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DNA extraction, a vital pre-requisite for most biological studies, continues to be studied extensively. According to some studies, DNA shows a certain degree of absorbability on filter paper made of plant fiber-based adsorbent material. However, the principle underlying such specific adsorption as well as plant species associated with plant fiber-based adsorbents and optimized extraction conditions have not yet been studied. This study demonstrates the tight correlation between crystallinity and hygroscopicity in plant fiber-based adsorbents used for DNA extraction and proposes the concept of DNA adsorption on plant fiber-based adsorbents, for the first time. We also explored optimal extracting and eluting conditions and developed a novel plant fiber-based DNA extraction method that was quadruple times more powerful than current approaches. Starting with the screening of various types of earthed plant fiber-based adsorbents, we went on to mine new plant fiber-based adsorbents, bamboo paper and degreased cotton, and succeeded in increasing their efficiency of DNA extraction to 4.2 times than that of current approaches. We found a very strong correlation between the crystallinity and hygroscopicity of plant fiber-based adsorbents which showed efficiency for DNA extraction, and thus propose a principle that potentially governs such specific adsorption processes, in the hope that this information may guide related multidisciplinary research studies in the future. Nanodrop, electrophoresis and PCR were selected to demonstrate the quantity, quality, integrity and utility of the extracted DNA. Furthermore, crystallinity, hygroscopicity, pore size distribution and composition of plant fiber-based adsorbents were studied to explore their correlation in an attempt to understand the principle underlying this particular type of adsorption. The findings of this study may be further extended to the extraction of other types of nucleic acids with similar biochemical properties.

In our planned study, we shall empirically study the assessment of cited papers within the framework of the anchoring-and-adjustment heuristic. We are interested in the question whether citation decisions are (mainly) driven by the quality of cited references. The design of our study is oriented towards the study by Teplitskiy, Duede [10]. We shall undertake a survey of corresponding authors with an available email address in the Web of Science database. The authors are asked to assess the quality of papers that they cited in previous papers. Some authors will be assigned to three treatment groups that receive further information alongside the cited paper: citation information, information on the publishing journal (journal impact factor), or a numerical access code to enter the survey. The control group will not receive any further numerical information. In the statistical analyses, we estimate how (strongly) the quality assessments of the cited papers are adjusted by the respondents to the anchor value (citation, journal, or access code). Thus, we are interested in whether possible adjustments in the assessments can not only be produced by quality-related information (citation or journal), but also by numbers that are not related to quality, i.e. the access code. The results of the study may have important implications for quality assessments of papers by researchers and the role of numbers, citations, and journal metrics in assessment processes.

Background A minority of scientific journals publishes the majority of scientific papers and receives the majority of citations. The extent of concentration of the most influential articles is less well known. Methods/Principal Findings The 100 most-cited papers in the last decade in each of 21 scientific fields were analyzed; fields were considered as ecosystems and their “species” (journal) diversity was evaluated. Only 9% of journals in Journal Citation Reports had published at least one such paper. Among this 9%, half of them had published only one such paper. The number of journals that had published a larger number of most-cited papers decreased exponentially according to a Lotka law. Except for three scientific fields, six journals accounted for 53 to 94 of the 100 most-cited papers in their field. With increasing average number of citations per paper (citation density) in a scientific field, concentration of the most-cited papers in a few journals became even more prominent (p

In newborn screening, samples suspected for congenital adrenal hyperplasia (CAH), a potentially lethal inborn error of steroid biosynthesis, need to be confirmed using liquid chromatography–tandem mass spectrometry. Daily quality controls (QCs) for the 2nd-tier CAH assay are not commercially available and are therefore generally prepared within the laboratory. For the first time, we aimed to compare five different QC preparation approaches used in routine diagnostics for CAH on the concentrations of cortisol, 21-deoxycortisol, 11-deoxycortisol, 4-androstenedione and 17-hydroxyprogesterone in dried blood spots. The techniques from Prep1 to Prep5 were tested at two analyte concentrations by spiking aliquots of a steroid-depleted blood, derived from washed erythrocyte suspension and steroid-depleted serum. The preparation processes differed in the sequence of the preparation steps and whether freeze-thaw cycles were used to facilitate blood homogeneity. The five types of dried blood spot QCs were assayed and quantitated in duplicate on five different days using a single calibration row per day. Inter-assay variations less than 15% and concentrations within ±15% of the nominal values were considered acceptable. Results obtained by means of the four dried blood spot QC preparation techniques (Prep1, Prep2, Prep4 and Prep5) were statistically similar and remained within the ±15% ranges in terms of both reproducibility and nominal values. However, concentration results for Prep3 (spiking prior to three freeze-thaw cycles) were significantly lower than the nominal values in this setting, with differences exceeding the ±15% range in many cases despite acceptable inter-assay variations. These findings have implications for the in-house preparation of QC samples in laboratory developed tests for CAH, including 2nd-tier assays in newborn screening.

Congenital cytomegalovirus (CMV) infection is a major cause of birth defects ranging from developmental disorders to stillbirth. Most newborns affected by CMV do not present with symptoms at birth but are at risk of sequelae at later stages of their childhood. Stored dried blood spots (DBS) taken at birth can be used for retrospective diagnosis of hereditary diseases, but detection of pathogens is challenged by potentially low pathogen concentrations in the small blood volume available in a DBS. Here we test four different extraction methods for optimal recovery of CMV DNA from DBS at low to high CMV titers. The recovery efficiencies varied widely between the different extractions (from 3% to 100%) with the most efficient method extracting up to 113-fold more CMV DNA than the least efficient and 8-fold more than the reference protocol. Furthermore, we amplified four immunomodulatory CMV genes from the extracted DNA: the UL40 and UL111A genes which occur as functional knockouts in some circulating CMV strains, and the highly variable UL146 and US28 genes. The PCRs specifically amplified the CMV genes at all tested titers with sufficient quality for sequencing and genotyping. In summary, we here report an extraction method for optimal recovery of CMV DNA from DBSs that can be used for both detection of CMV and for genotyping of polymorphic CMV genes in congenital CMV infection.

Effects of different pretreatment methods on sludge dewaterability and polycyclic aromatic hydrocarbons (PAHs) degradation during petrochemical sludge anaerobic digestion were studied. Results showed that the total biogas production volume in the thermal pretreatment system was 4 and 5 times higher than that in the ultrasound pretreatment and in the control system, and the corresponding volatile solid removal efficiencies reached 28%, 15%, and 8%. Phenanthrene, paranaphthalene, fluoranthene, benzofluoranthene, and benzopyrene removal rates reached 43.3%, 55.5%, 30.6%, 42.9%, and 41.7%, respectively, in the thermal pretreatment system, which were much higher than those in the ultrasound pretreatment and in the control system. Moreover, capillary suction time (CST) of sludge increased after pretreatment, and then reduced after 20 days of anaerobic digestion, indicating that sludge dewaterability was greatly improved after anaerobic digestion. The decrease of protein and polysaccharide in the sludge could improve sludge dewaterability during petrochemical sludge anaerobic digestion. This study suggested that thermal pretreatment might be a promising enhancement method for petrochemical sludge solubilization, thus contributing to degradation of the PAHs, biogas production, and improvement of dewaterability during petrochemical sludge anaerobic digestion.

The effects of the molecular weight (MW) and charge density (CD) of cationic polyacrylamide (CPAM) on sludge dewatering and moisture evaporation were investigated in this study. Results indicated that in sludge conditioning, the optimum dosages were 10, 6, 6, 4, and 4 mg g(-1) CPAM with 5 million MW and 20% CD, 5 million MW and 40% CD, 3 million MW and 40% CD, 8 million MW and 40% CD, and 5 million MW and 60% CD, respectively. The optimum dosage of CPAM was negatively correlated with its CD or MW if the CD or MW of CPAM was above 20% or 5 million. In the centrifugal dewatering of sludge, the moisture content in the conditioned sludge gradually decreased with the extension of centrifugation time, and the economical centrifugal force was 400×g. The moisture evaporation rates of the conditioned sludge were closely related to sludge dewaterability, which was in turn significantly correlated either positively with the solid content of sludge particles that were >2 mm in size or negatively with that of particles measuring 1 mm to 2 mm in diameter. During treatment, sludge moisture content was reduced from 80% to 20% by evaporation, and the moisture evaporation rates were 1.35, 1.49, 1.62, and 2.24 times faster in the sludge conditioned using 4 mg g(-1) CPAM with 5 million MW and 60% CD than in the sludge conditioned using 4 mg g(-1) CPAM with 8 million MW and 40% CD, 6 mg g(-1) CPAM with 5 million MW and 40% CD, 6 mg g(-1) CPAM with 3 million MW and 40% CD, and 10 mg g(-1) CPAM with 5 million MW and 20% CD, respectively. Hence, the CPAM with 5 million MW and 60% CD was ideal for sludge dewatering.

Ranked set sampling (RSS) has created a broad interest among researchers and it is still a unique research topic. It has at long last begun to find its way into practical applications beyond its initial horticultural based birth in the fundamental paper by McIntyre in the nineteenth century. One of the extensions of RSS is median ranked set sampling (MRSS). MRSS is a sampling procedure normally utilized when measuring the variable of interest is troublesome or expensive, whereas it might be easy to rank the units using an inexpensive sorting criterion. Several researchers introduced ratio, regression, exponential, and difference type estimators for mean estimation under the MRSS design. In this paper, we propose three new mean estimators under the MRSS scheme. Our idea is based on three-fold utilization of supplementary information. Specifically, we utilize the ranks and second raw moments of the supplementary information and the original values of the supplementary variable. The appropriateness of the proposed group of estimators is demonstrated in light of both real and artificial data sets based on the Monte-Carlo simulation. Additionally, the performance comparison is also conducted regarding the reviewed families of estimators. The results are empowered and the predominant execution of the proposed group of estimators is seen throughout the paper.

The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, raising the need to develop novel tools to provide rapid and cost-effective screening and diagnosis. Clinical reports indicated that COVID-19 infection may cause cardiac injury, and electrocardiograms (ECG) may serve as a diagnostic biomarker for COVID-19. This study aims to utilize ECG signals to detect COVID-19 automatically. We propose a novel method to extract ECG signals from ECG paper records, which are then fed into a one-dimensional convolution neural network (1D-CNN) to learn and diagnose the disease. To evaluate the quality of digitized signals, R peaks in the paper-based ECG images are labeled. Afterward, RR intervals calculated from each image are compared to RR intervals of the corresponding digitized signal. Experiments on the COVID-19 ECG images dataset demonstrate that the proposed digitization method is able to capture correctly the original signals, with a mean absolute error of 28.11 ms. Our proposed 1D-CNN model, which is trained on the digitized ECG signals, allows identifying individuals with COVID-19 and other subjects accurately, with classification accuracies of 98.42%, 95.63%, and 98.50% for classifying COVID-19 vs. Normal, COVID-19 vs. Abnormal Heartbeats, and COVID-19 vs. other classes, respectively. Furthermore, the proposed method also achieves a high-level of performance for the multi-classification task. Our findings indicate that a deep learning system trained on digitized ECG signals can serve as a potential tool for diagnosing COVID-19., Accepted with minor revision by Plos One

Currently, JavaScript malicious code detection methods are becoming more and more effective. Still, the existing methods based on deep learning are poor at detecting too long or too short JavaScript code. Based on this, this paper proposes an adaptive code length deep learning network JACLNet, composed of convolutional block RDCNet, BiLSTM and Transfrom, to capture the association features of the variable distance between codes. Firstly, an abstract syntax tree recombination algorithm is designed to provide rich syntax information for feature extraction. Secondly, a deep residual convolution block network (RDCNet) is designed to capture short-distance association features between codes. Finally, this paper proposes a JACLNet network for JavaScript malicious code detection. To verify that the model presented in this paper can effectively detect variable JavaScript code, we divide the datasets used in this paper into long text dataset DB_Long; short text dataset DB_Short, original dataset DB_Or and enhanced dataset DB_Re. In DB_Long, our method’s F1 − score is 98.87%, higher than that of JSContana by 2.52%. In DB_Short, our method’s F1-score is 97.32%, higher than that of JSContana by 7.79%. To verify that the abstract syntax tree recombination algorithm proposed in this paper can provide rich syntax information for subsequent models, we conduct comparative experiments on DB_Or and DB_Re. In DPCNN+BiLSTM, F1-score with abstract syntax tree recombination increased by 1.72%, and in JSContana, F1-score with abstract syntax tree recombination increased by 1.50%. F1-score with abstract syntax tree recombination in JACNet improved by 1.00% otherwise unused.

As an important part of a modern economic system, a modern industrial system is the key to promoting high-quality economic development. China’s modern industrial system construction focuses on industrial restructuring. At present, in order to strengthen the support and leading role of transportation in the modern economic system, China is actively promoting the construction of a competitive transportation power. Therefore, it is necessary to study whether large-scale investment in transportation infrastructure can promote industrial structure transformation and upgrade. This paper takes China as the research background. Firstly, a RAM model was employed to evaluate the unified economic and environmental efficiency of transportation infrastructure that measures the level of transportation infrastructure investment. Secondly, a PVAR model was built to evaluate the dynamic effects of transportation infrastructure investment on industrial structure transformation and upgrade. Finally, from the perspective of rational flow and optimal allocation of resource factors, the paper points out that transportation infrastructure investment can indirectly promote industrial structure transformation and upgrade through three paths, namely expanding market demand, reducing resource misallocation and increasing technological innovation, and the first half of these paths are positively regulated by policies. Then, an empirical test was done with the moderated mediation model. Research findings suggest that: first, improvements in the unified economic and environmental efficiency of transportation infrastructure can only promote industrial structure supererogation in a short time, but have no significant effect on promoting industrial structure rationalization in the short or long term. Second, in actual situation, transportation infrastructure investment can promote industrial structure transformation and upgrade only by expanding market demand and technological innovation, but not by reducing resource misallocation. Third, the first half of these paths through which transportation infrastructure investment promotes industrial structure transformation and upgrade are positively regulated by policies. This paper provided some theoretical reference for promoting industrial structure transformation and upgrade by virtue of the sustainable development of transportation.

With the rapid development of electric vehicle (EV) technology under the background of double carbon, a reasonable evaluation of the network planning of EV charging stations is conducive to the long-term stable development of the EV industry. Therefore, this paper proposes a comprehensive evaluation system and method for EV charging networks under the traffic network and power grid coupling. First, an EV travel model is developed based on the travel probability matrix to analyze the spatial and temporal characteristics of EVs. Secondly, the coupling relationship among users, charging network, road network, and the power grid is analyzed, four criteria of user feedback, charging network operation, road network operation impact, and power grid operation impact are proposed, and corresponding evaluation indexes are constructed under each criterion to form a comprehensive evaluation index system. Furthermore, the Analytic Hierarchy Process (AHP) and entropy weighting method are used to assign weights to the index layer and the criterion layer respectively, and the improved TOPSIS evaluation method is used to quantitatively analyze the impact of charging networks on different subjects for comprehensive evaluation. Finally, through the comprehensive evaluation of different charging network planning schemes, the effectiveness of the comprehensive evaluation method and model proposed in this paper is verified, which can provide a reference basis for the planning and improvement of EV charging station networks.

With the development of smart mobile devices and global positioning technology, people’s daily travel has become increasingly dependent on online car-hailing. Meanwhile, it has also become possible to use multi-source data to explore the factors influencing urban residents’ car-hailing trips. Using online data on car-hailing trajectories, points of interest (POIs) data and other auxiliary data, the paper explores how the built environment impacts online car-hailing passengers. Within a 200 x 200m research grid, the unique spatiotemporal patterns of weekday car-hailing trips during a one-week period are analyzed, using statistics on pick-ups and drop-offs at different time of the day. By combining these data with built environment variables and various economic and traffic indicators, a multi-scale geographically weighted regression (MGWR) model is developed for different time scales. The MGWR model outperforms the classical geographically weighted regression (GWR) model and the ordinary least squares (OLS) regression model in terms of goodness of fit and all other aspects. More importantly, this study finds a high degree of temporal and spatial heterogeneity in the impact of built environment factors on local car-hailing trips across different regions, and the paper analyzes the business residence coefficient in detail. The study provides valuable insights to help improve the level of urban transportation services, as well as urban transportation planning and construction.

In order to study the impact of inclusive finance on agricultural green development, This paper uses both static panel and dynamic panel (system GMM model) estimation methods to make empirical analysis of the impact of inclusive financial development on agricultural green development. The results both find that there is a significant positive correlation between the level of inclusive financial development, real GDP per capita, the proportion of the added value of agriculture, forestry, animal husbandry and fishery in GDP and agricultural green development. This paper puts forward some countermeasures and suggestions to promote agricultural green development, including vigorously developing inclusive finance, promoting economic growth, promoting the development of agriculture, forestry, animal husbandry and fishery, and increasing environmental protection expenditures.

Acute coronary syndrome (ACS) is a serious cardiovascular disease that can lead to cardiac arrest if not diagnosed promptly. However, in the actual diagnosis and treatment of ACS, there will be a large number of redundant related features that interfere with the judgment of professionals. Further, existing methods have difficulty identifying high-quality ACS features from these data, and the interpretability work is insufficient. In response to this problem, this paper uses a hybrid feature selection method based on gradient boosting trees and recursive feature elimination with cross-validation (RFECV) to reduce ACS feature redundancy and uses interpretable feature learning for feature selection to retain the most discriminative features. While reducing the feature set search space, this method can balance model simplicity and learning performance to select the best feature subset. We leverage the interpretability of gradient boosting trees to aid in understanding key features of ACS, linking the eigenvalue meaning of instances to model risk predictions to provide interpretability for the classifier. The data set used in this paper is patient records after percutaneous coronary intervention (PCI) in a tertiary hospital in Fujian Province, China from 2016 to 2021. In this paper, we experimentally explored the impact of our method on ACS risk prediction. We extracted 25 key variables from 430 complex ACS medical features, with a feature reduction rate of 94.19%, and identified 5 key ACS factors. Compared with different baseline methods (Logistic Regression, Random Forest, Gradient Boosting, Extreme Gradient Boosting, Multilayer Perceptron, and 1D Convolutional Networks), the results show that our method achieves the highest Accuracy of 98.8%.

Recommendation algorithms save a lot of valuable time for people to get the information they are interested in. However, the feature calculation and extraction process of each machine learning or deep learning recommendation algorithm are different, so how to obtain various features with different dimensions, i.e., how to integrate the advantages of each model and improve the model inference efficiency, becomes the focus of this paper. In this paper, a better deep learning model is obtained by integrating several cutting-edge deep learning models. Meanwhile, to make the integrated learning model converge better and faster, the parameters of the integrated module are initialized, constraints are imposed, and a new activation function is designed for better integration of the sub-models. Finally, the integrated large model is distilled for knowledge distillation, which greatly reduces the number of model parameters and improves the model inference efficiency.

Resource allocation problem is one of key issues in the field of revenue management. The traditional models usually rely on some restrictive assumptions about demand information or arrival process, which is sometimes out of line with reality. To overcome this shortcoming, the method of competitive analysis of online algorithms, which eliminates the need for the assumptions on demand and arrivals, is adopted to deal with the quantity-based revenue management problem. The current model in this paper considers two downgrade compatible levels of resources. Given the capacities and fares of both levels of resources, the objective is to accept appropriate customers and assign them to appropriate resources so as to maximize revenues. Compared with the existing literature, this paper generalizes the concerned resource allocation problem by considering multiple fares for each level of resources. From the perspective of online algorithms and competitive analysis, both an upper bound and an optimal online strategy are derived in this paper.

Despite savannas being known for their relatively sparse vegetation coverage compared to other vegetation ecosystems, they harbour functionally diverse vegetation forms. Savannas are affected by climate variability and anthropogenic factors, resulting in changes in woody plant species compositions. Monitoring woody plant species diversity is therefore important to inform sustainable biodiversity management. Remote sensing techniques are used as an alternative approach to labour-intensive field-based inventories, to assess savanna biodiversity. The aim of this paper is to review studies that applied remote sensing to assess woody plant species diversity in savanna environments. The paper first provides a brief account of the spatial distribution of savanna environments around the globe. Thereafter, it briefly defines categorical classification and continuous-scale species diversity assessment approaches for savanna woody plant estimation. The core review section divides previous remote sensing studies into categorical classification and continuous-scale assessment approaches. Within each division, optical, Radio Detection And Ranging (RADAR) and Light Detection and Ranging (LiDAR) remote sensing as applied to savanna woody species diversity is reviewed. This is followed by a discussion on multi-sensor applications to estimate woody plant species diversity in savanna. We recommend that future research efforts should focus strongly on routine application of optical, RADAR and LiDAR remote sensing of physiologically similar woody plant species in savannas, as well as on extending these methodological approaches to other vegetation environments.

Countries around the world have become concerned about their responsibility to protect the environment and resources. In this paper, we established a model of multi-period PSW-DID (weighted propensity score matching- differences-in-differences) to test the effect of China’s audit of natural resource. We found that: (1) local governments had a strategic incentive to reduce pollution, they paid more attention to environmental performance linked to individual promotion than to green innovation and development. (2) Compared with the long-term and complexity of water pollution control, they paid more attention to air pollution treatment. (3) In the long run, the environmental audit was indeed beneficial to the improvement of environmental quality, but the time of taking effect was the second year (one-year lag). (4) In addition, because of the contradiction between the neglect of human capital and the timeliness of environmental supervision, the local government did not show substantial pollution reduction. Therefore, local officials should foster the long-term responsibility consciousness of green innovation and pay more attention to the integration of human capital. The audit of natural resources should establish a long-term mechanism, which could establish a complete accountability system or change off-office audit to interim audit. The construction of audit big data platforms should pay more attention to substantive characteristic data, such as data on population inflow, which is not only a paper score of air pollution. This study can reveal the dilemma of pollution prevention and control in China, urge local governments to promote the rational flow of human resources, improve the innovation level, and achieve substantive pollution control and efficiency enhancement of green development.

We investigate the citation frequency of retracted scientific papers in science. For the period of five years before and after retraction, we counted the citations to papers in a sample of over 3,000 retracted, and a matched sample of another 3,000 non-retracted papers. Retraction led to a decrease in average annual citation frequency from about 5 before, to 2 citations after retraction. In contrast, for non-retracted control papers the citation counts were 4, and 5, respectively. Put differently, we found only a limited effect of retraction: retraction decreased citation frequency only by about 60%, as compared to non-retracted papers. Thus, retracted papers often live on. For effective self-correction the scientific enterprise needs to be more effective in removing retracted papers from the scientific record. We discuss recent proposals to do so.

The Chinese central government established eight pilot zones in five provinces for green finance reform and innovations (GFRI) in 2017. The pilot zones promote green finance development and explore the propagable and reproducible experiences regarding mechanisms and institutions. Adopting a sample of China’s listed companies from 2012 to 2021, this paper constructed a quasi-natural experiment and investigated the GFRI policy’s effect on firms’ total factor productivity (TFP) using the difference-in-differences (DID) method to verify the implementation effect of the GFRI policy. Furthermore, heterogeneity analysis and mechanism analysis were conducted to identify the guidance effect and deep mechanisms of the GFRI policy. The empirical results demonstrated that firms’ TFP in pilot zones increased substantially after implementing the GFRI pilot policy, confirming that the policy had a strong incentive effect. The corresponding promoting effect was particularly significant for non-state-owned companies, the eastern and central regions, and firms in the growth stage. Further mechanism analysis revealed that the GFRI pilot policy can stimulated firms’ TFP by promoting technological innovation and improving resource allocation efficiency. This paper’s empirical findings are essential in improving relevant policies and expanding the pilot zones.

When aerial cameras get aerial remote sensing images, the defocus will occur because of reasons such as air pressure, temperature and ground elevation changes, resulting in different image sharpness of continual aerial remote sensing images. Nowadays, the rapidly developing feature matching algorithm will rapidly reduce the registration rate between images with different image sharpness. Therefore, in order to enable aerial cameras to get image sharpness parameters according to the locations of aerial image feature points with inconsistent sharpness, this paper proposes a feature matching algorithm between aerial images with different sharpness by using DEM data and multiple constraints. In this paper, the feature matching range is extended according to the modified aerial imaging model and the nonlinear soft margin support vector machine. Then the relative moving speed and its variation of the feature points in the image are obtained by using the extended L-k optical flow, and finally the epipolar geometric constraint is introduced. To locate the feature points is obtained under multiple constraints, there is no need to calculate the feature point descriptors, and some mismatched point pairs are corrected, which improves the matching efficiency and precision. The experimental results show the feature matching precision of this algorithm is more than 90%, and the running time and matching precision can meet various application needs of aerial cameras.

In the past 40 years, the continuous strengthening of the greenhouse effect has led to a significant increase in the global average temperature. Although people’s understanding of climate change has been strengthened, the world has not yet witnessed a significant decline in pollutant emissions; hence it is imperative to get to the root cause. This paper is based on the STIRPAT model framework and uses the panel data of G20 countries over the period 1999–2019 to examine the role of financial development on carbon emissions under good governance. The results show that financial development significantly promotes carbon dioxide emissions, and the impact presents an inverted “U”-shaped trend when the quadratic term of financial development is introduced. Surprisingly, governance quality indicators increase carbon emissions. However, financial development accompanied by good governance suppresses carbon emissions. Moreover, according to the grouped results of developed and developing countries, different nations should adopt differentiated strategies in development finance to implement the carbon emission targets proposed by the G20. In addition, this paper also confirms the existence of the Environmental Kuznets Curve hypothesis. In light of this, policymakers should optimize the quality of governance while shifting their agendas toward environmentally responsible financial practices to promote financial development to improve environmental quality effectively. Furthermore, strengthen international cooperation, enhance public environmental protection concepts, and take joint actions to achieve low-carbon and win-win results.

Managing indoor ozone levels is important because ozone is a hazardous pollutant that has adverse effects on human health. Coffee is a popular daily beverage, and thus, coffee beans and spent coffee grounds are common in many places such as offices, homes, aircraft, cafeterias, and such. The most common material used to remove ozone is activated carbon which can be made from coffee beans or spent coffee grounds with proper activation processes. This paper presents a novel idea: to remove ozone at the level of an indoor environment using unactivated coffee products. This paper examines the ozone removal efficiency and the ozone deposition velocity at 130 ppb ozone for two types of coffee: solid coffee (powder) and liquid coffee (beverage). The activated carbon, the deionized water, and the seawater are also included for comparison and validation purposes. The tests show that the fine coffee powder has a removal efficiency of 58.5% and a deposition velocity of 0.62 cm/s. The liquid coffee has a removal efficiency of 34.4% and a deposition velocity of 0.23 cm/s. The chemical inspections indicate that the oxidation reactions with the carbohydrates in solid coffee and the metal/mineral elements in liquid coffee are responsible for ozone removal. These results have confirmed that ozone removal via coffee is effective, controlling indoor air quality by coffee products is thus becoming possible.

In the field of surface electromyography (sEMG) gesture recognition, how to improve recognition accuracy has been a research hotspot. The rapid development of deep learning provides a new solution to this problem. At present, the main applications of deep learning for sEMG gesture feature extraction are based on convolutional neural network (CNN) structures to capture spatial morphological information of the multichannel sEMG or based on long short-term memory network (LSTM) to extract time-dependent information of the single-channel sEMG. However, there are few methods to comprehensively consider the distribution area of the sEMG signal acquisition electrode sensor and the arrangement of the sEMG signal morphological features and electrode spatial features. In this paper, a novel multi-stream feature fusion network (MSFF-Net) model is proposed for sEMG gesture recognition. The model adopts a divide-and-conquer strategy to learn the relationship between different muscle regions and specific gestures. Firstly, a multi-stream convolutional neural network (Multi-stream CNN) and a convolutional block attention module integrated with a resblock (ResCBAM) are used to extract multi-dimensional spatial features from signal morphology, electrode space, and feature map space. Then the learned multi-view depth features are fused by a view aggregation network consisting of an early fusion network and a late fusion network. The results of all subjects and gesture movement validation experiments in the sEMG signal acquired from 12 sensors provided by NinaPro’s DB2 and DB4 sub-databases show that the proposed model in this paper has better performance in terms of gesture recognition accuracy compared with the existing models.

Immersive projection display system is widely adopted in virtual reality and various exhibition halls. How to maintain high display quality in an immersive projection environment with uneven illumination and the color deviation caused by the inter-reflection of light is still a challenging task. In this paper, we innovatively propose a deep learning-based radiation compensation for an L-shaped projector-camera system. This method employs complex reflection phenomena to simulate the light transport processing in an L-shaped environment, we also designed a Dark-Channel Enhanced-Compensation Net (DECNet) which composed of a convolutional neural network called Compensation Net, a DarkChannelNet and another subnet (such as sensing network) aiming at achieving high-quality reproduction of projected display images. The final output of DECNet is the compensation image to be projected. It is always a critical problem to establish appropriate evaluation and analysis indexes throughout the research of light pollution compensation algorithms. In this paper, PSNR, SSIM, and RMSE are proposed to quantitatively analyze the image quality. The experimental results show that this method has certain advantages in reducing the inter-reflection of the projection plane. And our method could also well replace the traditional process using the backlight transmission matrix. It can be concluded to a certain that this method can be extended to other more complex projection environments with strong scalability and inclusiveness.

Wind velocity field knowledge is crucial for the future air traffic management paradigm and is key in many applications, such as aircraft performance studies. This paper addresses the problem of spatio-temporal windc velocity field estimation. The north and east wind components within a given air space are estimated as a function of time. Both wind velocity field reconstruction in space for a past or present time instant and short-term prediction are performed. Wind data are obtained indirectly from the states of the aircraft broadcast by the Mode-S and ADS-B aircraft surveillance systems. The Gaussian process regression method, which is a flexible and universal estimator, is employed to solve both problems. Under general conditions, the method is statistically consistent, meaning that the method converges to the ground truth when increasingly more data are available, which is especially interesting, since aircraft data availability is expected to grow in the future through the deployment of the European System-Wide Information Management. Besides estimation, the Gaussian process regression method provides the probability distribution of any particular estimate, allowing confidence intervals to be computed. Moreover, the spatial modelling is performed using raw data without relying on grids and estimation can be performed at any spatio-temporal location. Furthermore, since the training phase of the method described in this paper is fast, requiring less than 5 minutes on a standard desktop computer, it can be used online to continuously track the state of the wind velocity field, thus allowing for data assimilation. In the case study presented in this paper, the Gaussian process regression method is tested on different days with different wind intensities. The available data set is split into several training and testing data sets, which are used to check the consistency of the results of wind velocity field reconstruction and prediction. Finally, the Gaussian process regression method is validated using the European Centre for Medium-Range Weather Forecasts ERA5 meteorological reanalysis data. The obtained results show that Gaussian process regression can be used to reliably estimate the wind velocity field from aircraft derived data.

With the development of information technology, improving the efficiency of public services with the help of the Internet has become an important work of local governments. However, under different institutional environments, the impact mechanism of Internet development on the supply efficiency of government public services is still unclear. Based on China’s interprovincial panel data from 2011 to 2019, this paper constructs a threshold effect model, sets the institutional environment as the threshold variable, and empirically analyzes the impact of Internet development on the supply efficiency of government public services. The results show that the difference in regional institutional environment will lead to the obvious threshold effect of Internet development on the supply efficiency of government public services: When the institutional environment is poor, the role of Internet development on the supply efficiency of government public services is not significant. With the improvement of the institutional environment, the role of Internet development in promoting the supply efficiency of government public services gradually appears, but the marginal intensity of promotion weakens. Compared with existing studies that mostly use linear models, this paper incorporates the institutional environment into the complex relationship between Internet development and government public service supply efficiency, and clarifies the role of the institutional environment in the process of Internet development affecting government public service supply efficiency and the non-linear relationship among the three. This paper reveals the mechanism of Internet development affecting the supply efficiency of government public services under different institutional environments and provides a new perspective for solving the shortage of public services.

This paper offers a comprehensive analysis model for android malware. The model presents the essential factors affecting the analysis results of android malware that are vision-based. Current android malware analysis and solutions might consider one or some of these factors while building their malware predictive systems. However, this paper comprehensively highlights these factors and their impacts through a deep empirical study. The study comprises 22 CNN (Convolutional Neural Network) algorithms, 21 of them are well-known, and one proposed algorithm. Additionally, several types of files are considered before converting them to images, and two benchmark android malware datasets are utilized. Finally, comprehensive evaluation metrics are measured to assess the produced predictive models from the security and complexity perspectives. Consequently, guiding researchers and developers to plan and build efficient malware analysis systems that meet their requirements and resources. The results reveal that some factors might significantly impact the performance of the malware analysis solution. For example, from a security perspective, the accuracy, F1-score, precision, and recall are improved by 131.29%, 236.44%, 192%, and 131.29%, respectively, when changing one factor and fixing all other factors under study. Similar results are observed in the case of complexity assessment, including testing time, CPU usage, storage size, and pre-processing speed, proving the importance of the proposed android malware analysis model.

This paper constructs a profit distribution model under centralized decision-making by taking the secondary agricultural supply chain as an example. Moreover, it introduces resources and risk as two factors to form significant weights and then designs a fair profit distribution mechanism of the supply chain according to the weights. Finally, this paper solves the optimal solution of the profit distribution coefficient by using fair entropy. Research results find that the distribution according to the optimal profit distribution coefficient can maximize the profit of each actor and achieves the matching of returns and inputs. Therefore, this paper puts forward some suggestions that include establishing a precise distribution mechanism based on contracts or property rights definitions, considering the cost and importance weight of each actor’s input when formulating the profit distribution coefficient, and paying attention to information sharing.

This paper presents the comparative evaluation of multiple compact and lightweight 2D laser scanners for their possible backpack based scanning and mapping applications. These scanners include Hokuyo URG-04LX, Slamtec RPLidar A1-M8 and Hokuyo UTM-30LX-EW scanners. Since the technical datasheets provide general information and limited working details, this research presents a thorough study on the performance of each scanner related explicitly to indoor mapping operations. A series of scanning experiments have been performed for the characterization of each scanner using statistical analysis. During the testing, all the scanning data has been recorded using Robot Operating System (ROS) and then computed in offline processing. In initial tests, each scanner’s drift effect on range measurements has been tested and presented in the relevant section of the paper. In continuation, the effect of various scanning distances on measurement accuracy has been evaluated and discussed. Later the impact of various materials typically found in indoor vicinities and their respective properties of color and smoothness have been tested and provided in the paper. Finally, a Kalman Filtering based mathematical formulation has been utilized to calibrate each scanner and to reduce the measuring uncertainties as observed in various tests for each scanner.

An electronic transition-based bare bones particle swarm optimization (ETBBPSO) algorithm is proposed in this paper. The ETBBPSO is designed to present high precision results for high dimensional single-objective optimization problems. Particles in the ETBBPSO are divided into different orbits. A transition operator is proposed to enhance the global search ability of ETBBPSO. The transition behavior of particles gives the swarm more chance to escape from local minimums. In addition, an orbit merge operator is proposed in this paper. An orbit with low search ability will be merged by an orbit with high search ability. Extensive experiments with CEC2014 and CEC2020 are evaluated with ETBBPSO. Four famous population-based algorithms are also selected in the control group. Experimental results prove that ETBBPSO can present high precision results for high dimensional single-objective optimization problems.

Objective Inter-day reliability of sweat measurements, including the absorbent patch and modified iodine-paper techniques, at rest and exercise were evaluated. We further evaluated the effect of iodine paper size and the method of establishing sweat gland activation (sweat gland counting or surface area covered) on reliability. Furthermore, the relationships between all measurement techniques and metabolic heat production [Ḣprod] and evaporative requirement for heat balance [Ėreq] were determined. Method Twelve participants were assessed for whole-body sweat loss (WBSL), local sweat rate (LSR; absorbent patch) and sweat gland activation (SGA; iodine-paper) during rest and sub-maximal cycling at ~200, ~250 and ~300 W/m2 Ḣprod in the heat. Variations in iodine paper (1 x 1 cm-9 x 9 cm) were used to quantify SGA by counting sweat glands or surface area covered. The ‘optimal’ area of SGA was also determined based on the highest density of recruited glands. Results All measures of the sweating response were positively related with Ḣprod and Ėreq (r = 0.53–0.84), with the 9 x 9 cm and 6 x 6 cm iodine paper sizes being the strongest (r = 0.66–0.84) for SGA. Superior inter-day reliability was found for all measures during exercise (CV% = 6–33.2) compared to rest (CV% = 33.5–77.9). The iodine-paper technique was most reliable at 9 x 9 cm (CV% = 15.9) or when the 1 x 1 cm (CV% = 17.6) and 3 x 3 cm (CV% = 15.5) optimal SGA was determined, particularly when measuring the sweat gland number. Significance WBSL, LSR and SGA measurement techniques are sufficiently reliable to detect changes in thermal sweating typically reported. We recommend 9 x 9 cm paper sizes or 1 x 1 cm-3 x 3 cm optimal areas, using either gland counting or surface area to determine SGA.

Networks are useful for representing phenomena in a broad range of domains. Although their ability to represent complexity can be a virtue, it is sometimes useful to focus on a simplified network that contains only the most important edges: the backbone. This paper introduces and demonstrates a substantially expanded version of the backbone package for R, which now provides methods for extracting backbones from weighted networks, weighted bipartite projections, and unweighted networks. For each type of network, fully replicable code is presented first for small toy examples, then for complete empirical examples using transportation, political, and social networks. The paper also demonstrates the implications of several issues of statistical inference that arise in backbone extraction. It concludes by briefly reviewing existing applications of backbone extraction using the backbone package, and future directions for research on network backbone extraction.

The rail transit vehicle system is an important subsystem with the most frequent operation accidents and the most direct impact on passengers. Based on the particularity of the vehicle system and the complexity of the system, the hierarchical analysis method (AHP) is used to evaluate its safety. High-order judgment matrix often has inconsistency, and the judgment matrix consistency guarantee is the key to the hierarchical analysis method applied. Based on the hierarchical analysis principle, this paper corrects the inconsistency judgment matrix and realizes the optimization calculation based on the genetic algorithm. This paper constructs a vehicle system safety evaluation index system including 26 indexes at three layers and uses the fuzzy comprehensive evaluation method to evaluate the system safety level. The results show that the calculation results based on the improved AHP-GA are significantly better than that based on the conventional AHP method. The comprehensive evaluation conclusion of the case is "average", and the safety level of the vehicle system of the case enterprise needs to be strengthened.

Global warming has seriously affected the local climate characteristics of cities, resulting in the frequent occurrence of urban waterlogging with severe economic losses and casualties. Aiming to improve the effectiveness of disaster emergency management, we propose a novel emergency decision model embedding similarity algorithms of heterogeneous multi-attribute based on case-based reasoning. First, this paper establishes a multi-dimensional attribute system of urban waterlogging catastrophes cases based on the Wuli-Shili-Renli theory. Due to the heterogeneity of attributes of waterlogging cases, different algorithms to measure the attribute similarity are designed for crisp symbols, crisp numbers, interval numbers, fuzzy linguistic variables, and hesitant fuzzy linguistic term sets. Then, this paper combines the best-worst method with the maximal deviation method for a more reasonable weight allocation of attributes. Finally, the hybrid similarity between the historical and the target cases is obtained by aggregating attribute similarities via the weighted method. According to the given threshold value, a similar historical case set is built whose emergency measures are used to provide the reference for the target case. Additionally, a case of urban waterlogging emergency is conducted to demonstrate the applicability and effectiveness of the proposed model, which exploits historical experiences and retrieves the optimal scheme for the current disaster emergency with heterogeneous multi attributes. Consequently, the proposed model solves the problem of diverse data types to satisfy the needs of case presentation and retrieval. Compared with the existing model, it can better realize the multi-dimensional expression and fast matching of the cases.

In this paper, we discuss a multi-period portfolio optimization problem based on uncertainty theory and prospect theory. We propose an uncertain multi-period portfolio selection model, in which the return utility and risk of investment are measured by prospect theory utility function and uncertain semivariance. More realistically, the influence of transaction costs and bankruptcy of investor are also considered. Moreover, to solve the portfolio model, this paper designs a new artificial bee colony algorithm by combining sine cosine search method. Finally, a numerical experiment is presented to demonstrate the proposed model and the effectiveness of the designed algorithm.

The Civil Code of the People’s Republic of China, known as the “Encyclopedia of social life”, has come into effect since January 1, 2021. It is the first law named after the “Code” and occupies a fundamental position in the China’s legal system. Therefore, translation of legal terms is vital in making target readers familiar with the China’s civil system and contributes to cultural communication and academic exchange. Previous research on translations of legal terms provides various translation principles and strategies. However, culture connotation in Chinese is usually implicit or lacks counterparts, leading to the difficulties in the equivalent translation of legal terms with Chinese characteristics. Therefore, this paper concentrates on the selection of legal term translation principles from the perspective of the Skopos theory and a Chinese translation theory named Eco-translatology, to explore the application and effect of selected principles in the translation of legal terms with Chinese characteristics. Guided by the Skopos theory and Eco-translatology, this paper collects 42 legal terms with Chinese characteristics from theThe Civil Code of the People’s Republic of Chinaand analyzes the application of 5 translation principles of accuracy, readability, traceability, conventionality, and unification. Through analysis, the legal terms are classified into newly-coined and inherited terms and rules of strategy application are therefore summarized. It is expected that this paper would provide systematic principles for the translation of legal terms with Chinese characteristics.

Background Childhood epilepsy is a serious and common neurological condition and can have life-long consequences and its impact can pervade all aspects of family life. Whilst the medical management of seizures is important, much of the day-to-day home management of epilepsy is invisible to people external to the family, including health care professionals, and parents’/caregivers’ fears and concerns can go unacknowledged and unaddressed by health care professionals. Objective This objective of this review was to examine parents’/caregivers’ fears and concerns regarding their child’s epilepsy, the impact of these fears and concerns on family life, the social and emotional well-being of parents/caregivers and any factors which mitigate these fears and concerns. Design Scoping review using a modified version of Arksey and O’Malley’s framework. Data sources Relevant studies were identified using key search terms in Scopus, Medline, CINAHL and PsychInfo databases in March 2021 with hand checking of reference lists. Search terms were developed using population (parents/caregivers of children aged ≤ 18 years with epilepsy, families); concept (parents’/caregivers’ fears, concerns, anxiety about their child’s epilepsy); and context (any setting). A further search was run in April 2022. Other inclusion criteria: English language empirical studies, 2010–2021. Study appraisal methods A minimum of two reviewers independently screened articles and undertook data extraction and decisions were consensually made. Methodological quality appraisal was undertaken using the Mixed Methods Appraisal Tool v2018. A data extraction table was created to chart all studies. The conduct and reporting of this study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidance for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) (S1 Table). There is no published copy of the review protocol. Main findings The search identified a total of 4077 papers (after duplicates were removed) of which 110 were assessed for eligibility. Twenty-four papers published between 2010–2021 were included in the review and each paper was treated as a separate study. The review findings indicate that parents’/caregivers’ fears and concerns stem from more than their child’s seizures and relate to many wider aspects of family life. These fears and concerns had far-reaching influences on their parenting/caregiving, and on the lifestyle and activities of their child and their family. What was less evident was what parents/caregivers wanted in terms of support or how they thought health professionals could acknowledge and/or allay their fears and concerns. The discussion is framed within the compassion-focused therapy model as a basis for generating new thinking about the impact of these fears and concerns and the need for a new agenda for clinical consultations in childhood epilepsy. Conclusions The review concludes with a proposal that a more compassionate agenda underpins the dialogue between parents/caregivers and clinicians to encompass and mitigate the wider emotional, psychosocial, and societal threats that impact on the parent/caregivers of children with epilepsy.

Industrial pollution reduction is a crucial issue in the pursuit of sustainable economic and environmental development. As a product of the deep integration of traditional finance and Internet information technology, digital finance has become an effective tool for regulating the use of funds and strengthening the effectiveness of policies in the context of the digital era, which has obvious effects on industrial pollution emissions. Using panel data of 260 prefecture-level cities in China from 2011–2019 and the digital inclusive finance index jointly compiled by Peking University and Ant Financial Services Group, this paper empirically analyzes the impact of digital finance on industrial pollution emissions through fixed effects model, mediating effects model and threshold effects model. The empirical results show that digital finance can effectively reduce industrial pollution and part of the impact is achieved through industrial structure. In the process of reducing industrial pollution by digital finance, there exists double threshold effects. When the development of digital finance breaks the threshold value, the industrial pollution emission reduction effect appears to accelerate. Finally, this paper puts forward targeted suggestions to promote industrial pollution reduction and environmental economic development.

Covid-19 has been front and center in the global landscape since the beginning of 2020. In response, the scientific field has dedicated enormous amounts of resources to researching the virus and its effects. The number of times Covid-19 publications are being cited throughout the literature appears remarkably high but has not been directly compared to non-Covid-19 papers in the same journals over an extended period. In our study, we use Clarivate’s Web of Science—Science Citation Index Expanded™ database to identify Covid-19 papers published in 24 major scientific journals over a period of 24 months from January 1, 2020 to December 31, 2021. We conduct our search using keywords “Covid-19”, “coronavirus”, and “sars-cov-2” to locate publications with these words in the title. We then quantify the number of citations these papers have received and compare rates to non-Covid-19 papers in the same journals over the same timeframe. We find that, across 24 open-access and subscription-based scientific journals, Covid-19 papers published in the past 2 years currently have a median citation rate of 120.79 compared to 21.63 for non-Covid-19 papers. When negative binomial regression is used to minimize the influence of other variables such as article number variation and field of research, Covid-19 papers have still experienced more than 80% increase in citations relative to non-Covid-19 papers. These novel findings demonstrate that Covid-19 papers are being cited at remarkably higher rates than non-Covid-19 articles contained within the same journals. This suggests that journal impact factor, which is a product of the number of citations that recently published articles receive, will likely be drastically influenced by the number of Covid-19 papers that a journal has included within its pages in the previous years.

Due to the weak monitoring equipment for low- and medium-pressure gas pipelines, it is not easy to identify small flow leaks. The detection methods are mostly traditional manual inspections or night pressure-maintaining leak detection methods, which cannot be automatically monitored and sensed immediately. The abnormal fluctuations in client traffic caused by pipeline leaks studied in this paper can locate and detect leak locations more effectively. This paper analyzes the theoretical formula of leakage location based on flow data and finds out how to use the abnormal fluctuation of user-side flow to detect and locate gas pipeline leakage. First of all, this article uses the simulation software Pipeline Studio to construct the medium and low-pressure pipeline model. On this basis, 6 sets of leakage conditions were designed and simulated dynamically. Finally, the simulation results are analyzed, and the results show that: 1) The advisable monitoring period for monitoring abnormal fluctuations of user-side traffic is 10s. 2) There are two relationships between abnormal flow fluctuations and leakage position. They are: when the leakage point is at the first 40% of the relative distance from the gas source, the disturbance amplitude first increases and then decreases, and at the last 60%, it continues to decrease.; The closer the leak is to the user end, the more significant the abnormal flow fluctuations will be. On the contrary, the smaller the abnormal flow fluctuations will be; 3) No matter where the leakage occurs, the abnormal flow fluctuations in the 2nd and 3rd seconds after the leak occurs tend to be consistent. The proposal of the advisable monitoring period and the relationship between abnormal fluctuations of flow and the location of leakage provides a theoretical basis for the use of abnormal fluctuations of user-side flow for gas pipeline leakage detection and location.

This paper examines the experiences of refugees in a developing country during its first COVID-19 lockdown by utilizing a two-stage qualitative data analysis of 39 interviews with refugees and asylum-seekers. We find that their experiences during the lockdown are shaped by identity, trauma and help from external parties–such as community leaders and local non-governmental organizations (NGOs). Experiences during the pandemic in turn moderate the relationship between policy changes and trust in domestic authority figures, which consequently affects attitudes towards and compliance with public health measures put in place to contain the pandemic. We then explore the role of identity in refugees’ pandemic experiences by comparing the differences between two refugee groups (Syrians and Rohingyas), validating them by utilizing comparative thematic analysis. Finally, the paper presents policy implications for crisis response in developing countries by suggesting improvements that can be made on the ground regarding the delivery of aid and assistance to vulnerable groups.

Previous studies have investigated the determinants of urban tourism development from the various attributes of neighborhood quality. However, traditional methods to assess neighborhood quality are often subjective, costly, and only on a small scale. To fill this research gap, this study applies the recent development in big data of street view images, deep learning algorithms, and image processing technology to assess quantitatively four attributes of neighborhood quality, namely street facilities, architectural landscape, green or ecological environment, and scene visibility. The paper collects more than 7.8 million Baidu SVPs of 232 prefecture-level cities in China and applies deep learning techniques to recognize these images. This paper then tries to examine the influence of neighborhood quality on regional tourism development. Empirical results show that both levels of street facilities and greenery environment promote tourism. However, the construction intensity of the landscape has an inhibitory influence on the development of tourism. The threshold test shows that the intensity of the influence varies with the city’s overall economic level. These conclusions are of great significance for the development of China’s urban construction and tourism economy, and also provide a useful reference for policymakers. The methodological procedure is reduplicative and can be applied to other challenging cases.

Background Currently, more than 30,200,000 COVID-19 cases have been diagnosed in Germany alone. However, data regarding prevalence of COVID-19 in children, both in Germany and internationally, are sparse. We sought to evaluate the number of infected children by measuring IgG antibodies. Methods Oropharyngeal swabs were collected between December 2020 and August 2021 to measure SARS-CoV-2, and capillary blood for the detection of SARS-CoV-2 antibodies (by rapid test NADAL® and filter paper test Euroimmun® ELISA); venous blood was taken for validation (Roche® ECLIA and recomLine Blot) in 365 German children aged 3–16 years from 30 schools and preschools. We used multiple serological tests because the filter paper test Euroimmun® ELISA performs better in terms of sensitivity and specificity than the rapid test NADAL®. The Roche® ECLIA test is used to detect SARS-CoV-2 spike protein, and the recomLine Blot test is used to rule out the possibility of infection by seasonal SARS-viruses and to test for specific SARS-CoV-2 proteins (NP, RBD and S1). In addition, one parent each (n = 336), and 4–5 teachers/caregivers (n = 90) per institution were tested for IgG antibodies from capillary blood samples. The total study duration was 4 months per child, including the first follow-up after 2 months and the second after 4 months. Results Of 364 children tested at baseline, 3.6% (n = 13) were positive for SARS-CoV-2 IgG antibodies using Euroimmun® ELISA. Seven children reported previously testing positive for SARS-CoV-2; each of these was confirmed by the Roche® Anti-SARS-CoV-2-ECLIA (antibody to spike protein 1) test. SARS-CoV-2 IgG antibodies persisted over a 4-month period, but levels decreased significantly (p = 0.004) within this timeframe. The median IgG values were 192.0 BAU/ml [127.2; 288.2], 123.6 BAU/ml [76.6; 187.7] and 89.9 BAU/ml [57.4; 144.2] at baseline, 2 months and 4 months after baseline, respectively. During the study period, no child tested positive for SARS-CoV-2 by oropharyngeal swab. A total of 4.3% of all parents and 3.7% of teachers/caregivers tested positive for IgG antibodies by Euroimmun® ELISA at baseline. Conclusion We noted a rather low seroprevalence in children despite an under-reporting of SARS-CoV-2 infections. Measurement of IgG antibodies derived from capillary blood appears to be a valid tool to detect asymptomatic infections in children. However, no asymptomatic active infection was detected during the study period of 4 months in the whole cohort. Further data on SARS-CoV-2 infections in children are needed, especially in the group of

The question of whether it is appropriate to attribute authorship to deceased individuals of original studies in the biomedical literature is contentious. Authorship guidelines utilized by journals do not provide a clear consensus framework that is binding on those in the field. To guide and inform the implementation of authorship frameworks it would be useful to understand the extent of the practice in the scientific literature, but studies that have systematically quantified the prevalence of this phenomenon in the biomedical literature have not been performed to date. To address this issue, we quantified the prevalence of publications by deceased authors in the biomedical literature from the period 1990–2020. We screened 2,601,457 peer-reviewed papers from the full text Europe PubMed Central database. We applied natural language processing, stringent filtering and manual curation to identify a final set of 1,439 deceased authors. We then determined these authors published a total of 38,907 papers over their careers with 5,477 published after death. The number of deceased publications has been growing rapidly, a 146-fold increase since the year 2000. This rate of increase was still significant when accounting for the growing total number of publications and pool of authors. We found that more than 50% of deceased author papers were first submitted after the death of the author and that over 60% of these papers failed to acknowledge the deceased authors status. Most deceased authors published less than 10 papers after death but a small pool of 30 authors published significantly more. A pool of 266 authors published more than 90% of their total publications after death. Our analysis indicates that the attribution of deceased authorship in the literature is not an occasional occurrence but a burgeoning trend. A consensus framework to address authorship by deceased scientists is warranted.

The humans of modern society are enjoying the luxuries and comforts today but future generations will be facing a more polluted environment and scarcity of natural resources. So the effects of global warming and climatic changes are a major policy concern nowadays around the world. The majority of the literature treats the Carbon Dioxide emissions as an indicator of environmental deterioration but this paper considers the environmental performance index as an indicator of environment. This paper addresses the role of institutional reforms for environmental performance that is hardly discussed in the earlier literature. It is argued that a novel approach of institutional reforms can provide some useful insights for environmental performance in developing countries. There is wide agreement that institutional quality is crucial for economic sustainability but rarely focused to explore the impacts of institutional reforms on environmental performance. The institutional reforms are generally divided into two categories; economic and political reforms. This paper investigated the impact of each category of institutional reforms for environmental performance by using panel data of 122 developing economies for a period of 1996–2020. Difference in differences technique is applied to determine the impact of each category of reforms on the environment. It is found that economic and political reforms significantly contribute to protecting the environment in developing countries, and it will be a good policy option to reform the economic and political institutions to preserve the environment in these countries along with sustainable development.

Segmentation of retinal vessels is important for doctors to diagnose some diseases. The segmentation accuracy of retinal vessels can be effectively improved by using deep learning methods. However, most of the existing methods are incomplete for shallow feature extraction, and some superficial features are lost, resulting in blurred vessel boundaries and inaccurate segmentation of capillaries in the segmentation results. At the same time, the “layer-by-layer” information fusion between encoder and decoder makes the feature information extracted from the shallow layer of the network cannot be smoothly transferred to the deep layer of the network, resulting in noise in the segmentation features. In this paper, we propose the MFI-Net (Multi-resolution fusion input network) network model to alleviate the above problem to a certain extent. The multi-resolution input module in MFI-Net avoids the loss of coarse-grained feature information in the shallow layer by extracting local and global feature information in different resolutions. We have reconsidered the information fusion method between the encoder and the decoder, and used the information aggregation method to alleviate the information isolation between the shallow and deep layers of the network. MFI-Net is verified on three datasets, DRIVE, CHASE_DB1 and STARE. The experimental results show that our network is at a high level in several metrics, with F1 higher than U-Net by 2.42%, 2.46% and 1.61%, higher than R2U-Net by 1.47%, 2.22% and 0.08%, respectively. Finally, this paper proves the robustness of MFI-Net through experiments and discussions on the stability and generalization ability of MFI-Net.

To evaluate actions taken to implement the Telecommunications Act of 1996, the primary goal of which was to foster competition in the industry, the FCC created a standardized form (Form 477) to collect information about broadband deployment and competition in local telephone service. These data represent the best publicly available record of broadband provision in the United States. Despite the potential benefits offered by this database, there are several nuances to these data related to shifting geographies and reporting requirements that uncorrected, prevent them from being used as an uninterrupted time series for longitudinal analyses. Given the analytical challenges associated with the FCC Form 477 data, the purpose of this paper is to present a solution to the fragmented nature of these data which prevents meaningful longitudinal analyses of the digital divide. Specifically, this paper develops and describes a procedure for producing an integrated broadband time series (BITS) for the last decade (2008–2018). This includes the procedures for using these data, their value to social and economic analysis, and their underlying limitations. The core contribution of this paper is the creation of data infrastructure for investigating the evolution of the digital divide.

This paper provides an institutional and empirical analysis of the highly concentrated market of academic publishing, characterized by over proportionally high profit margins for publishing companies. The availability of latest research findings is an important issue for researchers, universities and politicians alike. Open access (OA) publication provides a promising but also costly solution to overcome this problem. However, in this paper we argue that OA publication costs are an important, but by far not the only way for academic publishers to gain access to public funding. In contrast, our study provides a comprehensive overview of the channels through which public expenditure benefits big academic publishing companies. Furthermore, we offer the results of an explorative case study, where we estimate the annual financial flows of public expenditures in Austria for the field of social sciences. In all, these expenditures add up to about 66.55 to 103.2 million € a year, which amounts to a fourth of total public funding for this field. Against this background, we contribute to the debate whether and to what extent public subsidies are justified for economically successful companies.