Miracle-dz/UniFAR-FTUs huggingface dataset:
Morphological and genetic characteristics of garfish Belone belone (L., 1760) (Belonidae, Teleostei) population from the southern Bulgarian Black Sea coast

This study was conducted to investigate genetic and some morphometric and meristic characteristics of garfish Belone belone from Nesebar in the Bulgarian Black Sea coast. Twelve morphometric characters were measured, and six meristic characters were counted for each individual. Based on both sexes’ morphological and meristic analyses, no statistically significant sexual differences were observed. Additionally, DNA barcoding was done. The fragment of the cytochrome oxidase subunit I (COI) gene of mitochondrial DNA was sequenced to supplement the species identification and popu - lation diversity study. Two haplotypes were found out of 39 sequences, indicating a low level of haplotype diversity (0.146±0.072). Nucleotide diversity was also found to be low (0.00023±0.00011). The Nesebar population of B. belone requires conservation efforts, due to the highly decreased mtDNA genetic diversity.
Multiscale Analysis of Collective Decision{Making in Swarms: An Advection-Diusio n with Memory Approach

We propose a (time) multiscale method for the coarse-grained analysis of self{ propelled particle models of swarms comprising a mixture of 'nave' and 'informed' in- dividuals, used to address questions related to collective motion and collective decision{ making in animal groups. The method is based on projecting the particle conguration onto a single 'meta-particle' that consists of the group elongation and the mean group velocity and position. The collective states of the conguration can be associated with the transient and asymptotic transport properties of the random walk followed by the meta{particle. These properties can be accurately predicted by an advection-diusio n equation with memory (ADEM) whose parameters are obtained from a mean group ve- locity time series obtained from a single simulation run of the individual{based model.
Enhancing Spam Filter Using Naive Bayes and Count Vectorizer

This study delves into advancements in the realm of email spam filtration, a critical pillar in augmenting email security infrastructure. Given the un-ceasing challenges presented by unwarranted spam, the deployment of efficacious spam filtration methodologies remains imperative. Contemporary strategies encompass IP address filtering, rule-based filtering, and the employment of Naive Bayes algorithms. However, these methodologies often succumb to the continuously evolving spamming techniques. To counter these drawbacks, the current study proposes an enhanced spam filtering architecture anchored in machine learning techniques, which involves augmented spam data procurement, data processing, feature extraction via Term Frequency-Inverse Document Frequency (TF-IDF), and the implementation of machine learning models such as Naive Bayes and Support Vector Machines (SVM). This research conducts a comparative analysis of these machine learning classifiers and underscores the superior performance of the SVM Linear model in spam detection, achieving elevated accuracy levels while ensuring balanced precision and recall for both spam and non-spam emails. These findings underscore promising strides in the arena of email security. The study culminates by advocating for persistent research and the incorporation of advanced techniques to augment the accuracy and user-friendly nature of spam filtering systems.
Exploring Class Ordering Heuristics for Incremental Learning

Incremental Learning research is principally focused on overcoming the challenge of “catastrophic forgetting” to increase model accuracy. However, in this endeavor of increasing model accuracy, one factor that is almost consistently overlooked is the way training data is ordered with respect to their classes. Although the entirety of a dataset is always available to the authors before training, random or seeded orderings are typically used, but this is problematic when trying to reproduce studies and peak accuracy results. We bring to the table a method to sort classes deterministically such that the order in which they are presented to the IL algo-rithm will, on average, consistently yield greater mean accuracy than random orderings do. Thus, through this novel ordering heuristic, we provide the computer vision community with a tool to perform more easily re-produced and benchmarked results, as well as a trick to boost model accuracy by as much as 5%.
Grasping with Humanoid Hands based on In-Hand Vision and Hardware-accelerated CNNs

We present a vision-based grasping system for humanoid and prosthetic hands using hardware-accelerated CNNs for real-time object classification and class-aware pixel-wise segmentation. The system is implemented on the hand internal processing hardware of a humanoid hand using a System-on-Chip (SoC) comprising a Processor System (PS) and an FPGA. As a sensor system, the hand provides an integrated RGB camera, a multi-region Time-of-Flight (ToF) depth sensor, and an Inertial Measurement Unit (IMU). We propose an algorithm for 3D object shape estimation based on sensory information provided by the hand internal sensor system. The 3D object mesh in combination with the object relative pose of the hand is used as input for a reactive grasp controller. For the design of the CNN-based object recognition and segmentation networks, we use a resource-aware algorithm for Network-Architecture-Synthesis (NAS). We evaluate the visual perception accuracy and 3D model estimation accuracy in grasping experiments with six objects. We obtain a mean object segmentation accuracy of 84.4 % and a mean error for object diameter estimation of 44 mm.
Establishment of a Spontaneous Liver Fibrosis Model in NOD/SCID Mice Induced by Natural Aging

Simple Summary Liver fibrosis is a chronic disease that can lead to serious health problems, including liver cancer. However, the pathogenesis of liver fibrosis caused by natural aging is not fully understood. For researchers studying liver fibrosis, an important tool for conducting mechanism research and testing anti-liver fibrosis therapies is an in vivo model that can reflect the pathological causes of liver fibrosis. This study aimed to establish a model of spontaneous liver fibrosis in NOD/SCID mice induced by natural aging. This model’s validity was confirmed using cutting-edge fibrosis-related measurements. This study provides a useful tool for further research into the causes and potential treatments for liver fibrosis. The findings may help to develop new therapies for chronic liver diseases which affect millions of people worldwide. Abstract Liver fibrosis, a critical pathological feature of chronic liver diseases, arises from a multitude of pathogenic factors. Consequently, establishing an appropriate animal model to simulate liver fibrosis holds immense significance for comprehending its underlying pathogenesis. Despite the numerous methodologies available for generating liver fibrosis models, they often deviate substantially from the spontaneous age-related liver fibrosis process. In this study, compared with young (12 weeks) and middle-aged NOD/SCID mice (32 weeks), there were a large number of fibrous septum and collagen in the liver tissue of old NOD/SCID mice (43 weeks, 43 W). Immunohistochemical analysis unequivocally indicated heightened α-SMA content within the liver tissue of the 43 W mice, thereby underscoring aging’s role in triggering the epithelial-to-mesenchymal transition. In addition, SA-β-gal staining as well as P21 expression were increased, and SIRT1 and SIRT3 expression were decreased in 43 W mice. A comprehensive evaluation encompassing transmission electron microscopy and fluorescence quantitative analysis elucidated compromised mitochondrial function and reduced antioxidant capacity in hepatocytes of the 43 W mice. Furthermore, the aging process activated the pro-fibrotic TGF-β-SMAD pathway, concurrently inducing hepatocellular inflammation. The results of the present study not only validate the successful construction of a spontaneous liver fibrosis mouse model through natural aging induction but also provide initial insights into the mechanisms underpinning age-induced liver fibrosis.
a Subpocket in Trypanosoma brucei Phosphodiesterase

: Several trypanosomatid cyclic nucleotide phos-phodiesterases (PDEs) possess a unique, parasite-speci ﬁ c cavity near the ligand-binding region that is referred to as the P-pocket. One of these enzymes, Trypanosoma brucei PDE B1 (TbrPDEB1)
Classification of Flood-Prone Areas in Tomohon Using Landsat 8 OLI Satellite Imagery

— Natural disasters often occur unexpectedly, resulting in both material and nonmaterial losses. Floods are among natural disasters that often occurs in several regions in Indonesia, one of which is Tomohon. Tomohon is a city located in the highlands, so it is expected to have a low flood risk level. Nevertheless, in reality, flood still occurs in Tomohon, which then causes material and nonmaterial losses. The data used in this research were the satellite imagery of the Landsat 8 onboard operational land imager (OLI) accessed through the United States Geographical Survey (USGS). The land covers in Tomohon were classified using the supervised classification method with the minimum distance classification (MDC) algorithm. This method provided the advantage of classifying land covers by utilizing training data in Tomohon, achieving an accuracy rate of 99.56%. In addition, the calculations of normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and soil adjusted vegetation index (SAVI) were also utilized to determine the level of vegetation and surface soil moisture in Tomohon using the Quantum GIS (QGIS) application. Upon examining the land covers and calculating the index, weighting was once more performed in accordance with criteria. It was done to facilitate the classification of the area into three flood risk classifications: high, medium, and low. The results showed that green spaces in Tomohon are still greater than residential areas. However, NDVI, NDWI, and SAVI calculations indicated that some densely populated areas are susceptible to flood. These areas include Tomohon Selatan and Tomohon Tengah Subdistricts, which have a high level of flood risk and the Tomohon Timur Subdistrict, which has a medium level of flood risk.
Encoding molecular motions in voxels maps

—Understanding life at the atomic level requires the development of new methodologies, able to overcome the limitations of available experimental and computational techniques for the analysis of processes involving molecular motions. With this goal in mind, we develop new methods, combining robotic path planning algorithms and molecular modeling techniques, for computing large-amplitude motions. This paper builds on these new methods, and introduces voxel maps as a computational tool to encode and to represent such motions. Voxel maps can be used to represent relative motions of two molecules, as well as conformational changes in macromolecules. We investigate several applications and show results that illustrate the interest of such representation. In particular, voxel maps are used to display channels into proteins, to analyze protein-ligand speciﬁcity, and to represents protein loop and domain motions.
The Identiﬁcation of Selective Pathogenic Microbial Community Bioﬁlms in Different Distribution Pipeline Materials and Their Disinfection Kinetics

: Bioﬁlms in water distribution lines strongly affect water safety as they are the main carriers of pathogens. The current study investigated the bioﬁlm formation and identiﬁcation of selected pathogens in different distribution pipeline materials and their disinfection method in an annular reactor (AR). Initially, the quality of the ﬂowing water from each pipeline material was analyzed, i.e., pH, TDS, EC, turbidity, and salinity; then, the bioﬁlm formation was monitored for each material, i.e., ABS, PC, PVC, PP, and HDPE. Further, the disinfection kinetics of bioﬁlm at different chlorine doses, i.e., 0.5, 1.0, 1.5, and 2.0 mg/L, was investigated. The selected pathogens, i.e., E. coli , Pseudomonas , Shigella , Salmonella sp. , and Vibrio sp. were identiﬁed in bioﬁlms formed in different pipeline materials. The disinfection kinetics results showed that a chlorine dose of 2.0 mg/L was the most effective in disinfecting selected pathogens. Following the disinfection kinetics, it was observed that Salmonella sp. was disinfected within 7 days, whereas other pathogenic bioﬁlms were disinfected within 14 days. The efﬁcacy of chlorine disinfection was affected by the types of pipeline materials. The study outcomes could provide insights into bioﬁlms’ disinfection method and the selection of suitable pipeline materials to ensure drinking water safety.
Classi�cation of Ice Crystal Habits by Deep Transfer Learning

Ice crystal particle shape is an important factor affecting cloud microphysical processes. Accurately identifying the shapes of ice crystal particle within clouds is a fundamental requirement for calculating various cloud microphysical parameters. In this study, we set up an ice crystal image dataset, encompassing nine distinct habit categories with 8100 images. These images were captured using three probes with varying resolutions: the Cloud Particle Imager (CPI), the Two-dimensional Stereo Probe (2D-S), and the High Volume Precipitation Spectrometer (HVPS). In addition, we introduce a deep convolutional neural network (CNN) based on transfer learning for ice crystal particle shape classification model, TL-AlexNet, which demonstrates the capability to simultaneously classify ice crystal particle habits observed by both the Line Scan Imager and the Area Scan Imager. The results indicate that the TL-AlexNet model could achieve superior performance in ice crystal shapes classification for two types of imagers, and the classification with the accuracy of 97.16%. It is much higher than the traditional shape recognition methods, and has certain application value for Climate and cloud microphysics research.
Adaptation of English onset clusters across time in Hong Kong Cantonese : The role of the perception grammar

In the present article we argue that the initial adaptation of English obstruent-liquid clusters into Hong Kong Cantonese is taking place in the perception grammar, which is influenced by phonological restrictions, following the proposal by Boersma & Hamann (2009) on loan adaptations in Korean. Such a perception grammar account does away with loanword-specific devices invoked by previous phonological accounts of Hong Kong Cantonese adaptations (such as Silverman 1992; Yip 1993, 2006; Kenstowicz 2012). Furthermore, we show that diachronic changes seem to have occurred in the adaptation of these English words, and we argue that such changes are due to the acquisition of a different perception grammar by younger speakers: newer loan forms (with less native-like structure) are possible because recent generations have had much more exposure to English. As a result, they have less (strong) phonotactic restrictions against English-like phonological structures and are more sensitive to English perceptual cues. We argue that this proposal is preferable to a stratified lexicon approach (Ito & Mester 1999, 2001 for Japanese) that encodes diachronic differences in adaptations as different constraint rankings within one synchronic grammar.
Analysis of Financial Performance, Efficiency and Growth of Zakat Management Organizations for Zakat Collection in Indonesia

This study examines the financial performance, efficiency, and growth of Zakat Management Organizations (ZMOs) in Indonesia, focusing on zakat collection and distribution. Zakat, a form of almsgiving in Islam, plays a crucial role in wealth redistribution and reducing income inequality in Muslim-majority Indonesia. The study observes a significant growth in zakat collection, particularly during natural disasters and the Covid-19 pandemic, reflecting increased public income and economic growth. The research emphasizes the importance of good governance in ZMOs, ensuring trustworthiness, transparency, and professionalism. According to Law Number 23 Tahun 2011, ZMOs are responsible for the collection, distribution, utilization, reporting, and accountability of zakat funds. This study quantitatively measures the performance of ZMOs using financial statement analysis to understand their efficiency and financial health. The study investigates the influence of liquidity ratio, amil fund ratio, and activity ratio on zakat distribution, using efficiency and growth ratios as control variables. It analyzes secondary data from financial reports of eight ZMOs in Indonesia for the period 2016-2020. The study employs various econometric tests, including panel data regression, to evaluate the relationships between these financial metrics and zakat distribution. The results indicate that while the liquidity ratio and activity ratio do not significantly impact zakat distribution, the amil fund ratio does. The study also finds that control variables, efficiency and growth ratios, significantly influence zakat distribution. These findings suggest that efficient management and growth of ZMOs positively affect zakat distribution, highlighting the need for effective financial governance in these organizations. In conclusion, the study underscores the critical role of ZMOs in managing zakat funds. It contributes to understanding how financial performance and efficiency of these organizations impact their ability to distribute zakat effectively, thereby aiding in wealth redistribution and economic development in Indonesia
Biofilm Formation by Escherichia coli Isolated from Urinary Tract Infections from Aguascalientes, Mexico

Uropathogenic Escherichia coli (UPEC) strains are among the leading causes of urinary tract infections (UTIs) worldwide. They can colonize the urinary tract and form biofilms that allow bacteria to survive and persist, causing relapses of infections and life-threatening sequelae. Here, we analyzed biofilm production, antimicrobial susceptibility, virulence factors, and phylogenetic groups in 74 E. coli isolated from diagnosed patients with UTIs to describe their microbiological features and ascertain their relationship with biofilm capabilities. High levels of ceftazidime resistance are present in hospital-acquired UTIs. Isolates of multidrug resistance strains (p = 0.0017) and the yfcV gene (p = 0.0193) were higher in male patients. All the strains tested were able to form biofilms. Significant differences were found among higher optical densities (ODs) and antibiotic resistance to cefazolin (p = 0.0395), ceftazidime (p = 0.0302), and cefepime (p = 0.0420). Overall, the presence of fimH and papC coincided with strong biofilm formation by UPEC. Type 1 fimbriae (p = 0.0349), curli (p = 0.0477), and cellulose (p = 0.0253) production was significantly higher among strong biofilm formation. Our results indicated that high antibiotic resistance may be related to male infections as well as strong and moderate biofilm production. The ability of E. coli strains to produce biofilm is important for controlling urinary tract infections.
Transcriptome of American Oysters, Transcriptome of American Oysters, Crassostrea virginica, in , in Response to Bacterial Challenge: Insights into Potential Response to Bacterial Challenge: Insights into Potential Mechanisms of Disease Resistance Mechanisms of Disease Resistance

The American oyster Crassostrea virginica , an ecologically and economically important estuarine organism, can suffer high mortalities in areas in the Northeast United States due to Roseovarius Oyster Disease (ROD), caused by the gram-negative bacterial pathogen Roseovarius crassostreae . The goals of this research were to provide insights into: 1) the responses of American oysters to R. crassostreae, and 2) potential mechanisms of resistance or susceptibility to ROD. The responses of oysters to bacterial challenge were characterized by exposing oysters from ROD-resistant and susceptible families to R. crassostreae , followed by high-throughput sequencing of cDNA samples from various timepoints after disease challenge. Sequence data was assembled into a reference transcriptome and analyzed through differential gene expression and functional enrichment to uncover genes and processes potentially involved in responses to ROD in the American oyster. While susceptible oysters experienced constant levels of mortality when challenged with R. crassostreae, resistant oysters showed levels of mortality similar to non-challenged oysters. Oysters exposed to R. crassostreae showed differential expression of transcripts involved in immune recognition, signaling, protease inhibition, detoxification, and apoptosis. Transcripts involved in metabolism were enriched in susceptible oysters, suggesting that bacterial infection places a large metabolic demand on these oysters. Transcripts differentially expressed in resistant oysters in response to infection included the immune modulators IL-17 and arginase, as well as several genes involved in extracellular matrix remodeling. The identification of potential genes and processes responsible for defense against R. crassostreae in the American oyster provides insights into potential mechanisms of disease resistance.
Biochar - The Future of Agriculture

The amount of carbon in the soil is a direct indication of good quality of soil. Higher carbon stocks have a direct correlation with increased agricultural yields through improved soil health. In the current scenario of climate change and global warming, much of carbon in atmosphere has to be sequestrated into soil carbon pool so that increasing CO2 in the atmosphere and resulting warming could be reduced. Biochar is a solid material obtained from thermal decomposition of organic materials, in the process much of the carbon becomes “fixed” into a more stable form, and when the resulting biochar is applied to soils, the atmospheric carbon is effectively sequestered and soil carbon pool is increased, making soil of a good quality. Thus, the use of biochar can be a simple yet powerful tool to combat climate change by sequestering much of atmospheric carbon into soil as well as providing an opportunity for the processing of agricultural and other waste into useful clean energy.
University of Birmingham Silver nanoparticles synthesis in microfluidic and well-mixed reactors

We investigate the production of silver nanoparticles (AgNPs) under different operating conditions of pH, inlet flow rates, and reactants concentration via a combination of population balance modelling and computational fluid dynamics (PBM-CFD) to elucidate our experimental observations in a well-mixed batch reactor and a T-junction microfluidic device. The synthesis process considered herein is based on a reduction protocol involving silver nitrate as precursor and a mixture of tannic acid/trisodium citrate as reducing/stabilising agents. Our novel PBM-CFD framework expands the current kinetic models developed for well-mixed reactors to explicitly account for the spatio-temporal dependence of mixing dynamics for microfluidic reacting flows in order to predict the final particle size distribution of the produced AgNPs. Nucleation and growth models are based on the Finke–Watzky (F–W) two-step mechanism and are directly coupled to the reduction reaction kinetics and the convection–diffusion equations of each species. The T-junction results suggest that the higher residence times involved in operations with lower flow rates are responsible for a cascade of kinetic/reactive and hydrodynamic events, unveiled by our simulations, that lead to higher outlet particle sizes and volume fraction of the particles in the dispersion. The simulation results also provide a characterisation of the radial heterogeneous concentration of particles and particle sizes across the channel typical of continuous-flow microfluidics. Limitations and advantages of our novel modelling approach are discussed in the context of future lines of research.