured disturbances. The parameter tuning process is given. In the end, the performance of the proposed methods is verified via simulations and experiments. Compared with the Proportional integral derivative controller (PID) method, the proposed methods could suppress the model uncertainty and improve the tracking precision.
Intelligent Computing Methods for Contextual Driving in Smart Engineering Model Systems
Authors: Horváth László , Rudas Imre J.
Published: May 2022

An engineering model system (EMS) is a complex purposeful structure of representation and description type content that is developed during development and application; in other words, it represents the lifecycle of an industrial product. The essential improvements for systems operated that are autonomous, situation controlled, and include cyber physical features of products dramatically changed requirements relative to EMS. Engineering modeling platforms (EMPs) experienced major improvements to cope with new requirements by smart products. These improvements were related to system level representation, autonomous active model features, situation level reaction to changed contexts, system and physical level virtual execution, and the support of situation-level human intervention. The lifecycle support of smart product engineering requires EMS, which behaves as a digital twin of a physically existing products and has an active connection with its contextual world, particularly with cyber units of the cyber physical system that it represents. The work in this paper aimed to develop new methods that can be implemented in EMS under developments in EMP. This is a new area of research that is motivated and at the same time enforced by moving engineering activities into highly integrated and automated complex model spaces. This paper focuses on context driving and the autonomous features of EMS as well as utilization-organized intellectual-property-powered intelligent computing relative to the definition and application of contextually connected object parameters in EMS. It introduces several latest results by the authors as a contribution for EMS to cope with the new requirements. These contributions include a new scenario called the lifecycle representation of contexts (LRC) to enhance integration in EMS, handling contexts in contextually connected autonomous unit (CCAU), organizing the intelligent content of EMS in proposed level-sublevel structure of LRC, and examining findings about situation-level cooperation between human and autonomous process.
An Intelligent Perception Model and Parameters Adjust Method for Quality of Experience
Authors: Wu Yanqin , Zhang Le , Lv Tiantian , Guo Rongrong , Xing Liang , Wang Yanchuan
Published: May 2022

With the coming of the 5G network era, Quality of Experience (QoE), which is used to describe experience quality of customers in the Direct Connect (DC) network, is critical to operators and hence has been widely studied. The existing evaluation method for QoE is unable to dynamically adjust QoE parameters so there will be deviations to reflect the real experience of customers when complaint events occur. In this paper, a novel progressive QoE evaluation model for network products is formulated. Subsequently, considering the customer complaints occurrences which represent that the real customer experience and previously calculated QoE may mismatch, the rules based on application types to judge whether QoE parameters needs to be adjusted are made. Then, a dynamic adjustment model of QoE parameters is further developed to reflect complaint customer experience. Finally, the obtained experimental result verified that the proposed dynamic perception method for QoE can accurately characterize the real customer experience. The evaluation result for QoE model based on customer satisfaction questionnaires proves the effectiveness of the QoE-aware model. The evaluation result for adjusted QoE model based on business complaints shows that the adjusted QoE calculation variance reduction rate exceeds more than 50%, which indicates that the proposed method has better ability to reflect the real customer experience.
Adaptive Fuzzy Observer Control for Half-Car Active Suspension Systems with Prescribed Performance and Actuator Fault
Authors: Ho Cong Minh , Nguyen Cong Hung , Ahn Kyoung Kwan
Published: May 2022

In this paper, an adaptive fuzzy observer-based fault-tolerant controller is designed for a half-car active suspension system under the presence of uncertain parameters, unknown masses of passengers, and actuator failures. To improve the control performance, fuzzy logic systems (FLSs) are employed to approximate the unknown functions caused by uncertain dynamics of the suspension system. Then, an adaptive control design is developed to compensate for the effects of a non-ideal actuator. To improve passenger comfort, both vertical and angular motions are guaranteed simultaneously under the predefined boundaries by the prescribed performance function (PPF) method. Besides, the objectives of handling stability and driving safety are also considered to enhance the suspension performance. The system stability is proved according to the Lyapunov theory. Finally, the effectiveness of the developed approach is evaluated by comparative simulation examples on the half-car model. The simulation results show that the proposed control can improve the suspension performance as the RMS acceleration value is decreased by 68.1%.
Stochastic Cell- and Bit-Discard Technique to Improve Randomness of a TRNG
Authors: Nam Jae-Won , Kim Jaewoo , Hong Jong-Phil
Published: May 2022

This paper presents a post-processing algorithm for a true random number generator (TRNG). Once the randomness of security key generation deteriorates for any reason, the entire chain of the security system can be compromised, increasing the odds of it being exploited by an attacker to retrieve information. Considering the change in the distribution of the RNG output sequence due to variations in the operating environment or the occurrence of aging phenomena in silicon-integrated circuits, a robust post-processing algorithm must be applied to an intrinsic TRNG to ensure the sustainability of a security system. Targeting high-level cryptography systems complying with the NIST 800-22a requirements, the proposed algorithm significantly improves the Hamming weight (HW) and successfully passes the NIST criteria while sacrificing approximately 20% of the entire number of available bits. The proposed algorithm improves the randomness of the TRNG through a sequential cell- and bit-level discarding technique, a cell-discard method, and focuses on improving the overall HW of the TRNG while the subsequent bit- discard method performs a Chi-square (χ2) test. To prove the concept, we programmed the proposed algorithm in a FPGA and configured the output of the manufactured TRNG chip to be post-processed and stored into on-board memory in real time. For five different ring-oscillator-based TRNG prototypes (fully custom designed in the 65 nm CMOS process), the failed intrinsic TRNG output sequences were respectively post-processed, resulting in all surpassing the NIST 800-22a requirements.
aRTIC GAN: A Recursive Text-Image-Conditioned GAN
Authors: Alati Edoardo , Caracciolo Carlo Alberto , Costa Marco , Sanzari  Marta , Russo Paolo , Amerini Irene
Published: May 2022

Generative Adversarial Networks have recently demonstrated the capability to synthesize photo-realistic real-world images. However, they still struggle to offer high controllability of the output image, even if several constraints are provided as input. In this work, we present a Recursive Text-Image-Conditioned GAN (aRTIC GAN), a novel approach for multi-conditional image generation under concurrent spatial and text constraints. It employs few line drawings and short descriptions to provide informative yet human-friendly conditioning. The proposed scenario is based on accessible constraints with high degrees of freedom: sketches are easy to draw and add strong restrictions on the generated objects, such as their orientation or main physical characteristics. Text on its side is so common and expressive that easily enforces information otherwise impossible to provide with minimal illustrations, such as objects components color, color shades, etc. Our aRTIC GAN is suitable for the sequential generation of multiple objects due to its compact design. In fact, the algorithm exploits the previously generated image in conjunction with the sketch and the text caption, resulting in a recurrent approach. We developed three network blocks to tackle the fundamental problems of catching captions’ semantic meanings and of handling the trade-off between smoothing grid-pattern artifacts and visual detail preservation. Furthermore, a compact three-task discriminator (covering global, local and textual aspects) was developed to preserve a lightweight and robust architecture. Extensive experiments proved the validity of aRTIC GAN and show that the combined use of sketch and description allows us to avoid explicit object labeling.
Electric Vehicle Fire Trace Recognition Based on Multi-Task Semantic Segmentation
Authors: Pu Jiankun , Zhang Wei
Published: May 2022

Conflagration is the major safety issue of electric vehicles (EVs). Due to their well-kept appearance and structure, which demonstrate salient visual changes after combustion, EV bodies are recognized as an important basis for on-spot inspection of burnt EVs and make application using semantic segmentation possible. The combination of deep learning-based semantic segmentation and recognition of visual traces of burnt EVs would provide preliminary analytical results of fire spread trends and output status descriptions of burnt EVs for further investigation. In this paper, a dataset of image traces of burnt EVs was built, and a two-branch network structure that splits the whole task into two sub-tasks separately concentrated on foreground extraction and severity segmentation is proposed. The proposed network is trained on the dataset via the transfer learning method and is tested using 5-fold cross validation. The foreground extraction branch achieved a mean intersection over union (mIoU) of 95.16% in the burnt EV foreground extraction task, and the burnt severity branch achieved a mIoU of 66.96% for the severity segmentation task. By jointly training two branches and applying a foreground mask to 3-class severity output, the mIoU was improved to 68.92%.
A New Beamforming Approach Using 60 GHz Antenna Arrays for Multi-Beams 5G Applications
Authors: Al-Sadoon Mohammed A. G. , Patwary Mohammad N. , Zahedi Yasser , Parchin Naser Ojaroudi , Aldelemy Ahmad , Abd-Alhameed Raed A.
Published: May 2022

Recent studies and research have centred on new solutions in different elements and stages to the increasing energy and data rate demands for the fifth generation and beyond (B5G). Based on a new-efficient digital beamforming approach for 5G wireless communication networks, this work offers a compact-size circular patch antenna operating at 60 GHz and covering a 4 GHz spectrum bandwidth. Massive Multiple Input Multiple Output (M–MIMO) and beamforming technology build and simulate an active multiple beams antenna system. Thirty-two linear and sixty-four planar antenna array configurations are modelled and constructed to work as base stations for 5G mobile communication networks. Furthermore, a new beamforming approach called Projection Noise Correlation Matrix (PNCM) is presented to compute and optimise the fed weights of the array elements. The key idea of the PNCM method is to sample a portion of the measured noise correlation matrix uniformly in order to provide the best representation of the entire measured matrix. The sampled data will then be utilised to build a projected matrix using the pseudoinverse approach in order to determine the best fit solution for a system and prevent any potential singularities caused by the matrix inversion process. The PNCM is a low-complexity method since it avoids eigenvalue decomposition and computing the entire matrix inversion procedure and does not require including signal and interference correlation matrices in the weight optimisation process. The suggested approach is compared to three standard beamforming methods based on an intensive Monte Carlo simulation to demonstrate its advantage. The experiment results reveal that the proposed method delivers the best Signal to Interference Ratio (SIR) augmentation among the compared beamformers.
Reduction of the Risk of Inaccurate Prediction of Electricity Generation from PV Farms Using Machine Learning
Authors: Krechowicz Maria , Krechowicz Adam , Lichołai Lech , Pawelec Artur , Piotrowski Jerzy Zbigniew , Stępień Anna
Published: May 2022

Problems with inaccurate prediction of electricity generation from photovoltaic (PV) farms cause severe operational, technical, and financial risks, which seriously affect both their owners and grid operators. Proper prediction results are required for optimal planning the spinning reserve as well as managing inertia and frequency response in the case of contingency events. In this work, the impact of a number of meteorological parameters on PV electricity generation in Poland was analyzed using the Pearson coefficient. Furthermore, seven machine learning models using Lasso Regression, K–Nearest Neighbours Regression, Support Vector Regression, AdaBoosted Regression Tree, Gradient Boosted Regression Tree, Random Forest Regression, and Artificial Neural Network were developed to predict electricity generation from a 0.7 MW solar PV power plant in Poland. The models were evaluated using determination coefficient (R2), the mean absolute error (MAE), and root mean square error (RMSE). It was found out that horizontal global irradiation and water saturation deficit have a strong proportional relationship with the electricity generation from PV systems. All proposed machine learning models turned out to perform well in predicting electricity generation from the analyzed PV farm. Random Forest Regression was the most reliable and accurate model, as it received the highest R2 (0.94) and the lowest MAE (15.12 kWh) and RMSE (34.59 kWh).
Enhancing the Performance of Eskom’s Cahora Bassa HVDC Scheme and Harmonic Distortion Minimization of LCC-HVDC Scheme Using the VSC-HVDC Link
Authors: Davidson Innocent Ewean , Oni Oluwafemi Emmanuel , Aluko Anuoluwapo , Buraimoh Elutunji
Published: May 2022

Cahora Bassa, a thyristor-based High Voltage Direct (HVDC) link, transmits 1920 MW of power from a hydro-power plant in Zambezi River, north of Mozambique, to Apollo Substation in Johannesburg, South Africa. The high degree of harmonics distortion that is transferred into the AC side of the transmission network and the continuous increase in the rate at which commutation failure occurs during systems disturbance are both flaws in the utilization of this HVDC converter technology. AC and DC filters with rugged controllers are often used to minimize this effect but are limited in scope. Modern converter technology, such as the Voltage Source Converter (VSC), was proposed in this study to reduce harmonics content level, increase power transfer capabilities, enhance network stability, and reduce the rate of commutation failure occurrence. This paper, therefore, evaluates the performance analysis of the Cahora Bassa HVDC link and its level of harmonic distortion in the line commutated converters. A proposed method of utilizing VSC HVDC is provided as a suitable solution using three modular-level voltage source converter technology. Current and voltage waveform characteristics during a three-phase short circuits fault were analyzed, and the latest developments in the area of VSC HVDC were discussed. The results show a lower total harmonics distortion with the usage of VSC HVDC converter technology at the inverter station. The continuous occurrence of commutation failure was minimized by implementing a new converter architecture. The network simulation and analysis were carried out using the DIgSILENT PowerFactory engineering software tool.
Quantitative Analysis of Cenozoic Extension in the Qiongdongnan Basin, South China Sea: Insight on Tectonic Control for Hydrocarbon Reservoir Accumulation and Formation
Authors: Zhang Yan , Zhang Li , Mi Lijun , Lu Xiangyang , Wu Shiguo , Tang Lishan , Zhou Jie , Xiong Xiaofeng , Zhu Jitian
Published: May 2022

Cenozoic extension rates were calculated based on 20 seismic profiles across the Qiongdongnan Basin, South China Sea. The results confirmed that the Cenozoic rifting in the Qiongdongnan Basin exhibited multistage extension and spatiotemporal variation. In terms of the N–S striking seismic profiles, the structural forms of the northern and southern sags of the basin were characterized by narrow half grabens, while the structure at the center sag of the basin was characterized by wide and gentle grabens. The fault strikes in the west of the basin were mainly northeast–southwest trending, whereas those in the east of the basin changed from northeast–southwest trending to nearly east–west trending. The extension rate in the east sag was higher than that in the west area. The extension rate in the middle part was lower relative to the east and west sags. This was because the rifting was controlled by the distribution of the main boundary fault along the basin. Temporally, the Cenozoic extension could be divided into three periods: Eocene, Oligocene, and Miocene. The amount of stretching in the three extension stages was unevenly distributed in the entire basin. The maximum was mainly in the Oligocene Lingshui and Yacheng Formations. The Oligocene extension occurred in the entire basin, and the Eocene extension was limited to the Ledong and Changchang sags. Significant fault activity could be observed during the deposition period of the Yacheng and Lingshui Formations and could be attributed to strong extensional activity. The rifting tectonics controlled the distribution of source rocks and oil-generating window as well as hydrocarbon generation, reservoir formation, and accumulation.
Characterization of Wax Precipitation and Deposition Behavior of Condensate Oil in Wellbore: A Comprehensive Review of Modeling, Experiment, and Molecular Dynamics Simulation
Authors: Wang Yong , Liu Xiaoyu , Huang Zuonan , Wang Zhihua , Liu Yang
Published: May 2022

Condensate oil is increasingly valued as the high-quality conventional hydrocarbon resources generally decline. The efficient development of condensate oil, however, has always been a world problem; massive condensate oil will be retained in reservoirs in case of improper exploitation process, resulting in a significant resource waste and economic loss. One of the problems closely related to enhancing condensate oil recovery is wax precipitation and deposition in wellbore. Therefore, it is vital to investigate the characterization methods for the wax precipitation and deposition behavior in wellbores. The current status of research on modelling characterization methods, experimental characterization methods and molecular dynamics representation of wax precipitation and deposition behavior is reviewed in this paper; the applicability and limitation of modeling and experiment studies for characterizing wax precipitation and deposition of condensate oil in the wellbore are critically summarized and discussed. Moreover, the molecular dynamics simulation technique characterizes wax precipitation and deposition behavior from the micro scale, which makes up for the deficiencies of macroscopic experiment, enriches the investigation of wax precipitation and deposition, and provides important guidance and reference value for the development of unconventional hydrocarbon exploitation processes.
Analytics for Recovery and Reuse of Solid Wastes from Refineries
Authors: Apicella Barbara , Russo Carmela , Senneca Osvalda
Published: May 2022

Heavy fractions of petroleum have for long time been bypassed in favour of lighter fractions. Nowadays, in the framework of the “circular economy”, there is a growing interest in residual petroleum heavy fractions. The present work briefly reviews the use and characterization at laboratory scale of some low valuable solid or semi-solid products of the oil refinery industry: asphaltenes (bitumen/asphalt), pet-coke and pitch for use as fuels. The use of solid and semi-solid refinery residues, in particular, of coke as a coal substitute in thermochemical processes and of pitch and asphaltenes as material precursors, requires careful analysis, and an understanding of their structure at the molecular level is mandatory for the development of processing technology. Techniques for the characterization of typical petroleum heavy fractions such as pitches, asphaltenes and cokes are reviewed. An experimental protocol for investigating at the laboratory scale the thermochemical conversion behavior of solid and semi-solid refinery wastes is proposed.
The Resilience of Vegetation to the 2009/2010 Extreme Drought in Southwest China
Authors: Shao Hui , Zhang Yuandong , Yu Zhen , Gu Fengxue , Peng Zhongtong
Published: May 2022

The 2009/2010 extreme drought in southwest China (SWC) was a “once-in-a-century” drought event, which caused unprecedented damage to the regional ecology and socioeconomic development. The event provided a chance to explore the resilience of vegetation growth and productivity to the extreme drought. Here, we used the self-calibrating Palmer drought severity index (scPDSI) to describe the characteristics of the extreme drought. Vegetation growth and productivity indices, including the normalized difference vegetation index (NDVI), leaf area index (LAI), and gross primary productivity (GPP), were applied to analyze the resilience of different vegetation types to the extreme drought. Our results showed that the extreme drought event occurred mainly in Yunnan Province, Guizhou Province, central and northern Guangxi Zhuang Autonomous Region, and northwestern Sichuan Province. The spatial heterogeneity of the extreme drought was related to the temperature increase and water deficit. During the extreme drought, the vegetation growth and productivity of evergreen broadleaf forest were the least suppressed, whereas cropland was greatly suppressed. The recovery of cropland was higher than that of evergreen broadleaf forest. NDVI and LAI were recovered in more than 80% of the drought-affected area within 5 months, whereas GPP required a longer time to recover. Moreover, the results of multiple linear regression showed that an increase in surface soil moisture was able to significantly improve the resistance of vegetation NDVI and LAI in evergreen broadleaf forest, evergreen needleleaf forest, evergreen broadleaf shrubland, deciduous broadleaf shrubland, and grassland. Our study highlights the differences in the resilience of different vegetation types to extreme drought and indicates that surface soil moisture is an important factor affecting vegetation resistance in SWC.
Responses of Fungal Community Structure and Functional Composition to Short-Term Fertilization and Dry Season Irrigation in Eucalyptus urophylla × Eucalyptus grandis Plantation Soils
Authors: Gao Shangkun , He Qian , Huang Di , Wang Zhengmu , Mao Jianhui , Xie Xianan , Su Yan , Qiu Quan , Li Jiyue , Chen Zujing
Published: May 2022

Plantation forests productivity is severely limited by the seasonal drought and fertilization practices in South China. Soil nutrient and water availability influence soil fungal community, functional group diversity and the variation of plant productivity; however, the effects of irrigation and fertilization on fungal responses have rarely been studied. Here, we investigate the responses of fungal community structure and functional groups in Eucalyptus plantation soils to short-term fertilization (F), dry-season irrigation (W), short-term fertilization combined with dry-season irrigation (FW), and control (CK) treatments for ten months. A higher proportion of Basidiomycota was observed in the irrigation and/or fertilization treatments; conversely, lower proportions of Ascomycota and Mucoromycotina were observed in the only irrigation and fertilization treatments. Higher soil carbon contents and symbiotroph fungi (mainly Ectomycorrhizas) proportion were detected in the FW treatment, while low proportions of saprophytic and pathogenic fungi were observed in the FW treatment when compared with those in other treatments. These results may indicate that Eucalyptus tree growth under irrigation and fertilization condition was better than under fertilization only, irrigation only, or neither management. The results highlight that short-term fertilization and dry-season irrigation can shift fungal community structure and functional groups by regulating available soil moisture and nutrients. They also provide a theoretical basis for the development of more appropriate management approaches in the early stages of forest plantation.
Mapping China’s Forest Fire Risks with Machine Learning
Authors: Shao Yakui , Feng Zhongke , Sun Linhao , Yang Xuanhan , Li Yudong , Xu Bo , Chen Yuan
Published: May 2022

Forest fires are disasters that are common around the world. They pose an ongoing challenge in scientific and forest management. Predicting forest fires improves the levels of forest-fire prevention and risk avoidance. This study aimed to construct a forest risk map for China. We base our map on Visible Infrared Imaging Radiometer Suite data from 17,330 active fires for the period 2012–2019, and combined terrain, meteorology, social economy, vegetation, and other factors closely related to the generation of forest-fire disasters for modeling and predicting forest fires. Four machine learning models for predicting forest fires were compared (i.e., random forest (RF), support vector machine (SVM), multi-layer perceptron (MLP), and gradient-boosting decision tree (GBDT) algorithm), and the RF model was chosen (its accuracy, precision, recall, F1, AUC values were 87.99%, 85.94%, 91.51%, 88.64% and 95.11% respectively). The Chinese seasonal fire zoning map was drawn with the municipal administrative unit as the spatial scale for the first time. The results show evident seasonal and regional differences in the Chinese forest-fire risks; forest-fire risks are relativity high in the spring and winter, but low in fall and summer, and the areas with high regional fire risk are mainly in the provinces of Yunnan (including the cities of Qujing, Lijiang, and Yuxi), Guangdong (including the cities of Shaoguan, Huizhou, and Qingyuan), and Fujian (including the cities of Nanping and Sanming). The major contributions of this study are to (i) provide a framework for large-scale forest-fire risk prediction having a low cost, high precision, and ease of operation, and (ii) improve the understanding of forest-fire risks in China.
Optimal Proactive Caching for Multi-View Streaming Mobile Augmented Reality
Authors: Huang Zhaohui , Friderikos Vasilis
Published: May 2022

Mobile Augmented Reality (MAR) applications demand significant communication, computing and caching resources to support an efficient amalgamation of augmented reality objects (AROs) with the physical world in multiple video view streams. In this paper, the MAR service is decomposed and anchored at different edge cloud locations to optimally explore the scarce edge cloud resources, especially during congestion episodes. In that way, the proposed scheme enables an efficient processing of popular view streams embedded with AROs. More specifically, in this paper, we explicitly utilize the notion of content popularity not only to synthetic objects but also to the video view streams. In this case, popular view streams are cached in a proactive manner, together with preferred/popular AROs, in selected edge caching locations to improve the overall user experience during different mobility events. To achieve that, a joint optimization problem considering mobility, service decomposition, and the balance between service delay and the preference of view streams and embedded AROs is proposed. To tackle the curse of dimensionality of the optimization problem, a nominal long short-term memory (LSTM) neural network is proposed, which is trained offline with optimal solutions and provides high-quality real-time decision making within a gap between 5.6% and 9.8% during inference. Evidence from a wide set of numerical investigations shows that the proposed set of schemes owns around 15% to 38% gains in delay and hence substantially outperforms nominal schemes, which are oblivious to user mobility and the inherent multi-modality and potential decomposition of the MAR services.
The Effect of Hormonal Treatment on Selected Sperm Quality Parameters and Sex Steroids in Tropical Cyprinid Bala Shark Balantiocheilos melanopterus
Authors: Podhorec Peter , Knowles Jindřiška , Vysloužil Jakub , Boryshpolets Sergii , Sotnikov Anatolii , Holická Martina , Kouřil Jan , Dzyuba Borys
Published: May 2022

Hormone treatments are routinely employed with bala shark Balantiocheilos melanopterus male broodstock to stimulate the production of high-quality sperm. In the current trial, three spermiation-inducing treatments were evaluated: 20 μg/kg body weight (BW) recombinant hCG; 20 μg/kg BW [D-Ala6, Pro9, NEt]- gonadotropin-releasing hormone (GnRHa) with 20 mg/kg BW metoclopramide; and poly(lactic-co-glycolic acid) (PLGA) microparticles with slow release of 10 μg/kg BW GnRHa. A 0.9% saline AS negative control was also included. Administration of the GnRHa through the form of slow release of PLGA microparticles 24 h after treatment resulted in a significantly higher sperm volume, motility percentage, and total sperm count compared to the control. Injection of GnRHa with metoclopramide induced sperm parameters that did not differ from the control, with the exception of motility percentage. The lowest potency to induce spermiation in bala sharks was in the treatment with recombinant hCG. Both PLGA microparticles and GnRHa with metoclopramide significantly increased blood plasma concentrations of testosterone and 11-ketotestosterone compared to the control. The PLGA microparticle system with continuous release of 10 μg/kg BW GnRHa was the most effective treatment in inducing spermiation in bala sharks.
Assessing the Gel Quality and Storage Properties of Hypophythalmalmichthys molitrix Surimi Gel Prepared with Epigallocatechin Gallate Subject to Multiple Freeze-Thaw Cycles
Authors: Tian Zhihang , Jiang Xin , Xiao Naiyong , Zhang Qiang , Shi Wenzheng , Guo Quanyou
Published: May 2022

Epigallocatechin gallate (EGCG) with concentrations of 0–0.03% was added to manufacture surimi gels, respectively, while effects on gel quality and storage properties indicators during freeze-thaw (F-T) cycles were investigated. The results implied that the gel quality and storage properties of surimi gels added without EGCG were seriously destroyed during F-T cycles. The addition of EGCG could inhibit the decline of texture and gel strength. Moreover, EGCG has effect on inhibiting the microbial growth and the formation of off-odor compounds such as total volatile basic nitrogen (TVB-N) and malondialdehyde (MDA). Low-field nuclear magnetic resonance (LF-NMR) and water-holding capacity (WHC) results showed that immobilized water migrated to free water with the extension of F-T cycles. The scanning electron microscope (SEM) observed denser protein networks and smaller holes from the surimi gels added with EGCG. However, excessive (0.03%) EGCG showed the loose network structure and moisture loss. Overall, EGCG in 0.01–0.02% addition was good for resisting damage of surimi gels during F-T cycles.
Risk Assessment and Evaluation of Analytical Method of Polycyclic Aromatic Hydrocarbons (PAHs) for Deep-Fat Fried Pork Products in Korea
Authors: Kim Seo Yeon , Shin Hye Won , Kim Geon Hee , Kim Yong-Yeon , Kang Min-Jae , Shin Han-Seung
Published: May 2022

Polycyclic aromatic hydrocarbons (PAHs) are produced during incomplete combustion of organic matter. Many of them are likely to be carcinogenic and cause mutations. In this study, the PAH4 (benzo[a]pyrene (BaP), benz[a]anthracene (BaA), chrysene (CHR), benzo[b]fluoranthene (BbF)) content in deep-fat fried pork was evaluated according to temperature and time, and a risk assessment was conducted. The high performance liquid chromatography-fluorescence detection (HPLC-FLD) method for PAH4 analysis was validated by determining linearity (R2), recovery, limit of detection (LOD), and limit of quantitation (LOQ). The linearity was R2 ≥ 0.99. The PAH4 level was dependent on the temperature, time, and nature of the edible oil. Before heat treatment, the PAH4 content of pork was 0.38 μg/kg. The PAH4 content of deep-fat fried pork ranged from 0.86 to 6.86 μg/kg according to temperature (160, 180, 200 °C) and time (3, 6, 9 min). Exposure to PAH4 via the consumption of deep-fat fried pork for different age groups among the Korean population was 0.01–0.89 μg-TEQBaP/kg/day, with the margin of exposure calculated as 7.88 × 104–5.22 × 106. The PAH4 content and risk of exposure increased proportionally with the heat treatment temperature and time. The survey provided important information in terms of evaluating the health risks that PAH compounds can cause in people’s diets due to the heat treatment of pork.
Genome-Wide Identification and Analysis of the NAC Transcription Factor Gene Family in Garden Asparagus (Asparagus officinalis)
Authors: Li Caifeng , Zhang Jingyang , Zhang Qianqian , Dong Ang , Wu Qiuhong , Zhu Xingyu , Zhu Xuli
Published: May 2022

As a large plant-specific gene family, the NAC (NAM, ATAF1/2, and CUC2) transcription factor is related to plant growth, development, and response to abiotic stresses. Although the draft genome of garden asparagus (Asparagus officinalis) has been released, the genome-wide investigation of the NAC gene family is still unavailable. In this study, a total of 85 A. officinalis NAC genes were identified, and a comprehensive analysis of the gene family was performed, including physicochemical properties, phylogenetic relationship, chromosome localization, gene structure, conserved motifs, intron/exon, cis-acting elements, gene duplication, syntenic analysis, and differential gene expression analysis. The phylogenetic analysis demonstrated that there were 14 subgroups in both A. officinalis and Arabidopsis thaliana, and the genes with a similar gene structure and motif distribution were clustered in the same group. The cis-acting regulatory analysis of AoNAC genes indicated four types of cis-acting elements were present in the promoter regions, including light-responsive, hormone-responsive, plant-growth-and-development-related, and stress-responsive elements. The chromosomal localization analysis found that 81 NAC genes in A. officinalis were unevenly distributed on nine chromosomes, and the gene duplication analysis showed three pairs of tandem duplicated genes and five pairs of segmental duplications, suggesting that gene duplication is possibly associated with the amplification of the A. officinalis NAC gene family. The differential gene expression analysis revealed one and three AoNAC genes that were upregulated and downregulated under different types of salinity stress, respectively. This study provides insight into the evolution, diversity, and characterization of NAC genes in garden asparagus and will be helpful for future understanding of their biological roles and molecular mechanisms in plants.
Comprehensive Genome-Wide Analysis of Histone Acetylation Genes in Roses and Expression Analyses in Response to Heat Stress
Authors: Wu Quanshu , Huang Qiuyue , Guan Huilin , Zhang Xiaoni , Bao Manzhu , Bendahmane Mohammed , Fu Xiaopeng
Published: May 2022

Roses have high economic values as garden plants and for cut-flower and cosmetics industries. The growth and development of rose plants is affected by exposure to high temperature. Histone acetylation plays an important role in plant development and responses to various stresses. It is a dynamic and reversible process mediated by histone deacetylases (HDAC) and histone acetyltransferases (HAT). However, information on HDAC and HAT genes of roses is scarce. Here, 23 HDAC genes and 10 HAT genes were identified in the Rosa chinensis ‘Old Blush’ genome. Their gene structures, conserved motifs, physicochemical properties, phylogeny, and synteny were assessed. Analyses of the expression of HDAC and HAT genes using available RNAseq data showed that these genes exhibit different expression patterns in different organs of the three analyzed rose cultivars. After heat stress, while the expression of most HDAC genes tend to be down-regulated, that of HAT genes was up-regulated when rose plants were grown at high-temperature conditions. These data suggest that rose likely respond to high-temperature exposure via modification in histone acetylation, and, thus, paves the way to more studies in order to elucidate in roses the molecular mechanisms underlying rose plants development and flowering.
Evaluation of the Effects of Different Sample Collection Strategies on DNA/RNA Co-Analysis of Forensic Stains
Authors: Lacerenza Daniela , Caudullo Giorgio , Chierto Elena , Aneli Serena , Di Vella Giancarlo , Barberis Marco , Voyron Samuele , Berchialla Paola , Robino Carlo
Published: May 2022

The aim of this study was to evaluate the impact of different moistening agents (RNase-free water, absolute anhydrous ethanol, RNAlater®) applied to collection swabs on DNA/RNA retrieval and integrity for capillary electrophoresis applications (STR typing, cell type identification by mRNA profiling). Analyses were conducted on whole blood, luminol-treated diluted blood, saliva, semen, and mock skin stains. The effects of swab storage temperature and the time interval between sample collection and DNA/RNA extraction were also investigated. Water provided significantly higher DNA yields than ethanol in whole blood and semen samples, while ethanol and RNAlater® significantly outperformed water in skin samples, with full STR profiles obtained from over 98% of the skin samples collected with either ethanol or RNAlater®, compared to 71% of those collected with water. A significant difference in mRNA profiling success rates was observed in whole blood samples between swabs treated with either ethanol or RNAlater® (100%) and water (37.5%). Longer swab storage times before processing significantly affected mRNA profiling in saliva stains, with the success rate decreasing from 91.7% after 1 day of storage to 25% after 7 days. These results may contribute to the future development of optimal procedures for the collection of different types of biological traces.
Association of Blood Heavy Metal Levels and Renal Function in Korean Adults
Authors: Park Yoonjin , Lee Su-Jung
Published: May 2022

This study aimed to investigate the association between the levels of lead, mercury, cadmium, and nickel in the blood and renal function and to provide basic data for the development of health programs for the prevention of renal failure. This study included 1984 participants aged 19 and older who participated in the Korean National Health and Nutrition Examination Survey from 2019. Differences in values according to general characteristics and the estimated glomerular filtration rate (eGFR) stage were analyzed using a chi-square test, t-test, ANOVA, and the influencing factors were analyzed through multiple regression analysis. The concentrations of lead, mercury, cadmium, and nickel, and the risk of elevated eGFR were analyzed using linear regression. The correlation between the variables was determined using Pearson’s correlation analysis. Analysis of the correlation between blood lead, mercury, cadmium, and nickel levels and blood eGFR levels revealed that blood eGFR levels were significantly negatively correlated with blood lead, cadmium, and nickel levels (p ≤ 0.001). This study is significant in that it found a significant association between decreased eGFR and heavy metal levels and provided meaningful basic data on the association between heavy metals and renal function.
Development and Validation of a Clinical Practicum Assessment Tool for the NAACLS-Accredited Biomedical Science Program
Authors: Abunada Taghreed , Abdallah Atiyeh M. , Singh Rajvir , Abu-Madi Marawan
Published: May 2022

Student perspectives on their final year clinical placements in biomedical sciences at Qatar University are assessed using the clinical practicum assessment tool (CPAT), which was developed in-house following accreditation body requirements. The tool, which we call the CPAT-Qatar University (CPAT-QU), covers the three clinical practicum domains: practicum content, preceptors, and competencies. Here, we validate this tool. The CPAT-QU has 27 Likert-scale questions and free-text open questions. CPAT-QU readability was calculated using the Flesch–Kincaid Reading Ease (FKRE) instrument. Content validity was assessed using the average and universal average scale-level content validity indices (S-CVI/Average and S-CVI/UA). For construct validity, 50 employed graduates who had completed the practicum were consented for study participation, and the validity was calculated by a principal component analysis (PCA). Reliability was analyzed by Cronbach’s alpha. The S-CVI/Average and S-CVI/UA were 0.90 and 0.59, respectively, indicating that an adequate proportion of the content was relevant. The PCA extracted two core components, which explained 63% of the variance in the CPAT-QU. Cronbach’s alpha values for the items were within the acceptable range of 0.60–1.00, showing that internal consistency has a good level. CPAT-QU appears to be a useful tool for assessing student perspectives on their clinical placements; however, construct validity needs continuous improvement.
Cancer Screening Program Delivered by Community Health Workers for Chinese Married Immigrant Women in Korea
Authors: Kim Jiyun , Paik Yuna , Park Seungmi
Published: May 2022

This quasi-experimental study used a cancer prevention program delivered by community health workers (CHWs) as an intervention to improve health literacy and increase screening uptake. The intervention group was educated by trained CHWs and received information about the nearby hospitals. After education, participants received telephone counseling once a month for 6 months. In the intervention group, CHWs met the participants individually and delivered the CD-ROM containing conversation scenarios with voice during the cancer screening test. The control group was provided educational materials related to breast and cervical cancers and a booklet containing information on mammography and Pap test. This study assessed the difference in mammography and Pap tests between the intervention and control groups. The results showed that the participants’ knowledge improved, and the rate of cancer screening tests did not decrease in the intervention group. Therefore, it is necessary to develop and apply various programs that train CHWs and apply them to immigrant women to promote health-related behaviors under the health system that they are not familiar with while living in another country.