Miracle-dz/UniFAR-FTUs huggingface dataset:
Damaraland mole-rats do not rely on helpers for reproduction or survival

Abstract In eusocial invertebrates and obligate cooperative breeders, successful reproduction is dependent on assistance from non-breeding group members. Although naked (Heterocephalus glaber) and Damaraland mole-rats (Fukomys damarensis) are often described as eusocial and their groups are suggested to resemble those of eusocial insects more closely than groups of any other vertebrate, the extent to which breeding individuals benefit from the assistance of non-breeding group members is unclear. Here we show that, in wild Damaraland mole-rats, prospective female breeders usually disperse and settle alone in new burrow systems where they show high survival rates and remain in good body condition—often for several years—before being joined by males. In contrast to many obligate cooperative vertebrates, pairs reproduced successfully without non-breeding helpers, and the breeding success of experimentally formed pairs was similar to that of larger, established groups. Though larger breeding groups recruited slightly more pups than smaller groups, adult survival was independent of group size and group size had mixed effects on the growth of non-breeders. Our results suggest that Damaraland mole-rats do not need groups to survive and that cooperative breeding in the species is not obligate as pairs can—and frequently do—reproduce without the assistance of helpers. While re-emphasizing the importance of ecological constraints on dispersal in social mole-rats, the mixed effects of group size in our study suggest that indirect benefits accrued through cooperative behavior may have played a less prominent role in the evolution of mole-rat group-living than previously thought.
A Dual-Branch Spatio-Temporal-Spectral Transformer Feature Fusion Network for EEG-Based Visual Recognition

Recognizing visual objects from single-trial electroencephalograph (EEG) signals is a promising brain-computer interface technology. However, due to the redundant features from noisy multichannel EEG signals, it is still a challenging task to achieve high precision recognition. Recent deep learning approaches commonly extract spatio-temporal features of EEG signals, which neglect important spectral-temporal features and may degrade the EEG recognition performance. To address the deficiency, we propose a novel channel attention weighting and multilevel adaptive spectral aggregation based dual-branch spatio-temporal-spectral transformer feature fusion network (CAW-MASA-STST) for EEG-based visual recognition. Specially, we first develop a channel attention weighting (CAW) to automatically learn the channel weights of EEG signals. Then, a graph convolution-based MASA is employed to aggregate spectral-temporal features of different sub-bands. Finally, an STST is designed to fuse spatio-temporal and spectral-temporal features, which enhances the comprehensive learning ability by modeling the temporal dependencies of the fused features. Competitive experimental results on two public datasets demonstrate that the proposed method is able to achieve superior recognition performance compared with the state-of-the-art methods, indicating a feasible solution for visual recognition-based BCI technology. The code of our proposed method will be available at https://github.com/ljbuaa/VisualDecoding.
Model-Based Policy Synthesis and Test-Case Generation for Autonomous Systems

Autonomous systems are supposed to automatically plan their actions and execute the plan without human intervention. In this paper, we propose a model-based two-layer frame-work for policy synthesis and test-case generation for autonomous systems. At the high-level layer of the framework, we have two kinds of methods for synthesising policies whose correctness is guaranteed by model checking. The autonomous system’s controller executes synthesised policies at the low-level layer. As the kinematics of autonomous systems is often nonlinear and the environment may influence the results of their actions, formally verifying the controllers is extremely difficult. We propose a novel method for generating test cases for the controllers at the low-level layer. The method employs reinforcement learning for test-case generation and model checking to ensure that the test cases faithfully realise the execution of the policy. The framework is designed in Uppaal Stratego, which integrates model checkers and algorithms for policy synthesis. Therefore, the framework separates concerns and seamlessly interchanges the information between two layers.
Model-Reference Adaptive Control for Distributed Parameter Systems of Parabolic Type

During the last several years there have been presented a number of results in the field of model-reference adaptive control. In the most of the results, the process is assumed to be a lumped parameter system, which is described by ordinary differential equations. But real physical systems occupy a certain spatial domain and are not concentrated at a single spatial point. Such systems are called distributed parameter systems, which are modeled by partial differential equations. The principal contribution of this paper is to present a procedure for the design of model-reference adaptive control for an unknown distributed parameter system of parabolic type. We assume the spatial average of the states over some effective sensing region to be the output of the process, and adaptively synthesize the input so that the tracking error between the output of the process and the output of a prescribed reference model, which is a lumped parameter system, is regulated to zero asymptotically.
An Enhanced Approach to Virtually Increase Quasi-Stationarity Regions Within Geometric Channel Models for Vehicular Communications

Vehicular communication channels are intrinsically nonstationary, as they present high mobility and abundant dynamic scatterers. Quasi-stationary regions can assess the degree of nonstationarity within a determined scenario and time variant observation of the channel can be extracted. These regions can aid geometrical models as to increase channel sampling intervals or to develop hybrid stochastic-geometric channel models. In this work, a new methodology for the use of virtual quasi-stationary regions within geometric channel models is proposed, in order to leverage the inherent location information to virtually increase their size. Overall, the use of delay-shifted channel responses improves the mean correlation coefficient between consecutive locations, ultimately reducing computation time for time-variant geometric channel models.
A Control Method for Congested Traffic in the Car-Following Model

A simple control method to suppress traffic congestion is proposed for the car-following model. The stability conditions are derived by using the control method, and the feedback signals, which act on our traffic system, are extended to the car-following model. The control signals will play an effect only if the traffic is congested. The corresponding numerical simulation results agree well with our theoretical analysis, and our control method can successfully suppress traffic jams.
Identification of m7G-Related miRNA Signatures Associated with Prognosis, Oxidative Stress, and Immune Landscape in Lung Adenocarcinoma

The role of N7-methylguanosine(m7G)-related miRNAs in lung adenocarcinoma (LUAD) remains unclear. We used LUAD data from The Cancer Genome Atlas (TCGA) to establish a risk model based on the m7G-related miRNAs, and divided patients into high-risk or low-risk subgroups. A nomogram for predicting overall survival (OS) was then constructed based on the independent risk factors. In addition, we performed a functional enrichment analysis and defined the oxidative stress-related genes, immune landscape as well as a drug response profile in the high-risk and low-risk subgroups. This study incorporated 28 m7G-related miRNAs into the risk model. The data showed a significant difference in the OS between the high-risk and low-risk subgroups. The receiver operating characteristic curve (ROC) predicted that the area under the curve (AUC) of one-year, three-year and five-year OS was 0.781, 0.804 and 0.853, respectively. The C-index of the prognostic nomogram for predicting OS was 0.739. We then analyzed the oxidative stress-related genes and immune landscape in the high-risk and low-risk subgroups. The data demonstrated significant differences in the expression of albumin (ALB), estimated score, immune score, stromal score, immune cell infiltration and functions between the high-risk and low-risk subgroups. In addition, the drug response analysis showed that low-risk subgroups may be more sensitive to tyrosine kinase inhibitor (TKI) and histone deacetylase (HDAC) inhibitors. We successfully developed a novel risk model based on m7G-related miRNAs in this study. The model can predict clinical prognosis and guide therapeutic regimens in patients with LUAD. Our data also provided new insights into the molecular mechanisms of m7G in LUAD.
Novel piperazine‐based ionizable lipid nanoparticles allow the repeated dose of mRNA to fibrotic lungs with improved potency and safety

Abstract mRNA‐based protein replacement therapy has received much attention as a novel intervention in clinical disease treatment. Lipid nanoparticles (LNPs) are widely used for their therapeutic potential to efficiently deliver mRNA. However, clinical translation has been hampered by the immunogenicity of LNPs that may aggravate underlying disease states. Here, we report a novel ionizable LNP with enhanced potency and safety. The piperazine‐based biodegradable ionizable lipid (244cis) was developed for LNP formulation and its level of protein expression and immunogenicity in the target tissue was evaluated. It was found that 244cis LNP enabled substantial expression of the target protein (human erythropoietin), while it minimally induced the secretion of monocyte chemoattractant protein 1 (MCP‐1) as compared to other conventional LNPs. Selective lung targeting of 244cis LNP was further investigated in tdTomato transgenic mice with bleomycin‐induced pulmonary fibrosis (PF). The repeated administration of 244cis LNP with Cre recombinase mRNA achieved complete transfection of lung endothelial cells (~80%) and over 40% transfection of Sca‐1‐positive fibroblasts. It was shown that 244cis LNP allows the repeated dose of mRNA without the loss of activity due to its low immunogenicity. Our results demonstrate that 244cis LNP has great potential for the treatment of chronic diseases in the lungs with improved potency and safety.
Conditional Generative Adversarial Network-Based Tactile Stimulus Generation for Ultrasonic Tactile Display

Tactile rendering is a promising technology that is necessary to integrate into virtual reality, augmented reality, mixed reality, and even metaverse environments. One of the key technologies for realizing tactile rendering is a reproduction or a display of tactile sensation. This study developed a model that generates an appropriate input signal to an ultrasonic tactile display using a conditional generative adversarial network. Sensory evaluation scores and vibration data acquired by a tactile sensor were used as training data for the conditional generative adversarial network-based models. In this study, different cluster analysis conditions were used to create the input information for the models. Each model generated the input signals for an ultrasonic tactile display, and the accuracy of the models was evaluated through sensory evaluation experiments. The results showed that model accuracy improved with moderate cluster classification and that the reproducibility of tactile sensation created with the models developed in this study was improved when compared with the reproducibility of tactile sensation created without the models.
Impact of a stochastic parametrization of cumulus convection, using cellular automata, in a mesoscale ensemble prediction system

A stochastic parametrization for deep convection, based on cellular automata, has been evaluated in the high‐resolution (2.5 km) ensemble prediction system Hirlam Aladin Regional Mesoscale Operational NWP Ensemble Prediction System (HarmonEPS). We studied whether such a stochastic physical parametrization, whilst implemented in a deterministic forecast model, can have an impact on the performance of the uncertainty estimates given by an ensemble prediction system. Various feedback mechanisms in the parametrization were studied with respect to ensemble spread and skill, in both subgrid and resolved precipitation fields. It was found that the stochastic parametrization improves the model skill in general, by reducing a positive bias in precipitation. This reduction in bias, however, led to a reduction in ensemble spread of precipitation. Overall, scores that measure the accuracy and reliability of probabilistic predictions indicate that the net impact (improved skill, degraded spread) of the ensemble prediction system is improved for 6 h accumulated precipitation with the stochastic parametrization and is rather neutral for other quantities examined.
Bayesian analysis of circular distributions based on non-negative trigonometric sums

ABSTRACT Fernández-Durán [Circular distributions based on nonnegative trigonometric sums. Biometrics. 2004;60:499–503] developed a new family of circular distributions based on non-negative trigonometric sums that is suitable for modelling data sets that present skewness and/or multimodality. In this paper, a Bayesian approach to deriving estimates of the unknown parameters of this family of distributions is presented. Because the parameter space is the surface of a hypersphere and the dimension of the hypersphere is an unknown parameter of the distribution, the Bayesian inference must be based on transdimensional Markov Chain Monte Carlo (MCMC) algorithms to obtain samples from the high-dimensional posterior distribution. The MCMC algorithm explores the parameter space by moving along great circles on the surface of the hypersphere. The methodology is illustrated with real and simulated data sets.
Raising Leadership Self-Efficacy and Minimizing Organizational Burnout Among School Administrators in a GROW Model of Cognitive Behavioral Coaching

Poor leadership self-efficacy and high organizational burnout are context-based cognitive conditions that sabotage leadership outcomes across organizations. When school administrators who are the leaders and directors of school affairs struggle with poor leadership self-efficacy and elevated levels of burnout their leadership styles are negatively affected with far-reaching impacts on the teachers, students, and the overall school outputs. This randomized control trial sought to investigate the effectiveness of GROW-Model (GROW-M) of Cognitive behavioral Coaching in raising leadership self-efficacy and minimizing burnout symptoms in a sample of school administrators in Enugu State, Nigeria. Participants included 77 school administrators who were randomly assigned to GROW-M (N = 38) and waitlisted control (N = 39) groups. A 2-h GROW-M program was delivered to the GROW-M intervention group weekly for 9 weeks. Maslach Burnout Inventory-Educators’ Survey and the School Leaders’ Self-Efficacy Scale were employed to source data at preintervention, postintervention, and follow-up evaluations. Descriptive and inferential statistics were used to explore data using SPSS version 24. Results revealed that participants’ organizational burnout reduced, while their leadership self-efficacy improved significantly at posttest. The changes in burnout and leadership self-efficacy levels were sustained through a 3-month follow-up. We concluded that intervention using GROW-M minimizes organizational burnout and raises leadership self-efficacy as a pliability resource for coping with overwhelming work demands.
Federated Quantum Neural Network With Quantum Teleportation for Resource Optimization in Future Wireless Communication

The following study introduces FT-QNN, a federated and quantum teleportation –based quantum neural network, utilized to optimize resource allocation for future wireless communications. The proposed FT-QNN consists of edge quantum neural networks (QNNs) and a cloud QNN, while quantum teleportation allows the cloud QNN to obtain the outputs of edge QNNs without requiring prior measurements on the output states, allowing the cloud to process the outputs directly as quantum states. As a particular case to demonstrate its applicability for wireless resource allocation, FT-QNN is then employed to optimize transmit power allocation coefficients in a power domain non-orthogonal multiple access (NOMA)-based system, aiming to maximize the achievable sum-rate. FT-QNN yields lower complexity compared to a distributed QNN scheme without quantum teleportation, while the numerical results also demonstrated that the FT-QNN is capable to achieve a similar sum-rate compared to the scheme without quantum teleportation.
Comparative Performance between V/F and Rotor Flux-Oriented Controls of Induction Motor Drive

A comparison between the performances of two popular speed control techniques for three-phase induction motors are presented. The scalar control (also known as V/F control) of induction motors is widely used. But its performances under various operating conditions are not satisfactory. As a result, a vector control technique, i.e., a rotor flux-oriented control technique, is thought to improve its performance under different working states, allowing flexibility in various applications. Various tests are conducted using MATLAB-SIMULINK software, and results are provided that highlight the comparative performances between the control methods for induction motor drives. Superior parameters in speed and torque responses are noticed in case of rotor flux-oriented method.
An age and spatially structured model of tumor invasion with haptotaxis

A model of tumor growth into surrounding tissue is analyzed. The model consists of a system of nonlinear partial differential equations for the populations of tumor cells, extracellular matrix macromolecules, oxygen concentration, and extracellular matrix degradative enzyme concentration. The spatial growth of the tumor involves the directed movement of tumor cells toward the extracellular matrix through haptotaxis. Cell age is used to track progression of cells through the cell cycle. The existence of unique global solutions is proved using the theory of fractional powers of analytic semigroup generators.
Adaptive Multispectral Encoding Network for Image Demoiréing

Moiré often appears when photographing textured objects, which can seriously degrade the quality of captured photographs. Due to the wide distribution of moiré and the dynamic properties of moiré, it is a challenge to effectively remove moiré patterns. For this purpose, we present an adaptive multispectral encoding network (AMSDM) for image demoiréing. In AMSDM, we leverage a multiscale network structure to process moiré images at different spatial resolutions, which can relieve the issue of moiré with distributed frequency spectrum. To solve the issue of dynamic properties of moiré, we design an adaptive multispectral encoding (AMSE) module to encode moiré patterns adaptively, which helps AMSDM restore moiré images clearly. Besides, a demoiréing convolutional network block (DMCNB) in the AMSE module makes AMSDM have the adaptability and the long-range correlation; thus, it can learn both global and local information about moiré images. Extensive experimental results indicate that our proposed AMSDM significantly outperforms state-of-the-art (SOTA) methods and achieves a great balance between performance and efficiency.
Threshold Voltage Analysis of E-mode Recessed p-GaN Gate HEMT-A Simulation Based Study

In power-electronics applications or switching applications Normally-off AlGaN/GaN high electron mobility transistors (HEMTs) are needed with higher threshold voltage ($\mathrm{V}_{\mathrm{t}\mathrm{h}}$) and high drain current (Id). Previously a lot of experiments have been done to propose emode HEMT structures by using several gate engineering techniques like recessed metal-gate HEMT, p-GaN gate HEMT and thin-barrier layer HEMT etc. This paper depicts a unique enhancement-mode recessed p-GaN gate HEMT structure with incorporating recess gate (p-GaN) engineering techniques which have unique advantages of all above proposed structures. Proposed results got up to 2. 7V of threshold voltage and by analyzing the band diagram, it is also observed that the triangular well at AlGaN/GaN interface is uplifted to 2. 54eV from the Fermi-level, which is significantly higher than p-GaN gate HEMT (only 0. 42SeV from the Fermi level). The proposed HEMT structure is simulated in Silvaco Atlas software version 5. 0.22. R by changing the parameters like x-mole fraction of the barrier layer, recess depth ($\mathrm{R}_{\mathrm{d}}$), and doping concentration of p-GaN layer (NA). The trans-conductance graph is generated by Tony-plot tool available in the software.
Design and Evaluation of a Quasi-Passive Variable Stiffness Prosthesis for Walking Speed Adaptation in People With Transtibial Amputation

The biological ankle joint adjusts stiffness to adapt to changing walking speed, terrain, and load carriage. The most commonly used passive transtibial prostheses are unable to adjust device stiffness and therefore do not maximize potential energy storage and peak prosthesis power across speeds. We present a quasi-passive variable stiffness ankle–foot prosthesis with discrete stiffness adjustment from 352 to 479 Nm/radian, corresponding to the range of biological ankle quasi-stiffness exhibited during level ground walking at speeds from 0.75 to 1.5 m/s for a 77 kg person. We implement a novel parallel leaf spring mechanism that utilizes custom solenoid-driven linear actuators to constrain sliding of parallel leaf springs relative to a mechanical ground in order to control bending stiffness. The prosthesis is lower in mass than all existing variable stiffness prostheses, with a mass of 945 g. We present initial results from a pilot study with one participant with unilateral transtibial amputation, demonstrating an increase in range of motion, peak prosthesis power, and energy storage and return, and a decrease in contralateral knee external adduction moment across a range of walking speeds. This variable stiffness ankle–foot prosthesis demonstrates the potential to improve biomechanics of walking through the design of a low-mass, quasi-passive prosthesis.