Miracle-dz/UniFAR-FTUs huggingface dataset:
Animals in the family mini-special issue introduction and historiographical review

ABSTRACT In Britain today, pets are often at the heart of family life, but we know relatively little about the roles they played in families in the past. From the early nineteenth century, pets were a central focus of middle- and working-class homes in Britain but are almost completely unremarked in historical studies of the home and family. In this mini-special issue we present four new essays, developed from papers given at the panel, exploring the evolving relationship between pets and family life in Britain in the eighteenth, nineteenth and twentieth centuries. Taken together they demonstrate the relationship between changes in the way the family was understood and experienced and the development of pet keeping practices. In our introduction we bring together two important strands of recent scholarship – the history of the family and histories of animals. We will review the development of animal histories – paying attention to how they might usefully be brought to bear on the study of the family. As there has been significant research on the role of pet animals in family life across the social sciences, we will also review some of the key work in sociology, social geography and psychology, thinking through the implications of these studies for historians. Finally, we reflect on the cumulative findings of the four essays – and how they add a new dimension to our understandings of modern British family life.
A Computational Approach for One and Two Dimensional Fisher’s Equation Using Quadrature Technique

Abstract In this paper, a refined form of the differential quadrature method is proposed to compute the numerical solution of one and two-dimensional convection-diffusion Fisher’s equation. The cubic trigonometric B-spline basis functions are applied in the differential quadrature method in a modified form to obtain the weighting coefficients. The application of the method reduces nonlinear Fisher’s partial differential equation into a system of ordinary differential equations which can be solved by applying the Runge-Kutta method. Six numerical test problems of Fisher’s equation are analyzed numerically to establish the efficiency of the proposed method. The stability of the method is also discussed using the matrix method.
Noble Metal Structures: Photo-Thermal Properties Enhancement

Theoretically speaking, within the track of photothermal therapy, heat impacts to cancer cells localization is overspreading strategy for confronting cancer. It helps in decreasing the side impacts on cells close to tumors, enhances the quality of life of affected people, and by and large, and consequently, the hyperthermia impact is upgraded. The nanoparticles optical features are based on their form and size. The main objective of our work is a trial to upgrade the photo-thermal features some noble metal nanostructures by enhancing the absorption characteristics. This is accomplished by utilizing unused shapes in addition to some modern geometrical forms of nanoparticles self-similar clusters. The significance is of this work is its use in treating tumors, through distributed heating, especially caused by cancer and destroying the injured cells, while keeping the other cells safe. The nano-rod single element is used, achieving absorption of − 31.8 dB at 613.5 THz. Also, the proposed nano-sphere shape results in an optimum performance that exceeds literature reported results, achieving absorption of - 42.5 dB at 1035 THz.
A Comprehensive Survey on Coded Distributed Computing: Fundamentals, Challenges, and Networking Applications

Distributed computing has become a common approach for large-scale computation tasks due to benefits such as high reliability, scalability, computation speed, and cost-effectiveness. However, distributed computing faces critical issues related to communication load and straggler effects. In particular, computing nodes need to exchange intermediate results with each other in order to calculate the final result, and this significantly increases communication overheads. Furthermore, a distributed computing network may include straggling nodes that run intermittently slower. This results in a longer overall time needed to execute the computation tasks, thereby limiting the performance of distributed computing. To address these issues, coded distributed computing (CDC), i.e., a combination of coding theoretic techniques and distributed computing, has been recently proposed as a promising solution. Coding theoretic techniques have proved effective in WiFi and cellular systems to deal with channel noise. Therefore, CDC may significantly reduce communication load, alleviate the effects of stragglers, provide fault-tolerance, privacy and security. In this survey, we first introduce the fundamentals of CDC, followed by basic CDC schemes. Then, we review and analyze a number of CDC approaches proposed to reduce the communication costs, mitigate the straggler effects, and guarantee privacy and security. Furthermore, we present and discuss applications of CDC in modern computer networks. Finally, we highlight important challenges and promising research directions related to CDC.
Investigation of point cloud registration uncertainty for gap measurement of aircraft wing assembly

Point cloud registration is necessary to join multiple laser scanned data, but researchers have not treated point cloud registration in aircraft assembly in much detail. When applying laser scan metrology into industries, the effects of registration uncertainty on high precision assembly accuracy are not negligible. Based on the use of laser scanner and commercial software PolyWorks, this study investigates the registration uncertainty between part-level and assembly-level point cloud data in aircraft wing assembly. A spar-and-skin assembly with sphere artefacts is used as a case study. Registration uncertainty in gap measurement is also investigated. Results show that: (1) the use of sphere artefacts cannot improve registration accuracy in PolyWorks, but can improve efficiency. (2) Registration process could bring errors and these errors would be affected by the parameters settings during data processing. (3) Systematic errors would be associated with both part-level and assembly-level measurements, and calibration should be applied to eliminate their effects on the measurement of the desired dimensions, i.e. the gap size in this case. It is concluded that laser scanner and computational software can be used for high precision assembly, and evaluating registration uncertainty is a crucial step to improve assembly accuracy.
Experimental and numerical simulation study on the lifting and diffusion of explosion cloud

Lifting and diffusion of the explosion cloud is an essential process for evaluation of the so-called dirty bomb. In this work, we have performed an experimental work on the explosion cloud of 30kg TNT equivalent, and obtained the morphological characters during the lifting process of the explosion cloud. 3D numerical simulation is also implemented on the lifting and diffusion process of the explosion cloud, as well as the diffusion feature for the aerosol particles by using two-phase flow methodology on basis of the discrete model. The simulation results demonstrate that the variation law for height of the explosion cloud is in agreement the experimental data.
Change Detection in The Covariance Structure of High-Dimensional Gaussian Low-Rank Models

This paper is devoted to the problem of testing equality between the covariance matrices of L multivariate Gaussian time series with dimension M, in the context where each of the L covariance matrices is the sum of a low-rank K component and the identity matrix. Assuming N1, …, NL samples are available for each time series, a new test statistic, based on the eigenvalues of the L sample covariance matrices (SCM) of each time series as well as the eigenvalues of a pooled SCM mixing the N1+…+NL available samples, is pro-posed and proved to be consistent in the high dimensional regime in which M, N1, …, NL converge to infinity at the same rate, while K and L are kept fixed. Numerical simulations show that the proposed test statistic is competitive with other relevant methods for moderate values of M, N1, …, NL.
A dual-phase alloy with ultrahigh strength-ductility synergy over a wide temperature range

We present a precipitation-strengthening strategy to produce a strong and ductile alloy at a broad range of temperatures. High-entropy alloys (HEAs), as an emerging class of materials, have pointed a pathway in developing alloys with interesting property combinations. Although they are not exempted from the strength-ductility trade-off, they present a standing chance in overcoming this challenge. Here, we report results for a precipitation-strengthening strategy, by tuning composition to design a CoNiV-based face-centered cubic/B2 duplex HEA. This alloy sustains ultrahigh gigapascal-level tensile yield strengths and excellent ductility from cryogenic to elevated temperatures. The highest specific yield strength (~150.2 MPa·cm3/g) among reported ductile HEAs is obtained. The ability of the alloy presented here to sustain this excellent strength-ductility synergy over a wide temperature range is aided by multiple deformation mechanisms i.e., twins, stacking faults, dynamic strain aging, and dynamic recrystallization. Our results open the avenue for designing precipitation-strengthened lightweight HEAs with advanced strength-ductility combinations over a wide service temperature range.
Testing Uncorrelation of Multi-Antenna Signals Using Linear Spectral Statistics of the Spatio-Temporal Sample Autocorrelation Matrix

We consider the use of the spatio-temporal sample autocorrelation matrix in order to determine whether the signals received by a distributed antenna system are spatially correlated. The asymptotic behavior of linear spectral statistics built from this matrix is studied, assuming that (i) the number of antennas, (ii) the sample size and (iii) the number of tested time lags all converge to infinity. In this asymptotic regime, linear spectral statistics of the spatiotemporal sample correlation matrix are shown to be asymptotically equivalent to the corresponding functional average with respect to a Marchenko-Pastur distribution. This means that the eigenvalue distribution of the original sample autocorrelation matrix essentially behaves as a sample covariance matrix of spatio-temporal white noise with equivalent dimensions. This result turns out to be useful in order to address the problem of detecting the presence of wideband directional signals with a number of uncalibrated receivers distributed over a large area.
Accelerating Level 2 BLAS Based on ARM SVE

Scalable Vector Extension (SVE) is a special vector instruction set architecture recently released by ARM, which is a vector extension for A64 instruction set of ARMv8-A architecture. SVE introduces many significant features that not only take full advantage of the use of wide vector, but also enable efficient vectorization functions which can realize high performance. This paper described how to apply the main features of SVE to accelerate the Level 2 BLAS routines. We carried out the simulation on ARM Instruction Emulator (ARMIE) and Gem5 simulator. The results demonstrated that our SVE implementation reduced the number of executed instructions by 90% and achieved over 17x speedup compared with the NEON implementation.
Unknown Signal Detection in Interference and Noise Using Hidden Markov Models

A machine learning approach to detecting unknown or anomalous signals in a complicated background of interfering signals and noise is presented. A hidden Markov model (HMM) is trained to represent the interference and noise environment via a Bayesian nonparametric hierarchical Dirichlet process (HDP)-HMM technique. An unknown signal is detected if the Viterbi hidden state path of the test data is sufficiently unlikely under the learned background HMM. The detection scheme is derived as a generalized likelihood ratio test (GLRT) for an unknown deterministic signal in HMM noise. In simple additive white Gaussian noise (AWGN), the proposed scheme trivially reduces to an energy detector. However, experimental results on a software-defined radio (SDR) testbed demonstrate that the proposed scheme substantially outperforms energy detection in a more challenging interference and noise background. The approach can be employed in spectrum monitoring applications to efficiently detect transmissions that deviate from a typical signal environment.
The Optimal Control of an Electric Warship Driven by an Operational Vignette

The following paper presents a framework for the optimal control of an electric warship using a load profile derived from an operational vignette. This framework consists of three key components: a reduced order model of an electric ship, a discretization of the resulting constitutive equations using an orthogonal spline collocation method, and an optimization engine to solve the resulting formulation. Once assembled, this control framework is validated through its application to a four zone model of a medium voltage DC (MVDC) electric ship using a load profile from an operational vignette,
Degradation mechanism of AlGaN/GaN HEMTs during high temperature operation stress

The degradation behavior and its physical mechanism of AlGaN/GaN high electron mobility transistors (HEMTs) during high temperature operation (HTO) stress were investigated in this paper. The results show that the gate leakage current of AlGaN/GaN HEMTs after 1000 h HTO stress is two orders of magnitude larger than that of the fresh ones. The maximum transconductance of the AlGaN/GaN HEMTs obviously decreases from 0.31 to 0.21 s after 1000 h HTO stress, which indicates the gate degraded seriously. The mechanism for the gate degradation could be attributed to the Au diffusion that degrades the barrier. Moreover, the output characteristics are also deteriorated seriously, and the maximum variation of drain-to-source current is up to 119 mA under the conditions of gate-to-source voltage of 0 V and drain-to-source voltage of 5 V. The reason for the deterioration of output characteristics could be attributed to two mechanisms of gate degradation and structural damage such as pit-like, crack-like damages. The results may be useful in the design and application of AlGaN/GaN HEMTs.
A Data Driven Review of Board Game Design and Interactions of Their Mechanics

Board games have often been recognised as a tool to model complex concepts in abstract environments for entertainment, education, and research in fields such as military and artificial intelligence. With more board games being designed and published, it is timely to draw attention towards board game design strategies and mechanics which capture the attributes that drive game play. The game design and the mechanics used define the structure, functionality, and play experience of these games. Towards this end, this paper presents a data driven review of board game mechanics and play-related attributes, their interactions and relationships. The analysis expects to draw insights into how board games can be utilised across diverse domains as a tool to understand and explore complex concepts through abstract models. The investigations focus on identifying the trends and patterns of board games being published and their individual mechanics over time. Moreover, the correlation between mechanics and play-related attributes such as game complexity, rating, and duration are explored. The interactions and similarities between individual mechanics based on co-occurrence, mutual information, and clustering based approaches are also illustrated. The results show that the level of complexity and engagement of a game is not a simple function of the set of mechanics used, but rather the interactions that exist between mechanics, and the nature of their specific implementation are the critical factors in determining play experience of a board game.
Distribution Network Admittance Matrix Estimation With Linear Regression

Topology identification and parameter estimation form the basis for the operation and control of the distribution network with little or no observability. This paper proposes a method to estimate the admittance matrix for open-loop distribution networks based only on active/reactive power injection and voltage magnitude data. We derive the linear relationship between the admittance matrix and the measurements based on decoupled linear power flow (DLPF) equations. A total least squares regression method is proposed based on analysis of the characteristics of the open-loop grid admittance matrix. Case studies on the IEEE 33-bus system demonstrate the proposed method's effectiveness, accuracy, and efficiency.
SpaceDML: Enabling Distributed Machine Learning in Space Information Networks

Space information networks (SINs) have become a rapidly growing global infrastructure service. Massive volumes of high-resolution images and videos captured by low-orbit satellites and unmanned aerial vehicles have provided a rich training data source for machine learning applications. However, SIN devices' limited communication and computation resources make it challenging to perform machine learning efficiently with a swarm of SIN devices. In this article, we propose Spacedml, a distributed machine learning system for SIN platforms that applies dynamic model compression techniques to adapt distributed machine learning training to SINs' limited bandwidth and unstable connectivity. Spaced-ml has two key algorithm:s adaptive loss-aware quantization, which compresses models without sacrificing their quality, and partial weight averaging, which selectively averages active clients' partial model updates. These algorithms jointly improve communication efficiency and enhance the scalability of distributed machine learning with SIN devices. We evaluate Spacedml by training a LeNet-S model on the MNIST dataset. The experimental results show that Spacedml can increase model accuracy by 2–3 percent and reduce communication bandwidth consumption by up to 60 percent compared to the baseline algorithm.
Time-Instance Zero-Crossing Precoding with Quality-of-Service Constraints

Analog-to-digital converters (ADCs) of low-resolution are promising for future wireless communications systems that require low power consumption and low hardware complexity. In this study, we investigate a waveform design for a bandlimited multi-user MIMO downlink scenario with 1-bit quantization and oversampling at the receivers. The novel modulation implies that the information is conveyed into the time instances of zero-crossings. Different to prior studies, the proposed precoding method minimizes the transmit power while taking into account quality of service constraints in terms of the minimum distance to the decision threshold. Unlike the precoding methods with a total power constraint, the bit error rate decreases significantly faster as a function of the average SNR.
Switching Dynamics of Ag-Based Filamentary Volatile Resistive Switching Devices—Part I: Experimental Characterization

Volatile resistive switching random access memory (RRAM) devices are drawing attention in both storage and computing applications due to their high ON-/ OFF-ratio, fast switching speed, low leakage, and scalability. However, these devices are relatively new and the physical switching mechanisms are still under investigation. A thorough understanding and modeling of the physical dynamics underlying filament formation and self-dissolution are of utmost importance in view of future integration of volatile devices in neuromorphic systems and in memory arrays. To assess the physical mechanisms and develop appropriate models, though, the electrical properties of the device have to be characterized. In this article, we present an extensive study of Ag/SiOx-based volatile RRAM devices. Important parameters, such as switching time, switching voltage, and retention time are investigated as a function of the stimulation conditions. A physical explanation is provided and the applicability of the device in neuromorphic systems is discussed.
Silicon Nanotexture Surface Area Mapping Using Ultraviolet Reflectance

The enhanced surface area of silicon nanotexture is an important metric for solar cell integration as it affects multiple properties including optical reflectance, dopant diffusion, and surface recombination. Silicon nanotexture is typically characterized by its surface-area-to-projected-area ratio or enhanced area factor (EAF). However, traditional approaches for measuring EAF provide limited statistics, making correlation studies difficult. In this article, silicon's dominant ultraviolet reflectance peak, R(E2), which is very sensitive to surface etching, is applied to EAF spatial mapping. A clear decay correlation between R(E2) and EAF is shown for multiple textures created using reactive ion etching and metal catalyzed chemical etching. This correlation is applied to R(280 nm) reflectance mapping to yield accurate, high-resolution full-wafer EAF spatial mapping of silicon nanotextures. R(280 nm) mapping is also shown to be sensitive enough to correlate the impact of nanotexture spatial variation on post-diffusion sheet resistance. Finite-difference time-domain simulations of several nanoscale pyramid textures confirm a decay band for R(E2) versus EAF, consistent with our measurements. We suggest that R(E2) mapping may prove useful for other silicon nanotexture properties and applications where EAF is important.
Transcriptome Analysis Reveals the Important Role of WRKY28 in Fusarium oxysporum Resistance

Root rot of Populus davidiana × P. alba var. pyramidalis Louche (Pdpap) is caused by Fusarium oxysporum. We used RNA sequencing to study the molecular mechanisms and response pattern of Pdpap infected by F. oxysporum CFCC86068. We cloned the PdpapWRKY28 transcription factor gene and transformed the recombinant vector pBI121-PdpapWRKY28 into Pdpap. The resistance function of PdpapWRKY28 was verified using physiological and biochemical methods. By means of RNA sequencing, we detected 1,403 differentially expressed genes (DEGs) that are common in the different treatments by F. oxysporum. Furthermore, we found that overexpression of the PdpapWRKY28 gene may significantly improve the resistance of Pdpap plants to F. oxysporum. Our research reveals a key role for PdpapWRKY28 in the resistance response of Pdpap to F. oxysporum. Additionally, our results provide a theoretical basis for in-depth research on resistance breeding to combat root rot.