Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Economization Using Connectivity-Based Eco-Routing and Driving for Fleet of Battery Electric Vehicles

Energy economizing using Connectivity-based eco-Routing and Driving (CeRD) is a key to conserve the resources of a fleet. This work takes maximum advantage by advising optimal energy-consumption-based route and velocity. Product delivery to several universities by a group of battery electric vehicles (BEVs) is considered. Problem contains certain constraints, i.e., BEV load capacity, time window, resources restriction, speed bounds, battery charging/discharging limits, and traffic. Simulation of urban mobility (SUMO) is used for the estimation of traffic and routes. Selection of route is based on energy consumption of traffic’s velocity. Dijkstra’s algorithm is applied to select the route with least energy consumption, and Clarke and Wright (C&W) algorithm is used to find the optimal sequence of visiting universities. Pontryagin’s minimum principle (PMP) is then applied to find the optimal velocity profiles for BEVs on the optimized routes. Results have shown 60%–70% reduction in energy consumption along with improvement in charging rates when compared with the vehicles without CeRD assistance in different scenarios. Furthermore, the results are validated for distance and time using online route planner, for energy efficiency using electric vehicles’ database, and for energy consumptions using a driver model. CeRD has also improved 30% route distance and 40% trip time.
Precision tests of the electroweak interaction

Recent measurements of various charged current and neutral current processes are presented and compared with the prediction of the Standard Model of the Electroweak Interaction. No discrepancy was found in the experiments’ precision which is often higher than 0.5%. A Standard Model fit to the data allows an indirect determination of the top mass mtop=145±25 GeV.
The Density of Zeros of Dirichlet's L-Functions

Let L(s, χ) be a Dirichlet L-function and let N(σ, T, χ) denote the number of zeros ρ of L(s, χ), counted according to multiplicity, in the rectangle σ ≦ Re(ρ) ≦ 1, |Im(ρ)| ≦ T, (T ≦ 1). In this paper we shall prove several new estimates for the sum where Σ* denotes summation over primitive characters only. These estimates will all be of the type (1) where denotes any fixed positive quantity.
NAMRTNet: Automatic Classification of Sleep Stages Based on Improved ResNet-TCN Network and Attention Mechanism

Sleep, as the basis for regular body functioning, can affect human health. Poor sleep conditions can lead to various physical ailments, such as poor immunity, memory loss, slow cognitive development, and cardiovascular diseases. Along the increasing stress in society comes with a growing surge in conditions associated with sleep disorders. Studies have shown that sleep stages are essential for the body’s memory, immune system, and brain functioning. Therefore, automatic sleep stage classification is of great medical practice importance as a basis for monitoring sleep conditions. Although previous research into the classification of sleep stages has been promising, several challenges remain to be addressed: (1) The EEG signal is a non-smooth signal with harrowing feature extraction and high requirements for model accuracy. (2) Some existing network models suffer from overfitting and gradient descent. (3) Correlation between long time sequences is challenging to capture. This paper proposes NAMRTNet, a deep model architecture based on the original single-channel EEG signal to address these challenges. The model uses a modified ResNet network to extract features from sub-epochs of individual epochs, a lightweight attention mechanism normalization-based attention module (NAM) to suppress insignificant features, and a temporal convolutional network (TCN) network to capture dependencies between features of long time series. The recognition rate of 20-fold cross-validation with the NAMRTNet model for Fpz-cz channel data in the public sleep dataset Sleep-EDF was 86.2%. The experimental results demonstrate the network’s superiority in this paper, surpassing some state-of-the-art techniques in different evaluation metrics. Furthermore, the total time to train the network was 5.1 h, which was much less than the training time of other models.
Fuzzy Soft Sets and their Algebraic Structures

In this paper, we have studied the algebraic operations of fuzzy soft sets to establish their basic properties. We have discussed different algebraic structures of fuzzy soft sets under the restricted and extended operations of union and intersection in a comprehensive manner. Logical equivalences have also been made in order to give a complete overview of these structures.
The Boguslawski Melting Model

The anharmonic vibrator, whose expression of potential energy contains second and third powers of coordinates, is treated on the basis of dynamical procedure, which presents the state of motion by means of mean position and mean amplitude of vibration. The divergent statistical integral comes here not into consideration. The free energy is represented through mean atomic displacement and developed in power series, retaining fourth degree. The graphs show that at certain temperature, the minimum in free energy disappears, and the atom escapes from the potential pit. A simple atomic model that represents this phenomenon is proposed and the influence of model dimension and pressure on melting temperature will be presented.
Three-dimensional simulation of microwave-induced helium plasma under atmospheric pressure

A three-dimensional model is presented to investigate helium plasma generated by microwave under atmospheric pressure in this paper, which includes the physical processes of electromagnetic wave propagation, electron and heavy species transport, gas flow, and heat transfer. The model is based on the fluid approximation calculation and local thermodynamic equilibrium assumption. The simulation results demonstrate that the maxima of the electron density and gas temperature are 4.79 × 1017 m−3 and 1667 K, respectively, for the operating conditions with microwave power of 500 W, gas flow rate of 20 l/min, and initial gas temperature of 500 K. The electromagnetic field distribution in the plasma source is obtained by solving Helmholtz equation. Electric field strength of 2.97 × 104 V/m is obtained. There is a broad variation on microwave power, gas flow rate, and initial gas temperature to obtain deeper information about the changes of the electron density and gas temperature.
The Cleavage-Specific Tau 12A12mAb Exerts an Anti-Amyloidogenic Action by Modulating the Endocytic and Bioenergetic Pathways in Alzheimer’s Disease Mouse Model

Beyond deficits in hippocampal-dependent episodic memory, Alzheimer’s Disease (AD) features sensory impairment in visual cognition consistent with extensive neuropathology in the retina. 12A12 is a monoclonal cleavage specific antibody (mAb) that in vivo selectively neutralizes the AD-relevant, harmful N-terminal 20–22 kDa tau fragment(s) (i.e., NH2htau) without affecting the full-length normal protein. When systemically injected into the Tg2576 mouse model overexpressing a mutant form of Amyloid Precursor Protein (APP), APPK670/671L linked to early onset familial AD, this conformation-specific tau mAb successfully reduces the NH2htau accumulating both in their brain and retina and, thus, markedly alleviates the phenotype-associated signs. By means of a combined biochemical and metabolic experimental approach, we report that 12A12mAb downregulates the steady state expression levels of APP and Beta-Secretase 1 (BACE-1) and, thus, limits the Amyloid beta (Aβ) production both in the hippocampus and retina from this AD animal model. The local, antibody-mediated anti-amyloidogenic action is paralleled in vivo by coordinated modulation of the endocytic (BIN1, RIN3) and bioenergetic (glycolysis and L-Lactate) pathways. These findings indicate for the first time that similar molecular and metabolic retino-cerebral pathways are modulated in a coordinated fashion in response to 12A12mAb treatment to tackle the neurosensorial Aβ accumulation in AD neurodegeneration.
Optimization of Power System Stabilizers Using Proportional-Integral-Derivative Controller-Based Antlion Algorithm: Experimental Validation via Electronics Environment

A robust, optimized power system stabilizer (PSS) is crucial for oscillation damping, and thus improving electrical network stability. Additionally, real-time testing methods are required to significantly reduce the likelihood of software failure in a real-world setting at the user location. This paper presents an Antlion-based proportional integral derivative (PID) PSS to improve power system stability during real-time constraints. The Antlion optimization (ALO) is developed with real-time testing methodology, using hardware-in-the-loop (HIL) that can communicate multiple digital control schemes with real-time signals. The dynamic power system model runs on the dSPACE DS1104, and the proposed PSS runs on the field programmable gate arrays (FPGA) (NI SbRIO-9636 board). The optimized PSS performance was compared with a modified particle swarm optimization (MPSO)-based PID-PSS, through different performance indices. The test cases include other step load perturbations and several short circuit faults at various locations. Twelve different test cases have been applied, through real-time constraints, to prove the robustness of the proposed PSS. These include 5 and 10% step changes through 3 different operating conditions and single, double, and triple lines to ground short circuits through 3 different operating conditions, and at various locations of the system transmission lines. The analysis demonstrates the effectiveness of ALO and MPSO in regaining the system’s stability under the three loading conditions. The integral square of the error (ISE), integral absolute of the error (IAE), integral time square of the error (ITSE), and integral time absolute of the error (ITAE) are used as performance indices in the analysis stage. The simulation results demonstrate the effectiveness of the proposed PSS, based on the ALO algorithm. It provides a robust performance, compared to the traditional PSS. Regarding the applied indices, the proposed PSS, based on the ALO algorithm, obtains significant improvement percentages in ISE, IAE, ITSE, and ITAE with 30.919%, 23.295%, 51.073%, and 53.624%, respectively.
On Twisted Smash Products of Monoidal Hom–Hopf Algebras

Let (H, α) be a monoidal Hom-Hopf algebra and (A, β) be an (H, α)-Hom-bimodule algebra. In this article, we first introduce the notion of a twisted Hom-smash product A☆H and then find some sufficient and necessary conditions making it into a monoidal Hom-bialgebra. Furthermore, we give a Maschke-type theorem for the twisted Hom-smash product over a semisimple monoidal Hom-Hopf algebra (H, α). Finally, we form an associated Morita context [AbiH, AbiHAA☆H, A☆HAAbiH, A☆H] in the category .
Polymeric Based Hydrogel Membranes for Biomedical Applications

The development of biomedical applications is a transdisciplinary field that in recent years has involved researchers from chemistry, pharmacy, medicine, biology, biophysics, and biomechanical engineering. The fabrication of biomedical devices requires the use of biocompatible materials that do not damage living tissues and have some biomechanical characteristics. The use of polymeric membranes, as materials meeting the above-mentioned requirements, has become increasingly popular in recent years, with outstanding results in tissue engineering, for regeneration and replenishment of tissues constituting internal organs, in wound healing dressings, and in the realization of systems for diagnosis and therapy, through the controlled release of active substances. The biomedical application of hydrogel membranes has had little uptake in the past due to the toxicity of cross-linking agents and to the existing limitations regarding gelation under physiological conditions, but now it is proving to be a very promising field This review presents the important technological innovations that the use of membrane hydrogels has promoted, enabling the resolution of recurrent clinical problems, such as post-transplant rejection crises, haemorrhagic crises due to the adhesion of proteins, bacteria, and platelets on biomedical devices in contact with blood, and poor compliance of patients undergoing long-term drug therapies.
Multiple Lineages of Hantaviruses Harbored by the Iberian Mole (Talpa occidentalis) in Spain

The recent detection of both Nova virus (NVAV) and Bruges virus (BRGV) in European moles (Talpa europaea) in Belgium and Germany prompted a search for related hantaviruses in the Iberian mole (Talpa occidentalis). RNAlater®-preserved lung tissue from 106 Iberian moles, collected during January 2011 to June 2014 in Asturias, Spain, were analyzed for hantavirus RNA by nested/hemi-nested RT-PCR. Pairwise alignment and comparison of partial L-segment sequences, detected in 11 Iberian moles from four parishes, indicated the circulation of genetically distinct hantaviruses. Phylogenetic analyses, using maximum-likelihood and Bayesian methods, demonstrated three distinct hantaviruses in Iberian moles: NVAV, BRGV, and a new hantavirus, designated Asturias virus (ASTV). Of the cDNA from seven infected moles processed for next generation sequencing using Illumina HiSeq1500, one produced viable contigs, spanning the S, M and L segments of ASTV. The original view that each hantavirus species is harbored by a single small-mammal host species is now known to be invalid. Host-switching or cross-species transmission events, as well as reassortment, have shaped the complex evolutionary history and phylogeography of hantaviruses such that some hantavirus species are hosted by multiple reservoir species, and conversely, some host species harbor more than one hantavirus species.
Efficient Generation of Multiple Seamless Point Mutations Conferring Triazole Resistance in Aspergillus fumigatus

Aspergillus fumigatus is a common human fungal pathogen that can cause a range of diseases. Triazoles are used to treat A. fumigatus infections, but resistance is increasing due to mutations in genes such as cyp51A, hmg1 and overexpression of efflux pumps. Verifying the importance of these mutations is time-consuming, and although the use of CRISPR-Cas9 methods has shortened this process, it still relies on the construction of repair templates containing a selectable marker. Here, employing in vitro-assembled CRISPR-Cas9 along with a recyclable selectable marker, we devised a quick and easy way to effectively and seamlessly introduce mutations conferring triazole resistance in A. fumigatus. We used it to introduce, alone and in combination, triazole resistance-conferring mutations in cyp51A, cyp51B and hmg1. With the potential to seamlessly introduce genes imparting resistance to additional existing and novel antifungals, toxic metals, and environmental stressors, this technique can considerably improve the ability to introduce dominant mutations in A. fumigatus.
The Effect of Work Environment Condition and Job Satisfaction on Employees Engagement and Organizational Citizenship Behavior on Non-Medical Workers

This study uses to describe the Work Environment Condition, Job Satisfaction and Employee Engagement on OCB in hospitals in Banjarbaru city. The purpose of this paper is to find out how big the impact of Work Environment Condition and job satisfaction on Employees Engagement and Organizational Citizenship Behavior in the health industry, especially in Banjarbaru City. The type of data that researchers collect for this study is use quantitative research and researcher examines the relationship of each variable to be hypothesized, and finds out whether the variable is influenced by other variables. This study used data collection techniques with the likert scale method. Population in this study were non-medical employees at the Banjarbaru City Hospitals. This study used PLS with boothstrap resampling methodology to test hypotheses. The test result of this analysis shows that all variables have an impact on another one. Then it can be concluded, organizations that are able to build OCB attitudes towards their employees tend to be better in terms of performance because non-medical employees work wholeheartedly in providing services so that hospitals need to reward non-medical employees with good performance so that they feel satisfied in their work.
Compassion in Hotels: Does Person–Organization Fit Lead Staff to Engage in Compassion-Driven Citizenship Behavior?

Existing research on person–organization (P-O) fit (POF) has ignored other-centered mechanisms in explaining why high levels of fit with the work environment increase employee’s organizational citizenship behavior (OCB). Using four factors involved in the compassion process (common humanity, empathic concern, kindness, mindfulness), this article investigates employee compassion as a mediator that explains why POF affects OCB directed toward individuals (OCBI) and the organization (OCBO). Our structural equation modeling analysis on 280 Spanish hotel employees reveals that compassion mediates the relationship between POF and OCBs. However, considering the employment contract of the sample (temporary, permanent), the mediation for OCBO was partial and full among permanent and temporary staff, respectively. These findings suggest that staff who fit their hotel are more likely to be sensitive to others’ setbacks and misfortunes, lessening or alleviating them (compassionately) by performing OCBs. However, compared with permanent staff, temporary staff’s OCBO response to POF is more compassion-driven.
The Impact of Sustainable Exercise and the Number of Pregnancies on Self-Efficacy, Self-Esteem, and Assertiveness Levels in Pregnant Women

This study examined the variations in self-efficacy, self-esteem, and assertiveness levels among pregnant women engaging in sustainable exercise compared to those performing no physical activity. The study also explored the connection between these changes and the number of pregnancies. The sample included 220 pregnant women engaging in sustainable exercise and 210 pregnant women performing no physical activity. Sustainable exercisers were chosen from those engaged in physical activity for at least 30 min, twice a week. The participants were in the fourth to seventh month of their pregnancy. A simple random sampling technique was used to choose participants and a total of 430 pregnant women volunteered to participate in the study. The mean age of the participants was 31.45 ± 12.11 years. Data collection tools were the Self-Efficacy Scale (SES), the Coopersmith Self-Esteem Inventory (CSEI), and the Rathus Assertiveness Schedule (RAS). In data analysis, the impact of independent variables on self-efficacy, self-esteem, and assertiveness was evaluated by one-way ANOVA in groups of more than two, t-test in paired groups, the relationship between some independent variables and scales was evaluated by correlation, and descriptive features were shown as percentages. In cases where variance analyses were significant at 0.05 (p < 0.05), Tukey’s test was used as a post hoc test. The study’s results indicated a significant disparity between the mean self-efficacy and self-esteem scores of women engaged in sustainable exercise compared to those who were not. However, there was no significant difference between the two groups in terms of assertiveness levels. The mean scores of self-efficacy, self-esteem, and assertiveness differed significantly in respect to the number of pregnancies in exercising women. However, there were no significant differences in mean scores of self-efficacy, self-esteem, and assertiveness scores in terms of the number of pregnancies in women who did not exercise.
YOLO Based Deep Learning Model for Segmenting the Color Images

The first stage is to extract fine details from a picture using Red Green Blue (RGB) colour space is colour image segmentation. Most grayscale and colour picture segmentation algorithms use original or updated fuzzy c-means (FCM) clustering. However, due to two factors, the majority of these methods are inefficient and fail to produce the acceptable segmentation results for colour photos. The inclusion of local spatial information often results in a high level of computational complexity due to the repetitive distance computation between clustering centres and pixels within a tiny adjacent window. The second reason is that a typical neighbouring window tends to mess up the local spatial structure of images. Color picture segmentation has been improved by introducing Deep Convolution Neural Networks (CNNs) for object detection, classification and semantic segmentation. This study seeks to build a light-weight for object detector that uses a depth and colour image from a publically available dataset to identify objects in a scene. It's likely to output in the depth way by expanding the YOLO network's network architecture. Using Taylor based Cat Salp Swarm algorithm (TCSSA), the weight of the suggested model is modified to improve the accuracy of region extraction findings. It is possible to test the detector's efficacy by comparing it to various datasets. Testing showed that the suggested model is capable of segmenting input into multiple metrics using bounding boxes. The results shows that the proposed model achieved 0.20 of Global Consistency Error (GCE) and 1.85 of Variation of Information (VOI) on BSDS500 dataset, where existing techniques achieved nearly 1.96 to 1.86 of VOI and 0.25 to 0.22 of GCE for the same dataset.
A method for numerical modelling of convection–reaction–diffusion using electrical analogues

In this paper, a new method, called the lumped‐component circuit method (LCM), is developed for one‐dimensioal and two‐dimensional convection–reaction–diffusion with low to moderate Peclet numbers, tested for modelling both steady‐state and transient problems, and compared with standard finite volume method (FVM) schemes. The method has been developed principally for solving equations with piecewise‐constant coefficients using nodes that are not positioned to correspond to the coefficient discontinuities. In such situations, the FVM solutions do not converge consistently as the node spacing is decreased, but LCM solutions do. In general, the LCM method is more accurate than the FVM schemes tested, and, while the computational cost of LCM is higher, results suggest that it can be more efficient. Like the transmission line method (TLM), it is an indirect scheme in which the problem to be solved is first represented by an analogous transmission line (TL). Unlike with TLM, however, the TL is then modelled using a lumped‐component circuit, the voltages at nodes within that circuit being calculated. Copyright © 2015 John Wiley & Sons, Ltd.
Robust Multi-Domain Mitosis Detection

Domain variability is a common bottle neck in developing generalisable algorithms for various medical applications. Motivated by the observation that the domain variability of the medical images is to some extent compact, we propose to learn a target representative feature space through unpaired image to image translation (CycleGAN). We comprehensively evaluate the performanceand usefulness by utilising the transformation to mitosis detection with candidate proposal and classification. This work presents a simple yet effective multi-step mitotic figure detection algorithm developed as a baseline for the MIDOG challenge. On the preliminary test set, the algorithm scoresan F1 score of 0.52.