Miracle-dz/UniFAR-FTUs huggingface dataset:
Satellite DNAs—From Localized to Highly Dispersed Genome Components

According to the established classical view, satellite DNAs are defined as abundant non-coding DNA sequences repeated in tandem that build long arrays located in heterochromatin. Advances in sequencing methodologies and development of specialized bioinformatics tools enabled defining a collection of all repetitive DNAs and satellite DNAs in a genome, the repeatome and the satellitome, respectively, as well as their reliable annotation on sequenced genomes. Supported by various non-model species included in recent studies, the patterns of satellite DNAs and satellitomes as a whole showed much more diversity and complexity than initially thought. Differences are not only in number and abundance of satellite DNAs but also in their distribution across the genome, array length, interspersion patterns, association with transposable elements, localization in heterochromatin and/or in euchromatin. In this review, we compare characteristic organizational features of satellite DNAs and satellitomes across different animal and plant species in order to summarize organizational forms and evolutionary processes that may lead to satellitomes’ diversity and revisit some basic notions regarding repetitive DNA landscapes in genomes.
Metalorganic Vapor Phase Epitaxial Growth and Properties of GaN/Al0.1Ga0.9N Layered Structures

High-quality, well-controlled GaN/Al0.1Ga0. 9N layered structures with periodicity varying from 4.5 nm to 60 nm have been successfully grown on (0001) sapphire substrates by Metalorganic Vapor Phase Epitaxy (MOVPE). The layered structure has been confirmed by double-crystal X-ray diffractometry. Photoluminescence peak energy showed a shift toward the higher-energy side with decreasing thickness of the GaN well layer, which is in good agreement with the calculation obtained from the Kronig-Penny analysis.
Access Control Mechanism Based on Game Theory in the Internet of Things Environment

In order to solve the problem that the traditional “centralized” access control technology can no longer guarantee the security of access control in the current Internet of Things (IoT)environment, a dynamic access control game mechanism based on trust is proposed. According to the reliability parameters of the recommended information obtained by the two elements of interaction time and the number of interactions, the user's trust value is dynamically calculated, and the user is activated and authorized to the role through the trust level corresponding to the trust value. The trust value and dynamic adjustment factor are introduced into the income function to carry out game analysis to avoid malicious access behavior of users. The hybrid Nash equilibrium strategy of both sides of the transaction realizes the access decision-making work in the IoT environment. Experimental results show that the game mechanism proposed in this paper has a certain restraining effect on malicious nodes and can play a certain incentive role in the legitimate access behavior of IoT users.
ECA-YOLOv5: Multi scale infrared salient target detection algorithm based on anchor free network

Aiming at the problems of poor detail texture features, low contrast and poor target detection effect in infrared images, a multi-scale infrared salient target detection algorithm based on anchor free network ECA-YOLOv5 is proposed. The model first introduces ECA attention mechanism to reduce noise interference and improve the infrared feature extraction capability of the network. Then, the shallow high-resolution feature information is supplemented, and the multi-scale feature fusion module is rebuilt to improve the utilization of feature information. Finally, an enhanced feature map output scale is added to further expand the infrared target detection size range and improve the detection capability. The experimental results show that, compared with the relevant detection model, the detection accuracy of this model for infrared targets has been significantly improved, and the recall rate and accuracy have been improved.
An approach to the effects of longevity, sexual maturity, and reproduction on telomere length and oxidative stress in different Psittacidae species

Introduction: Aging is a multifactorial process that includes molecular changes such as telomere shortening. Telomeres shorten progressively with age in vertebrates, and their shortening rate has a significant role in determining the lifespan of a species. However, DNA loss can be enhanced by oxidative stress. The need for novel animal models has recently emerged as a tool to gather more information about the human aging process. Birds live longer than other mammals of the same size, and Psittacidae species are the most persevering of them, due to special key traits. Methods: We aimed to determine telomere length by qPCR, and oxidative stress status using colorimetric and fluorescence methods in different species of the order Psittaciformes with different lifespans. Results: We found that telomeres shorten with age for both long- and short-lived birds (p < 0.001 and p = 0.004, respectively), with long-lived birds presenting longer telomeres than short-lived ones (p = 0.001). In addition, short-lived birds accumulated more oxidative stress products than long-lived birds (p = 0.013), who showed a better antioxidant capacity (p < 0.001). Breeding was found related to telomere shortening in all species (p < 0.001 and p = 0.003 for long- and short-lived birds). Short-lived birds, especially breeding females, increased their oxidative stress products when breeding (p = 0.021), whereas long-lived birds showed greater resistance and even increased their antioxidant capacity (p = 0.002). Conclusion: In conclusion, the relationship between age and telomere length in Psittacidae was verified. The influence of breeding increased cumulative oxidative damage in short-lived species, while long-lived species may counteract this damage.
Variable free reasoning on finite trees

In this paper we examine three modal languages that have been proposed in the model-theoretic syntax literature for describing finite ordered trees. We compare their expressive power, and then examine a key complexity-theoretic issue: how expensive is it to decide --- given a theory specifying a certain class of trees --- whether a formula describes a model? Our main result is that for two of these languages this problem is EXPTIME-complete.
A Dual-Port Bessel Beam Launcher for Microwave Wireless Power Transfer

A dual-port fed Bessel beam launcher designed for 10 GHz microwave wireless power transfer is proposed in this article. The annular slot rings placed on the top plane is excited with a two-port coaxial probe fed circular loop placed in the middle layer. The feed circular loop is designed such that the structure exhibits a parallel combiner feature at the design frequency. The proposed structure is designed and simulated in the full wave numerical solver of ANSYS HFSS. The obtained results indicate a Bessel beam generation within a non-diffracting range of $6\lambda$ and parallel combiner nature at 10 GHz.
Visible and Infrared Object Tracking via Convolution-Transformer Network With Joint Multimodal Feature Learning

The existing Transformer-based redgreenblue-thermal (RGBT) tracker mainly focuses on the enhancement of features extracted by convolutional neural network (CNN). The potential of the Transformer in representation learning remains underexplored. In this letter, we propose a Convolution-Transformer network with joint multimodal feature learning (JMFL), in which both representation learning and feature fusion leverage Transformer. Specifically, we use the multibranch Convolution-Transformer feature extraction network to process the extraction task of local modality-independent features and global modality-shared features, respectively. Several simplified Transformer encoder layers form the Transformer backbone network, which is more suitable for real-time object tracking. Besides, we found that intermodality correlation is an important factor for modality interactions and mutual exploitation. Therefore, we propose a JMFL module, which uses cross-attention to capture the dependencies of cross-modal and enhance multimodal fusion by bidirectional guidance of multimodal information. The proposed method is fully experimented on two large benchmark datasets and compared with some current well-performing methods. The experimental results show that the proposed method performs well in terms of tracking accuracy and speed.
Rheology as a Tool for Fine-Tuning the Properties of Printable Bioinspired Gels

Over the last decade, efforts have been oriented toward the development of suitable gels for 3D printing, with controlled morphology and shear-thinning behavior in well-defined conditions. As a multidisciplinary approach to the fabrication of complex biomaterials, 3D bioprinting combines cells and biocompatible materials, which are subsequently printed in specific shapes to generate 3D structures for regenerative medicine or tissue engineering. A major interest is devoted to the printing of biomimetic materials with structural fidelity after their fabrication. Among some requirements imposed for bioinks, such as biocompatibility, nontoxicity, and the possibility to be sterilized, the nondamaging processability represents a critical issue for the stability and functioning of the 3D constructs. The major challenges in the field of printable gels are to mimic at different length scales the structures existing in nature and to reproduce the functions of the biological systems. Thus, a careful investigation of the rheological characteristics allows a fine-tuning of the material properties that are manufactured for targeted applications. The fluid-like or solid-like behavior of materials in conditions similar to those encountered in additive manufacturing can be monitored through the viscoelastic parameters determined in different shear conditions. The network strength, shear-thinning, yield point, and thixotropy govern bioprintability. An assessment of these rheological features provides significant insights for the design and characterization of printable gels. This review focuses on the rheological properties of printable bioinspired gels as a survey of cutting-edge research toward developing printed materials for additive manufacturing.
Family functioning and learning engagement of junior high school students in rural China: the mediating effect of academic self-efficacy

Abstract This study examined academic self-efficacy as a mediator in the relationship between family functioning and learning engagement by constructing a structural equation model. The participants were 817 students from public junior high schools in rural China. The data were collected by measuring family functioning, academic self-efficacy, and learning engagement using self-report assessments. The results indicated that family functioning was positively associated with learning engagement; academic self-efficacy significantly mediated the correlation between family functioning and learning engagement. In addition, the mediating effect of academic self-efficacy varied across dimensions of family functioning. These findings contribute to understanding the internal relationship between family functioning and learning engagement, and have implications for improving the learning engagement of disadvantaged students in rural areas.
Backward Edge Pointer Protection Technology Based on Dynamic Instrumentation

Buffer overflow is an important security issue. Many countermeasures have been proposed, e.g., DEP, shadow stack. However, existing DEP is facing the problem of being bypassed by attackers through information leakage attacks. To address the shortcomings of current backward edge pointer integrity protection in preventing buffer overflow attacks, this paper proposes a shadow stack based backward edge pointer integrity protection scheme through dynamic instrumentation, and implements a prototype system based on the DynamoRIO framework. Shadow stack mechanism can achieve effective protection for return address integrity by maintaining a one-to-one mapping relationship between the shadow stack and the program stack. Meanwhile, test cases were designed to evaluate the effectiveness, runtime performance and compatibility of the plugin. Experiment results show that the security plugin is compatible with most programs and can run with an average overhead of about 10% to 15%. This is acceptable when comparing to stack shield with 22% overhead.
Developing digital twins of multi-camera metrology systems in Blender

Blender is an open-source three-dimensional animation software, which can be used as a simulation tool in metrology, to build numerical models that can be used in the design and optimisation of camera-based measurement systems. In this work, the relevance of using Blender to model camera-based measurement systems was explored. Two experiments were conducted in real-world and Blender modelled environments, one using individual cameras for a simple measurement task, the other considering multi-camera position optimisation. The objective was to verify whether the virtual cameras created in Blender can perceive and measure objects in the same manner as the real cameras in an equivalent environment. The results demonstrate that in its native modelling format Blender satisfies the optical metrology characteristics of measurement, but the correlation between Blender output and real-world results is highly sensitive to initial modelling parameters such as illumination intensity, camera definitions and object surface texture.
Optical hyperbolic metamaterials based on nanoparticles doped liquid crystals

Self-aligned liquid crystals containing randomly dispersed nanoparticles are proposed to realise hyperbolic metamaterials at visible spectrum. Opposite signs for anisotropic permittivity tensors originated from the scattering of metallic nanoparticles in liquid crystals are achieved. Tunable effective wavelength properties are also demonstrated by choosing dielectric-metal core-shell nanoparticles.
Wavelet neural networks and receptive field partitioning

The use of wavelet functions as basis functions is proposed. Wavelets have many advantages over other basis functions. Orthonormal sets of wavelets can easily be constructed. Thus, network weights can be computed directly and independently. Wavelets can be used to provide a multiresolution approximation of the discriminant functions and offer localization in space and frequency. These properties are put to good advantage by the proposed method, which constructs a sparse wavelet network by including and positioning wavelets from increasing levels of resolution to maximize the classification score. <<ETX>>
Indoor Localization Based on Weighted Gaussian Hybrid Filter and Adaptive Neural Network

Low-cost ranging techniques, such as received signal strength indicator(RSSI), often lead to inaccurate location estimation in wireless sensor networks. To reduce the errors caused by ranging techniques, this paper uses a non-ranging method based on RSSI. We arrange several access points(APs) with known coordinates in the indoor environment, and then select a large number of reference points(RPs) to collect RSSI. A weighted Gaussian hybrid filter algorithm is applied to received RSSI to obtain reference value and construct a RSSI database. We use the DBSCAN clustering algorithm to pre-process the database for classification, and then construct an environmental adaptive neural network localization model to achieve high-precision localization of target nodes in complex indoor environments.
Light mode and dark mode: Which one is suitable when using public‐facing web maps? An experimental evaluation using eye‐tracking

Dark maps, which are also called dark modes or dark themes, have gained popularity, but their usability has not been experimentally evaluated. This article presents a user experiment that uses eye‐tracking to assess the usability of dark and light maps. Here, two widely used web maps, Google Maps and Gaode Maps, are selected at the street and city scales. Eight map‐use tasks are designed and cover four operations (identify, compare, rank, and associate) with space‐alone and attributes‐in‐space distinctions. Four pairs of map‐use tasks (light‐during‐the‐day, dark‐during‐the‐day, dark‐at‐night, and light‐at‐night) are examined from three aspects: effectiveness, efficiency, and cognitive load. The results provide preliminary evidence that the light‐during‐the‐day performance is generally the best in most cases, followed by the dark‐at‐night performance; the dark‐during‐the‐day performance is the worst in all cases, followed by the light‐at‐night performance, which suggests that aligning the map design with the environment (i.e., lighting environment) is critical for better communication. The light‐during‐the‐day performance is the best for space‐alone tasks, and the dark‐at‐night performance is the best for attributes‐in‐space tasks. Our investigation also indicates that dark maps are far less favored in practice, which suggests that users' preference for using the dark mode of public‐facing web maps needs to be shaped. Since light and dark maps are associated with photopic and scotopic vision, respectively, the results indicate the need for future studies on how to leverage scotopic vision to design better dark maps.
Applications of Machine Learning in Medicine: Current Trends and Prospects

With the continuous development of information technology and medical data information, more and more clinicians recognize artificial intelligence or will completely change medical practice by using advanced machine learning methods. The potential of using machine learning and predictive analysis to customize specific treatments for individuals is currently under research. Machine learning can learn a large number of medical data and explore the dependencies in data concentration, forming a corresponding medical model that can quickly and accurately predict new data, which is conducive to the early diagnosis of diseases and assisted clinical decisions. Clinical medicine faces the status quo of the relative shortage of medical resources and the identification and rapid diagnosis and treatment of critically ill emergency patients. In the era of big data, clinical demand is driven by clinical needs, and intelligent medical care provided by machines is the key to tackling the aforementioned issues.
Application of early arrival waveform inversion with pseudo-deconvolution misfit function by source convolution

Abstract Typical seismic inversion methods require a source wavelet estimated from the data. Inaccurate wavelet estimation may severely undermine the seismic inversion results. We propose a pseudo-deconvolution method that mitigates the requirement for accurate wavelet estimation in early arrival waveform inversion as follows. For a pair of observed and predicted data-sets, we first extract the respective source wavelets from the near-offset part of the two data-sets. Next, we convolve the former data-set with the latter wavelet, and the latter data-set with the former wavelet. The mismatch between these two is then minimized as in standard seismic inversion. Conveniently implemented in the frequency domain, this method is both fast and robust. This is verified by numerical tests on both synthetic and field data.
Wireless Network Infrastructure for Electric Vehicles Communications: Khobar City as a Case Study

Abstract-As moving to the EVs is essential due to the environmental cues, we need to solve the problems and undertake the challenges of the EVs to expedite this move. One of these challenges is the long recharging time compared to the traditional refueling process and the consequence of this issue is overcrowding in charging stations. To solve this problem, a charging management system is needed. However, an effective charging management system requires efficient communication networks that facilitate the exchange of information between the EVs, charging stations and the management server. In this work, we study the feasibility of exchanging EV’s data using V2V and V2I communications infrastructure considering Al Khobar city in Saudi Arabia as a case study. SUMO GUI and OMNET++ simulators have been used to set up the environment. The simulation results show that the minimum data throughput requirements are 0.83,1.11, and 59.19 Bytes for V2V, V2I, and I2I respectively. This paper has contributed to finding the minimum requirement of the data throughputs of the communication links in vehicular communication.
Varying performance of eight evapotranspiration products with aridity and vegetation greenness across the globe

The wide application of the evapotranspiration (ET) products has deepened our understanding of the water, energy and carbon cycles, driving increased interest in regional and global assessments of their performance. However, evaluating ET products at a global scale with varying levels of dryness and vegetation greenness poses challenges due to a relative lack of reference data and potential water imbalance. Here, we evaluated the performance of eight state-of-the-art ET products derived from remote sensing, Land Surface Models, and machine learning methods. Specifically, we assessed their ability to capture ET magnitude, variability, and trend, using 1,381 global watershed water balance ET as a baseline. Furthermore, we created aridity and vegetation categories to investigate performance differences among products under varying environmental conditions. Our results demonstrate that the spatial and temporal performances of the ET products were strongly affected by aridity and vegetation greenness. The poorer performances, such as underestimation of interannual variability and misjudged trend, tend to occur in abundant humidity and vegetation. Our findings emphasize the significance of considering aridity and vegetation greenness into ET product generation, especially in the context of ongoing global warming and greening. Which hopefully will contribute to the directional optimizations and effective applications of ET simulations.
Dual-Polarization High-Gain Array Antenna without Power Division Network for 5G Base Station

A dual-polarization high-gain array antenna without power division network is proposed. A crossed dielectric resonator antenna (DRA) is applied as a driven element. A coupled rectangular metal patch is placed above the DRA radiating as a three-unit antenna array. In order to improve the isolation between the two dual polarized ports, tree shorted pins are designed across the dielectric resonator. Otherwise, the slots with different sizes is etched on the metal patch to suppress the cross polarization. A ±45° dual-polarization antenna array with high gain upper than 11dBi and beamwidth of 23° at vertical plane (V-plane) and 65° at horizontal plane (H-plane) in the frequency of 3.4Ghz - 3.6Ghz have been achieved. The proposed design have promising applications for 5G base station with simpler architecture and lower feeding loss.
Optimal experiment design for identification of linear distributed-parameter systems: Frequency domain approach

In this paper optimality conditions for experiment design are derived. The experiment is planned for identification of linear time invariant distributed-parameter systems. The determinant of the averaged information matrix is expressed in the terms of input spectral density matrix and spatial density of measurements, and then used as a measure of estimation accuracy. Presented results are applied to find optimal sensors positions and input signals in several examples.