Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlation Analysis of Fitness Landscapes

Fitness landscapes underlie the dynamics of evolutionary processes and are a key concept of evolutionary theory. Recent research on molecular folding and on evolutionary algorithms has demonstrated that such landscapes are also important for understanding problems of chemistry and of combinatorial optimization. In these cases free energy or cost functions are used instead of biological fitness functions defined on genotypes. However, the image of a three dimensional landscape with many peaks and valleys turns out to be misleading. Genotypes tend to differ in numerous characteristics, resulting in multidimensional fitness landscapes. Properties of these landscapes are very different from those of low dimensional ones. The main intention of this study is to investigate how these features affect the duration of adaptive walks on such landscapes. For this purpose we focus on the Traveling Salesman Problem (TSP), which amounts to finding the shortest tour visiting a given set of locations. By comparing theoretical predictions for the duration of adaptive walks to the actual waiting times observed for an evolutionary algorithm we demonstrate that a sufficiently fine-grained correlation matrix succeeds in capturing essential structural features of the TSP fitness landscape. To test the performance of correlation-based predictions for a class of fitness landscapes with varying degree of neutrality, we have analyzed evolutionary waiting times on NKp fitness landscapes. We show that for low degrees of neutrality, correlation statistics again prove to be an excellent basis for predicting waiting times, while for very high degrees of neutrality, a population's drift along neutral networks turns out to require incorporation of additional information on network topologies.
The effect of controller selection in collision avoidance system on fuel consumption for unmanned surface vessel: a case study

ABSTRACT A proposed mathematical model has been constructed to avoid both fixed and moving obstacles encountered on the ship's route. The mathematical model includes the manoeuvring model, MMG (Manoeuvring Modeling Group), and controller parts, MPC (Model Predictive Control), and PD controllers, separately. First, the turning and zigzag tests have been performed to validate the mathematical model. Especially, the turning test results are crucial since they are used as data in the control algorithm. The distance between the controlled ship and the obstacles is calculated in a time interval and the rudder is commanded according to these results. Second, a course including moving and fixed obstacles is determined for the case study. Finally, the path-following simulations for the determined course have been obtained thanks to the controllers. As a result, the ship reached the target coordinates without any collision and the fuel consumption differed depending on the controller selection.
Image error correction in x-ray phase contrast imaging by convolutional neural networks

When there is mechanical drift in X-ray phase contrast imaging system, the position of the grating will produce random error, and the intensity of the obtained image will have a certain deviation, and the information retrieved by using the phase step method may be accompanied with Moire artifacts. In order to overcome this limitation, we introduce the convolutional neural network (CNN) to address it. The training data is downloaded from Kaggle, and the fringe graph with random deviation is combined as the network input, while the label is defined as the first-order difference image along the horizontal direction of the image. Both simulation and experiment show that CNN can not only retrieve the phase signal of the sample, but also remove some Moire artifacts with regular shape to improve the image quality. As a result, the utilization rate of X-ray in imaging system can be improved.
Plant Plasma Membrane Proton Pump: One Protein with Multiple Functions

In plants, the plasma membrane proton pump (PM H+-ATPase) regulates numerous transport-dependent processes such as growth, development, basic physiology, and adaptation to environmental conditions. This review explores the multifunctionality of this enzyme in plant cells. The abundance of several PM H+-ATPase isogenes and their pivotal role in energizing transport in plants have been connected to the phenomena of pleiotropy. The multifunctionality of PM H+-ATPase is a focal point of numerous studies unraveling the molecular mechanisms of plant adaptation to adverse environmental conditions. Furthermore, PM H+-ATPase is a key element in plant defense mechanisms against pathogen attack; however, it also functions as a target for pathogens that enable plant tissue invasion. Here, we provide an extensive review of the PM H+-ATPase as a multitasking protein in plants. We focus on the results of recent studies concerning PM H+-ATPase and its role in plant growth, physiology, and pathogenesis.
Generation of Segmented Triangular Meshes From CT Images Based on Centroidal Voronoi Tessellation and the Graph Cut Method

Mesh generation from X-ray computed tomography (CT) images of mechanical parts is an important consideration in industrial application, and boundary surface meshes in multimaterial parts can be extracted by generating segmented meshes from segmented images. In this paper, the authors outline a new approach for achieving segmented mesh generation. The image is first subjected to centroidal Voronoi tessellation and Delaunay tessellation steered by a density map to create a triangular mesh while maintaining discontinuities between materials. Given an input domain and a number of initial sites, the energy function is minimized automatically by iteratively updating the Voronoi tessellation and relocating sites to produce optimized domain discretization and form the mesh. Thus, the mesh in question is effectively and quickly segmented into different parts via this new graph cut method. The proposed approach is considered more efficient because there are fewer triangles than pixels, which reduces computation time and memory usage.
[Commentary] The new nucleic acid based COVID-19 vaccines: a glittering achievement, yet disturbed by a black stain that does need to be identified and swept away

Some two years ago, a that far untold plague fell onto our heads without notice. Presenting initially with a harsh pneumonia destroying lung alveoli, SARS COVID 2 (SCVID2) reached rapidly the feat to kill over 80% of the older sick people in retirement shelters, and met the requirements to be classified “pandemia” since March 11, 2020. To honor the trust placed on it right at the beginning, SCVID2 promptly trespassed the age limits of candidates to the infection. Sweeping away thousands of youngsters and middle-aged, the virus soon made outdated the label of an “older-bound” pathogen, and, to make the old story short, today’s dash board is giving: 645.630. 482 infected, and 6.634. 816 fatalities. The overall mortality might be 12-2%. It seems that no disease presentation, whether human or animal has been missed by this protean virus. Has the COVID 19 syndrome disappeared, or (at least) is it walking its sunset strip? This issue is being increasingly debated by media in the last weeks. The very fact that is being “debated” might be appalling per se. The simple possibility that the infection adopts an endemic course (meaning: silent low level viremia affecting the people only episodically), is reported to erratically switch on panic in those who never-the-less tolerate close contact with unrelated pathogens : thus, our brain seems to be incurable in conceiving SCVID2 as the “pre-historic evil”. The ability of SCVID2 to mimic the true COVID syndrome even when given as a vaccine, is now making things even harder, fostering the irrational disappointment of those raised since their infancy with the dogma that vaccines “can only be good”. In the attached Editorial, we chose to concentrate on these cases, trying to help knock down rising superstition, now the best allied of COVID 19. On the turn of the second year, pandemias are known to try and escape counter-measures, by hiding beneath a cloud of forgetfulness. Allowing this outcome to terminate our COVID combat now, means deadly defeat. Teaming up to devise and launch the final hit must hold in our scope to the very end.
Extrinsic factors of pressure injuries in patients during surgery: A frequency matched retrospective study

This study was to clarify the connection between extrinsic factors and the risk of perioperative pressure injuries (PIs) through the case–control approach, which involved making the intrinsic factors of the patients in the control group with no PIs consistent with those of the case group with PIs. We collected samples from a teaching hospital in Taiwan. We found a total of 132 patients deemed to have developed perioperative PIs. Using 1:2 frequency matching, we matched these cases with patients who had not developed PIs by gender, age, and BMI. Binary logistic regression analysis of the odds ratios of the extrinsic factors and PI risk revealed that the independent variables with statistical significance included duration of anaesthesia 3 h, amount of blood loss, use of electric blankets, diastolic blood pressure below 60 mmHg during surgery, and oxyhemoglobin saturation by pulse oximetry (SPO2) below 93% during surgery. Emphasis should be placed on cooperation among the medical team during surgery, less use of electric blankets, control over the duration of anaesthesia and blood loss, continuous monitoring of the patient during surgery for any emergencies, and the maintenance of patient diastolic blood pressure and blood oxygen levels to reduce the risk of PIs.
Kinematic characteristics of air flowing into and out of precipitating convection over the west Pacific warm pool: An airborne Doppler radar survey

Air flowing into and out of 25 cloud systems over the west Pacific warm pool was sampled by Doppler radars on board two NOAA WP‐3D aircraft and the NCAR Electra aircraft during the Tropical Ocean/Global Atmosphere Coupled Ocean‐Atmosphere Response Experiment (TOGA‐COARE). In convective cells, updraught inflows and downdraught outflows tended to be positioned adjacent to each other at sharp updraught‐downdraught interfaces sloping at various angles and directions. The updraught inflows were sometimes shallow and sometimes deep, often extending well above the boundary layer. Downdraught outflow magnitudes were usually stronger than updraught inflows, and the horizontal directions of the inflow and outflow were frequently offset from each other at angles of ˜|90°|.
Fused least absolute shrinkage and selection operator for credit scoring

Credit scoring can be defined as the set of statistical models and techniques that help financial institutions in their credit decision makings. In this paper, we consider a coarse classification method based on fused least absolute shrinkage and selection operator (LASSO) penalization. By adopting fused LASSO, one can deal continuous as well as discrete variables in a unified framework. For computational efficiency, we develop a penalization path algorithm. Through numerical examples, we compare the performances of fused LASSO and LASSO with dummy variable coding.
Review of Botnet Attack Detection in SDN-Enabled IoT Using Machine Learning

The orchestration of software-defined networks (SDN) and the internet of things (IoT) has revolutionized the computing fields. These include the broad spectrum of connectivity to sensors and electronic appliances beyond standard computing devices. However, these networks are still vulnerable to botnet attacks such as distributed denial of service, network probing, backdoors, information stealing, and phishing attacks. These attacks can disrupt and sometimes cause irreversible damage to several sectors of the economy. As a result, several machine learning-based solutions have been proposed to improve the real-time detection of botnet attacks in SDN-enabled IoT networks. The aim of this review is to investigate research studies that applied machine learning techniques for deterring botnet attacks in SDN-enabled IoT networks. Initially the first major botnet attacks in SDN-IoT networks have been thoroughly discussed. Secondly a commonly used machine learning techniques for detecting and mitigating botnet attacks in SDN-IoT networks are discussed. Finally, the performance of these machine learning techniques in detecting and mitigating botnet attacks is presented in terms of commonly used machine learning models’ performance metrics. Both classical machine learning (ML) and deep learning (DL) techniques have comparable performance in botnet attack detection. However, the classical ML techniques require extensive feature engineering to achieve optimal features for efficient botnet attack detection. Besides, they fall short of detecting unforeseen botnet attacks. Furthermore, timely detection, real-time monitoring, and adaptability to new types of attacks are still challenging tasks in classical ML techniques. These are mainly because classical machine learning techniques use signatures of the already known malware both in training and after deployment.
Studies on Bone Remodeling Theory Based on Microcracks Using Finite Element Computations

The investigation of the microcrack theory supports the research work of understanding the microstructural behaviour of physiological loaded bones. Microcracks in cortical bone are assumed to have a stimulatory effect on osteocytes – the sensor cells for the bone remodeling process. In this contribution an approch to simulate microcrack initiation and propagation inside a 3D anisotropic and inhomogenious FEA model of cortical bone tissue will be shown. The numerical formulations are based on computational continuum damage mechanics. (© 2009 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Strength in the gap: A rapid review of principles and practices for urgent care centres

Responding to a provincial government decision to develop two Urgent Care Centres (UCCs) in Saskatchewan, we undertook a rapid review of published literature with the objective of determining best practices for their creation and functioning. Two English-limited PubMed database searches combining “after-hours care,” “ambulatory care,” “emergency medicine,” “urgent care,” “minor emergency,” “walk-in,” and “Canada” over the past 10 years were the sources of articles for our review. Articles were independently reviewed by two authors and synthesized collaboratively. From 833 articles, 44 were utilized in the review. Six considerations in the following areas were subsequently outlined: expected impact, preferred location, healthcare services collaboration, available services, staffing priorities, and community partnerships. These principles were considered against the backdrop of currently successful Canadian UCCs. This review indicates that general principles for the successful development of UCCs exist; these may guide the establishment and functioning of UCCs both in Saskatchewan and elsewhere.
Inflow of Gas to the Solar Neighborhood during Evolution of the Galactic Disk

We discuss several lines of evidence indicating that gas is flowing into the solar neighborhood and go on to offer a theoretical framework to explain this phenomenon, which is important for any model of the chemical and dynamical evolution of the Galaxy. We derive a theoretical age distribution for the G dwarf stars in the solar neighborhood that fits well the distribution observed recently in the generally increasing trend of the amplitude of oscillation with time, leading to star formation peaks at several epochs. Our model is based on the interference between two effects: the variable rate of gas inflow to the solar vicinity due to the density wave pattern in the disk, and the arrival of gas from the local intergalactic medium broken cyclically by star formation processes. The model is shown to be consistent with a scenario in which low-metallicity gas falls continually to the Galactic plane from the intergalactic medium, notably in the form of high-velocity clouds.
Spectrally-Null-Constrained ZCZ Sequences for MIMO-OFDM Channel Estimation Over Non-Contiguous Carriers

In cognitive radio (CR) networks with non-continuous carriers, designing spectrally-null-constrained (SNC) sequences with good correlation properties is an important research task. In this letter, two types of SNC sequences with zero correlation zone (ZCZ) property are considered: single-channel SNC-ZCZ sequences and multi-channel SNC-Z-periodic complementary sequences (SNC-ZPCSs), which have potential applications for CR-multiple-input multiple-output (MIMO) channel estimation and CR-quasi-synchronization communications to achieve interference-free performance. The resultant SNC-ZCZ sequence sets are optimal with respect to the theoretical bound and the spectral-null constraint can be set flexibly. Simulation results demonstrate that such sequences are suitable for pilot design in CR-MIMO-orthogonal frequency division multiplexing (OFDM) system. Besides, a construction of multi-channel SNC-ZPCS sets was provided based on single-channel SNC-ZCZ sequence sets.
One-Pot Synthesis of Pt High Index Facets Catalysts for Electrocatalytic Oxidation of Ethanol

Direct ethanol fuel cell (DEFC) has attracted wide attention due to its wide range of fuel sources, cleanliness, and high efficiency. However, the problems of low catalytic efficiency and poor catalyst stability still exist in DEFC catalysts, which restrict its rapid development. With chloroplatinic acid (H2PtCl6·6H2O) as the precursor, Polyvinylpyrrolidone (PVP) plays the role of surfactant, stabilizer, and reducing agent in the experiment. Glycine is the surface control agent and co-reducing agent. Pt high-index facets nanocatalyst was prepared with the one-pot hydrothermal method by adjusting the amount of PVP and glycine. X-Ray Diffraction (XRD), transmission electron microscope (TEM), and scanning electron microscope (SEM) were used to characterize the micro-structure of the nanocatalyst, and the influence of PVP and glycine on the synthesis of high-index facets catalyst was studied. The electrocatalytic performance of the catalyst was tested with an electrochemical workstation, and it was found that the performance of the prepared catalyst was better than that of the commercial catalyst. When the mass ratio of PVP and Pt was 50:1 and the molar ratio of glycine and Pt was 24:1, Pt nanocatalysts with {310}, {520} and {830} high exponential facets were prepared. The electrochemical test results showed that the peak current density of ethanol oxidation was 2.194 m2/g, and the steady-state current density was 0.241 mA/cm2, which was 5.7 times higher than that of commercial catalyst. The results of this paper show that due to the defects such as steps and kinks on the surface of the high-index facets, the active sites are increased, thus showing excellent electrocatalytic performance. This study provides a theoretical basis for the development and commercial application of high index facets nanocatalysts.
Modeling and Optimization of the Isolation of Blackcurrant and Black Cumin Seeds Oils Using Supercritical Fluid Extraction

Supercritical fluid extraction is a powerful analytical tool and it is willingly used by researchers for the isolation of various components from different matrices. In our study, the carbon dioxide in the supercritical state was used for the extraction of oils from blackcurrant and black cumin seeds. To determine the optimal conditions for the process (temperature, pressure and time), the method of statistical experiment planning and the Box–Behnken design was applied and the yield of the oils and the content of fatty acids (FAs) were taken into consideration. It has been found that an increase in pressure causes an increase in extraction yield (W), and an increase in temperature, both at constant pressure and time, does not significantly change the yield value. Optimal yield values were obtained for both materials under almost similar extraction parameters: 306 bar/ 43 min/ 50 °C (blackcurrant) and 282 bar/ 40 min/ 50 °C (black cumin). The influence of the above parameters (T, p, t) on the content of FAs in the extracts has a slightly different trend. The use of supercritical carbon dioxide for the extraction of blackcurrant and black cumin seeds allowed for high process yield and high-quality, rich in polyunsaturated fatty acids oils which can be used as a substrate or final product for industry.
An update to ‘Numerical Map Generalization’

Mathematical procedures for the generalization of isarithmic maps are derived. It is shown that the level of generalization for different purposes may be specified in advance, and that under certain conditions the original map can be recreated from the generalized map. Les procedures mathematiques utilisees pour la generalisation des cartes d'isarithmes sont derivees. L'article montre que le niveau de generalisation peut etre specifie a l'avance en fonction des buts vises et que, sous certaines conditions, la carte originale peut etre recree a partir de la carte generalisee.
Impact of Inappropriate Empirical Antibiotic on Outcomes in Community-acquired Third Generation Cephalosporin Resistant Enterobacterales Bacteremia

Background Currently, third generation cephalosporin resistant Enterobacterales (3GCRE) are becoming more common in community-acquired infection, leading to increasing consumption of carbapenems. Because community-acquired 3GCRE infections are generally less severe and of lower pathogenicity, the impact of inappropriate empirical antibiotics among patients with community-acquired 3GCRE bacteremia remains unknown. Materials and Methods This prospective cohort study included adult patients with 3GCRE bacteremia from April 2018 to December 2021. Participants were followed for 30 days to measure the primary outcome of mortality. Propensity score analysis was performed to adjust for treatment selection bias. Results A total of 155 patients met the eligible criteria (42 participants in the appropriate antibiotics group, and 113 participants in the inappropriate antibiotics group). Eight participants in the inappropriate antibiotics group never received appropriate antibiotics, three of whom died before microbiological results were made available. The most common clinical syndromes were urinary tract infection (56.8%) and biliary tract infection (22.6%). The overall 30-day mortality rate was 12.9%, 14.3% in the appropriate empirical antibiotics group and 12.4% in the inappropriate empirical antibiotics group. After propensity score weighted adjustment, the 30-day mortality rate in the inappropriate group was non-inferior to the appropriate group (mean difference 1.9%; 95% confidence interval: -10.1 - 14.0). From the multivariate analysis, acute respiratory failure and primary bacteremia were associated with 30-day mortality. Conclusion Among patients with community-acquired 3GCRE bacteremia, inappropriate empirical treatment given within 24 hours after the onset of bacteremia was non-inferior to appropriate antibiotics. In the setting of a high prevalence of 3GCRE carriage in community, adjustment to carbapenem might be tolerable among patients with community-acquired infections. Trial Registration ClinicalTrials.gov Identifier: NCT03765749