Miracle-dz/UniFAR-FTUs huggingface dataset:
Simple models to quickly estimate the probable range of data‐limited riverine fishes

Species distribution models provide biologists with insight into species ranges, ecological niches, and spatial predictions of suitable habitat, but require significant training, data, and time for biologists to obtain reliable results. Alternatively, ad hoc inferences from sparse occurrence records leave biologists to face conservation decisions with high uncertainty. Simple predictive models can fill the gap between these two alternatives. One method to predict the probable range of riverine fishes is to add the average distance between occurrences to the most distal occurrence (parametric estimator). However, there are drawbacks to this method when the distances between occurrences deviate from a non‐uniform distribution. Herein, estimators that are robust to non‐uniformity, a non‐parametric and an optimal linear estimator, were adapted and compared to the parametric estimator using simulations. The non‐parametric estimator showed the least bias and variance in bias for the probable range endpoint. The optimal linear estimator best balanced type I and type II errors in the maximum probable range estimate (i.e., upper CI). Application of the robust estimators were made to demonstrate how sampling can be planned based on available effort (Gulf Coast walleye, Sander vitreus) and where early detection sampling may be focused (silver carp, Hypophthalmichthys molitrix). This study provides biologists with a suite of simple options (R code provided as supplementary material) to bridge the gap between ad hoc inference and complex modeling over a range of scenarios. Future studies should focus on accounting for false‐positives, variable effort, and develop online apps for accessibility.
Designing CNNs for Multimodal Image Restoration and Fusion via Unfolding the Method of Multipliers

Multimodal, alias, guided, image restoration is the reconstruction of a degraded image from a target modality with the aid of a high quality image from another modality. A similar task is image fusion; it refers to merging images from different modalities into a composite image. Traditional approaches for multimodal image restoration and fusion include analytical methods that are computationally expensive at inference time. Recently developed deep learning methods have shown a great performance at a reduced computational cost; however, since these methods do not incorporate prior knowledge about the problem at hand, they result in a “black box” model, that is, one can hardly say what the model has learned. In this paper, we formulate multimodal image restoration and fusion as a coupled convolutional sparse coding problem, and adopt the Method of Multipliers (MM) for its solution. Then, we use the MM-based solution to design a convolutional neural network (CNN) encoder that follows the principle of deep unfolding. To address multimodal image restoration and fusion, we design two multimodal models which employ the proposed encoder followed by an appropriately designed decoder that maps the learned representations to the desired output. Unlike most existing deep learning designs comprising multiple encoding branches followed by a concatenation or a linear combination fusion block, the proposed design provides an efficient and structured way to fuse information at different stages of the network, providing representations that can lead to accurate image reconstruction. The proposed models are applied to three image restoration tasks, as well as two image fusion tasks. Quantitative and qualitative comparisons against various state-of-the-art analytical and deep learning methods corroborate the superior performance of the proposed framework.
TOWARDS AN INTEGRATED CO-OPERATIVE SUPERVISION SYSTEM FOR ACTIVATED SLUDGE PROCESSES OPTIMISATION

Abstract This paper deals with the design of an integrated co-operative supervision system to optimise activated sludge wastewater treatment. For this purpose a biological wastewater treatment pilot plant, suitable for industrial up scaling and composed of a biological reactor, a settler, sludge recirculation and aeration systems, has been carried out. The reactor configuration is adjustable to treat both urban and industrial wastewater. The pilot plant scale allows to reproduce industrial hydrodynamics and is illustrated by RTD (Residence Time Distribution) measurements. The supervisory system includes instrumentation and industrial supervisor. The integrated co-operative supervision approach emphases the importance of process behavior anticipation. Information for the human operator will be extracted from a suitable model and a state estimator, which results are presented.
Does renewable energy consumption promote economic growth? An empirical analysis of panel threshold based on 54 African countries

Purpose The purpose of this study is to investigate the relationship between renewable energy consumption and economic growth, using the threshold variables of non-renewable energy consumption, urbanization level and per-capita income. Design/methodology/approach This study used a panel threshold regression model, on combined African countries and divided African countries into five regions (northern, western, central, southern and eastern Africa). The study used panel data from 54 African countries, from 1990 to 2018. Findings This study established a threshold interval where the significant negative impact of renewable energy consumption on the economic growth of combined African countries is different at each split asymmetric phase, meaning the relationship is negative and non-linear. Further, the study established the threshold effect of divided African countries into regions, revealed a negative effect of renewable energy consumption on economic growth and compared the differences of threshold effect and coefficient in the regions, which further highlight the varying resource and renewable energy development across African countries. Practical implications This study recommends strategies and investment priorities on energy transition, through optimizing renewable energy in Africa, hence aggressive investment in the renewable energy sector is highly encouraged especially for the oil-producing state to promote clean and sustainable energy. Originality/value The contribution of this study is the establishment of a non-linear panel threshold model to examine the asymmetric effect of renewable energy consumption on economic growth which to the best of the authors’ knowledge is pioneer research in Africa. Additionally, an in-depth analysis of renewable energy consumption’s effect on the economic growth of all the regions in Africa.
A High-Performance Convolutional Neural Network for Ground-Level Ozone Estimation in Eastern China

Having a high-quality historical air pollutant dataset is critical for environmental and epidemiological research. In this study, a novel deep learning model based on convolutional neural network architecture was developed to estimate ground-level ozone concentrations across eastern China. A high-resolution maximum daily average 8-h (MDA8) surface ground ozone concentration dataset was generated with the support of the total ozone column from the satellite Tropospheric Monitoring Instrument, meteorological data from the China Meteorological Administration Land Data Assimilation System, and simulations of the WRF-Chem model. The modeled results were compared with in situ measurements in five cities that were not involved in model training, and the mean R2 of predicted ozone with observed values was 0.9, indicating the good robustness of our model. In addition, we compared the model results with some widely used machine learning techniques (e.g., random forest) and recently published ozone datasets, showing that the accuracy of our model is higher and that the spatial distributions of predicted ozone are more coherent. This study provides an efficient and exact method to estimate ground-level ozone and offers a new perspective for modeling spatiotemporal air pollutants.
Improving the Output Power of PEM Fuel Cell with PI + ASM Combined Controller Designed for Boost Converter

Received: July 21-2021 Accepted: September 30-2021 Proton-exchange membrane (PEM) is one of the most common fuel cells for renewable energy generation. In this paper, the structure of DC/DC boost converter is presented to improve the energy efficiency and the combination of proportionalintegral controller with adaptive sliding mode method is designed to achieve high output power as well as constant output voltage. The proposed method has the ability to cover uncertainty effects with an unknown upper bound due to the use of adaptive technique and in addition to improving the quality of output power and voltage of the PEM, also eliminates permanent tracking error and ensures the stability of the closed loop system. The stability of the closed-loop system has been obtained using Lyapunov method and the simulation and comparison results in MATLAB environment show the optimal performance of the system under the proposed method and high efficiency compared to the existing methods.
Interactive Trajectory Prediction Using a Driving Risk Map-Integrated Deep Learning Method for Surrounding Vehicles on Highways

Accurate trajectory prediction of surrounding vehicles is vital for automated vehicles to achieve high-level driving safety in complex situations. However, most state-of-the-art approaches for multi-vehicle trajectory prediction ignore vehicle motion uncertainty caused by different driving styles. Moreover, the interrelationship between the vehicle and the environment is seldom considered. To address the above problems, this paper proposes a driving risk map-integrated deep learning (DRM-DL) method for interactive trajectory prediction of surrounding vehicles, which comprehensively considers the motion uncertainty, trajectory intention uncertainty and interactions among vehicles, lane lines and road boundaries. Specifically, we adopt a conditional variational autoencoder (CVAE) to generate the candidate trajectories, in which the motion uncertainty is considered using a conditional Gaussian distribution. Furthermore, a driving risk map is constructed to realize a unified and interpretable representation of vehicle-vehicle and vehicle-environment interactions. The probability of each candidate trajectory is assigned using a trajectory probability model and a random selection is adopted to select a guided trajectory, which simulates the driver’s trajectory intention uncertainty. Finally, a relearning module is designed to obtain the precise trajectory prediction for surrounding vehicles. The proposed method is evaluated on the HighD dataset, and the results demonstrate a more accurate and reliable trajectory prediction for surrounding vehicles compared with state-of-the-art methods.
Association between rs738409 and rs2896019 single-nucleotide polymorphisms of phospholipase domain-containing protein 3 and susceptibility to nonalcoholic fatty liver disease in Greek children and adolescents

Background Several studies have detected a strong association linking rs738409 and rs2896019 polymorphisms in the patatin-like phospholipase domain-containing protein 3 (PNPLA3) gene with hepatic steatosis and steatohepatitis. In the present study we aimed to determine the association of those PNPLA3 variants with nonalcoholic fatty liver disease (NAFLD) susceptibility in obese and nonobese Greek children and adolescents. Methods The study recruited 91 children and adolescents of Greek descent with NAFLD or biopsy-proven nonalcoholic steatohepatitis, and 91 healthy subjects of normal weight (control group) with sex distribution similar to the patient group. DNA samples were amplified using polymerase chain reaction with specifically designed primers. Data were analyzed using the statistical software SPSS version 24.0. Results A significant correlation was shown between the rs738409 polymorphism (CG and GG genotypes) and the rs2896019 polymorphism (TG genotype) with the development of hepatic steatosis (P<0.001). The incidences of rs738409 GG, rs738409 CG and rs2896019 TG genotypes were found to be increased in patients with hepatic steatosis (obese and nonobese), but not in obese patients without liver disease. The combined expression of the 2 polymorphisms was associated with a lower age of diagnosis of hepatic steatosis in nonobese patients. Conclusions We confirmed a strong association between the 2 polymorphisms and hepatic steatosis. The association of the rs2896019 single-nucleotide polymorphism with hepatic steatosis in obese and nonobese pediatric patients, and the combined study of both polymorphisms in a pediatric population of Greek origin are described for the first time.
Influence of HVDC P2P Links on Static Voltage Stability in Transmission Grids with High Shares of Renewable Energy

The Energiewende with the massive installation of renewable energy sources (RES) in the distribution grid leads to major changes in the transmission system. RES are often installed at sites with a high yield of primary energy, which results in higher average transmission distances. Thus, reactive power demand increases, which affects static voltage stability. To address these challenges, technologies like High Voltage Direct Current (HVDC) Point-to-Point (P2P) links will be used in the transmission grid. Within this paper an iterative load-flowalgorithm is proposed to evaluate voltage stability margins, i.e. the distance from the actual operating point to the stability limit regarding active power transmission. Due to the possibility of modern Voltage Source Converters (VSC) to control active and reactive power independently, voltage stability limit is investigated for different loadings of the HVDC P2P links. A 58bus-transmission grid representing the German transmission system is used, where regional distributed renewable feed-in as well as three HVDC P2P links are applied. The dispatch of conventional power plants is determined through a merit-order approach. Results show that HVDC-links can have a positive effect on voltage stability depending on their power transmission. The choice of the operating point can therefore be crucial for voltage stability.
Police perspectives on interviewing older adult victims and witnesses: Preliminary findings and call for future research

ABSTRACT As the proportion of older adults (OAs) in the population continues to increase, so too will the frequency of police interactions with OAs and the need to gather accurate and detailed accounts from them. Yet, research on police information-gathering with OAs remains relatively scarce. This qualitative study begins to address this gap. We conducted semi-structured interviews with an experienced Canadian police sample (N = 10) regarding their involvement with, and perspectives on, interviewing OA victims and witnesses. Participants reported heterogeneous interactions with OAs, identified barriers that OAs face (e.g., shame), and affirmed the unavailability of training and official guidance on information-gathering with OAs. Our findings highlight existing practical challenges and future research avenues.
FUNCTIONAL CLASSIFICATION OF CEREAL PROTEINS USING SUPPORT VECTOR MACHINE

Abiotic stress factors severely limit plant growth and development as well as crop yield. Predicting the function of an unknown protein is an essential goal in bioinformatics. A highly accurate prediction method capable of identifying protein function, based on physicochemical properties of protein has been described. On the basis of 34 features extracted solely from the protein sequence, models were built to predict the functions of different proteins from a range of stresses namely cold, heat, drought, salt and ABA. Classification of cereal proteins has been done using Support Vector Machine (SVM). Feature selection was performed by stepwise Logistic Regression. This method analyses and identifies specific features of the protein sequence that are highly correlated with certain protein functions. SVM was trained using different kernel functions. Different measures such as sensitivity, specificity, accuracy has been calculated. The accuracy of protein function prediction using SVM with different kernel functions ranged from 60% to 100%. Prediction accuracy of different models with different kernel functions was validated using bootstrap cross validation.
Topical toxicity of four active ingredients on Atta sexdens workers

Leaf-cutting ants are economically important pest insects in the Neotropical region. They forage on fresh leaves and flowers that serve as substrates for the cultivation of their mutualistic fungus, which they feed upon. The main active ingredient for their control is sulfluramid, but the restriction of its use is under discussion. In this study, the topical toxicity of four active ingredients on Atta sexdens workers was evaluated, determining the median lethal dose (LD50), median lethal time (LT50), and control efficiency, with the prospect of identifying them as potential contact formicides. Values of LD50 were obtained after observation of mortality and subsequent probit analysis. Sulfluramid presented the highest value (245.33 μg mL-1), followed by diafenthiuron (42.53 μg mL-1) and indoxacarb (7.29 μg mL-1). The imidacloprid LD50 was about 40-fold lower than the sulfluramid LD50 (6.27 μg mL-1). The LT50 values were higher for sulfluramid and diafenthiuron. Indoxacarb and imidacloprid presented control efficiency greater than 90%, and the linear model was significant for all active ingredients. The results obtained in this study indicate that indoxacarb and imidacloprid are fast-acting potential formicides for contact formulations, but not are feasible for use in formicide baits.
Exploring Factors Contributing to the Smoking Behaviour among Hong Kong Chinese Young Smokers during COVID-19 Pandemic: A Qualitative Study

COVID-19 has significant impacts on young smokers in their smoking behaviors. This qualitative study summarises the lived experience of young smokers during COVID-19. Moreover, through their lived experience, we aim to understand how the COVID-19 pandemic influence tobacco use behaviours in this population. A purposive sampling of 48 smokers aged between 17–25 years old is individually interviewed for 30 to 45 min. All interviews are transcribed in verbatim and analysed by two researchers separately using Colaizzi’s method of descriptive phenomenology. The results reveal the following six important themes, which could explain the mixed pattern of smoking behaviour changes in young smokers: (1) perceptions of COVID-19 and its association with smoking, (2) more time at home, (3) taking masks off to smoke, (4) the effects of COVID-19 on smokers’ financial status and academic performance, (5) reduced social gatherings, and (6) restricted access to tobacco products. To conclude, this pandemic and the anti-pandemic measures, i.e., mask mandates, stay-at-home and work-from-home orders, and class suspension, result in both new obstacles and new advantages for smoking cessation among young people. More studies should be performed to monitor any transition of tobacco products and the trajectory of use in this population during this pandemic, thus informing public health policy making.
Cytochrome c oxidase subunit 1 gene reveals species composition and phylogenetic relationships of Oesophagostomum spp. infecting pigs in northeastern Brazil

Abstract Helminths of the genus Oesophagostomum cause enteric diseases and affect domestic animals such as pigs. The aim of this study was to explore the species composition and genetic diversity of Oesophagostomum spp. infecting pigs in close contact with humans in the state of Piauí, Brazil. Eighty-seven fecal samples were collected for parasitological tests and molecular analysis. Through microscopy, the overall positivity rate for strongyliform eggs was 81.6% among the pigs studied. Forty-two strongyliform egg samples were subjected to PCR and six cox1 sequences (637 bp) were identified for the genus Oesophagostomum. The sequences were identified as Oesophagostomum dentatum, O. quadrispinulatum and O. columbianum. In the phylogenetic tree and haplotype network, 89 sequences were separated into seven clusters, which also included reference sequences from GenBank. Oesophagostomum dentatum and O. quadrispinulatum were seen to be closely related species and formed a monophyletic group related to O. aculeatum. Oesophagostomum columbianum showed similarity with sequences from parasites infecting small ruminants and the clade was positioned closer to O. bifurcum. High interspecific diversity was found and intraspecific diversity varied according to the species. This was the first study to characterize Oesophagostomum DNA sequences obtained from pigs in Brazil.
Simultaneous prediction of blast-induced flyrock and fragmentation in opencast limestone mines using back propagation neural network

Goal of blasting operations is to achieve desired fragment size to operate the mine and plant economically while maintaining safety that includes prevention of flyrock accidents. This paper focuses on the simultaneous prediction of flyrock distance and fragmentation using back propagation neural network techniques. Thus, linear charge concentration, burden, spacing, stemming length, specific charge, unconfined compressive strength and rock quality designation are taken as input. Flyrock distance and fragment size are chosen as output. The predicted outputs by back propagation neural network (BPNN), multi variate regression analysis (MVRA) have been compared. The quite lower root mean square error (RMSE) and mean absolute error (MAE) in BPNN than MVRA prove that BPNN is a better prediction method. Also, the predicted output in BPNN correlates better with the observed output than MVRA. Sensitivity analysis for both independent variables for BPNN and MVRA is also included in this paper.
nnU-Net Deep Learning Method for Segmenting Parenchyma and Determining Liver Volume From Computed Tomography Images

Background: Recipient donor matching in liver transplantation can require precise estimations of liver volume. Currently utilized demographic-based organ volume estimates are imprecise and nonspecific. Manual image organ annotation from medical imaging is effective; however, this process is cumbersome, often taking an undesirable length of time to complete. Additionally, manual organ segmentation and volume measurement incurs additional direct costs to payers for either a clinician or trained technician to complete. Deep learning-based image automatic segmentation tools are well positioned to address this clinical need. Objectives: To build a deep learning model that could accurately estimate liver volumes and create 3D organ renderings from computed tomography (CT) medical images. Methods: We trained a nnU-Net deep learning model to identify liver borders in images of the abdominal cavity. We used 151 publicly available CT scans. For each CT scan, a board-certified radiologist annotated the liver margins (ground truth annotations). We split our image dataset into training, validation, and test sets. We trained our nnU-Net model on these data to identify liver borders in 3D voxels and integrated these to reconstruct a total organ volume estimate. Results: The nnU-Net model accurately identified the border of the liver with a mean overlap accuracy of 97.5% compared with ground truth annotations. Our calculated volume estimates achieved a mean percent error of 1.92% + 1.54% on the test set. Conclusions: Precise volume estimation of livers from CT scans is accurate using a nnU-Net deep learning architecture. Appropriately deployed, a nnU-Net algorithm is accurate and quick, making it suitable for incorporation into the pretransplant clinical decision-making workflow.