Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Characterization of Multipath Routing Protocol in Different Mobility Scenarios

MANET is a self-organizing, distributed network with mobile, independent terminals that is simple to deploy and doesn’t need infrastructure. Due to such features, MANETs always seem to be vital for defence applications, particularly in nations like India with extensive geographical diversity. Routing protocols have a significant impact on the efficiency of a MANET because of its mobility. In this paper, the performance of AOMDV routing protocol is evaluated for a variety of mobility scenarios, such as realistic mobility models, random movement-based models as well as controlled movement-based models. Multiple mobility scenarios can be created using BonnMotion and MOVE tools in Network Simulator 2.35.
A decomposition multi-objective evolutionary algorithm based on vector arrangement and strategy adaptation mechanism

When a decomposition-based multi-objective evolutionary algorithm is used to solve complex multi-objective optimization problems (MOPs), the distribution of weight vectors cannot adapt to the different shapes of Pareto front surfaces, resulting in a poor distribution of feasible solutions. This paper proposes a decomposition multi-objective evolutionary algorithm (MOEA/D-VAS) based on vector programming and a strategy adaptive mechanism (SaM). In this algorithm, virtual vectors are added to the initial decomposition vector set, and weight vectors are screened by Pareto advantage and an adaptive function to improve the diversity of solutions. Additionally, the differential evolution operator uses SaM to select different greedy strategies for each individual, which improves the convergence speed of the algorithm and the accuracy of the weight vector to predict the true Pareto surface while effectively balancing the diversity and convergence of the solution. DTLZ and WFG are used to compare different algorithms. The results show that the convergence and distribution of the proposed algorithm are greatly improved, and the MOEA/D-VAS algorithm can adapt to more types of MOPs.
Metal-Free High-Overtone Bulk Acoustic Resonators With Outstanding Acoustic Match and Thermal Stability

This letter presents a novel type of epitaxial gallium nitride (GaN) on semi-insulating silicon carbide (4H-SiC)-based high-overtone bulk acoustic resonators (HBARs) that eliminates the need for metal electrodes. The device is piezoelectrically transduced through a quantum well induced two-dimensional electron gas (2DEG) and a heavily-doped GaN layer as the top and bottom electrodes, respectively. Room to cryogenic temperature evolution for 50 HBAR phonon modes near the fundamental transduction envelope (2.032 GHz) is demonstrated. It shows an exceptionally low temperature coefficient of frequency (TCF) at −5.709 ppm/K, a converged free spectral range (FSR) at 17.9 MHz, and a figure of merit <inline-formula> <tex-math notation="LaTeX">${f} \times {Q}$ </tex-math></inline-formula> at <inline-formula> <tex-math notation="LaTeX">$6.7\times 10^{{13}}$ </tex-math></inline-formula> Hz at 15 K (<inline-formula> <tex-math notation="LaTeX">$1.7\times 10^{{13}}$ </tex-math></inline-formula> Hz at 298 K). This is the result of complete acoustic and thermal impedance match due to a metal-free design, which is promising for sensing, microwave signal processing, and quantum acoustodynamics (QAD).
Dynamic Modeling and Real-time Simulation of Soft Manipulator

The simulation for a soft manipulator model is complicated due to the large number of free degrees and the high nonlinearity. In order to model the soft manipulator, a novel real-time simulation framework is established in this paper. Numerical integration and collision detection are separated from one another to decrease complexity and increase computing efficiency while maintaining accuracy standards. For numerical integration, modal derivatives are proposed for model reduction, they not only boost simulation efficiency but also make it easier to produce reduced order matrices, and the bounding box is utilized to get rid of superfluous collision pairs to speed up calculation. To confirm that the proposed technique is practical for configuration design and analysis, a soft manipulator with three cable actuations is simulated.
Ground Beetles (Coleoptera: Trachypachidae and Carabidae) of the Yukon: Geographical Distribution, Ecological Aspects, and Origin of the Extant Fauna

The Yukon ground-beetle fauna is analyzed in terms of species composition, geographical distribution, Wisconsinan and early Holocene fossils, habitat associations, and patterns of hindwing development. This analysis provides the basis for an interpretation about the development of the extant ground-beetle fauna of the Yukon Territory. In total, 209 species or subspecies of ground beetles are recorded from the Yukon Territory, of which at least 98, representing 46% of the extant ground-beetle fauna, are hypothesized to have been present in northwestern refugia during the Wisconsinan glaciation. This figure was derived from a combination of distributional and fossil evidence. The range of habitat associations exhibited by this autochthonous component suggests that the Beringian environment may have been more complex than suggested by the "mammoth steppe" or "polar desert" models. Our data are most consistent with the "mosaic model", in which a variety of habitats are hypothesized to have persisted within Beringia. The fauna associated with tundra, riparian zones, and marshes provides evidence that such habitats probably existed in Beringia during late Wisconsinan time, whereas the fauna associated with forest, grassy meadow, and other habitats shows only limited evidence of a long history in northwestern North America. This latter group is hypothesized to have been derived primarily from refugia south of Wisconsinan-age ice. The high degree of endemism in present-day tundra environments indicates long-term stability of that habitat. Analysis of the pattern of hindwing development indicates that brachyptery is most prevalent among the Beringian endemic component, whereas macroptery is dominant among the emigrant and immigrant components. This suggests the importance of flight in insect dispersal. The extensive ranges of the emigrant species provide evidence that Beringia was an important centre for repopulating the north following deglaciation. The corollary is that the number of species present in Beringia during Wisconsinan time may be underestimated if only restrictive distributional information is considered.
Virtual Fence Environment Perception Algorithm for Industrial Robots Based on 3D Vision

In order to solve the inconvenient problem of installing a physical fence for robots, especially the problem that the physical fence does not solve the problem of making robots actively perceive the environment, this paper proposes a multi-level virtual fence control system based on an RGBD camera. This strategy generates the spatial virtual fence point cloud in advance, and the virtual fence exists in the space of multiple levels. After the camera was installed above the robot and hand-eye calibration was carried out, the ambient point cloud captured by the RGBD camera was mapped to the coordinate system of the robot base. The ground is removed, the objects are extracted from the environmental point cloud, and the objects are segmented into clusters, and then the fence is divided into layers to construct KD-tree. In terms of object judgment, an improved KNN hierarchical search model with weight assignment of control points is proposed. Experiments show that the method is effective.
Magnetodisc modelling in Jupiter's magnetosphere using Juno magnetic field data and the paraboloid magnetic field model

Abstract. One of the main features of Jupiter's magnetosphere is its equatorial magnetodisc, which significantly increases the field strength and size of the magnetosphere. Analysis of Juno measurements of the magnetic field during the first 10 orbits covering the dawn to pre-dawn sector of the magnetosphere (∼03:30–06:00 local time) has allowed us to determine optimal parameters of the magnetodisc using the paraboloid magnetospheric magnetic field model, which employs analytic expressions for the magnetospheric current systems. Specifically, within the model we determine the size of the Jovian magnetodisc and the magnetic field strength at its outer edge.
Physiological Constraints and Comparative Economic Development

It is a well known fact that economic development and distance to the equator are positively correlated variables in the world today. It is perhaps less well known that as recently as 1500 C.E. it was the other way around. The present paper provides a theory of why the "latitude gradient" seemingly changed sign in the course of the last half millennium. In particular, we develop a dynamic model of economic and physiological development in which households decide upon the number and nutrition of their offspring. In this setting we demonstrate that relatively high metabolic costs of fertility, which may have emerged due to positive selection towards greater cold tolerance in locations away from the equator, would work to stifle economic development during pre-industrial times, yet allow for an early onset of sustained growth. As a result, the theory suggests a reversal of fortune whereby economic activity gradually shifts away from the equator in the process of long-term economic development.
Causal Discovery and Hidden Driving Force Estimation from Nonstationary/Heterogeneous Data

It is commonplace to encounter nonstationary or heterogeneous data. Such a distribution shift feature presents both challenges and opportunities for causal discovery, of which the underlying generating process changes over time or across domains. In this paper, we develop a principled framework for causal discovery from such data, called Constraint-based causal Discovery from NOnstationary/heterogeneous Data (CD-NOD), which addresses two important questions. First, we propose an enhanced constraint-based procedure to detect variables whose local mechanisms change and recover the skeleton of the causal structure over observed variables. Second, we present a way to determine causal orientations by making use of independent changes in the data distribution implied by the underlying causal model, benefiting from information carried by changing distributions. After learning the causal structure, next, we investigate how to efficiently estimate the `driving force' of the nonstationarity of a causal mechanism. That is, we aim to extract from data a low-dimensional and interpretable representation of changes. The proposed methods are totally nonparametric, with no restrictions on data distributions and causal mechanisms, and do not rely on window segmentation. Furthermore, we find that nonstationarity benefits causal structure identification with particular types of confounders. Finally, we show the tight connection between nonstationarity/heterogeneity and soft intervention in causal discovery. Experimental results on various synthetic and real-world data sets (task-fMRI and stock data) are presented to demonstrate the efficacy of the proposed methods.
A Uniform Control Allocation Method for Distributed Electric Propulsion Aircraft using Iterative-based Approach and Coordinate Transformation

This paper presents a novel control allocation method which can be used in all flight modes for Distributed Electric Propulsion (DEP) Vertical Takeoff and Landing (VTOL) aircraft. Since the multiple sets of tiltable propulsors of this type of aircraft would cause nonlinearity in the control effective model and add extra constraints to the model, a constant control allocation matrix can not be directly applied as in the case of quadrotors. To deal with the difficulties, a cascade two-step allocation method is proposed so that the constraints on the same propulsion sets can be naturally satisfied. Based on this, an iterative allocation method based on coordinate transformation is developed to adapt the unique geometry of the propulsors, the nonlinearity problem is then solved by breaking the control effective model into two subsystems, and the nonlinear problem can be iteratively solved from two linear system. Coordinate transformation is also introduced into the allocation to deal with the constantly changing geometry of the propulsors so that the method can work in all flight modes. Finally, the proposed method is implemented in our self-developed platform to carry out simulations and experiments, the results show our method is well suited to the unique features of this type of aircraft in all the flight modes.
Stock Price Prediction by Machine Learning Technique: Case Study of Ho Chi Minh Stock Exchange

The stock price is a principal component by investors. Stock prices need to be predicted in determining further stability. Predictive value is particularly challenging in marketing efficiency. This study aims to make precise forecasting. The method used in this study is the Artificial Neural Network (ANN) algorithm. Data processed sourced from the Ho Chi Minh Stock Exchange (HOSE) includes all securities in the VN30 portfolio from 28 July 2000 to 30 July 2021. The results obtained are two layers with an average Root Means Square Error (RMSE) in the training set of 0.185 and 0.21 in the test set. The results of the T-test show that the model is still overfitting.
Automatic Generation of Vectorizing Compilers for Customizable Digital Signal Processors

Embedded applications extract the best power–performance trade-off from digital signal processors (DSPs) by making extensive use of vectorized execution. Rather than hand-writing the many customized kernels these applications use, DSP engineers rely on auto-vectorizing compilers to quickly produce effective code. Building these compilers is a large and error-prone investment, and each new DSP architecture or application-specific ISA customization must repeat this effort to derive a new high-performance compiler. We present Isaria, a framework for automatically generating vectorizing compilers for DSP architectures. Isaria uses equality saturation to search for vectorized DSP code using a system of rewrite rules. Rather than hand-crafting these rules, Isaria automatically synthesizes sound rewrite rules from an ISA specification, discovers phase structure within these rules that improve compilation performance, and schedules their application at compile time while pruning intermediate states of the search. We use Isaria to generate a compiler for an industrial DSP architecture, and show that the resulting kernels outperform existing DSP libraries by up to 6.9 × and are competitive with those generated by expert-built compilers. We also demonstrate how Isaria can speed up exploration of new ISA customizations by automatically generating a high-quality vectorizing compiler.
iMAT: Energy-Efficient In-Memory Acceleration for Ternary Neural Networks With Sparse Dot Product

Ternary Neural Networks (TNNs) achieve an excellent trade-off between model size, speed, and accuracy, quantizing weights and activations into ternary values {+1, 0, -1}. The ternary multiplication operations in TNNs equal light-weight bitwise operations, favorably in In-Memory Computing (IMC) platforms. Therefore, many IMC-based TNN accelerators have been proposed. They build dedicated ternary multiplication cells or utilize efficient bitwise operations on IMC architectures. However, existing ternary value accumulation schemes on IMC architectures are inefficient. They extend the sign bit of integer operands or conduct two-round accumulation with specially designed encoding, bringing long latency and extra memory write overhead. Moreover, existing IMC-based TNN accelerators overlook TNNs' sparsity and conduct operations on zero weights, resulting in unnecessary power consumption and latency. In this paper, we propose iMAT to accelerate TNNs with operator-, architecture- and layer-level optimizations. First, we propose a single-round Ternary Variable-Bitwidth Accumulation scheme, which efficiently extends the addition result sign bit without extra memory write overhead. Second, we propose an in-memory accelerator with enhanced sensing circuits for the accumulation scheme and a Sparse Dot Product Unit to exploit TNNs' weight sparsity, utilizing zero weights to skip unnecessary operations. Further, we propose Fused Scaling Functions which combine the scaling, activation, normalization, and quantization layers to reduce the hardware complexity without affecting the model accuracy. Simulation results show that compared with dense in-memory TNN accelerators, our iMAT achieves up to 2.7× speedup and 3.7×energy efficiency on ternary ResNet-18.
Quantized Sliding Mode Control for Semi-Markovian Jump Systems with Partially Accessible Transition Rates and Asymmetric Dead Zone Input

The problem of sliding-mode control (SMC) design is investigated for semi-Markovian jump systems (S-MJSs) with uncertain parameters. Compared with previous literature, the effects of signals quantization and asymmetric dead-zone input are considered simultaneously. To ensure stochastic stability of the corresponding system with generally uncertain transition rate (TR), we present sufficient conditions by employing the weak infinitesimal operator theory. Subsequently, the SMC law is designed to drive the states towards the predefined sliding surface, even under conditions of signals quantization and asymmetric dead-zone input. Finally, our theoretical findings are validated through a numerical simulation example, which demonstrates their effectiveness.
indexes and compliance with mandatory disclosure — The case of intangible assets in the Italian market

Disclosure of ﬁnancial statements is an important topic both for investors and for preparers as disclosure allows investors to understand the application of the accounting principles used by companies. This research examines the 2010 ﬁnancial statements under IFRS of 189 Italian listed groups and their compliance with mandatory disclosure on intangible assets and presents an in depth empirical analysis of the Italian market – that belongs to the Continental European accounting cluster. Different variables were tested to analyze the compliance with the mandatory disclosure such as size variables, performance variables, ﬁnancial interest variables and market variables. Many studies on mandatory disclosure are based on one disclosure index method and results are affected by the different approaches used: Cooke’s dichotomous approach, Partial Compliance method, weighted and unweighted. In this paper, the decision was taken to run all the previous methods: results show that the only signiﬁcant variable for all Dscore indexes is the weight of interests on revenues and this result is a distinctive feature of the Italian market where the role of the banking systems is more important than in other countries.
Traffic Sign Recognition

: Traffic sign detection and recognition are significant in the development of intelligent vehicles. And enhanced traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. Firstly, the HSV is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, the model is considerably improved on the basis of the classical convolutional neural network model by using Gabor kernel as the initial convolutional kernel, adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm. Finally, the traffic sign categorization and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark. The favourable prediction and specific recognition of traffic signs are achieved through the continuous training and testing of the network model. Experimental results show that the specific recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. Compared with other algorithms, the proposed algorithm has remarkable accuracy and real-time performance, strong stimulus generalization ability and high training efficiency. The specific recognition rate and average processing time are markedly improved. This improvement is of considerable importance to reduce the accident rate and enhance the road traffic safety situation, providing a strong technical guarantee for the steady development of intelligent vehicle driving assistance.
Establishment of Disturbance Model for Coke Oven Flue Gas Denitrification System

The modeling and control of coke oven flue gas denitrification system has always been acknowledged as a challenging problem due to the large delay and strong disturbance of the system. The disturbance model of the denitrification system is described using the CARMA model in this paper. The batch least squares identification method is used to obtain the model parameters after the disturbance model of the controlled object has been established based on the identification of data collected in the industrial field. According to the results, the disturbance model which was developed for flue gas denitrification system has a high degree of accuracy.
Analysis on suitable distribution area of oriental fruit fly (Bactrocera dorsalis) based on GARP ecological niche model.

[Objective] The paper was to provide the scientific basis for effective monitoring and early warning on oriental fruit fly in Jiangxi Province. [Method] Using the distribution data and environmental factor data of oriental fruit fly,the potential distribution area of oriental fruit fly in Jiangxi Province was predicted by GARP ecological niche model. [Result]The total suitable area for oriental fruit fly in Jiangxi province was over two thirds of the whole province.Most areas in southern Jiangxi basin ranged from 24° to 26° N were the high suitable area for oriental fruit fly,where the annual average temperature was 19-20℃. Part areas in north Ganzhou and small areas in southwest Ji'an,Wuzhou,Yingtan,southeast Shangrao ranged from 26° to 27° N were the moderate suitable areas for oriental fruit fly,where the annual average temperature was 18-19℃. The low suitable area was ranged from 26° to 30° N including Binhu,Ganjiang River,Wuhe,Yuanshui area and Southwestern Mountain of Jiangxi Province,where the annual average temperature was 17-18℃. Northeast and northwest areas in Jiangxi Province and coastal areas along the Yangtze River was predicted to be non-suitable area for oriental fruit fly,where the annual average temperature was 16-17℃. [Conclusion] The results showed that the actual distribution of oriental fruit fly basically was consistent with the distribution predicted by GARP.