bluuebunny/crossref_metadata_2025 huggingface dataset:
Effectiveness of Educational Interventions for Health Workers on Antibiotic Prescribing in Outpatient Settings in China: A Systematic Review and Meta-Analysis
Authors: Zheng Kunhua , Xie Ying , Dan Lintao , Mao Meixian , Chen Jie , Li Ran , Wang Xuanding , Hesketh Therese
Published: June 2022

Educational interventions are considered an important component of antibiotic stewardship, but their effect has not been systematically evaluated in outpatient settings in China. This research aims to evaluate the effectiveness of educational interventions for health workers on antibiotic prescribing rates in Chinese outpatient settings. Eight databases were searched for relevant randomized clinical trials, non-randomized trials, controlled before–after studies and interrupted time-series studies from January 2001 to July 2021. A total of 16 studies were included in the systematic review and 12 in the meta-analysis. The results showed that educational interventions overall reduced the antibiotic prescription rate significantly (relative risk, RR 0.72, 95% confidence interval, CI 0.61 to 0.84). Subgroup analysis demonstrated that certain features of education interventions had a significant effect on antibiotic prescription rate reduction: (1) combined with compulsory administrative regulations (RR With: 0.65 vs. Without: 0.78); (2) combined with financial incentives (RR With: 0.51 vs. Without: 0.77). Educational interventions can also significantly reduce antibiotic injection rates (RR 0.83, 95% CI 0.74 to 0.94) and the inappropriate use of antibiotics (RR 0.61, 95% CI 0.51 to 0.73). The limited number of high-quality studies limits the validity and reliability of the results. More high-quality educational interventions targeting the reduction of antibiotic prescribing rates are needed.
Active Fault-Tolerant Control Scheme for Unmanned Air-Ground Attitude System with Time-Varying Delay Faults
Authors: Zhao Xianfa , Li Xingwei , Zhang Jiazhe , Zhang Zheng
Published: June 2022

This paper reports a designed method of fault diagnosis, estimation, and fault-tolerant control aiming at solving the problems of time–delay variation of system parameters, actuator time-varying failure, and external disturbance under the flight mode of air-ground platform. Firstly, a robust fault observer is designed to accurately detect the fault of unmanned air-ground attitude system with time-varying parameter delay and reduce the false alarm rate through reasonable assumptions; secondly, considering the actual computing power of the system, the method of estimating the overall fault size of the system instead of estimating each sub fault separately is adopted to reduce the memory space and computation. Then, based on the fault diagnosis and estimation, the fault-tolerant control rate is designed, the integral term is reasonably introduced to eliminate the chattering problem in the fault-tolerant control, and the appropriate nonlinear function is selected as the ideal control input to optimize the transient performance of the system. Finally, the stability of the system is proved, and the effectiveness of the proposed method is verified by simulation.
Fault Diagnosis of a Solenoid Valve Based on Multi-Feature Fusion
Authors: Ma Dong , Liu Zhihao , Gao Qinhe , Huang Tong
Published: June 2022

This paper presents a non-intrusive solenoid valve fault diagnosis method, which adopts the eigenvalue extraction scheme and combines the time-frequency analysis and time-domain analysis of the current signal through the solenoid valve fault pattern recognition method. Specifically, the research group selected the solenoid valve driving end current value and its second-order change rate curve as the characteristic curve, and then it extracted the time-domain parameters and the corresponding frequency band energy of the second-order change rate as the characteristic value. Then, the research group conducted time-frequency analysis on the basis of the current signal, created a multi-feature fusion feature vector and then input it into a multi-class support vector machine (SVM) for solenoid valve fault diagnosis. To evaluate the method, the research group developed a working solenoid valve model that integrated a magnetic circuit, circuit and spool motion, and the research group examined four typical solenoid valve states (normal, spring broken, spool stuck and spool slightly stuck), for which the research group collected the current signals during solenoid valve energization and closing. The trials involved analyzing the current characteristics of the drive end, both in simulation and experimental setups, and the research group demonstrated that the proposed drive end current detection diagnosis method can improve the accuracy of recognition by 15.22% compared with traditional neural network recognition methods. The SVM strategy exploiting a multi-feature fusion strategy can improve recognition accuracy by 8.7% and verification accuracy by 42.11% compared with SVM on the basis of the energy feature values.
Autonomous Temporal Pseudo-Labeling for Fish Detection
Authors: Veiga Ricardo J. M. , Ochoa Iñigo E. , Belackova Adela , Bentes Luís , Silva João P. , Semião Jorge , Rodrigues João M. F.
Published: June 2022

The first major step in training an object detection model to different classes from the available datasets is the gathering of meaningful and properly annotated data. This recurring task will determine the length of any project, and, more importantly, the quality of the resulting models. This obstacle is amplified when the data available for the new classes are scarce or incompatible, as in the case of fish detection in the open sea. This issue was tackled using a mixed and reversed approach: a network is initiated with a noisy dataset of the same species as our classes (fish), although in different scenarios and conditions (fish from Australian marine fauna), and we gathered the target footage (fish from Portuguese marine fauna; Atlantic Ocean) for the application without annotations. Using the temporal information of the detected objects and augmented techniques during later training, it was possible to generate highly accurate labels from our targeted footage. Furthermore, the data selection method retained the samples of each unique situation, filtering repetitive data, which would bias the training process. The obtained results validate the proposed method of automating the labeling processing, resorting directly to the final application as the source of training data. The presented method achieved a mean average precision of 93.11% on our own data, and 73.61% on unseen data, an increase of 24.65% and 25.53% over the baseline of the noisy dataset, respectively.
Extraoral Scanning of Implant Prosthetic Components under Difficult Conditions Using a Transfer Device
Authors: Petre Alexandru Eugen , Drafta Sergiu , Oancea Luminita
Published: June 2022

Obtaining an extremely precise virtual model is decisive for the final success of prosthetic restorations on implants using CAD–CAM technology. Intraoral digital implant impression can be challenging under specific conditions (e.g., narrow spaces, lack of visibility, or subgingival margins) because sometimes the scan body does not reach an optimal supragingival level so that it can be scanned accurately. We propose an easy and quick solution, which uses an extraoral transfer device that requires minimum time and cost investment.
Analysis of the Effects and Causes of Driver Horn Use on the Acoustic Environment at Urban Intersections in Taiwan
Authors: Takada Masayuki , Tsunekawa Shoki , Hashimoto Kazuma , Inada Tamaki , Kim Ki-Hong , Oeda Yoshinao , Yamauchi Katsuya , Iwamiya Shin-ichiro
Published: June 2022

Car horns were originally installed in vehicles for safety. However, many urban areas in several countries face noise problems related to the use of car and motorbike horns. To propose measures to suppress the use of horns, relationships between horn use and factors including driver awareness and behavior, traffic environment, and the transportation system should be investigated. The present study therefore conducted surveys to grasp the current circumstances of horn use and traffic at urban intersections in Taiwan. The relationship between horn use and the traffic volume of standard-sized vehicles was found. According to an analysis of horn use during traffic signal cycles, in many cases, horns were honked after entering intersections to turn left. In particular, horns were honked when the driver waited more than 4 s for the car in front to start moving after the green light allowing left turns was turned on. An analysis of noise levels at intersections showed that the maximum noise level value (LAmax) could be reduced if vehicle horns were not used. Multiple regression analysis also indicated that LAmax values increased with the frequency of horn use. The equivalent continuous A-weighted sound pressure level (LAeq,10min) did not change with driver horn use, and increased with the traffic volume of motorcycles.
Smart Agriculture Applications Using Deep Learning Technologies: A Survey
Authors: Altalak Maha , Ammad uddin Mohammad , Alajmi Amal , Rizg Alwaseemah
Published: June 2022

Agriculture is considered an important field with a significant economic impact in several countries. Due to the substantial population growth, meeting people’s dietary needs has become a relevant concern. The transition to smart agriculture has become inevitable to achieve these food security goals. In recent years, deep learning techniques, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), have been intensely researched and applied in various fields, including agriculture. This study analyzed the recent research articles on deep learning techniques in agriculture over the previous five years and discussed the most important contributions and the challenges that have been solved. Furthermore, we investigated the agriculture parameters being monitored by the internet of things and used them to feed the deep learning algorithm for analysis. Additionally, we compared different studies regarding focused agriculture area, problems solved, the dataset used, the deep learning model used, the framework used, data preprocessing and augmentation method, and results with accuracy. We concluded in this survey that although CNN provides better results, it lacks in early detection of plant diseases. To cope with this issue, we proposed an intelligent agriculture system based on a hybrid model of CNN and SVM, capable of detecting and classifying plant leaves disease early.
Inhibition of TP53 Mutant Oral Cancer by Reactivating p53
Authors: Kang Yei-Jin , Kim Dae-Won , Che Xiangguo , Choi Je-Yong , Kim Seong-Gon
Published: June 2022

Background: Mutation of p53 is a frequent event, and mutant p53 exhibits low levels of acetylation and phosphorylation. This study aimed to investigate the effect of the histone deacetylase (HDAC) inhibitor, 4-hexylresorcinol (4HR), on the acetylation and phosphorylation of mutant p53 carcinoma cells and its therapeutic effects in a xenograft model. Methods: To determine the effect of 4HR on the acetylation and phosphorylation of p53, western blot analysis was performed using YD-9 and YD-15 cells. p53 siRNA was used to examine whether 4HR acts in a p53-dependent or independent manner. This was evaluated using a xenograft model. Results: In in vitro experiments when the concentration of 4HR was increased, the expression levels of HDAC4, acetylated p53 (Ac-p53), and phosphorylated p53 (p-p53) increased. Transfection with TP53 siRNA successfully suppressed p53 protein and TP53 mRNA expression. When 4HR was administered to a xenograft model, the tumour expansion rate was suppressed compared with the control, and the mice exhibited a higher survival rate. Conclusions: Our findings reveal that 4HR is a potential agent that restores loss of function in mutant p53 cancer cells via acetylation and phosphorylation of p53 as well as inhibition of HDAC4.
Modelling and Control of Mechatronic and Robotic Systems, Volume II
Authors: Gasparetto Alessandro , Seriani Stefano , Scalera Lorenzo
Published: June 2022

In modern times, mechatronic and robotic systems are developing at a faster pace than in the past [...]
Study on Risk of Long-Steep Downgrade Sections of Expressways Based on a Fuzzy Hierarchy Comprehensive Evaluation
Authors: Zhao Yifei , Li Jingru , Ying Xinzhi
Published: June 2022

The long-steep downgrade sections of expressways are characterized by a large elevation difference, poor horizontal and vertical alignment, and the easy failure of brakes on large trucks. They are sections with a high overall operation safety risk. It is necessary to strengthen the research on traffic risk evaluation. In order to study the traffic safety risks of long-steep downgrade parts of expressways, the fuzzy hierarchical comprehensive evaluation method is used to establish the calculation model. First, an evaluation index system including the target level, rule level, first-level index level and second-level index level is established. The qualitative and quantitative indicators are processed by the set value statistical method and the linear standard method, respectively, so that all indicators can be quantitatively evaluated together. Then, each indicator is assigned a score and divided into five risk levels, and a ridge-shaped fuzzy distribution is used to constitute a membership function for each level. A hierarchical structure model is established with the analytic hierarchy process to determine the affiliation between the upper and lower levels, and the relative weight of each level to the upper level also can be obtained. Finally, according to the hierarchical relevance of each evaluation indicator, a three-level fuzzy comprehensive evaluation model is constructed. The traffic risk evaluation level for long-steep downgrade sections can be obtained, and the probability of the corresponding risk evaluation level can be calculated. Through the risk evaluation of the long-steep downgrade sections of the Fuzhou Yinchuan Expressway in China, this shows that the risk evaluation conclusion obtained by using this evaluation method is consistent with the actual traffic safety situation, which shows that the traffic safety risk evaluation model based on a fuzzy hierarchy comprehensive evaluation is operable.
MBHAN: Motif-Based Heterogeneous Graph Attention Network
Authors: Hu Qian , Lin Weiping , Tang Minli , Jiang Jiatao
Published: June 2022

Graph neural networks are graph-based deep learning technologies that have attracted significant attention from researchers because of their powerful performance. Heterogeneous graph-based graph neural networks focus on the heterogeneity of the nodes and links in a graph. This is more effective at preserving semantic knowledge when representing data interactions in real-world graph structures. Unfortunately, most heterogeneous graph neural networks tend to transform heterogeneous graphs into homogeneous graphs when using meta-paths for representation learning. This paper therefore presents a novel motif-based hierarchical heterogeneous graph attention network algorithm, MBHAN, that addresses this problem by incorporating a hierarchical dual attention mechanism at the node-level and motif-level. Node-level attention aims to learn the importance between a node and its neighboring nodes within its corresponding motif. Motif-level attention is capable of learning the importance of different motifs in the heterogeneous graph. In view of the different vector space features of different types of nodes in heterogeneous graphs, MBHAN also aggregates the features of different types of nodes, so that they can jointly participate in downstream tasks after passing through segregated independent shallow neural networks. MBHAN’s superior network representation learning capability has been validated by extensive experiments on two real-world datasets.
Correlations between Anthropometric Measurements and Sports Discipline Aptitude
Authors: Wiacek Magdalena , Tomasiuk Ryszard , Zubrzycki Igor Z.
Published: June 2022

Background: Sports specialization is required for the advancement of elite-level skills of a competitor. Therefore, this study attempted to assess the applicability of anthropometric measurements for a tailored selection of sports disciplines. Methods: The sports disciplines studied in this report were wrestling, triple jumping, badminton, and tennis. The data used in this study were obtained from a PubMed search. Literature-derived data were used as a template to build a random Gaussian population of N = 500 subjects used for ratio calculation using the error propagation approach. The obtained ratios encompassed height/sitting height, height/length, height/arm length, height/waist circumference, height/chest circumference, sitting height/leg length, sitting height/arm length, sitting height/waist circumference, sitting height/chest circumference, arm/leg length, and arm length/forearm length. Results: There is a clear relationship between a sports discipline and the distribution of the anthropometric ratio. The anthropometric measurements of wrestlers are the most outstanding among the disciplines studied. The use of machine learning algorithms, that is, the decision tree classifier, allows for building a model able to distinguish between the disciplines of sports studied. Conclusions: The presented approach allows for selection of a specific sports discipline for a young person. Moreover, an extension of the model built by other sports disciplines and anthropometric measurement may be a practical tool for selecting sports subjects.
Supercritical Extraction of a Natural Pyrethrin-Rich Extract from Chrysanthemum Cinerariifolium Flowers to Be Impregnated into Polypropylene Films Intended for Agriculture Applications
Authors: Maya Claudia , Mantell Casimiro , Martínez de la Ossa Enrique J. , Casas Lourdes
Published: June 2022

The extensive use of synthetic pesticides and their addition to the field presents significant environmental problems that must be minimized. The use of natural insecticides and their addition using techniques that minimize their impact in the field are widely studied by the current scientific community. In this work an extraction method based on supercritical CO2 to obtain a pyrethrin-rich natural extract from different varieties of chrysanthemum flowers is analyzed. This extract would be used in a supercritical solvent impregnation (SSI) process to produce a commercial polypropylene (PP) film with insecticidal properties to be used in greenhouses. The extract selected for the impregnation process was that obtained from the Atlantis variety at 35 °C and 10 MPa pressure. The amount of insecticide impregnated into the polymer at 55 °C and under two different pressure levels (10 MPa and 40 MPa) have been determined. A batch impregnation method (BM) with 5 h constant impregnation time and low depressurization rates were used to favor the impregnation process. The results demonstrated that this procedure was suitable to produce pyrethrin-loaded PP films that could to be used in greenhouses as a protection against pests, while allowing a more rational and moderate use of other chemical pesticides.
Surface Electromagnetic Waves near a Black Hole Event Horizon and Their Observational Consequences
Authors: Smolyaninov Igor I.
Published: June 2022

Localization phenomena in light, scattering from random fluctuations of matter fields and space–time metrics near a black hole horizon, were predicted to produce a pronounced peak in the angular distribution of second-harmonic light in the direction normal to the horizon. Therefore, the detection of second-harmonic generation may become a viable observational tool to study spacetime physics near event horizons of astronomical black holes. The light localization phenomena near the horizon may be facilitated by the existence of surface electromagnetic wave solutions. In this communication, we study such surface electromagnetic wave solutions near the horizon of a Schwarzschild metric, describing a black hole in vacuum. We demonstrate that such surface wave solutions must appear when quantum gravity effects are taken into account. Potential observational evidence of this effect is also discussed.
Study on Determination of Excessive Emissions of Heavy Diesel Trucks Based on OBD Data Repaired
Authors: He Weinan , Zheng Xiaobin , Zhang Yumeng , Han Yuan
Published: June 2022

It has been recognized that emission control for heavy diesel trucks should be given priority, as a massive amount of pollutants (e.g., NOx) are emitted from heavy diesel trucks. Although pollutants can be filtered to a considerable extent by after-treatment devices equipment, emissions can still exceed the designated standards when after-treatment devices function improperly. To timely identify excessive emissions, we propose a general and systematic framework, including a data quality assessment and a data repairing and excessive emission determination process, based on the data sensed from the on-board diagnostics (OBD) monitoring system. To overcome the adverse effects of poor data quality, a set of approaches have been developed for the different statuses of data quality. When all variables contain missing or abnormal values, data repairing algorithms can be employed to improve data quality. Two strategies have been developed for the situation where only the NOx data is problematic. One is to improve data quality by using the other variables before identifying excessive emissions, and the other is to directly predict whether the emissions exceed recommendations by using other variables without the data quality problem. To reduce the impact of noise and extreme values, three methods based on the moving average principle have been developed to generate an aggregated emission level for the determination of excessive emissions. In the experimental study, we employed a number of machine learning algorithms to achieve data repairing and prediction. The support vector machine (SVM) algorithm slightly outperforms the random forests (RF) and gradient boosting decision tree (GBDT) in the prediction of the excessive emission possibility in terms of prediction accuracy. The experimental results indicate that the most accurate data repairing can be achieved by probabilistic principal component analysis (PPCA), as compared to non-negative matrix factorization (NNMF) and k-nearest neighbor (KNN). However, the proposed approach does not restrict other algorithms from achieving the functions of data repairing and prediction.
Comparison of the Forecast Performance of WRF Using Noah and Noah-MP Land Surface Schemes in Central Asia Arid Region
Authors: Ju Chenxiang , Li Huoqing , Li Man , Liu Zonghui , Ma Yufen , Mamtimin Ali , Sun Mingjing , Song Yating
Published: June 2022

The land surface scheme (LSS) plays a very important role in the forecast of surface meteorological elements in the Central Asia arid region. Therefore, two LSSs viz. Noah and Noah-MP were evaluated over the Central Asia arid region in January and July 2017 using the Weather Research and Forecasting (WRF) model at a 3-km horizontal grid resolution. The objective was to assess the performance of WRF LSSs by calculated the mean error (ME) and the root mean squared error (RMSE) of simulated hourly meteorological elements, such as 2-m air temperature, 10-m wind speed, soil temperature, soil moisture at 5 and 25 cm thickness, and surface soil heat flux at 5 cm thickness. The results showed that, compared to Noah, Noah-MP modeled less surface sensible heat flux in the northern Xinjiang (15~20 W∙m−2) and surface latent heat flux in most areas of Xinjiang (<10 W∙m−2) in January, and mainly generated less sensible heat flux in most areas of north Xinjiang and the mountainous regions of southern Xinjiang (≤20 W·m−2) which on the contrary, generated more surface latent heat flux in most parts of Xinjiang (15~20 W·m−2) in July. Meanwhile, the surface soil heat flux generated from Noah-MP was closer to the observations at Hongliuhe and Kelameili stations in January, the ME increased by 17.5% and reduced by 80.7%, respectively, the RMSE decreased by 44.4% and 61.7%, respectively, and closer to the observations at Xiaotang station in July, the ME and RMSE reduced by 19.1% and 20.5%, respectively. Compared to Noah, Noah-MP improved the overall simulation of soil temperature and soil moisture over the northern and eastern Xinjiang (at 10 cm thickness), the ME and RMSE of simulated soil temperature reduced by 85.0% and 13.4% in January, decreased by 78.6% and increased by 6.2% in July, respectively, and the ME and RMSE of simulated soil moisture reduced by 67.2% and 14.9% in January, reduced by 33.3% and 2.8% in July, respectively. Compared to Noah, Noah-MP’s results were lowered for the simulated 10-m wind speed and 2-m air temperature, especially the simulated 2-m air temperature over the cold climate regions of northern Xinjiang, was improved significantly, the ME and RMSE of simulated 10-m wind speed reduced by 0.8% and 4.9% in January, decreased by 6.7% and 2.8% in July, respectively, the ME and RMSE of simulated 2-m air temperature reduced by 2.8% and 1.0% in July, respectively. This study demonstrated the advantage of coupled Noah-MP over the Central Asia arid region, providing the basis for WRF/Noah-MP in future operational applications in the Central Asia arid region.
Meteonetwork: An Open Crowdsourced Weather Data System
Authors: Giazzi Marco , Peressutti Gianandrea , Cerri Luca , Fumi Matteo , Riva Isabella Francesca , Chini Andrea , Ferrari Gianluca , Cioni Guido , Franch Gabriele , Tartari Gianni , Galbiati Flavio , Condemi Vincenzo , Ceppi Alessandro
Published: June 2022

Citizen science has shown great potential for bringing large groups of people closer to science, thanks in part to cooperation with universities and research centers. In this context, amateur weather networks played a major role in the last few decades thanks to a constant growth in technology. An example is given by the Meteonetwork association, born in 2002, and mainly composed by atmospheric science enthusiasts, who built up in time a huge weather station network in Italy. In recent years, they have enlarged their horizons over Europe, displaying real time observations and daily maps coming from both personal weather stations and official networks. This study described how Meteonetwork has set up an open crowdsourced weather data system, how data are validated, and which products are generated and freely accessible for scientists and stakeholders for their own purposes. Two concrete use cases were described as examples: the Weatherness Project, which selects a subnet of Meteonetwork data for biometeorological and health purposes and the data assimilation process implemented to improve the initial conditions into the WRF meteorological model for daily weather forecasts.
Summer Precipitation Forecast Using an Optimized Artificial Neural Network with a Genetic Algorithm for Yangtze-Huaihe River Basin, China
Authors: Zhang Zhi-Cheng , Zeng Xin-Min , Li Gen , Lu Bo , Xiao Ming-Zhong , Wang Bing-Zeng
Published: June 2022

Owing to the complexity of the climate system and limitations of numerical dynamical models, machine learning based on big data has been used for climate forecasting in recent years. In this study, we attempted to use an artificial neural network (ANN) for summer precipitation forecasts in the Yangtze-Huaihe River Basin (YHRB), eastern China. The major ANN employed here is the standard backpropagation neural network (BPNN), which was modified for application to the YHRB. Using the analysis data of precipitation and the predictors/factors of atmospheric circulation and sea surface temperature, we calculated the correlation coefficients between precipitation and the factors. In addition, we sorted the top six factors for precipitation forecasts. In order to obtain accurate forecasts, month (factor)-to-month (precipitation) forecast models were applied over the training and validation periods (i.e., summer months over 1979–2011 and 2012–2019, respectively). We compared the standard BPNN with the BPNN using a genetic algorithm-based backpropagation (GABP), support vector machine (SVM) and multiple linear regression (MLR) for the summer precipitation forecast after the model training period, and found that the GABP method is the best among the above methods for precipitation forecasting, with a mean absolute percentage error (MAPE) of approximately 20% for the YHRB, which is substantially lower than the BPNN, SVM and MLR values. We then selected the best summer precipitation forecast of the GABP month-to-month models by summing up monthly precipitation, in order to obtain the summer scale forecast, which presents a very successful performance in terms of evaluation measures. For example, the basin-averaged MAPE and anomaly rate reach 4.7% and 88.3%, respectively, for the YHRB, which can be a good recommendation for future operational services. It appears that sea surface temperatures (SST) in some key areas dominate the factors for the forecasts. These results indicate the potential of applying GABP to summer precipitation forecasts in the YHRB.
Analysis of Pre-Earthquake Space Electric Field Disturbance Observed by CSES
Authors: Li Zhong , Yang Baiyi , Huang Jianping , Yin Huichao , Yang Xuming , Liu Haijun , Zhang Fuzhi , Lu Hengxin
Published: June 2022

In order to explore the abnormal disturbance of the space electric field caused by earthquakes using the electric field data of the ULF and VLF frequency bands of the electric field observed by the ZH-1 satellite, and taking the Mw7.7 earthquake in the Caribbean Sea in the southern sea area of Cuba on 29 January 2020 as an example, the signal-to-noise ratio of the NAA and NLK artificial source VLF transmitting stations in the Northern Hemisphere and the height of the lower ionosphere was calculated. The disturbance of the electric field in the ULF band was extracted using the S-G filtering method. The results indicate that: (1) The ionospheric anomaly caused by this earthquake appeared 20 days before the earthquake, and before the earthquake, there were significant anomalous changes in all parameters within the pregnant seismic zone. The signal-to-noise ratios of the NAA and NLK artificial source transmitter stations decreased by 30%, and the height of the low ionosphere decreased by 5–10 km, while there were anomalous perturbations in several orbits of the ULF electric field, and the magnitude of the perturbations exceeded three times the standard deviation. (2) The SNR of the artificial source transmitting stations before and after the earthquake was significantly reduced in the third period before the earthquake and recovered after the earthquake. (3) The low ionospheric height appears to be reduced before the earthquake and recovers after the earthquake. (4) The decrease in the S/N ratio occurred simultaneously with the decrease in ionospheric height 15 days–10 days before the earthquake. This provides a reference for extracting pre-earthquake ionospheric precursor anomalies.
Characteristics of High-Latitude Climate and Cloud Simulation in Community Atmospheric Model Version 6 (CAM6)
Authors: Baek Eun-Hyuk , Bae Jungeun , Sung Hyun-Joon , Jung Euihyun , Kim Baek-Min , Jeong Jee-Hoon
Published: June 2022

Many global climate models (GCMs) have difficulty in simulating climate variabilities over high northern latitudes. One of the main reasons is the inability of GCMs to simulate proper cloud fraction and the amount of liquid-containing cloud over the region. This study assessed the impact of cloud simulation in high latitudes by comparing the long-term parallel simulations of Community Atmosphere Model version 6 (CAM6) and CAM5, the previous version. The results show that the CAM6 simulation exhibits a considerable improvement in the Arctic, especially by reducing the cold bias of CAM5 throughout the year. Over the sub-Arctic region, however, CAM6 produces an excessive cold bias in summer and a warm bias in winter compared to the observation, which is closely related to the overestimation of cloud fraction and the amount of cloud liquid. In summer, the overestimation of the cloud in CAM6 tends to alleviate the cold bias compared to CAM5 due to an increase in downward longwave radiation over the high latitudes, while causing the excessive cold bias by blocking downward shortwave radiation over the sub-Arctic land area. In winter, when there is little incidence of shortwave radiation, the overestimation of the cloud in CAM6 increases the downward longwave radiation, which alleviates the cold bias in CAM5 over the Arctic but induces an excessive warm bias over the sub-Arctic land. The excessive cloudiness in CAM6 could weaken the high-latitude internal variability, exacerbating the deteriorating climate variability and long-term trend simulations in the region.
Trends in Temperature, Precipitation, Potential Evapotranspiration, and Water Availability across the Teesta River Basin under 1.5 and 2 °C Temperature Rise Scenarios of CMIP6
Authors: Das Soumik , Datta Pritha , Sharma Dreamlee , Goswami Kishor
Published: June 2022

Considering the linkages between climate change and water management, a lack of effort has been observed in analyzing the imprints of climate change over the transboundary Teesta river basin, where the changing climatic conditions can trigger substantial changes in eco-hydrological and socio-politico-economic setups. Therefore, to stimulate effective basin management, we investigated the trends in temperature, precipitation, potential evapotranspiration, and water availability under 1.5 and 2 °C warming levels across the transboundary Teesta river basin. The ensemble median of five bias-corrected model outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) was used for this purpose. The results indicate that the temperature is expected to significantly increase (decrease) in the near (far) future, along with an overall significant increasing trend in monsoon precipitation. The evaporation paradox is found in the near future, and the water availability is likely to increase, with some exceptions for the pre-monsoon season. The perpetuation of such changes might result in environmental degradation through snow melting, glacial recession, and floods. Anticipating the changing climatic scenarios and their possible impacts, in this study, we recommend a variety of short- and long-term strategies for the concerned stakeholders to implement the Sustainable Development Goal 13, i.e., “Climate Action”, over the Teesta river basin.
Impact of the ‘Coal-to-Natural Gas’ Policy on Criteria Air Pollutants in Northern China
Authors: Meng He , Shen Yanjie , Fang Yuan , Zhu Yujiao
Published: June 2022

During the last decades, China had issued a series of stringent control measures, resulting in a large decline in air pollutant concentrations. To quantify the net change in air pollutant concentrations driven by emissions, we developed an approach of determining the closed interval of the deweathered percentage change (DPC) in the concentration of air pollutants on an annual scale, as well as the closed intervals of cumulative DPC in a year compared with that in the base year. Thus, the hourly mean mass concentrations of criteria air pollutants to determine their interannual variations and the closed intervals of their DPCs during the heating seasons from 2013 to 2019 in Qingdao (a coastal megacity) were analyzed. The seasonal mean SO2 concentration decreased from 2013 to 2019. The seasonal mean CO, NO2, and PM2.5 concentrations also generally decreased from 2013 to 2017, but increased unexpectedly in 2018 (from 0.9 mg m−3 (CO), 42 µg m−3 (NO2), and 51 µg m−3 (PM2.5) in 2017 to 1.1 mg m−3, 48 µg m−3, and 64 µg m−3 in 2018, respectively). The closed intervals of DPC in concentrations of CO, NO2, and PM2.5 from the 2017 heating season (2017/2018) to the 2018 heating season (2018/2019) were obtained at (27%, 30%), (15%, 18%), and (30%, 33%), respectively. Such high positive endpoint values of the closed intervals, in contrast to their small interval lengths, indicate increased emissions of these pollutants and/or their precursors in 2018/2019 compared with 2017/2018, by minimizing the meteorological influences. The rebounds of CO, NO2, and PM2.5 in 2018/2019 were likely associated with a doubled increase in natural gas (NG) consumption implemented by the “coal-to-NG” project, as the total energy consumption showed little difference. Our results suggested an important role of the “coal-to-NG” project in driving concentrations of air pollutant increases in China in 2018/2019, which need integrated assessments.
Evaluation of Observed and Future Climate Change Projection for Uttarakhand, India, Using CORDEX-SA
Authors: Tyagi Neetu , Jayal Tripti , Singh Mukesh , Mandwal Vipan , Saini Atul , Nirbhav  , Sahu Netrananda , Nayak Sridhara
Published: June 2022

The climate change scenarios, especially global warming, have raised significant concerns, and the Himalayan regions such as Uttarakhand are highly vulnerable to such climatic shifts. Considering 10 Coordinated Regional Climate Downscaling Experiments in South Asia (CORDEX-SA), experiments with 3 regional climate models (RCMs), driven by 13 global climate models, historical estimates and future projections are analyzed from the mid-century (MC) i.e., from 2021–2050 to the end of the century (EC) i.e., from 2070–2099 to characterize annual and seasonal variations in precipitation and temperature. The analysis shows a decrease in the annual average precipitation by 5.92% at MC and an increase of 5.97% at EC for the Representative Climate Pathway (RCP) 4.5, while precipitation may likely increase from 2.83% to 15.89% towards MC and EC in the RCP 8.5. The maximum temperature may likely increase from 0.42 °C to 3.07 °C from MC to EC in the RCP 4.5 and from 0.83 °C to 5.49 °C in the RCP 8.5. In addition, the minimum temperature may increase from 0.80 °C to 3.25 °C from MC to EC in the RCP 4.5 and from 0.30 °C to 5.86 °C from MC to EC in the RCP 8.5. Notably, a decrease in the pre-monsoon precipitation at EC and a higher increase in the maximum temperature during the monsoon season are observed. An increase in the maximum temperature along with precipitation may lead to an increase in the frequency of the monsoon season’s extreme rainfall events.
Monitoring the Water Mass Balance Variability of Small Shallow Lakes by an ERA5-Land Reanalysis and Water Level Measurement-Based Model. An Application to the Trasimeno Lake, Italy
Authors: Bongioannini Cerlini Paolina , Saraceni Miriam , Silvestri Lorenzo , Meniconi Silvia , Brunone Bruno
Published: June 2022

Climate change has a strong impact on inland water bodies such as lakes. This means that the increase in lake temperature recorded in recent decades-in Europe as well-can change the evaporation regime of the lakes. This, together with the variation of the water cycle, in particular precipitation, implies that the water mass balance of lakes may vary due to climate change. Water mass balance modeling is therefore of paramount importance to monitor lakes in the context of global warming. Although many studies have focused on such a modeling, there is no shared approach that can be used for any lake across the globe, irrespective of the size. This becomes even more problematic for shallow and small lakes, for which few studies exist. For this reason, in this paper the use of reanalysis data, in particular ERA5-Land provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), is proposed for the mass balance modeling. In fact, ERA5-Land has a global coverage and it is the only data source comprising a specific model for lakes, the Fresh-water Lake model (FLake). The chosen case study is the Trasimeno lake, a small and shallow lake located in Central Italy. The use of the reanalysis was preceded by data validation by considering both ground-based and satellite observations. The results show that there is a good agreement between the observed monthly variation of the lake level, ΔH, and the corresponding values of the water storage, δ, computed by means of the ERA5-Land data (Pearson coefficient larger than 70%). Discrepancies between observations and the ERA5-Land data happen in periods characterized in Europe by an extreme climate anomaly. This promising result encourages the use of ERA5-Land for other lakes.
Environmental Viability Analysis of Connected European Inland–Marine Waterways and Their Services in View of Climate Change
Authors: Némethy Sándor A. , Ternell Anna , Bornmalm Lennart , Lagerqvist Bosse , Szemethy László
Published: June 2022

Inland waterways and their connections to marine transport systems constitute a substantial resource for the establishment of green infrastructures, flood prevention, and environmental conservation. However, these developments have numerous inherent environmental hazards such as water and air pollution, a loss of habitats, increased coastal erosion, the transfer of invasive species between connected watercourses and lakes, and the transport of pollutants through watercourses to coastal areas. Climate change may aggravate these environmental problems through changing temperatures, reduced precipitation, enhancing the adverse impact of excess nutrient discharge, and the entry of invasive species. In this study, we analyse the main European inland waterway corridors and their branches to assess the ecological viability of a pan-European inland waterway network. The environmental viability of such network depends on the right assessment of ecosystem services and protection of biodiversity. A model structure for landscape conservation, green infrastructure development, water replenishment, and ecosystem reconstruction is proposed, considering a sustainable combination of multimodal inland waterway and rail transport.
A Review of Additive Manufacturing Studies for Producing Customized Ankle-Foot Orthoses
Authors: Silva Rui , Veloso António , Alves Nuno , Fernandes Cristiana , Morouço Pedro
Published: June 2022

Ankle-foot orthoses (AFO) are prescribed to improve the patient’s quality of life. Supporting weak muscles or restraining spastic muscles leads to smoother and more stable locomotion. Commonly, AFO are made using thermoplastic vacuum forming, which requires a long time for production and has limited design options. Additive manufacturing (AM) can solve this problem, leading to a faster and cheaper solution. This review aimed to investigate what is the state-of-art using AM for AFO. Evaluating the used manufacturing processes, customization steps, mechanical properties, and biomechanical features in humans would provide significant insights for further research. The database searches combined AM and AFO with no year or publication type restrictions. Studies must have examined outcomes on human participants with the orthoses built by AM. Other types of orthotic devices or different manufacturing techniques were excluded. Nineteen studies met the inclusion criteria. As stated by having all studies conducted in the last nine years, this is a very recent domain. Different AM processes have been used, with the majority relying on Fused Deposition Modeling. Overall, the manuscripts’ quality is deficient, which is critical to promoting further studies with higher samples. Except for one paper, AM-printed AFO was comparable or superior to the thermoplastic vacuum forming AFO in mechanical tests, kinematics, kinetics, and participant feedback.
MAGE: An Open-Source Tool for Meta-Analysis of Gene Expression Studies
Authors: Tamposis Ioannis A. , Manios Georgios A. , Charitou Theodosia , Vennou Konstantina E. , Kontou Panagiota I. , Bagos Pantelis G.
Published: June 2022

MAGE (Meta-Analysis of Gene Expression) is a Python open-source software package designed to perform meta-analysis and functional enrichment analysis of gene expression data. We incorporate standard methods for the meta-analysis of gene expression studies, bootstrap standard errors, corrections for multiple testing, and meta-analysis of multiple outcomes. Importantly, the MAGE toolkit includes additional features for the conversion of probes to gene identifiers, and for conducting functional enrichment analysis, with annotated results, of statistically significant enriched terms in several formats. Along with the tool itself, a web-based infrastructure was also developed to support the features of this package.
Efficient Viral Transduction in Fetal and Adult Human Inner Ear Explants with AAV9-PHP.B Vectors
Authors: van Beelen Edward S. A. , van der Valk Wouter H. , Verhagen Thijs O. , de Groot John C. M. J. , Madison Margot A. , Shadmanfar Wijs , Hensen Erik F. , Jansen Jeroen C. , van Benthem Peter Paul G. , Holt Jeffrey R. , Locher Heiko
Published: June 2022

Numerous studies have shown the recovery of auditory function in mouse models of genetic hearing loss following AAV gene therapy, yet translation to the clinic has not yet been demonstrated. One limitation has been the lack of human inner ear cell lines or tissues for validating viral gene therapies. Cultured human inner ear tissue could help confirm viral tropism and efficacy for driving exogenous gene expression in targeted cell types, establish promoter efficacy and perhaps selectivity for targeted cells, confirm the expression of therapeutic constructs and the subcellular localization of therapeutic proteins, and address the potential cellular toxicity of vectors or exogenous constructs. To begin to address these questions, we developed an explant culture method using native human inner ear tissue excised at either fetal or adult stages. Inner ear sensory epithelia were cultured for four days and exposed to vectors encoding enhanced green fluorescent protein (eGFP). We focused on the synthetic AAV9-PHP.B capsid, which has been demonstrated to be efficient for driving eGFP expression in the sensory hair cells of mouse and non-human primate inner ears. We report that AAV9-PHP.B also drives eGFP expression in fetal cochlear hair cells and in fetal and adult vestibular hair cells in explants of human inner ear sensory epithelia, which suggests that both the experimental paradigm and the viral capsid may be valuable for translation to clinical application.
Development of Nomograms for Predicting Prognosis of Pancreatic Cancer after Pancreatectomy: A Multicenter Study
Authors: Yoon So Jeong , Park Boram , Kwon Jaewoo , Lim Chang-Sup , Shin Yong Chan , Jung Woohyun , Shin Sang Hyun , Heo Jin Seok , Han In Woong
Published: June 2022

Surgical resection is the only curative treatment for pancreatic ductal adenocarcinoma (PDAC). Currently, the TNM classification system is considered the standard for predicting prognosis after surgery. However, the prognostic accuracy of the system remains limited. This study aimed to develop new predictive nomograms for resected PDAC. The clinicopathological data of patients who underwent surgery for PDAC between 2006 and 2015 at five major institutions were retrospectively reviewed; 885 patients were included in the analysis. Cox regression analysis was performed to investigate prognostic factors for recurrence and survival, and statistically significant factors were used for creating nomograms. The nomogram for predicting recurrence-free survival included nine factors: sarcopenic obesity, elevated carbohydrate antigen 19–9, platelet-to-lymphocyte ratio, preoperatively-identified arterial abutment, estimated blood loss (EBL), tumor differentiation, size, lymph node ratio, and tumor necrosis. The nomogram for predicting overall survival included 10 variables: age, underlying liver disease, chronic kidney disease, preoperatively found portal vein invasion, portal vein resection, EBL, tumor differentiation, size, lymph node metastasis, and tumor necrosis. The time-dependent area under the receiver operating characteristic curve for both nomograms exceeded 0.70. Nomograms were developed for predicting survival after resection of PDAC, and the platforms showed fair predictive performance. These new comprehensive nomograms provide information on disease status and are useful for determining further treatment for PDAC patients.