bluuebunny/crossref_metadata_2025 huggingface dataset:
Femtosecond Laser-Based Additive Manufacturing: Current Status and Perspectives
Authors: Kaligar Atiq Basha , Kumar Hemnath Anandan , Ali Asghar , Abuzaid Wael , Egilmez Mehmet , Alkhader Maen , Abed Farid , Alnaser Ali Sami
Published: January 2022

The ever-growing interest in additive manufacturing (AM) is evidenced by its extensive utilisation to manufacture a broad spectrum of products across a range of industries such as defence, medical, aerospace, automotive, and electronics. Today, most laser-based AM is carried out by employing continuous-wave (CW) and long-pulsed lasers. The CW and long-pulsed lasers have the downside in that the thermal energy imparted by the laser diffuses around the irradiated spot and often leads to the creation of heat-affected zones (HAZs). Heat-affected zones may degrade the material strength by producing micro-cracks, porous structures and residual stresses. To address these issues, currently, attempts are being made to employ ultrafast laser sources, such as femtosecond (fs) lasers, in AM processes. Femtosecond lasers with pulse durations in the order of 10−15 s limit the destructive laser–material interaction and, thus, minimise the probability of the HAZs. This review summarises the current advancements in the field of femtosecond laser-based AM of metals and alloys. It also reports on the comparison of CW laser, nanosecond (ns)/picosecond (ps) lasers with fs laser-based AM in the context of heat-affected zones, substrate damage, microstructural changes and thermomechanical properties. To shed light on the principal mechanisms ruling the manufacturing processes, numerical predictions are discussed and compared with the experimental results. To the best of the authors’ knowledge, this review is the first of its kind to encompass the current status, challenges and opportunities of employing fs lasers in additive manufacturing.
Carbon Footprint and Feedstock Quality of a Real Biomass Power Plant Fed with Forestry and Agricultural Residues
Authors: Ilari Alessio , Duca Daniele , Boakye-Yiadom Kofi Armah , Gasperini Thomas , Toscano Giuseppe
Published: January 2022

Phasing out fossil fuels to renewables is currently a global priority due to the climate change threat. Advocacy for biomass use as an energy source requires assessing the quality biomass and ecological impacts of bioenergy supply chains. This study evaluated the quality of biomass residues from orchards and silviculture transported from different Northern and Central Italy locations and the carbon footprint of a biomass power plant. The total greenhouse emissions were calculated based on primary data for 2017 according to the ISO/TS 14067. All the residue samples showed their suitability for biofuel use. Ash content was relatively low on average (3–5% d.m.), except for grapevine residues (18% d.m.). The lower heating value was within the expected range of 15–21 MJ kg−1 for plant species. The average GHG emission from the power plant was 17.4 g CO2 eq./MJ of electrical energy, with the energy conversion (38%) and transportation of biomass (34%) phases being the main impact contributors. For this study, impacts of residual agricultural residue were about half that of residues from forest management, mainly due to chipping and greater transport distance. Results show that sourcing residual biomass materials for electricity generation close to power plants significantly reduce GHG emissions compared to conventional fossil fuels.
From the Great Recession to the COVID-19 Pandemic: The Risk of Expansionary Monetary Policies
Authors: Echarte Fernández Miguel Ángel , Náñez Alonso Sergio Luis , Reier Forradellas Ricardo , Jorge-Vázquez Javier
Published: January 2022

Central banks have been pursuing an expansionary monetary policy since before the pandemic, although the health and economic crisis of COVID-19 has boosted asset purchase programmes. After the Great Recession, a new phase began, characterised by low interest rates and liquidity injections. These policies spilled over into financial markets and are leading to higher inflation. These policies stabilised the situation in the short term, but if they continue indefinitely there is a risk of debt overhang, investment mistakes and high inflation in the future. The aim of this article is to analyse monetary policy developments from the Great Recession to the COVID-19 crisis. Correlations between different macroeconomic variables will be shown through IBM SPSS Statistics. For this purpose, bi-variate correlations were used. For the predictions and confidence of the model data, Tableau Desktop Edition was used, which in turn was used for the generation of the graphs. There is a strong correlation between the growth of monetary aggregates and public debt and stock market capitalisation for the selected indicators. The main contribution of this research is the analysis of the long-term effects of a monetary policy.
Spectral Comparison of UAV-Based Hyper and Multispectral Cameras for Precision Viticulture
Authors: Di Gennaro Salvatore Filippo , Toscano Piero , Gatti Matteo , Poni Stefano , Berton Andrea , Matese Alessandro
Published: January 2022

Analysis of the spectral response of vegetation using optical sensors for non-destructive remote monitoring represents a key element for crop monitoring. Considering the wide presence on the market of unmanned aerial vehicle (UAVs) based commercial solutions, the need emerges for clear information on the performance of these products to guide the end-user in their choice and utilization for precision agriculture applications. This work aims to compare two UAV based commercial products, represented by DJI P4M and SENOP HSC-2 for the acquisition of multispectral and hyperspectral images, respectively, in vineyards. The accuracy of both cameras was evaluated on 6 different targets commonly found in vineyards, represented by bare soil, bare-stony soil, stony soil, soil with dry grass, partially grass covered soil and canopy. Given the importance of the radiometric calibration, four methods for multispectral images correction were evaluated, taking in account the irradiance sensor equipped on the camera (M1–M2) and the use of an empirical line model (ELM) based on reference reflectance panels (M3–M4). In addition, different DJI P4M exposure setups were evaluated. The performance of the cameras was evaluated by means of the calculation of three widely used vegetation indices (VIs), as percentage error (PE) with respect to ground truth spectroradiometer measurements. The results highlighted the importance of reference panels for the radiometric calibration of multispectral images (M1–M2 average PE = 21.8–100.0%; M3–M4 average PE = 11.9–29.5%). Generally, the hyperspectral camera provided the best accuracy with a PE ranging between 1.0% and 13.6%. Both cameras showed higher performance on the pure canopy pixel target, compared to mixed targets. However, this issue can be easily solved by applying widespread segmentation techniques for the row extraction. This work provides insights to assist end-users in the UAV spectral monitoring to obtain reliable information for the analysis of spatio-temporal variability within vineyards.
An Effective Method for InSAR Mapping of Tropical Forest Degradation in Hilly Areas
Authors: Carstairs Harry , Mitchard Edward T. A. , McNicol Iain , Aquino Chiara , Burt Andrew , Ebanega Médard Obiang , Dikongo Anaick Modinga , Bueso-Bello José-Luis , Disney Mathias
Published: January 2022

Current satellite remote sensing methods struggle to detect and map forest degradation, which is a critical issue as it is likely a major and growing source of carbon emissions and biodiveristy loss. TanDEM-X InSAR phase height (hϕ) is a promising variable for measuring forest disturbances, as it is closely related to the mean canopy height, and thus should decrease if canopy trees are removed. However, previous research has focused on relatively flat terrains, despite the fact that much of the world’s remaining tropical forests are found in hilly areas, and this inevitably introduces artifacts in sideways imaging systems. In this paper, we find a relationship between hϕ and aboveground biomass change in four selectively logged plots in a hilly region of central Gabon. We show that minimising multilooking prior to the calculation of hϕ strengthens this relationship, and that degradation estimates across steep slopes in the surrounding region are improved by selecting data from the most appropriate pass directions on a pixel-by-pixel basis. This shows that TanDEM-X InSAR can measure the magnitude of degradation, and that topographic effects can be mitigated if data from multiple SAR viewing geometries are available.
Predicting Attack Pattern via Machine Learning by Exploiting Stateful Firewall as Virtual Network Function in an SDN Network
Authors: Prabakaran Senthil , Ramar Ramalakshmi , Hussain Irshad , Kavin Balasubramanian Prabhu , Alshamrani Sultan S. , AlGhamdi Ahmed Saeed , Alshehri Abdullah
Published: January 2022

Decoupled data and control planes in Software Defined Networks (SDN) allow them to handle an increasing number of threats by limiting harmful network links at the switching stage. As storage, high-end servers, and network devices, Network Function Virtualization (NFV) is designed to replace purpose-built network elements with VNFs (Virtualized Network Functions). A Software Defined Network Function Virtualization (SDNFV) network is designed in this paper to boost network performance. Stateful firewall services are deployed as VNFs in the SDN network in this article to offer security and boost network scalability. The SDN controller’s role is to develop a set of guidelines and rules to avoid hazardous network connectivity. Intruder assaults that employ numerous socket addresses cannot be adequately protected by these strategies. Machine learning algorithms are trained using traditional network threat intelligence data to identify potentially malicious linkages and probable attack targets. Based on conventional network data (DT), Bayesian Network (BayesNet), Naive-Bayes, C4.5, and Decision Table (DT) algorithms are used to predict the target host that will be attacked. The experimental results shows that the Bayesian Network algorithm achieved an average prediction accuracy of 92.87%, Native–Bayes Algorithm achieved an average prediction accuracy of 87.81%, C4.5 Algorithm achieved an average prediction accuracy of 84.92%, and the Decision Tree algorithm achieved an average prediction accuracy of 83.18%. There were 451 k login attempts from 178 different countries, with over 70 k source IP addresses and 40 k source port addresses recorded in a large dataset from nine honeypot servers.
A 3D Attitude Estimation Method Based on Attitude Angular Partial Feedback for Polarization-Based Integrated Navigation System
Authors: Hu Pengwei , Huang Panpan , Qiu Zhenbing , Yang Jian , Liu Xin
Published: January 2022

Polarization (POL) navigation is inspired by insects’ behavior of precepting celestial polarization patterns to orient themselves. It has the advantages of being autonomous and having no accumulative error, which allows it to be used to correct the errors of the inertial navigation system (INS). The integrated navigation system of the POL-based solar vector with INS is capable of 3D attitude determination. However, the commonly used POL-based integrated navigation system generally implements the attitude update procedure without considering the performance difference with different magnitudes of the angles between the solar-vector and body-axes of the platform (S-B angles). When one of the S-B angles is small enough, the estimated accuracy of the attitude angle by the INS/POL is worse than that of the strapdown inertial navigation system. To minimize the negative impact of POL in this situation, an attitude angular adaptive partial feedback method is proposed. The S-B angles are used to construct a partial feedback factor matrix to adaptively adjust the degree of error correction for INS. The results of simulation and real-world experiments demonstrate that the proposed method can improve the accuracy of 3D attitude estimation compared with the conventional all-feedback method for small S-B angles especially for yaw angle estimation.
Evaluation of Random Forests (RF) for Regional and Local-Scale Wheat Yield Prediction in Southeast Australia
Authors: Pang Alexis , Chang Melissa W L , Chen Yang
Published: January 2022

Wheat accounts for more than 50% of Australia’s total grain production. The capability to generate accurate in-season yield predictions is important across all components of the agricultural value chain. The literature on wheat yield prediction has motivated the need for more novel works evaluating machine learning techniques such as random forests (RF) at multiple scales. This research applied a Random Forest Regression (RFR) technique to build regional and local-scale yield prediction models at the pixel level for three southeast Australian wheat-growing paddocks, each located in Victoria (VIC), New South Wales (NSW) and South Australia (SA) using 2018 yield maps from data supplied by collaborating farmers. Time-series Normalized Difference Vegetation Index (NDVI) data derived from Planet’s high spatio-temporal resolution imagery, meteorological variables and yield data were used to train, test and validate the models at pixel level using Python libraries for (a) regional-scale three-paddock composite and (b) individual paddocks. The composite region-wide RF model prediction for the three paddocks performed well (R2 = 0.86, RMSE = 0.18 t ha−1). RF models for individual paddocks in VIC (R2 = 0.89, RMSE = 0.15 t ha−1) and NSW (R2 = 0.87, RMSE = 0.07 t ha−1) performed well, but moderate performance was seen for SA (R2 = 0.45, RMSE = 0.25 t ha−1). Generally, high values were underpredicted and low values overpredicted. This study demonstrated the feasibility of applying RF modeling on satellite imagery and yielded ‘big data’ for regional as well as local-scale yield prediction.
Oligoclonal Band Straightening Based on Optimized Hierarchical Warping for Multiple Sclerosis Diagnosis
Authors: Haddad Farah , Boudet Samuel , Peyrodie Laurent , Vandenbroucke Nicolas , Poupart Julien , Hautecoeur Patrick , Chieux Vincent , Forzy Gérard
Published: January 2022

The detection of immunoglobulin G (IgG) oligoclonal bands (OCB) in cerebrospinal fluid (CSF) by isoelectric focusing (IEF) is a valuable tool for the diagnosis of multiple sclerosis. Over the last decade, the results of our clinical research have suggested that tears are a non-invasive alternative to CSF. However, since tear samples have a lower IgG concentration than CSF, a sensitive OCB detection is therefore required. We are developing the first automatic tool for IEF analysis, with a view to speeding up the current visual inspection method, removing user variability, reducing misinterpretation, and facilitating OCB quantification and follow-up studies. The removal of band distortion is a key image enhancement step in increasing the reliability of automatic OCB detection. Here, we describe a novel, fully automatic band-straightening algorithm. The algorithm is based on a correlation directional warping function, estimated using an energy minimization procedure. The approach was optimized via an innovative coupling of a hierarchy of image resolutions to a hierarchy of transformation, in which band misalignment is corrected at successively finer scales. The algorithm’s performance was assessed in terms of the bands’ standard deviation before and after straightening, using a synthetic dataset and a set of 200 lanes of CSF, tear, serum and control samples on which experts had manually delineated the bands. The number of distorted bands was divided by almost 16 for the synthetic lanes and by 7 for the test dataset of real lanes. This method can be applied effectively to different sample types. It can realign minimal contrast bands and is robust for non-uniform deformations.
Epileptic-Net: An Improved Epileptic Seizure Detection System Using Dense Convolutional Block with Attention Network from EEG
Authors: Islam Md Shafiqul , Thapa Keshav , Yang Sung-Hyun
Published: January 2022

Epilepsy is a complex neurological condition that affects a large number of people worldwide. Electroencephalography (EEG) measures the electrical activity of the brain and is widely used in epilepsy diagnosis, but it usually requires manual inspection, which can be hours long, by a neurologist. Several automatic systems have been proposed to detect epilepsy but still have some unsolved issues. In this study, we proposed a dynamic method using a deep learning model (Epileptic-Net) to detect an epileptic seizure. The proposed method is largely heterogeneous and comprised of the dense convolutional blocks (DCB), feature attention modules (FAM), residual blocks (RB), and hypercolumn technique (HT). Firstly, DCB is used to get the discriminative features from the EEG samples. Then, FAM extracts the essential features from the samples. After that, RB learns more vital parts as it entirely uses information in the convolutional layer. Finally, HT retains the efficient local features extracted from the layers situated at the different levels of the model. Its performance has been evaluated on the University of Bonn EEG dataset, divided into five distinct classes. The proposed Epileptic-Net achieves the average accuracy of 99.95% in the two-class classification, 99.98% in the three-class classification, 99.96% in the four-class classification, and 99.96% in classifying the complicated five-class problem. Thus the proposed approach shows more competitive results than the existing model to detect epileptic seizures. We also hope that this method can support experts in achieving objective and reliable results, lowering the misdiagnosis rate, and assisting in decision-making.
Cooperative-PHD Tracking Based on Distributed Sensors for Naval Surveillance Area
Authors: Lima Kleberson Meireles de , Costa Ramon Romankevicius
Published: January 2022

Brazil has an extensive coastline and Exclusive Economic Zone (EEZ) area, which are of high economic and strategic importance. A Maritime Surveillance System becomes necessary to provide information and data to proper authorities, and target tracking is crucial. This paper focuses on a multitarget tracking application to a large-scale maritime surveillance system. The system is composed of a sensor network distributed over an area of interest. Due to the limited computational capabilities of nodes, the sensors send their tracking data to a central station, which is responsible for gathering and processing information obtained by the distributed components. The local Multitarget Tracking (MTT) algorithm employs a random finite set approach, which adopts a Gaussian mixture Probability Hypothesis Density (PHD) filter. The proposed data fusion scheme, utilized in the central station, consists of an additional step of prune & merge to the original GM PHD filter algorithm, which is the main contribution of this work. Through simulations, this study illustrates the performance of the proposed algorithm with a network composed of two stationary sensors providing the data. This solution yields a better tracking performance when compared to individual trackers, which is attested by the Optimal Subpattern Assignment (OSPA) metric and its location and cardinality components. The presented results illustrate the overall performance improvement attained by the proposed solution. Moreover, they also stress the need to resort to a distributed sensor network to tackle real problems related to extended targets.
A Comprehensive Survey on the Internet of Things with the Industrial Marketplace
Authors: Salih Kazhan Othman Mohammed , Rashid Tarik A. , Radovanovic Dalibor , Bacanin Nebojsa
Published: January 2022

There is no doubt that new technology has become one of the crucial parts of most people’s lives around the world. By and large, in this era, the Internet and the Internet of Things (IoT) have become the most indispensable parts of our lives. Recently, IoT technologies have been regarded as the most broadly used tools among other technologies. The tools and the facilities of IoT technologies within the marketplace are part of Industry 4.0. The marketplace is too regarded as a new area that can be used with IoT technologies. One of the main purposes of this paper is to highlight using IoT technologies in Industry 4.0, and the Industrial Internet of Things (IIoT) is another feature revised. This paper focuses on the value of the IoT in the industrial domain in general; it reviews the IoT and focuses on its benefits and drawbacks, and presents some of the IoT applications, such as in transportation and healthcare. In addition, the trends and facts that are related to the IoT technologies on the marketplace are reviewed. Finally, the role of IoT in telemedicine and healthcare and the benefits of IoT technologies for COVID-19 are presented as well.
Persistence and Attrition among Participants in a Multi-Page Online Survey Recruited via Reddit’s Social Media Network
Authors: Spennemann Dirk H.R.
Published: January 2022

Participant attrition is a major concern for the validity of longer or complex surveys. Unlike paper-based surveys, which may be discarded even if partially completed, multi-page online surveys capture responses from all completed pages until the time of abandonment. This can result in different item response rates, with pages earlier in the sequence showing more completions than later pages. Using data from a multi-page online survey administered to cohorts recruited on Reddit, this paper analyses the pattern of attrition at various stages of the survey instrument and examines the effects of survey length, time investment, survey format and complexity, and survey delivery on participant attrition. The participant attrition rate (PAR) differed between cohorts, with cohorts drawn from Reddit showing a higher PAR than cohorts targeted by other means. Common to all was that the PAR was higher among younger respondents and among men. Changes in survey question design resulted in the greatest rise in PAR irrespective of age, gender or cohort.
Do the Invasive Earthworms Amynthas agrestis (Oligochaeta: Megascolecidae) and Lumbricus rubellus (Oligochaeta: Lumbricidae) Stimulate Oxalate-Based Browser Defenses in Jack-in-the-Pulpit (Arisaema triphyllum) by Their Presence or Their Soil Biogeochemical Activity?
Authors: Melnichuk Ryan D. S. , Tecimen Hüseyin Barış , Görres Josef H.
Published: January 2022

The introduction of invasive earthworms initiates physical and chemical alterations in previously earthworm-free forest soils, which triggers an ecological cascade. The most apparent step is the shift in the herbaceous plant community composition. However, some species, such as Arisaema triphyllum (jack-in-the-pulpit), persist where earthworms are present. It has been hypothesized that A. triphyllum produces insoluble oxalate, an herbivory deterrent, in the presence of earthworms. This study aimed to distinguish between the effects of earthworm-induced changes in soils and the physical presence of earthworms on oxalate production. As such, a two-way factorial greenhouse trial was conducted using uninvaded soils to test this hypothesis for two invasive earthworm species (Amynthas agrestis and Lumbricus rubellus). The sequential extraction of oxalates in A. triphyllum corms was performed with absolute ethanol, deionized water, acetic acid and HCl, representing fractions of decreasing solubility. Earthworm presence increased water-soluble (p = 0.002) and total oxalate (p = 0.022) significantly, but only marginally significantly for HCl-soluble oxalate (p = 0.065). The corms of plants grown in soils previously exposed to the two species did not differ in oxalate production when earthworms were not present. However, the data suggest that earthworms affect corm oxalate concentrations and that the sequence of invasion matters for oxalate production by A. triphyllum.
A Multidisciplinary Investigation into the Talent Development Processes in an English Premiership Rugby Union Academy: A Preliminary Study through an Ecological Lens
Authors: Dimundo Francesco , Cole Matthew , Blagrove Richard C. , Till Kevin , Kelly Adam L.
Published: January 2022

(1) Background: The progression of youth rugby union (RU) players towards senior professional levels can be the result of various different constraints. The aim of this study was to examine characteristics that differentiated playing positions and player rankings in an English Premiership RU academy. (2) Methods: Thirty players (mean age = 18.5 ± 2.8 years) were divided by playing positions (forwards = 18, backs = 12) and ranked (one to thirty) by coaches based on their potential to achieve senior professional status. Players were analysed across 32 characteristics from eight overreaching factors based on task, environmental, and performer constraints. MANOVA and ANOVA were used to calculate differences among variables in players’ positions (i.e., forwards vs. backs) and ranks (i.e., top 10 vs. bottom 10), with a Welch’s t-test applied to identify individual differences amongst groups and effect sizes calculated. (3) Results: Large effect sizes were found between groups for socioeconomic, sport activity, anthropometric, physical, and psychological factors. Moreover, environmental and performer constraints differentiated playing positions, whereas task and environmental constraints discriminated player ranks. (4) Conclusion: Present findings showed that playing positions and player ranks can be distinguished according to specific constraints.
Coaches’ Criteria for Talent Identification of Youth Male Soccer Players
Authors: Fuhre Jan , Øygard Arild , Sæther Stig Arve
Published: January 2022

Introduction: The main aim of this study was to examine which criteria coaches considered in the talent identification of youth male soccer players aged 13–16. The second aim was to describe how the coaches considered these criteria when identifying players for their club or regional teams and how these criteria take the impact of the relative age effect into consideration. Methods: We conducted qualitative, semi-structured interviews with six male coaches from a professional club academy or a regional team within the Norwegian Football Federation. Results: In line with earlier research, the results showed that the coaches considered the technical, tactical, and mental factors as the most important in talent identification. Further in line with earlier research, they considered that the physiological and sociological factors were of secondary importance, while anthropometric measures were considered the least important. Regarding the relative age effect, the coaches were aware of the effect and its consequences, while few of them had ways to reduce the effect and its impact on their talent identification process. Even so, the coaches highlighted the importance of considering a holistic approach to talent identification. Conclusion: The results show similarities with earlier research, but there is still a need for more longitudinal studies that investigate criteria for talent identification in youth football.
Effects of Leader-Member Exchange, Organizational Identification and Leadership Communication on Unethical Pro-Organizational Behavior: A Study on Bank Employees in Turkey
Authors: Kelebek Ezgi Erbas , Alniacik Esra
Published: January 2022

Unethical behavior of employees threatens business development and sustainability by damaging the image and reputation of companies. Unethical pro-organizational behavior (UPB) must also be considered in this context, and its antecedents should be analyzed. This study aims to advance what is known about how leader-member exchange (LMX) and organizational identification affect employees’ intentions to perform UPB, by incorporating the effect of leadership communication. Within this context, the mediating role of organizational identification and the moderating role of leader’s message framing (gain or loss) are examined. The research sample consists of 306 employees working for state and private banks operating in Turkey. Participants were divided into two groups and message framing was manipulated with a hypothetical story using vignettes. Research hypotheses were tested by structural equation modeling (SEM) and multi-group analysis. Results confirmed positive effects of LMX and organizational identification on UPB. Organizational identification also mediated the effect of LMX on UPB. Moreover, leader’s communication style moderated the effects of LMX and organizational identification on UPB. When leaders used loss framing instead of gain framing, the effect of LMX on UPB was augmented whilst the effect of organizational identification diminished. Our study contributes to the literature by documenting how a leader’s communication style can trigger a shift towards UPB among highly identified employees. Research and managerial implications of the findings are discussed.
Applying Solar Energy in the Combination of Solar Dryer with Olive Harvesting Machine to Reduce Energy Losses
Authors: Zare Fatemeh , Najafi Gholamhassan , Ghiasi Pedram , Fayyazi Ebrahim , Yusaf Talal , Mazlan Mohamed
Published: January 2022

In recent years, leveraging the amount of energy loss occurring in different fields of human activity has captured the attention of researchers. Harvesting and drying processes can be integrated in order to reduce energy losses. The present research work seeks to pinpoint the association between the harvesting and drying processes as well as to make optimal use of both processes so as to decrease the level of energy loss and apply the renewable energies to the food supply chain. The olive harvesting machine was designed and evaluated, and the olives harvested in the solar dryer were placed in the solar dryer as the input materials. To obtain the evaluation of the experimental tests’ purpose, Mari cultivar was used. Following this trend was the evaluation of the olive harvesting machine and its comparison with the manual harvesting method. Having separated the olives from the tree through use of the harvesting machine designed and made, a solar dryer was used to accommodate the olives in order to make the final examination concerning any damage to olives. Findings of the study indicated up to 92% separation of the olive fruits by the olive harvester. It was also found that there is a 29.47 harvest efficiency for the olive harvester. In addition, evaluation of the solar dryer emphasized that an increase in the temperature and velocity of the inlet air results in a rapid decrease in the olive moisture.
Multidimensional Spatial Match of Hierarchical Healthcare Facilities Considering Floating Population: A Case of Beijing, China
Authors: Cai Xingfei , Wang Hao , Ning Xiaogang , Du Qiyong , Jia Peng
Published: January 2022

Good health and well-being are key to achieving the main goals of the UN Sustainable Development Goals (SDGs), especially after the outbreak of the COVID-19 epidemic. What is a concern for both government and society is how to understand the spatial match of hierarchical healthcare facilities and residential areas in terms of quantity and capacity, to meet the challenges of various diseases and build a healthy life. Using hierarchical healthcare data and cellphone signaling data in Beijing, China, we used the kernel density estimation, a bivariate spatial autocorrelation model, and a coupling index to explore the spatial relationships between hierarchical healthcare facilities and residential areas. We found large numbers of both healthcare facilities and residential areas in the urban center, and small numbers of both at the urban edge. The hospitals and designated retail pharmacies in the densely populated areas do not have enough capacity to meet the need of the population. In addition, the capacity of primary healthcare institutions can meet people’s needs. Our findings would serve as a reference for urban planning, optimization of hierarchical healthcare facilities, and research on similar themes.
Environmental Quality and Compliance with Animal Welfare Legislation at Swedish Cattle and Sheep Farms
Authors: Hultgren Jan , Hiron Matthew , Glimskär Anders , Bokkers Eddie A. M. , Keeling Linda J.
Published: January 2022

Conflicts between different goals can obstruct progress in sustainability, but interests may also coincide. We evaluated relationships between environmental quality and animal welfare on Swedish farms with grazing livestock, using publicly available databases. Data were collected from 8700 official animal welfare inspections on 5808 cattle farms and 2823 inspections on 2280 sheep farms in 2012–2017. Compliance with three animal-based checkpoints was modeled using logistic regression, including a random farm effect to account for repeated inspections. Compliance was regressed on semi-natural grassland area, participation in the National Meadow and Pasture Inventory, Agri-Environmental Scheme (AES) grassland payments, presence of indicator plant species, and the presence of Natura 2000 habitats. Cattle farms complied more often if they received AES payments for grasslands of special values compared with if they did not apply for them (OR = 1.55–1.65; p ≤ 0.0001) and there was a similar tendency for cattle farms that applied for but were denied such payments (OR = 1.29; p = 0.074). There was also a strong tendency for Natura 2000 habitats on cattle farms to be associated with higher compliance (OR = 1.36; p = 0.059). These results suggest a direct or indirect causal effect of biodiversity on cattle welfare. The same associations could not be shown in sheep.
A Fuzzy-Interval Dynamic Optimization Model for Regional Water Resources Allocation under Uncertainty
Authors: Suo Meiqin , Xia Feng , Fan Yurui
Published: January 2022

In this study, a fuzzy-interval dynamic programming (FIDP) model is proposed for regional water management under uncertainty by combining fuzzy-interval linear programming (FILP) and dynamic programming (DP). This model can not only tackle uncertainties presented as intervals, but also consider the dynamic characteristics in the allocation process for water resources. Meanwhile, the overall satisfaction from users is considered in the objective function to solve the conflict caused by uneven distribution of resources. The FIDP model is then applied to the case study in terms of water resources allocation under uncertainty and dynamics for the City of Handan in Hebei Province, China. The obtained solutions can provide detailed allocation schemes and water shortage rates at different stages. The calculated comprehensive benefits of economy, water users’ satisfaction and pollutant discharge (i.e., COD) are [2264.72, 2989.33] × 108 yuan, [87.50, 96.50] % and [1.23, 1.65] × 108 kg respectively with a plausibility degree (i.e., λopt±) ranging within [0.985, 0.993]. Moreover, the benefit from FIDP model under consideration of dynamic features is more specific and accurate than that of FILP model, whilst the water shortage rate from FIDP is [5.10, 9.10] % lower than that of FILP model.
PFAS Molecules: A Major Concern for the Human Health and the Environment
Authors: Panieri Emiliano , Baralic Katarina , Djukic-Cosic Danijela , Buha Djordjevic Aleksandra , Saso Luciano
Published: January 2022

Per- and polyfluoroalkyl substances (PFAS) are a group of over 4700 heterogeneous compounds with amphipathic properties and exceptional stability to chemical and thermal degradation. The unique properties of PFAS compounds has been exploited for almost 60 years and has largely contributed to their wide applicability over a vast range of industrial, professional and non-professional uses. However, increasing evidence indicate that these compounds represent also a serious concern for both wildlife and human health as a result of their ubiquitous distribution, their extreme persistence and their bioaccumulative potential. In light of the adverse effects that have been already documented in biota and human populations or that might occur in absence of prompt interventions, the competent authorities in matter of health and environment protection, the industries as well as scientists are cooperating to identify the most appropriate regulatory measures, substitution plans and remediation technologies to mitigate PFAS impacts. In this review, starting from PFAS chemistry, uses and environmental fate, we summarize the current knowledge on PFAS occurrence in different environmental media and their effects on living organisms, with a particular emphasis on humans. Also, we describe present and provisional legislative measures in the European Union framework strategy to regulate PFAS manufacture, import and use as well as some of the most promising treatment technologies designed to remediate PFAS contamination in different environmental compartments.
Vacuum Energy in Saez-Ballester Theory and Stabilization of Extra Dimensions
Authors: Singh Pheiroijam Suranjoy , Singh Kangujam Priyokumar
Published: January 2022

In this work, we study a spherically symmetric metric in 5D within the framework of Saez-Ballester Theory, where minimal dark energy-matter interaction occurs. We predict that the expanding isotropic universe will be progressively DE dominated. We estimate few values of the deceleration parameter, very close to the recently predicted values. We obtain the value of the DE EoS parameter as ω=−1. Additionally, we measure the value of the overall density parameter as Ω=0.97(≈1), in line with the notion of a close to or nearly (not exactly) flat universe. We predict that the model universe starts with the Big-Bang and ends at the Big Freeze singularity. In general, we cannot find conditions for stabilization of extra dimensions in general relativity, and all dimensions want to be dynamical. Here, we present two possible conditions to solve this stabilization problem in general relativity.
Codon Usage for Genetic Diversity, and Evolutionary Dynamics of Novel Porcine Parvoviruses 2 through 7 (PPV2–PPV7)
Authors: Xie Changzhan , Tao Yimo , Zhang Ying , Zhang Ping , Zhu Xiangyu , Ha Zhuo , Zhang He , Xie Yubiao , Xia Xianzhu , Jin Ningyi , Lu Huijun
Published: January 2022

Porcine parvovirus (PPV) is the main pathogen of reproductive disorders. In recent years, a new type of porcine parvovirus has been discovered and named porcine parvovirus 2 to 7 (PPV2–PPV7), and it is associated with porcine circovirus type 2 in pigs. Codon usage patterns and their effects on the evolution and host adaptation of different PPV sub-types are still largely unknown. Here, we define six main sub-types based on the Bayesian method of structural proteins of each sub-type of PPV, including PPV2, PPV3, PPV4, PPV5, PPV6, and PPV7, which show different degrees of codon usage preferences. The effective number of codons (ENC) indicates that all PPV sub-types have low codon bias. According to the codon adaptation index (CAI), PPV3 and PPV7 have the highest similarity with the host, which is related to the main popular tendency of the host in the field; according to the frequency of optimal codons (FOP), PPV7 has the highest frequency of optimal codons, indicating the most frequently used codons in its genes; and according to the relative codon deoptimization index (RCDI), PPV3 has a higher degree. Therefore, it is determined that mutational stress has a certain impact on the codon usage preference of PPV genes, and natural selection plays a very decisive and dominant role in the codon usage pattern. Our research provides a new perspective on the evolution of porcine parvovirus (PPV) and may help provide a new method for future research on the origin, evolutionary model, and host adaptation of PPV.
Targeting the Virus Capsid as a Tool to Fight RNA Viruses
Authors: Hozáková Lucie , Vokatá Barbora , Ruml Tomáš , Ulbrich Pavel
Published: January 2022

Several strategies have been developed to fight viral infections, not only in humans but also in animals and plants. Some of them are based on the development of efficient vaccines, to target the virus by developed antibodies, others focus on finding antiviral compounds with activities that inhibit selected virus replication steps. Currently, there is an increasing number of antiviral drugs on the market; however, some have unpleasant side effects, are toxic to cells, or the viruses quickly develop resistance to them. As the current situation shows, the combination of multiple antiviral strategies or the combination of the use of various compounds within one strategy is very important. The most desirable are combinations of drugs that inhibit different steps in the virus life cycle. This is an important issue especially for RNA viruses, which replicate their genomes using error-prone RNA polymerases and rapidly develop mutants resistant to applied antiviral compounds. Here, we focus on compounds targeting viral structural capsid proteins, thereby inhibiting virus assembly or disassembly, virus binding to cellular receptors, or acting by inhibiting other virus replication mechanisms. This review is an update of existing papers on a similar topic, by focusing on the most recent advances in the rapidly evolving research of compounds targeting capsid proteins of RNA viruses.
Identifying Inhibitors of −1 Programmed Ribosomal Frameshifting in a Broad Spectrum of Coronaviruses
Authors: Munshi Sneha , Neupane Krishna , Ileperuma Sandaru M. , Halma Matthew T. J. , Kelly Jamie A. , Halpern Clarissa F. , Dinman Jonathan D. , Loerch Sarah , Woodside Michael T.
Published: January 2022

Recurrent outbreaks of novel zoonotic coronavirus (CoV) diseases in recent years have highlighted the importance of developing therapeutics with broad-spectrum activity against CoVs. Because all CoVs use −1 programmed ribosomal frameshifting (−1 PRF) to control expression of key viral proteins, the frameshift signal in viral mRNA that stimulates −1 PRF provides a promising potential target for such therapeutics. To test the viability of this strategy, we explored whether small-molecule inhibitors of −1 PRF in SARS-CoV-2 also inhibited −1 PRF in a range of bat CoVs—the most likely source of future zoonoses. Six inhibitors identified in new and previous screens against SARS-CoV-2 were evaluated against the frameshift signals from a panel of representative bat CoVs as well as MERS-CoV. Some drugs had strong activity against subsets of these CoV-derived frameshift signals, while having limited to no effect on −1 PRF caused by frameshift signals from other viruses used as negative controls. Notably, the serine protease inhibitor nafamostat suppressed −1 PRF significantly for multiple CoV-derived frameshift signals. These results suggest it is possible to find small-molecule ligands that inhibit −1 PRF specifically in a broad spectrum of CoVs, establishing frameshift signals as a viable target for developing pan-coronaviral therapeutics.
Venous Thrombosis within 30 Days after Vaccination against SARS-CoV-2 in a Multinational Venous Thromboembolism Registry
Authors: Bikdeli Behnood , Jiménez David , Demelo-Rodriguez Pablo , Galeano-Valle Francisco , Porras José Antonio , Barba Raquel , Ay Cihan , Malý Radovan , Braester Andrei , Imbalzano Egidio , Rosa Vladimir , Lecumberri Ramón , Siniscalchi Carmine , Fidalgo Ángeles , Ortiz Salvador , Monreal Manuel ,   for the RIETE Investigators
Published: January 2022

Background: Venous thromboembolism (VTE)—including deep vein thrombosis, pulmonary embolism, and cerebral venous sinus thrombosis (CVST)—may occur early after vaccination against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We sought to describe the site, clinical characteristics, and outcomes of VTE after vaccination against SARS-CoV-2. Methods: In a prospective study using the Registro Informatizado de Enfermedad TromboEmbólica (RIETE) platform, patients with VTE 4–30 days after vaccination against SARS-CoV-2 (1 February 2021 through 30 April 2021) were included. VTE patients recruited from the same centers into RIETE in the same months in 2018–2019 were selected as the reference group. All-cause mortality and major bleeding were the main study outcomes. Results: As of 30 April 2020, 102 patients with post-vaccination VTEs had been identified (28 after adenovirus-based vaccination [ChAdOx1 nCov-19; AstraZeneca] and 74 after mRNA-based vaccination [mRNA-1273; Moderna, and BNT162b2; Pfizer]). Compared with 911 historical controls, patients with VTE after adenovirus-based vaccination more frequently had CVST (10.7% vs. 0.4%, p < 0.001) or thrombosis at multiple sites (17.9% vs. 1.3%, p < 0.001), more frequently had thrombocytopenia (40.7% vs. 14.7%, p < 0.001), and had higher 14-day mortality (14.3% vs. 0.7%; odds ratio [OR]: 25.1; 95% confidence interval [CI]: 6.7–94.9) and major bleeding rates (10.3% vs. 1.0%, OR: 12.03, 95% CI: 3.07–47.13). The site of thrombosis, accompanying thrombocytopenia, and 14-day mortality rates were not significantly different for patients with VTE after mRNA-based vaccination, compared with historical controls. Conclusions: Compared with historical controls, VTE after adenovirus-based vaccination against SARS-CoV-2 is accompanied by thrombocytopenia, occurs in unusual sites, and is associated with worse clinical outcomes.
Rapid Detection of Genotype II African Swine Fever Virus Using CRISPR Cas13a-Based Lateral Flow Strip
Authors: Wei Ning , Zheng Bohan , Niu Junjun , Chen Tao , Ye Jing , Si Youhui , Cao Shengbo
Published: January 2022

The African swine fever virus (ASFV) is a dsDNA virus that can cause serious, highly infectious, and fatal diseases in wild boars and domestic pigs. The ASFV has brought enormous economic loss to many countries, and no effective vaccine or treatment for the ASFV is currently available. Therefore, the on-site rapid and accurate detection of the ASFV is key to the timely implementation of control. The RNA-guided, RNA-targeting CRISPR effector CRISPR-associated 13 (Cas13a; previously known as C2c2) exhibits a “collateral effect” of promiscuous RNase activity upon the target recognition. The collateral cleavage activity of LwCas13a is activated to degrade the non-targeted RNA, when the crRNA of LwCas13a binds to the target RNA. In this study, we developed a rapid and sensitive ASFV detection method based on the collateral cleavage activity of LwCas13a, which combines recombinase-aided amplification (RAA) and a lateral flow strip (named CRISPR/Cas13a-LFD). The method was an isothermal detection at 37 °C, and the detection can be used for visual readout. The detection limit of the CRISPR/Cas13a-LFD was 101 copies/µL of p72 gene per reaction, and the detection process can be completed within an hour. The assay showed no cross-reactivity to eight other swine viruses, including classical swine fever virus (CSFV), and has a 100% coincidence rate with real-time PCR detection of the ASFV in 83 clinical samples. Overall, this method is sensitive, specific, and practicable onsite for the ASFV detection, showing a great application potential for monitoring the ASFV in the field.