bluuebunny/crossref_metadata_2025 huggingface dataset:
Elevation Regimes Modulated the Responses of Canopy Structure of Coastal Mangrove Forests to Hurricane Damage
Authors: Gao Qiong , Yu Mei
Published: March 2022

Mangrove forests have unique ecosystem functions and services, yet the coastal mangroves in tropics are often disturbed by tropical cyclones. Hurricane Maria swept Puerto Rico and nearby Caribbean islands in September 2017 and caused tremendous damage to the coastal mangrove systems. Understanding the vulnerability and resistance of mangrove forests to disturbances is pivotal for future restoration and conservation. In this study, we used LiDAR point clouds to derive the canopy height of five major mangrove forests, including true mangroves and mangrove associates, along the coast of Puerto Rico before and after the hurricanes, which allowed us to detect the spatial variations of canopy height reduction. We then spatially regressed the pre-hurricane canopy height and the canopy height reduction on biophysical factors such as the elevation, the distance to rivers/canals within and nearby, the distance to coast, tree density, and canopy unevenness. The analyses resulted in the following findings. The pre-hurricane canopy height increased with elevation when elevation was low and moderate but decreased with elevation when elevation was high. The canopy height reduction increased quadratically with the pre-hurricane canopy height, but decreased with elevation for the four sites dominated by true mangroves. The site of Palma del Mar dominated by Pterocarpus, a mangrove associate, experienced the strongest wind, and the canopy height reduction increased with elevation. The canopy height reduction decreased with the distance to rivers/canals only for sites with low to moderate mean elevation of 0.36–0.39 m. In addition to the hurricane winds, the rainfall during hurricanes is an important factor causing canopy damage by inundating the aerial roots. In summary, the pre-hurricane canopy structures, physical environment, and external forces brought by hurricanes interplayed to affect the vulnerability of coastal mangroves to major hurricanes.
Trusted Data Storage Architecture for National Infrastructure
Authors: Wang Yichuan , Fan Rui , Liang Xiaolong , Li Pengge , Hei Xinhong
Published: March 2022

National infrastructure is a material engineering facility that provides public services for social production and residents’ lives, and a large-scale complex device or system is used to ensure normal social and economic activities. Due to the problems of difficult data collection, long project period, complex data, poor security, difficult traceability and data intercommunication, the archives management of most national infrastructure is still in the pre-information era. To solve these problems, this paper proposes a trusted data storage architecture for national infrastructure based on blockchain. This consists of real-time collection of national infrastructure construction data through sensors and other Internet of Things devices, conversion of heterogeneous data source data into a unified format according to specific business flows, and timely storage of data in the blockchain to ensure data security and persistence. Knowledge extraction of data stored in the chain and the data of multiple regions or fields are jointly modeled through federal learning. The parameters and results are stored in the chain, and the information of each node is shared to solve the problem of data intercommunication.
Behavioural Classification of Cattle Using Neck-Mounted Accelerometer-Equipped Collars
Authors: Pavlovic Dejan , Czerkawski Mikolaj , Davison Christopher , Marko Oskar , Michie Craig , Atkinson Robert , Crnojevic Vladimir , Andonovic Ivan , Rajovic Vladimir , Kvascev Goran , Tachtatzis Christos
Published: March 2022

Monitoring and classification of dairy cattle behaviours is essential for optimising milk yields. Early detection of illness, days before the critical conditions occur, together with automatic detection of the onset of oestrus cycles is crucial for obviating prolonged cattle treatments and improving the pregnancy rates. Accelerometer-based sensor systems are becoming increasingly popular, as they are automatically providing information about key cattle behaviours such as the level of restlessness and the time spent ruminating and eating, proxy measurements that indicate the onset of heat events and overall welfare, at an individual animal level. This paper reports on an approach to the development of algorithms that classify key cattle states based on a systematic dimensionality reduction process through two feature selection techniques. These are based on Mutual Information and Backward Feature Elimination and applied on knowledge-specific and generic time-series extracted from raw accelerometer data. The extracted features are then used to train classification models based on a Hidden Markov Model, Linear Discriminant Analysis and Partial Least Squares Discriminant Analysis. The proposed feature engineering methodology permits model deployment within the computing and memory restrictions imposed by operational settings. The models were based on measurement data from 18 steers, each animal equipped with an accelerometer-based neck-mounted collar and muzzle-mounted halter, the latter providing the truthing data. A total of 42 time-series features were initially extracted and the trade-off between model performance, computational complexity and memory footprint was explored. Results show that the classification model that best balances performance and computation complexity is based on Linear Discriminant Analysis using features selected through Backward Feature Elimination. The final model requires 1.83 ± 1.00 ms to perform feature extraction with 0.05 ± 0.01 ms for inference with an overall balanced accuracy of 0.83.
Joint Beamforming, Power Allocation, and Splitting Control for SWIPT-Enabled IoT Networks with Deep Reinforcement Learning and Game Theory
Authors: Liu JainShing , Lin Chun-Hung Richard , Hu Yu-Chen , Donta Praveen Kumar
Published: March 2022

Future wireless networks promise immense increases on data rate and energy efficiency while overcoming the difficulties of charging the wireless stations or devices in the Internet of Things (IoT) with the capability of simultaneous wireless information and power transfer (SWIPT). For such networks, jointly optimizing beamforming, power control, and energy harvesting to enhance the communication performance from the base stations (BSs) (or access points (APs)) to the mobile nodes (MNs) served would be a real challenge. In this work, we formulate the joint optimization as a mixed integer nonlinear programming (MINLP) problem, which can be also realized as a complex multiple resource allocation (MRA) optimization problem subject to different allocation constraints. By means of deep reinforcement learning to estimate future rewards of actions based on the reported information from the users served by the networks, we introduce single-layer MRA algorithms based on deep Q-learning (DQN) and deep deterministic policy gradient (DDPG), respectively, as the basis for the downlink wireless transmissions. Moreover, by incorporating the capability of data-driven DQN technique and the strength of noncooperative game theory model, we propose a two-layer iterative approach to resolve the NP-hard MRA problem, which can further improve the communication performance in terms of data rate, energy harvesting, and power consumption. For the two-layer approach, we also introduce a pricing strategy for BSs or APs to determine their power costs on the basis of social utility maximization to control the transmit power. Finally, with the simulated environment based on realistic wireless networks, our numerical results show that the two-layer MRA algorithm proposed can achieve up to 2.3 times higher value than the single-layer counterparts which represent the data-driven deep reinforcement learning-based algorithms extended to resolve the problem, in terms of the utilities designed to reflect the trade-off among the performance metrics considered.
How Validation Methodology Influences Human Activity Recognition Mobile Systems
Authors: Bragança Hendrio , Colonna Juan G. , Oliveira Horácio A. B. F. , Souto Eduardo
Published: March 2022

In this article, we introduce explainable methods to understand how Human Activity Recognition (HAR) mobile systems perform based on the chosen validation strategies. Our results introduce a new way to discover potential bias problems that overestimate the prediction accuracy of an algorithm because of the inappropriate choice of validation methodology. We show how the SHAP (Shapley additive explanations) framework, used in literature to explain the predictions of any machine learning model, presents itself as a tool that can provide graphical insights into how human activity recognition models achieve their results. Now it is possible to analyze which features are important to a HAR system in each validation methodology in a simplified way. We not only demonstrate that the validation procedure k-folds cross-validation (k-CV), used in most works to evaluate the expected error in a HAR system, can overestimate by about 13% the prediction accuracy in three public datasets but also choose a different feature set when compared with the universal model. Combining explainable methods with machine learning algorithms has the potential to help new researchers look inside the decisions of the machine learning algorithms, avoiding most times the overestimation of prediction accuracy, understanding relations between features, and finding bias before deploying the system in real-world scenarios.
Technology Developments and Impacts of Connected and Autonomous Vehicles: An Overview
Authors: Ahmed Hafiz Usman , Huang Ying , Lu Pan , Bridgelall Raj
Published: March 2022

The scientific advancements in the vehicle and infrastructure automation industry are progressively improving nowadays to provide benefits for the end-users in terms of traffic congestion reduction, safety enhancements, stress-free travels, fuel cost savings, and smart parking, etc. The advances in connected, autonomous, and connected autonomous vehicles (CV, AV, and CAV) depend on the continuous technology developments in the advanced driving assistance systems (ADAS). A clear view of the technology developments related to the AVs will give the users insights on the evolution of the technology and predict future research needs. In this paper, firstly, a review is performed on the available ADAS technologies, their functions, and the expected benefits in the context of CVs, AVs, and CAVs such as the sensors deployed on the partial or fully automated vehicles (Radar, LiDAR, etc.), the communication systems for vehicle-to-vehicle and vehicle-to-infrastructure networking, and the adaptive and cooperative adaptive cruise control technology (ACC/CACC). Secondly, for any technologies to be applied in practical AVs related applications, this study also includes a detailed review in the state/federal guidance, legislation, and regulations toward AVs related applications. Last but not least, the impacts of CVs, AVs, and CAVs on traffic are also reviewed to evaluate the potential benefits as the AV related technologies penetrating in the market. Based on the extensive reviews in this paper, the future related research gaps in technology development and impact analysis are also discussed.
Decadal Changes of Organic Carbon, Nitrogen, and Acidity of Austrian Forest Soils
Authors: Jandl Robert , Leitgeb Ernst , Englisch Michael
Published: March 2022

Repeated soil surveys provide opportunities to quantify the effect of long-term environmental change. In recent decades, the topics of forest soil acidification as a consequence of acidic deposition, the enrichment of forest ecosystems with nitrogen, and the loss of carbon due to climate change have been discussed. We used two forest soil surveys that were 20 years apart, in order to establish the direction and magnitude of changes in soil carbon, nitrogen, and soil acidity. Soils have been initially sampled in the late 1980s. The plots were revisited twenty years later. Archived soil samples from the first survey were reanalyzed with the same protocol as the new samples. We found changes in the stocks of soil organic carbon, soil nitrogen, and soil pH. However, the changes were inconsistent. In general, as many sites have gained soil organic carbon, as sites have lost carbon. Most soils have been slightly enriched with nitrogen. The soil pH has not changed significantly. We conclude that changes in the evaluated soil chemical properties are mainly driven by forest management activities and ensuing forest stand dynamics, and atmospheric deposition. We have no convincing evidence that climate change effects have already changed the soil organic carbon stock, irrespective of bedrock type.
Research on Permeability Coefficient of Fine Sediments in Debris-Flow Gullies, Southwestern China
Authors: Wang Qinjun , Xie Jingjing , Yang Jingyi , Liu Peng , Chang Dingkun , Xu Wentao
Published: March 2022

Fine sediments in debris-flow gullies are quaternary sediments with a particle size of less than 2 mm. Since they are easy to suspend in flowing water, their stability plays a key “probe” role in early debris-flow warning. The permeability coefficient is the main internal control factor of fine sediment stability in debris flow. However, there is no quantitative model between the permeability coefficient and its influencing factors, which seriously affects the quantitative evaluation of debris flow sediments. Taking the debris-flow gullies in Laobeichuan County, Sichuan Province, China as the research area, we carried out experiments on the permeability coefficient and its influencing factors. A model between the permeability coefficient and its influencing factors was established by the least-squares multivariate statistical analysis method. The results showed that cohesion was the closest factor to the permeability coefficient, followed by porosity and density. Each factor passed the t-test and significantly correlated with the model in 99.99% probability. With a correlation coefficient of 0.72, the model had a good prediction ability. Therefore, the model not only provides a theoretical basis for analyzing the stability of fine sediments in Laobeichuan County, but also points out the direction for detecting the fine sediment stability in debris-flow gullies.
A Multi-Agent Model-Based Evolutionary Model of Port Service Value Network and Decision Preferences
Authors: Zhou Yu , Shi Shanshan , Wang Shaohua
Published: March 2022

The rise of value networks in the new economic era and the emergence of a common competitive advantage pose enormous challenges to the value-added approach of enterprises based on traditional value chains. Based on the customer-centered concept, the complementary effect of agents, and other competition characteristics of the value network, this paper constructs a multi-agent-based value network evolution model. The influencing mechanism of customer preference on the number of value network entities and overall income is discussed through simulation analysis. The research shows that the influence of customer preference on the evolution and steady-state of the overall value of the value network is not significant. Customer preference plays a decisive role in the number of service subjects and the evolution of attributes. In different situations, the influence of customer preference on the number of subjects is significantly different. This means that customer power preference in the value network of port services is much higher than other preferences. The research results provide a decision-making basis for enterprises to break through the shackles of traditional value-added concepts, actively seek value network optimization schemes, and achieve timely responses to customer preferences.
Penalty Function Optimization in Dual Response Surfaces Based on Decision Maker’s Preference and Its Application to Real Data
Authors: Aziz Nasuhar Ab. , Midi Habshah
Published: March 2022

The dual response surface methodology is a widely used technique in industrial engineering for simultaneously optimizing both the process mean and process standard deviation functions of the response variables. Many optimization techniques have been proposed to optimize the two fitted response surface functions that include the penalty function method (PM). The PM method has been shown to be more efficient than some existing methods. However, the drawback of the PM method is that it does not have a specific rule for determining the penalty constant; thus, in practice, practitioners will find this method difficult since it depends on subjective judgments. Moreover, in most dual response optimization methods, the sample mean and sample standard deviation of the response often use non-outlier-resistant estimators. The ordinary least squares (OLS) method is also usually used to estimate the parameters of the process mean and process standard deviation functions. Nevertheless, not many statistics practitioners are aware that the OLS procedure and the classical sample mean and sample standard deviation are easily influenced by the presence of outliers. Alternatively, instead of using those classical methods, we propose using a high breakdown and highly efficient robust MM-mean, robust MM-standard deviation, and robust MM regression estimators to overcome these shortcomings. We also propose a new optimization technique that incorporates a systematic method to determine the penalty constant. We call this method the penalty function method based on the decision maker’s (DM) preference structure in obtaining the penalty constant, denoted as PMDM. The performance of our proposed method is investigated by a Monte Carlo simulation study and real examples that employ symmetrical factorial design of experiments (DOE). The results signify that our proposed PMDM method is the most efficient method compared to the other commonly used methods in this study.
Multi-Parameter Quantum Integral Identity Involving Raina’s Function and Corresponding q-Integral Inequalities with Applications
Authors: Vivas-Cortez Miguel , Awan Muhammad Uzair , Talib Sadia , Kashuri Artion , Noor Muhammad Aslam
Published: March 2022

Convexity performs its due role in the theoretical field of inequalities according to the nature and conduct of the properties it displays. A correlation connectivity, which is visible between the two variables symmetry and convexity, enhances its importance. In this paper, we derive a new multi-parameter quantum integral identity involving Raina’s function. Applying this generic identity as an auxiliary result, we establish some new generalized quantum estimates of certain integral inequalities pertaining to the class of Rs-convex functions. Moreover, we give quantum integral inequalities for the product of Rs1- and Rs2-convex functions as well as another quantum result for a function that satisfies a special condition. In order to demonstrate the efficiency of our main results, we offer many important special cases for suitable choices of parameters and finally for Rs-convex functions that are absolute-value bounded.
A Novel Classification Framework for Hyperspectral Image Data by Improved Multilayer Perceptron Combined with Residual Network
Authors: Wang Aili , Li Meixin , Wu Haibin
Published: March 2022

Convolutional neural networks (CNNs) have attracted extensive attention in the field of modern remote sensing image processing and show outstanding performance in hyperspectral image (HSI) classification. Nevertheless, some hyperspectral images have fixed position priors and parameter sharing between different positions, so the common convolution layer may ignore some important fine and useful information and cannot guarantee to effectively capture the optimal image features. This paper proposes an improved multilayer perceptron (IMLP) and IMLP combined with ResNet (IMLP-ResNet) two models for HSI classification. Combined with the characteristics of hyperspectral data, we design IMLP based on three improvements. Specifically, a depthwise over-parameterized convolutional layer is introduced to increase learnable parameters of the model in IMLP, which speeds up the convergence of the model without increasing the computational complexity. Secondly, a Focal Loss function is used to suppress the useless ones in the classification task and enhance the critical spectral–spatial features, which allow the IMLP network to learn more useful hyperspectral image information. Furthermore, to enhance the convergence speed of the network, cosine annealing is introduced to further improve the training performance of IMLP. Furthermore, the IMLP module is combined with a residual network (IMLP-ResNet) to construct a symmetric structure, which extracts more advanced semantic information from hyperspectral images. The proposed IMLP and IMLP-ResNet are tested on the two public HSI datasets (i.e., Indian Pines and Pavia University) and a real hyperspectral dataset (Xuzhou). Experimental results demonstrate the superiority of the proposed IMLP-ResNet method over several state-of-the-art methods with the highest OA, which outperforms CNN by 8.19%, 6.28%, 5.59% and outperforms ResNet by 3.52%, 3.54%, 2.67% on Indian Pines, Pavia University and Xuzhou datasets, respectively, and demonstrates that the well-designed MLPs can also obtain remarkable classification performance of HSI.
A Method for the Design of Bicomplex Orthogonal DSP Algorithms for Applications in Intelligent Radio Access Networks
Authors: Valkova-Jarvis Zlatka , Poulkov Vladimir , Stoynov Viktor , Mihaylova Dimitriya , Iliev Georgi
Published: March 2022

In this paper, a new method for designing orthogonal bicomplex digital signal processing (DSP) algorithms is developed. In contrast to those previously reported on, the method proposed is universal, since it is not affected by the order or the type of the real digital processing algorithm employed as a prototype. The method is based on a transformation starting with either real or complex orthogonal DSP algorithms represented in the z-domain, and transforming them into orthogonal bicomplex algorithms. The proposed new method is applied in the design of bilinear orthogonal bicomplex DSP systems with a canonical number of elements, the main advantage of which is that the order of the digital system is reduced by a factor of four. As well as being canonical, the orthogonal bicomplex digital systems are also symmetrical structures, as a result of which they offer parallelism and subsequent unification. It is experimentally shown that bicomplex orthogonal DSP algorithms acquire the properties of the initial algorithm prototype, irrespective of whether it is real or complex. Since the new design method is universally applicable, it can be used to develop bicomplex orthogonal digital algorithms of any order and type. Being simple, canonical, and symmetrical—and, thus, leading to lower equipment cost, reduced complexity, and higher energy efficiency—these structures may well be appropriate for the enhancement of the implementation of intelligent algorithms in next-generation radio access networks.
Research on Financing Risk Factors of Expressway REITs in China with a Hybrid Approach
Authors: Song Yimeng , Hao Shengyue
Published: March 2022

Finding sustainable measures for expressway financing has long been a significant issue owing to high demand for funds of expressway construction and maintenance of existing facilities. In the Chinese context of expressway financing it has become imperative to shift from indirect bank financing to Real Estate Investment Trusts (REITs). This research investigates the impact of various factors on the financing risk of expressway REITs and estimates the weight of the impact of various aspects and the link between the factors. We used literature review, keyword co-occurrence analysis and keyword cluster analysis methods to identify 19 risk factors that affect the financing of expressway REITs, then we classified factors into six dimensions: credit risk, underlying asset risk, operational risk, market risk, liquidity risk, and other risk. In addition, a multi-level hierarchical structure model was established by the Integrated Decision-Making and Trial Evaluation Laboratory (DEMATEL) and an interpretative structural model (ISM). The research finds that the project’s future cash flow under-expected risk, price risk, and counterparty limited risk are direct factors, the bankruptcy isolation of the underlying assets risk is a deep factor affecting the financing of expressway REITs, and other factors are indirect factors. This study fills the gap in financing risk of expressway REITs in the context of China and contributes to exploring and establishing the financing risks identification approach and risk factors in expressway REITs based on Chinese contexts. This research presents a theoretical foundation and methodologies for reducing the financing risk of expressway REITs projects and improving financing safety.
A Subtle Aspect of Minimal Lengths in the Generalized Uncertainty Principle
Authors: Bishop Michael , Contreras Joey , Singleton Douglas
Published: March 2022

In this work, we point out an overlooked and subtle feature of the generalized uncertainty principle (GUP) approach to quantizing gravity: namely that different pairs of modified operators with the same modified commutator, [X^,P^]=iħ(1+βp2), may have different physical consequences such as having no minimal length at all. These differences depend on how the position and/or momentum operators are modified rather than only on the resulting modified commutator. This provides guidance when constructing GUP models since it distinguishes those GUPs that have a minimal length scale, as suggested by some broad arguments about quantum gravity, versus GUPs without a minimal length scale.
Flu Vaccination Coverage and Predictors of Non-Vaccination in Military Health Corps Personnel 2016–2017 and 2019–2021
Authors: Ajejas Bazán María Julia , Pérez-Rivas Francisco Javier , Wärnberg Julia , Fuentes Mora Carlos , Ballester Orcal Lucía Elena , Gómez Crespo Jose Manuel , López-López Candelas , Domínguez-Fernández Silvia , Rico-Blázquez Milagros , Pérez-Farinós Napoleón
Published: March 2022

(1) Background: Vaccination is the most effective intervention to control seasonal influenza morbidity and mortality. The present study aimed to determine the influenza vaccination coverage in the Military Health Corps personnel in the 2020–2021 season, as well as the time trend and the possible influence of the pandemic on coverage, in order to study the reasons that led to the non-vaccination of health professionals and to analyze adverse drug reactions (ADRs). (2) Methods: A descriptive, cross-sectional study was conducted from February to May 2021. All FAS CMS personnel were included. A self-administered questionnaire was sent by e-mail to the selected personnel. (3) Results: Vaccination coverage in the 2016–2017 season was 15.8% (n = 276), in the 2019–2020 season it was 17.41% (n = 424), and in the 2020–2021 season it was 24.22% (n = 590). The percentage of vaccinated men was higher than the percentage of women. In 2019 and 2020 the most vaccinated group was 31–40 years old. Lieutenants had the highest vaccination uptake in 2019 and 2020. The personnel with the highest uptake of vaccines were those in the specialty of nursing in each of 2016, 2019 and 2020, with >30 years of time worked in 2016. In terms of factors leading to refusal of vaccination, the most reported was “not considered a risk group” (23.0%), and the least reported was “avoidance of vaccine administration” (2.2%). Eighty individuals presented adverse reactions after vaccine administration (9.6%). (4) Conclusions: The rate of influenza vaccination among healthcare professionals was lower during the 2020 season compared to the previous season, but was expected to increase in the upcoming 2021 season.
Anti-SARS-CoV-2 Antibody Responses 5 Months Post Complete Vaccination of Moroccan Healthcare Workers
Authors: Assaid Najlaa , Arich Soukaina , Charoute Hicham , Akarid Khadija , Ezzikouri Sayeh , Maaroufi Abderrahmane , Sarih M’hammed
Published: March 2022

Data about the duration of antibodies after vaccination show that the protection against SARS-CoV-2 infection begins to decline over time. This study aims to determine anti-SARS-CoV-2 anti-S IgG levels in healthcare workers five months after the second vaccination dose. We collected samples from 82 participants who were fully vaccinated with ChAdOx1 nCoV-19 or BBIBP-CorV. We assessed anti-SARS-CoV-2 IgG antibodies using a Euroimmun ELISA and an Abbott Architect ™ SARS-CoV-2 IgG test. Of the 82 participants, 65.85% were seropositive for IgG using ELISA, and 86.59% were positive for IgG according to the Abbott Architect ™ test. Individuals vaccinated with the ChAdOx1 nCoV-19 vaccine had a median anti-S1 antibody level of 1.810 AU/mL [interquartile range (IQR), 1.080–3.7340] and 171.7 AU/mL [79.9–684.6] according to the Euroimmun ELISA and Abbott Architect test, respectively. These tests indicated that people vaccinated with BBIBP-CorV had a median anti-S1 antibody level of 1.840 AU/mL [0.810–2.960] and 126.7 AU/mL [54.9–474.3], respectively. Statistical analysis showed no significant difference between the positivity rates of the vaccinated individuals, either for gender or for age. In addition, we found no significant difference between the two vaccines. Our study provides information on the longevity of the anti-SARS-CoV-2 IgG antibodies in people at least five months after vaccination.
An Outbreak of Equine Herpesvirus-4 in an Ecological Donkey Milk Farm in Romania
Authors: Mureşan Alexandra , Mureşan Cosmin , Siteavu Madalina , Avram Electra , Bochynska Diana , Taulescu Marian
Published: March 2022

Equine herpesviruses are important pathogens causing significant economic loss in equine and asinine populations. EHV-1/4 strains are mainly associated with respiratory distress. The aim of this study is to report the first EHV 4-associated respiratory disease in donkeys in Romania. Thirty-seven of three hundred jennies in an ecological donkey farm in southwest Romania started initially showing signs of severe upper respiratory tract disease, with ten concomitant late abortions/neonatal deaths and three neurological cases. There were nine fatalities. Pathological examination was performed, and samples were collected for Real-Time PCR analysis and histology. In addition, serum samples from 28 individuals with respiratory symptoms were collected and tested using indirect ELISA. RT-PCR identified the EHV-4 strain. Acute, diffuse necrotizing bronchointerstitial pneumonia with occasional intraepithelial intranuclear viral inclusion bodies was identified. Additionally, EHV-1/4-specific antibodies were found in 15 of the 28 sampled animals. Few studies on donkeys and herpesviruses have been published, and this is the first reported case of EHV-4 outbreak in Romania. There is a need for more extensive seroprevalence studies as, currently, the status of EHV-4 infection in donkeys in Romania is unknown.
Effectiveness of BNT162b2 and mRNA-1273 Vaccines against COVID-19 Infection: A Meta-Analysis of Test-Negative Design Studies
Authors: Chang Shuailei , Liu Hongbo , Wu Jian , Xiao Wenwei , Chen Sijia , Qiu Shaofu , Duan Guangcai , Song Hongbin , Zhang Rongguang
Published: March 2022

Although numerous COVID-19 vaccines are effective against COVID-19 infection and variants of concern (VOC) in the real world, it is imperative to obtain evidence of the corresponding vaccine effectiveness (VE). This study estimates the real-world effectiveness of the BNT162b2 and mRNA-1273 vaccines against COVID-19 infection and determines the influence of different virus variants on VE by using test-negative design (TND) studies. We systematically searched for published articles on the efficacy of BNT162b2 and mRNA-1273 against COVID-19 infection. Two researchers independently selected and extracted data from eligible studies. We calculated the VE associated with different vaccine types, SARS-CoV-2 variants, and vaccination statuses, using an inverse variance random-effects model. We selected 19 eligible studies in the meta-analysis from 1651 records. For the partially vaccinated group, the VE of BNT162b2 and mRNA-1273 was 61% and 78% against COVID-19 infection, respectively. For the completely vaccinated group, the VE of BNT162b2 and mRNA-1273 was 90% and 92% against COVID-19 infection, respectively. During subgroup analyses, the overall VE of BNT162b2 and mRNA-1273 against the Delta variant was 53% and 71%, respectively, for the partially vaccinated group; the respective VE values were 85% and 91% for the fully vaccinated group. Irrespective of the BNT162b2 or mRNA-1273 vaccines, the Delta variant significantly weakened vaccine protection for the partially vaccinated group, while full vaccination was highly effective against COVID-19 infection and various VOC. The mRNA-1273 vaccine is more effective against COVID-19 infection and VOC than the BNT162b2 vaccine, especially for the partially vaccinated group. Overall, the results provide recommendations for national and regional vaccine policies.
Factors Influencing Level and Persistence of Anti SARS-CoV-2 IgG after BNT162b2 Vaccine: Evidence from a Large Cohort of Healthcare Workers
Authors: Costa Cristina , Migliore Enrica , Galassi Claudia , Scozzari Gitana , Ciccone Giovannino , Coggiola Maurizio , Pira Enrico , Scarmozzino Antonio , La Valle Giovanni , Cassoni Paola , Cavallo Rossana ,   on behalf of the Collaborative Group
Published: March 2022

We aimed at evaluating quantitative IgG response to BNT162b2 COVID-19 vaccine among health care workers (HCW), and exploring the role of demographic, clinical, and occupational factors as predictors of IgG levels. On May 2021, among 6687 HCW at the largest tertiary care University-Hospital of Northwestern Italy, at a median of 15 weeks (Interquartile range-IQR 13.6–16.0) after second-dose, serological response was present in 99.8%. Seropositivity was >97% in all the subgroups, except those self-reporting immunodeficiency (94.9%). Overall, the median serological IgG value was 990 BAU/mL (IQR 551–1870), with most of subjects with previous SARS-CoV-2 infection or with shorter time lapse (2–8 weeks) between vaccination and serology with values in the highest quintile (>2080). At multivariable analysis, significant predictors of lower values were increasing age, male, current smoking, immunodeficiency, recent occupational contacts, and increasing time lapse from vaccination; conversely, previous infection and recent household contacts were significantly associated with higher IgG levels. Subjects with previous infection kept a very high level (around 2000 BAU/mL) up to 120 days. These results, besides supporting a high serological response up to 4–5 months, suggest predictive factors of faster decay of IgG levels that could be useful in tailoring vaccination strategies.
Knowledge, Attitudes, Perceptions and Vaccination Acceptance/Hesitancy among the Community Pharmacists of Palermo’s Province, Italy: From Influenza to COVID-19
Authors: Costantino Claudio , Graziano Giorgio , Bonaccorso Nicole , Conforto Arianna , Cimino Livia , Sciortino Martina , Scarpitta Francesco , Giuffrè Chiara , Mannino Salvatore , Bilardo Mario , Ledda Caterina , Vitale Francesco , Restivo Vincenzo , Mazzucco Walter
Published: March 2022

In Italy, following the start of the SARS-CoV-2 vaccination campaign, community pharmacies (CPs) were recruited on a voluntary basis in order to administer COVID-19 vaccines as part of their activities. The aim of the present study was to investigate the knowledge, attitudes, and practices regarding SARS-CoV-2 infection prevention, and vaccine acceptance/hesitancy towards COVID-19 and influenza vaccinations among the community pharmacists operating in the Palermo Province. A cross-sectional study was conducted, with two different questionnaires administered before and after the conduction of the vaccination campaign against SARS-CoV-2 at the COVID-19 vaccination center of the Palermo University Hospital (PUH). The baseline survey showed that 64% of community pharmacists (CPs) declared that they planned to vaccinate against SARS-CoV-2, and 58% were vaccinated against influenza during the 2020/2021 season. Factors significantly associated with willingness to receive the COVID-19 vaccination were confidence in vaccines (adjOR 1.76; CI 1.11–2.80), fear of contracting SARS-CoV-2 infection (adjOR 1.50; CI 1.06–2.11), considering COVID-19 vaccination to be the best strategy to counteract SARS-CoV-2 (adjOR 1.79; CI 1.39–2.29), and adherence to influenza vaccination during the 2020/2021 season (adjOR 3.25; CI 2.23–4.25). The adherence among CPs of the Palermo Province to COVID-19 vaccination was 96.5%. From the post-vaccination survey, the main reasons for changing opinions on vaccination adherence were the introduction of mandatory vaccinations, fear of contracting COVID-19, and limitations on work activities in the case of vaccine refusal. The achievement of very high COVID-19 vaccination coverage rates among healthcare professionals (HCPs) in the present study was mainly due to the mandatory vaccination policies; nevertheless, a willingness for COVID-19 vaccination was relatively high among pharmacists before the beginning of the vaccination campaign. HCPs and CPs should receive training on vaccination, which is recommended in the national immunization plan and is also suggested by the respondents in our study, in order to routinely re-evaluate their own vaccination profiles, as well as those of their patients.
Comparison of Anterior Corneal Aberrometry, Keratometry and Pupil Size with Scheimpflug Tomography and Ray Tracing Aberrometer
Authors: Ashena Zahra , Gallagher Sean , Naveed Hasan , Spalton David J. , Nanavaty Mayank A.
Published: March 2022

This study aimed to assess the anterior corneal wavefront aberrations, keratometry, astigmatism vectors and pupil size between Pentacam HR® (Oculus Optikgeraete GmbH, Wetzlar, Germany) and iTrace® (Tracey Technologies Corp., Houston, TX, USA). In this observational study, 100 eyes (50 healthy volunteers) were scanned in mesopic light condition with a Pentacam HR® and iTrace®. Anterior corneal aberrations (spherical aberration (Z40), vertical coma (Z3 − 1), horizontal coma (Z3 + 1)), keratometry in the flattest (K1) and steepest meridian (K2), mean astigmatism, astigmatic vectors (J0 and J45), and pupil size were measured. We found a significant difference in Z40 (Pentacam®: +0.30 ± 0.11 µm and iTrace®: −0.03 µm ± 0.05 µm; p < 0.01) with no correlation between the devices (r = −0.12, p = 0.22). The devices were in complete agreement for Z3 − 1 (p = 0.78) and Z3 + 1 (p = 0.39), with significant correlation between the machines (r = −0.38, p < 0.01 and r = −0.6, p < 0.01). There was no difference in K1, K2 and mean astigmatism. J0 was negative with both devices (against-the-rule astigmatism), but there was no correlation. J45 was negative with the Pentacam HR® (more myopic oblique astigmatism) but significantly correlated between the devices. Pupil size was smaller with Pentacam HR® (p < 0.01). In summary, these devices cannot be used interchangeably. Corneal Z40 was significantly different with more negative Z40 with iTrace® compared to Pentacam HR®. iTrace® operates with lower illumination, giving larger pupil size than Pentacam HR®, which uses intense blue light during measurement. No correlation was found for J0. Pentacam HR® had a trend to record more negative J45 (myopic oblique astigmatism).
Granular Natural Zeolites: Cost-Effective Adsorbents for the Removal of Ammonium from Drinking Water
Authors: Eberle Stephan , Börnick Hilmar , Stolte Stefan
Published: March 2022

Increasing food demand has resulted in an ever increasing demand for nitrogen fertilizers. Ammonium is the main constituent of these fertilizers and is a threat to aquatic environments around the world. With a focus on the treatment of drinking water, the scope of this study was to investigate the influence of key parameters on the suitability of granular natural zeolites as adsorbents for ammonium. Sorption experiments were performed in artificial matrices by varying the grain size, contact time, ammonium concentration, pH, content of competing ions, and regeneration solutions used. Additionally, natural matrices and the point of zero charge (pzc) were investigated. With an initial ammonium concentration of 10 mgN/L, the grain size was shown to have no significant effect on the sorption efficiency (97–98%). The experimental data obtained was best described by the Langmuir adsorption model (R2 = 0.99). Minor effects on sorption were observed at different pH values and in the presence of competing anions. In addition, the pHPZC was determined to be between pH 6.24 and pH 6.47. Potassium ions were shown to be better than sodium ions for the regeneration of previously loaded zeolites, potassium is also the main competitor to ammonium sorption. The use of tap, bank filtrate, river, and groundwater matrices decreased the ammonium sorption capacity of granular natural zeolites by up to 8%. Based on our results, granular natural zeolites are promising cost-effective adsorbents for drinking water treatment, especially in threshold and developing countries.
Modeling Cyanobacteria Vertical Migration
Authors: Overman Corina , Wells Scott
Published: March 2022

Cyanobacteria often cause harmful algal blooms and release toxic substances that can harm humans and animals. Accurately modeling these phytoplankton is a step towards predicting, preventing, and controlling such blooms. Certain cyanobacteria species are known to migrate vertically in the water column on a daily cycle. Capturing this behavior is one aspect of modeling their dynamics. Previous studies on modeling cyanobacterial vertical migration are reviewed and summarized. Several models of cyanobacteria vertical movement are tested using data from field studies. These models are applied using both continuum and particle-tracking frameworks. Models range in complexity from simple functions of time to more complicated calculations of cyanobacteria buoyancy. Simple models were often able to predict cyanobacteria migration at low values of vertical diffusion in both types of modeling frameworks. More complicated models of buoyancy change performed better in the particle-tracking framework than in the continuum framework. Analysis of the models developed and tested provides information on the applicability of these models in more complex hydrodynamic and water quality models.
Following the Occurrence and Origin of Titanium Dioxide Nanoparticles in the Sava River by Single Particle ICP-MS
Authors: Vidmar Janja , Zuliani Tea , Milačič Radmila , Ščančar Janez
Published: March 2022

Titanium dioxide nanoparticles (TiO2NPs) are widely produced and used NPs in different applications. To evaluate the risk from anthropogenic TiO2NPs, more information is needed on their occurrence in the environment. For the first time, this study reports the levels of TiO2NPs in waters and sediments at selected sampling sites along the Sava River using inductively coupled plasma mass spectrometry in single particle mode (spICP-MS). The highest concentrations of TiO2NPs were determined in river water at Vrhovo (VRH), Jasenovac (JAS), and Slavonski Brod (SLB) sampling locations impacted by urban, agricultural, and/or industrial activities, suggesting that these NPs are likely of anthropogenic origin. The results further showed that hydrological conditions and sediment composition significantly influence the levels of TiO2NPs in river water at most locations. Moreover, the Ti/Al elemental concentration ratios of NPs in water and sediments at JAS were higher than the natural background ratios, further confirming their anthropogenic origin. The outcome of this study provides first information on the presence of (anthropogenic) TiO2NPs in different environmental compartments of the Sava River, contributing to more reliable risk assessments and better regulation of TiO2NPs emissions in the future.
Plankton under Pressure: How Water Conditions Alter the Phytoplankton–Zooplankton Link in Coastal Lagoons
Authors: D’Alelio Domenico , Russo Luca , Del Gaizo Gabriele , Caputi Luigi
Published: March 2022

Transitional waters (TWs), such as coastal lagoons, are bodies of surface water at the transition between saline and freshwater domains. These environments play a vital role in guaranteeing ecosystem services, including provision of food, protection against meteorological events, as anthropogenic carbon sinks, and in filtering of pollutants. Due to the escalating overpopulation characterising coastlines worldwide, transitional systems are over-exploited, degraded, and reduced in their macroscopic features. However, information on the impact of anthropogenic pressures on planktonic organisms in these systems is still scanty and fragmented. Herein, we summarise the literature, with a special focus on coastal lagoons undergoing anthropogenic pressure. Specifically, we report on the implications of human impacts on the ecological state of plankton, i.e., a fundamental ecological component of aquatic ecosystems. Literature information indicates that human forces may alter ecosystem structures and functions in coastal lagoons, as in other TWs such as estuaries, hampering the phytoplankton–zooplankton link, i.e., the main trophic process occurring in those communities, and which sustains aquatic productivity. Changes in the dominance and lifestyle of key planktonic players, plus the invasion of ‘alien’ species, and consequent regime shifts, are among the most common outcomes of human disturbance.
Seawater with Added Monosodium Glutamate Residue (MSGR) Is a Promising Medium for the Cultivation of Two Commercial Marine Microalgae
Authors: Yu Siteng , Yu Ze , Hou Qingjie , Pei Haiyan
Published: March 2022

Phaeodactylum tricornutum and Nannochloropsis oceanica, with their satisfactory performance in accumulating lipids and other high-value products, have been successfully used for commercial production in recent years. However, costly chemicals in culture media greatly increase the price of the resulting bioproducts. To control the cultivation cost, this paper assessed the potential of seawater supplemented with monosodium glutamate residue wastewater at a ratio of 1/500 (S-MSGR) to serve as a growing medium for these two marine species. Compared with the standard chemical culture medium, Erdschreiber’s medium (EM), both the algal growth and metabolite accumulation of P. tricornutum and N. oceanica were greatly promoted in S-MSGR. The maximum biomass concentrations of P. tricornutum and N. oceanica reached 0.93 and 0.36 g/L, which were, respectively, 1.5 and 1.9 times higher than those in EM medium. For lipid accumulation, P. tricornutum exhibited an excellent lipid productivity of 22.9 mg/L/day in S-MSGR, a 64% increase compared to EM medium. Furthermore, the average yield coefficients indicated good performance of P. tricornutum and N. oceanica in transferring the nitrogen in S-MSGR to the biomass, at 74.8 and 174.8 mg/g of nitrogen. In addition, compared with EM, the costs of the medium for lipid production of P. tricornutum and N. oceanica cultured in S-MSGR were USD 2.3 and 5.8/(kg lipid), which saved 96.9% and 97.6%, respectively. Therefore, this paper demonstrates that S-MSGR is a suitable nutrient resource for P. tricornutum and N. oceanica, and it has a great potential to cut the cultivation cost during real commercial production.