Miracle-dz/UniFAR-FTUs huggingface dataset:
Link Prediction in Dynamic Networks Based on the Selection of Similarity Criteria and Machine Learning

The study’s findings showed that link prediction utilizing the similarity learning model in dynamic networks (LSDN) performed better than other learning techniques including neural network learning and decision tree learning in terms of the three criteria of accuracy, coverage, and efficiency. , Compared to the random forest approach, the LSDN learning algorithm’s link prediction accuracy increased from 97% to 99%. The proposed method’s use of oversampling, which improved link prediction accuracy, was the cause of the improvement in area under the curve (AUC). To bring the ratio of the classes closer together, the suggested strategy attempted to produce more samples from the minority class. In addition, similarity criteria were chosen utilizing feature selection techniques based on correlation that had a strong link with classes. This technique decreased over-fitting and improved the suggested method’s test data generalizability. Based on the three criteria (accuracy, coverage, and efficiency), the research’s findings demonstrated that link prediction utilizing the similarity LSDN outperformed other learning techniques including neural network learning and decision tree learning. Compared to the random forest algorithm, the LSDN algorithm’s link prediction accuracy increased from 97% to 99%. The oversampling in the suggested strategy, which increased link prediction accuracy, is what caused the increase in AUC. To bring the ratio of the classes closer together, the suggested strategy attempted to produce more samples from the minority class. In addition, similarity criteria were chosen utilizing feature selection techniques based on correlation that had a strong link with classes. This technique decreased over-fitting and improved the suggested method’s test data generalizability.
Novel, computational IgE‐clustering in a population‐based cross‐sectional study: Mapping the allergy burden

Abstract Background Even though the prevalence of allergies is increasing, population‐based data are still scarce. As a read‐out for chronic inflammatory information, new methods are needed to integrate individual biological measurements and lifestyle parameters to mitigate the consequences and costs of allergic burden for society. Methods More than 480.000 data points were collected from 1462 Luxembourg adults during the representative, cross‐sectional European Health Examination Survey, spanning health and lifestyle reports. Deep IgE‐profiles based on unsupervised clustering were correlated with data of the health survey. Findings 42.6% of the participants reported a physician‐diagnosed allergy and 44% were found to be IgE‐positive to at least one allergen or extract. The main sensitization sources were tree pollens followed by grass pollens and mites (52.4%, 51.8% and 40.3% of sensitized participants respectively), suggesting seasonal as well as perennial burden. The youngest group of participants (25–34 years old) showed the highest burden of sensitization, with 18.2% of them having IgE to 10 or more allergen groups. Unsupervised clustering revealed that the biggest cluster of 24.4% of participants was also the one with the highest medical need, marked by their multi‐sensitization to respiratory sources. Interpretation Our novel approach to analyzing large biosample datasets together with health information allows the measurement of the chronic inflammatory disease burden in the general population and led to the identification of the most vulnerable groups in need of better medical care.
Augmented Reality For Learning Children With Asd Software Usability Evaluation With Heuristic Rules

The objective of this research is to develop an augmented reality mobile application for learning mathematics and reading for children between 5 and 7 years of age who suffer from autism spectrum disorders. An own methodology for the development of the mobile application is presented and as results the finished mobile application is shown, which was evaluated by the tutors of the autistic children. 90% of the tutors affirmed that augmented reality facilitated educational understanding, 80% assured that learning through augmented reality applications is more attractive for children and finally 90% affirmed that they would recommend the use of the mobile application to tutors with similar situations.
Aerosol Optical Depth Retrieval for Sentinel-2 Based on Convolutional Neural Network Method

Atmospheric aerosol significantly affects the climate environment and public health, and Aerosol Optical Depth (AOD) is a fundamental optical characteristic parameter of aerosols, so it is important to develop methods for obtaining AOD. In this work, a novel AOD retrieval algorithm based on a Convolutional Neural Network (CNN) method that could provide continuous and detailed aerosol distribution is proposed. The algorithm utilizes data from Sentinel-2 and Aerosol Robotic Network (AERONET) spanning from 2016 to 2022. The CNN AOD data are consistent with the AERONET measurements, with an R2 of 0.95 and RMSE of 0.049 on the test dataset. CNN demonstrates superior performance in retrieving AOD compared with other algorithms. CNN retrieves AOD well on high reflectance surfaces, such as urban and bare soil, with RMSEs of 0.051 and 0.042, respectively. CNN efficiently retrieves AOD in different seasons, but it performs better in summer and winter than in spring and autumn. In addition, to study the relationship between image size and model retrieval performance, image datasets of 32 × 32, 64 × 64 and 128 × 128 pixels were created to train and test the CNN model. The results show that the 128-size CNN performs better because large images contain rich aerosol information.
Thromboelastometry-Guided Individualized Fibrinolytic Treatment for COVID-19-Associated Severe Coagulopathy Complicated by Portal Vein Thrombosis: A Case Report

COVID-19-associated coagulopathy (CAC), mainly characterized by hypercoagulability leading to micro- and macrovascular thrombotic events due to the fibrinolysis shutdown phenomenon, is a life-threatening complication of severe SARS-CoV-2 infection. However, optimal criteria to assess patients with the highest risk for progression of severe CAC are still unclear. Bedside point-of-care viscoelastic testing (VET) appears to be a promising tool to recognize CAC, to support the appropriate therapeutic decisions, and to monitor the efficacy of the treatment. The ClotPro VET has the potential to reveal fibrinolysis resistance indicated by a clot lysis time (LT) > 300 s on the TPA-test. We present a case of severe SARS-CoV-2 infection complicated by CAC-resulting portal vein thrombosis (PVT) and subsequent liver failure despite therapeutic anticoagulation. Since fibrinolysis shutdown (LT > 755 s) caused PVT, we performed a targeted systemic fibrinolytic therapy. We monitored the efficacy of the treatment with repeated TPA assays every three hours, while the dose of recombinant plasminogen activator (rtPA) was adjusted until fibrinolysis shutdown completely resolved and portal vein patency was confirmed by an ultrasound examination. Our case report highlights the importance of VET-guided personalized therapeutic approach during the care of severely ill COVID-19 patients, in order to appropriately treat CAC.
Editorial: Deep facial attribute analysis

COPYRIGHT © 2023 Kong, Zhang, Zhang and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Deep facial attribute analysis
PERCEIVED EMPLOYEE FINANCIAL WELL-BEING IN THE BANKING INDUSTRY IN THE 4IR PERIOD: ASSESSING PREDICTORS’ IMPACTS

This paper evaluates the influences of financial behavior, work engagement, and work engagement on financial well-being within Nigeria’s Banking Industry. Its sample was taken from four (4) banks in the Nigerian states of Lagos and Oyo. They include First Bank of Nigeria, First City Monument Bank Plc, United Bank for Africa Plc, and Zenith Bank Plc. The present researcher dispersed the questionnaires at random for this cross-sectional survey. Nevertheless, 382 of the 400 questionnaires sent met the criteria for investigation and were examined using SPSS version 28. The present investigation conducted standard multiple regression and zero-order correlation analyses to test the stated hypotheses and conclude that positive financial behaviors and employee work engagement considerably increase financial well-being. In Nigeria's banking industry, financial stress, on the other hand, worsens financial well-being in the 4IR period. It further establishes a link between financial behavior and financial stress and a good correlation between financial behavior and work engagement. Furthermore, the study discovered a negative association between financial stress and work involvement in Nigeria's banking industry during the 4IR period. It also confirms that work engagement, financial stress, and financial behaviors substantially determine financial well-being. Consequently, this paper advocates banks helping employees become more financially literate and responsible by offering financial education to their staff. Also, employers must take action to reduce financial stress among employees. Resources and assistance can help reduce financial stress and enhance employee well-being in the present 4IR period. Moreover, banks may use tactics including offering chances for employee growth, encouraging work-life balance, and cultivating a positive work environment to boost engagement in the banking industry.
POX and RYU Controller Performance Analysis on Software Defined Network

From the last decades different types of network schemes are pitched to enhance the user performance. Software Defined Networks (SDN) is also considered as important factor for different network schemes and its proper administration or management. Due to major deployment in today’s networking era SDN are further sub divided in to commercial and open-source controllers. Commercial and open-source controllers are utilized in different type of businesses. According to our knowledge considerable amount of literature is available on these controllers but did not provide or analyse performance of these controllers on different network parameters. This paper evaluates and compares the performance of two well-known SDN open-source controllers POX and RYU with two performance assessments. The first assessment is the implementation of optimal path by using Dijkstra's algorithm from source to destination. Second assessment is the creation of a custom topology in our desired tool (MiniNet emulator). Then, the performance in terms of QoS parameters such as Jitter, throughput, packet loss, and packet delivery ratio are computed by two end hosts in each network. After the assessments, the performance of POX are optimal as compare to the RYU and best suited to be deployed in any scenario.
Ultrasonic Estimation of Effective Scatterer Diameter for Micro-Macro Parameter Based Improved Breast Lesion Classification

ESD is an important micro-parameter for ultrasonic tissue characterization. However, classifying between benign and malignant breast lesions from a broad dataset remains a challenge. In this work, we propose a new technique for the estimation of ESD of breast tissues from the diffuse component of backscattered RF data. This allows us to combine ESD with MSS and other ultrasonic macro-parameters for binary classification of lesions. In order to separate the diffuse component from the coherent component of the backscattered data, EEMD is performed. K-S test is used to automatically select the IMFs responsible for diffuse scattering. To ensure proper minimization of the system effects, a multi-step process is adopted where the RF data is deconvolved and filtered as the first two steps prior to normalization. As the ESD is supposed to have a fairly consistent value over a small tissue subregion, it is estimated, from the slope of the average regression line, computed from the exponential weighted average of lines, fitted over the log-power spectra of the neighboring blocks. Using our method, the ESD for malignant lesions is estimated to be 123.05{\pm}8.85 {\mu}m and that for fibroadenomas is estimated to be 98.88{\pm}9.48 {\mu}m. ESD values estimated for inflammatory breast tissues (~76 {\mu}m) are close to normal tissues. When the ESD values are used to classify 159 benign and malignant lesions, we obtain sens. , spec. , and acc. values of 91.07%, 96.11%, and 94.34%, respectively. ESD is also combined with the MSS and 27 previously reported macro-parameters, estimated from the UB and UE, for classification. This results in high sens. , spec. , and acc. values of 96.43%, 98.06%, and 97.48%, respectively. The proposed classification approach based on this hybrid feature set, therefore, show great potential to be used as a CAD tool for breast lesion classification.
Unraveling the Kinetics of the 10–23 RNA-Cleaving DNAzyme

DNA-based enzymes, or DNAzymes, are single-stranded DNA sequences with the ability to catalyze various chemical reactions, including the cleavage of the bond between two RNA nucleotides. Lately, an increasing interest has been observed in these RNA-cleaving DNAzymes in the biosensing and therapeutic fields for signal generation and the modulation of gene expression, respectively. Additionally, multiple efforts have been made to study the effects of the reaction environment and the sequence of the catalytic core on the conversion of the substrate into product. However, most of these studies have only reported alterations of the general reaction course, but only a few have focused on how each individual reaction step is affected. In this work, we present for the first time a mathematical model that describes and predicts the reaction of the 10–23 RNA-cleaving DNAzyme. Furthermore, the model has been employed to study the effect of temperature, magnesium cations and shorter substrate-binding arms of the DNAzyme on the different kinetic rate constants, broadening the range of conditions in which the model can be exploited. In conclusion, this work depicts the prospects of such mathematical models to study and anticipate the course of a reaction given a particular environment.
The Interpretation of a Tertiary Tillite at Mount Feather, Southern Victoria Land, Antarctica

Abstract A Tertiary tillite occurs at an altitude of 2750 m on Mount Feather in southern Victoria Land. The tillite mantles a remnant of the floor of a south-east-trending glacial valley that pre-dates the cutting of the existing glacial drainage system in southern Victoria Land. Petrographic evidence suggests that ice which deposited the tillite drained a relatively distant source area.
Numerical Simulation Study on the Impacts of Tropospheric O3 and CO2 Concentration Changes on Winter Wheat. Part I: Model Description

Ozone is well documented as the air pollutant most damaging to agricultural crops and other plants. It is reported that tropospheric O3 concentration increases rapidly in recent 20 years. Evaluating and predicting impacts of ozone concentration changes on crops are drawing great attention in the scientific community. In China, main study method about this filed is controlled experiments, for example, Open Top Chambers. But numerical simulation study about impacts of ozone on crops with crop model was developed slowly, what is more, the study about combined impacts of ozone and carbon dioxide has not been reported. The improved agroecosystem model is presented to evaluate simultaneously impacts of tropospheric O3 and CO2 concentration changes on crops in the paper by integrating algorithms about impacts of ozone on photosynthesis with an existing agroecosystem biogeochemical model (named as DNDC). The main physiological processes of crop growth (phenology, leaf area index, photosynthesis, respiration, assimilated allocation and so on) in the former DNDC are kept. The algorithms about impacts of ozone on photosynthesis and winter wheat leaf are added in the modified DNDC model in order to reveal impacts of ozone and carbon dioxide on growth, development, and yield formation of winter wheat by coupling the simulation about impacts of carbon dioxide on photosynthesis of winter wheat which exists in the former DNDC. In the paper, firstly assimilate allocation algorithms and some genetic parameters (such as daily thermal time of every development stage) were modified in order that DNDC can be applicable in North China. Secondly impacts of ozone on crops were simulated with two different methods- one was impacts of ozone on light use efficiency, and the other was direct effects of ozone on leaves photosynthesis. The latter simulated results are closer to experiment measurements through comparing their simulating results. At last the method of direct impacts of ozone on leaf growth is adopted and the coefficients about impacts of ozone on leaf growth and death are ascertained. Effects of climate changes, increasing ozone, and carbon dioxide concentration on agroecosystem are tried to be simulated numerically in the study which is considered to be advanced and credible.
A Study of the Phytochemical Properties and Synergistic Antibacterial Activity of Annona comosus (LINN) Merr. Peel and Citrus senensis Peel Extracts on Aeromonas hydrophila and Salmonella species .

In Nigerian traditional medicine, the combination of A. comosus and C. senensis peels has antibacterial effect against typhoid fever and diarrhoea due to Aeromonas hydrophila. A. comosus and C. senensis peels were extracted using percolation method and ethanol solvent. The antibacterial potential of ethanolic extract of A. comosus and C. senensis peels were investigated by disc diffusion and broth dilution techniques. The extracts were subjected to phytochemical screening using standard procedures. This study was aimed at verifying the synergistic effects of the two plant extracts against some clinical isolates: six Salmonella paratyphi B, one S. typhi and three A. hydrophila. The antibacterial activity of the combined extracts was 7-12mm and from 15- 42mm for the standard antibiotic disc. The minimum inhibitory concentration (MIC) of the combined extracts was 0.25-12.50mg/ml while the minimum bactericidal concentration (MBC) was 0.50-50.00mg/ml. Phytochemical investigation of the extracts revealed the presence secondary metabolites like alkaloids, flavonoids, saponins, tannins, steroids, triterpenoids and phytosterols in C. senensis peels while alkaloids, flavonoids, saponins, tannins, triterpenoids and phytosterols were found in A. comosus peels. In-vitro antibacterial potential was confirmed and no synergism was demonstrated at a combination ratio of 1:1 of the extracts tested (P = 0.05). Keywords : - Anti-bacterial, Annona comosus , Citrus senensis , synergism, Extracts
FDTD Analysis of Nearby Lightning Surges Flowing Into a Distribution Line via Groundings

This paper investigates lightning surges on a distribution line reported using a finite-difference time-domain (FDTD) method. FDTD simulation results show a satisfactory agreement with measured lightning currents when lightning strikes ground nearby a distribution line. The current and voltage characteristics along the line, which were not measured in the test, are calculated by the FDTD simulation. Parametric analysis shows that lightning currents and voltages are dependent on the lightning position, distance from the lightning to the nearest pole, and pole grounding resistance. A large portion of phase-wire voltage is induced by the lightning channel current, and correspondingly, a large portion of phase-wire current is produced by the induced voltage. When the lightning channel is inclined, the peak current and voltage vary by +10% to −10% depending on the inclined angle. The effect of neutral wire position in the line is also investigated, and the induced voltage at the nearest pole to the lightning can be effectively suppressed by installing two neutral wires above and under the phase wires. EMTP simulation results agree qualitatively well with the measurement and FDTD results.
Prototyping: tools and techniques: improving software and documentation quality through rapid prototyping

Nina Wishbow has a Ph.D. in Rhetoric from Carnegie Mellon University. She was a technical writer and documentation manager for six years, and is now the Manager of LEGENT% Human Interface Group. Michael Thompson has a Masters in Prof=ional Writing from Carnegie Mellon University. He was an intern in the Usability Group at Apple Computer and has been a member of LEGENT% Human Interface Group for more than a year.
Tandem Neural Networks for Electric Machine Inverse Design

In electric motor design tasks, multiple design goals often need to be placed on a single motor, and multi-objective optimization plays a significant role. Trade-offs and Pareto front searching are needed, as these design goals or responses cannot be optimized concurrently due to their interdependent nature. However, tuning the motor parameters in the iterative optimization process is typically ineffective and heavily dependent on the expertise of the engineers due to the large number of time-consuming finite-element simulations required to evaluate each motor design candidate. In this paper, we propose an inverse design approach for electric machines based on a tandem neural network, which can effectively provide desired motor design candidates for various design targets without iteration. The one-to-many mapping problem can be avoided by the tandem neural network, which constructs loss functions based on the responses of the generated motor designs. The proposed intelligent design strategy is generally applicable for the design tasks of different types of electric motors.
Contribution from Intrinsic Charm Production to Fixed-Target Interactions at the LHC

Background: Intrinsic charm, nonperturbative charm in the hadron wavefunction, has long been speculated but has never been satisfactorily proven. Open charm and J/ψ measurements in a ﬁxed-taget conﬁguration at the LHCb searched for this contribution but reported no evidence. Purpose: D meson and J/ψ production is calculated for the SMOG ﬁxed-target conﬁguration in the LHCb experiment using a combination of perturbative QCD and intrinsic charm to see whether intrinsic chaarm would indeed be observable in the SMOG kinematics. Methods: Open charm and J/ψ production is calculated to next-to-leading order in perturbative QCD. Because a gas jet nuclear target is used, cold nuclear matter eﬀects are included in the perturbative calculations. The intrinsic charm is calculated assuming production from a | uudcc i Fock state. Results: The diﬀerential rapidity and transverse momentum distributions in p + Ne, p + He and p + Ar ﬁxed-target interactions are calculated in the SMOG acceptance and compared to data. The predicted asymmetries between D (leading charm) and D (nonleading charm) are also shown. Conclusions: The contribution from intrinisic charm is small and decreases with center of mass energy. The calculations agree well with the current SMOG data, with or without intrinsic charm.