Search Results (9)

Machine learning (ML) models are extensively used in spatial predictive modeling, including landslide susceptibility prediction. The performance statistics of these models are vital for assessing their reliability, which is typically obtained using the random cross-validation (R-CV) method. However, R-CV has a major drawback, i.e., it ignores the spatial autocorrelation (SAC) inherent in spatial datasets when partitioning the training and testing sets. We assessed the impact of SAC at three crucial phases of ML modeling: hyperparameter tuning, performance evaluation, and learning curve analysis. As an alternative to R-CV, we used spatial cross-validation (S-CV). This method considers SAC when partitioning the training and testing subsets. This experiment was conducted on regional landslide susceptibility prediction using different ML models: logistic regression (LR), k-nearest neighbor (KNN), linear discriminant analysis (LDA), artificial neural networks (ANN), support vector machine (SVM), random forest (RF), and C5.0. The experimental results showed that R-CV often produces optimistic performance estimates, e.g., 6–18% higher than those obtained using the S-CV. R-CV also occasionally fails to reveal the true importance of the hyperparameters of models such as SVM and ANN. Additionally, R-CV falsely portrays a considerable improvement in model performance as the number of variables increases. However, this was not the case when the models were evaluated using S-CV. The impact of SAC was more noticeable in complex models such as SVM, RF, and C5.0 (except for ANN) than in simple models such as LDA and LR (except for KNN). Overall, we recommend S-CV over R-CV for a reliable assessment of ML model performance in large-scale LSM. Full article

Taking into account the requirements to reduce the release of harmful emissions into the environment, the EU’s environmental standards when transitioning to the Euro 7 standard in 2025 will actually lead vehicles having to operate without producing emissions in all driving situations. Carmakers believe that the new, much stricter regulations will mark the end of the internal combustion engine era. For example, in 2030, the manufacturer SEAT will cease its activities, leaving behind the Cupra brand, which will be exclusively electric in the future. This trend will apply not only to private vehicles (passenger cars), but also to utility vehicles, which is the subject of our research, namely the spatial tubular frame in the Bogdan ERCV27 garbage truck, presented in the form of a solid model. The peculiarity of the studied model is the installation of a battery block behind the driver’s cabin, causing an additional load to be placed on the spatial frame of the garbage truck, which in terms of its architecture is more like the body of a bus. During the conditions involving various modes of operation of a full-scale Bogdan ERCV27 garbage truck sample, questions about the strength and uniformity of its load-bearing spatial frame inevitably arise, which are decisive, even at the stage of designing and preparing the technical documentation. The main static load mode, which, despite its name, also covers dynamic conditions, was modeled using the appropriate coefficient k_d = 2.0. The maximum stresses on the model during the “bending” mode were 381.13 MPa before structure optimization and 270.5 MPa as a result of the improvement measures. The spatial frame mass was reduced by 4.13%. During the “torsion” mode, the maximum deformation values were 12.1–14.5 mm, which guarantees the normal operation of the aggregates and units of the truck. Full article

Pathogenic lagoviruses (Rabbit hemorrhagic disease virus, RHDV) are widely spread across the world and are used in Australia and New Zealand to control populations of feral European rabbits. The spread of the non-pathogenic lagoviruses, e.g., rabbit calicivirus (RCV), is less well studied as the infection results in no clinical signs. Nonetheless, RCV has important implications for the spread of RHDV and rabbit biocontrol as it can provide varying levels of cross-protection against fatal infection with pathogenic lagoviruses. In Chile, where European rabbits are also an introduced species, myxoma virus was used for localised biocontrol of rabbits in the 1950s. To date, there have been no studies investigating the presence of lagoviruses in the Chilean feral rabbit population. In this study, liver and duodenum rabbit samples from central Chile were tested for the presence of lagoviruses and positive samples were subject to whole RNA sequencing and subsequent data analysis. Phylogenetic analysis revealed a novel RCV variant in duodenal samples that likely originated from European RCVs. Sequencing analysis also detected the presence of a rabbit astrovirus in one of the lagovirus-positive samples. Full article

Estimation of error variance in the case of genomic selection is a necessary step to measure the accuracy of the genomic selection model. For genomic selection, whole-genome high-density marker data is used where the number of markers is always larger than the sample size. This makes it difficult to estimate the error variance because the ordinary least square estimation technique cannot be used in the case of datasets where the number of parameters is greater than the number of individuals (i.e., p > n). In this article, two existing methods, viz. Refitted Cross Validation (RCV) and kfold-RCV, were suggested for such cases. Moreover, by considering the limitations of the above methods, two new methods, viz. Bootstrap-RCV and Ensemble method, have been proposed. Furthermore, an R package “varEst” has been developed, which contains four different functions to implement these error variance estimation methods in the case of Least Absolute Shrinkage and Selection Operator (LASSO), Least Squares Regression (LSR) and Sparse Additive Models (SpAM). The performances of the algorithms have been evaluated using simulated and real datasets. Full article

Rabbit haemorrhagic disease virus (RHDV), European brown hare syndrome virus (EBHSV), rabbit calicivirus (RCV), and hare calicivirus (HaCV) belong to the genus Lagovirus of the Caliciviridae family that causes severe diseases in rabbits and several hare (Lepus) species. Previously, Lagoviruses were classified into two genogroups, e.g., GI (RHDVs and RCVs) and GII (EBHSV and HaCV) based on partial genomes, e.g., VP60 coding sequences. Herein, we provide a robust phylogenetic classification of all the Lagovirus strains based on full-length genomes, grouping all the available 240 strains identified between 1988 and 2021 into four distinct clades, e.g., GI.1 (classical RHDV), GI.2 (RHDV2), HaCV/EBHSV, and RCV, where the GI.1 clade is further classified into four (GI.1a–d) and GI.2 into six sub-clades (GI.2a–f). Moreover, the phylogeographic analysis revealed that the EBHSV and HaCV strains share their ancestor with the GI.1, while the RCV shares with the GI.2. In addition, all 2020–2021 RHDV2 outbreak strains in the USA are connected to the strains from Canada and Germany, while RHDV strains isolated in Australia are connected with the USA-Germany haplotype RHDV strain. Furthermore, we identified six recombination events in the VP60, VP10, and RNA-dependent RNA polymerase (RdRp) coding regions using the full-length genomes. The amino acid variability analysis showed that the variability index exceeded the threshold of 1.00 in the ORF1-encoded polyprotein and ORF2-encoded VP10 protein, respectively, indicating significant amino acid drift with the emergence of new strains. The current study is an update of the phylogenetic and phylogeographic information of Lagoviruses that may be used to map the evolutionary history and provide hints for the genetic basis of their emergence and re-emergence. Full article

The engineering practices for applying the microbial precipitation of carbonates require a design of the blending biocement solution (BCS). The BCS is usually blended with concentrated strains NO-A10, reaction media, such as urea and CaCl₂, and a solvent, i.e., water or seawater. To characterize the BCS, the unknown microbial characteristics, such as the cell viability, are complex factors. Therefore, the optical density (OD) was redefined as Rcv OD*, in which OD* was the tentative OD of the BCS used and Rcv was the conversion rate concerning the cell viability. To determine Rcv values, a standard precipitation curve based on the precipitation rate at 24 h was determined. It was found that the curve was expressed by λ₁ OD+ λ₂ OD², in which λ₁ and λ₂ were 8.46 M and −17.633 M, respectively. With this, the Rcv and OD values of unknown BCS were estimated from the results of precipitation tests using arbitrary OD* values. By extending the testing time, the second order term of OD or OD* was negligible. Accordingly, the precipitation amount is expressed as 8.46 OD, in which the OD can be estimated by precipitation tests using arbitrary OD* values of BCSs. Unless the Ca²⁺ value is dominant, the optimum blending of BCS can be determined by OD. Thus, it is concluded that the blending design of BCS is achieved using 8.46 OD, or 8.46 Rcv OD*, and the standard precipitation curve was defined in this study. Full article

In order to study the relationship between human physical activity and the design of the built environment, it is important to measure the location of human movement accurately. In this study, we compared an inexpensive GPS receiver (Holux RCV-3000) and a frequently used Garmin Forerunner 35 smart watch, with a device that has been validated and recommended for physical activity research (Qstarz BT-Q1000XT). These instruments were placed on six geodetic points, which represented a range of different environments (e.g., residential, open space, park). The coordinates recorded by each device were compared with the known coordinates of the geodetic points. There were no differences in accuracy among the three devices when averaged across the six sites. However, the Garmin was more accurate in the city center and the Holux was more accurate in the park and housing estate areas compared to the other devices. We consider the location accuracy of the Holux and the Garmin to be comparable to that of the Qstarz. Therefore, we consider these devices to be suitable instruments for locating physical activity. Researchers must also consider other differences among these devices (such as battery life) when determining if they are suitable for their research studies. Full article

With the initiative of sustainable smart city space, services and structures (3S), progress towards zero-emission municipal services has advanced the deployment of electric refuse collection vehicles (eRCVs). However, eRCVs are commonly equipped with oversized batteries which not only contribute to the majority of the weight of the vehicles but also remain a consistent weight, independent of the stage of charge (SoC), thus crucially jeopardising the significance of eRCVs in sustainability and economic strategies. Hence, customising the battery capacity in such a way that minimises its weight while storing ample energy for stalwart serviceability could significantly enhance its sustainability. In this study, taking only addresses as input, through an emergent two-stage data analysis, the energy required to collect refuse from a group of addresses was predicted. Therefore, predictions of the battery capacity requirement for the target location are possible. The theories and techniques presented in this paper were evaluated using real-life data from eRCV trials. For the same group of addresses, predicted results show an averaged error rate of 8.44%, which successfully demonstrates that using the proposed address-driven energy prediction approach, the energy required to collect refuse from a set of addresses can be predicted, which can provide a means to optimise the vehicle’s battery requirement. Full article

The determination of levels of rabies virus-neutralizing antibody (VNA) provides the foundation for the quantitative evaluation of immunity effects. The traditional fluorescent antibody virus neutralization test (FAVN) using a challenge virus standard (CVS)-11 strain as a detection antigen and staining infected cells with a fluorescein isothiocyanate (FITC)-labeled monoclonal antibody, is expensive and high-quality reagents are often difficult to obtain in developing countries. Indeed, it is essential to establish a rapid, economical, and specific rabies virus neutralization test (VNT). Here, we describe a recombinant virus rCVS-11-eGFP strain that stably expresses enhanced green fluorescent protein (eGFP) based on a reverse genetic system of the CVS-11 strain. Compared to the rCVS-11 strain, the rCVS-11-eGFP strain showed a similar growth property with passaging stability in vitro and pathogenicity in vivo. The rCVS-11-eGFP strain was utilized as a detection antigen to determine the levels of rabies VNAs in 23 human and 29 canine sera; this technique was termed the FAVN-eGFP method. The good reproducibility of FAVN-eGFP was tested with partial serum samples. Neutralization titers obtained from FAVN and FAVN-eGFP were not significantly different. The FAVN-eGFP method allows rapid economical, specific, and high-throughput assessment for the titration of rabies VNAs. Full article