Miracle-dz/UniFAR-FTUs huggingface dataset:
A unifying method-based classification of robot swarm spatial self-organisation behaviours

Self-organisation in robot swarms can produce collective behaviours, particularly through spatial self-organisation. For example, it can be used to ensure that the robots in a swarm move collectively. However, from a designer’s point of view, understanding precisely what happens in a swarm that allows these behaviours to emerge at the macroscopic level remains a difficult task. The same behaviour can come from multiple different controllers (ie the control algorithm of a robot) and a single controller can give rise to multiple different behaviours, sometimes caused by slight changes in self-organisation. To grasp the causes of these differences, it is necessary to investigate the relationships between the many methods of self-organisation that exist and the various behaviours that can be obtained. The work presented here addresses self-organisation in robot swarms by focusing on the main behaviours that lead to spatial self-organisation of the robots. First, we propose a unified definition of the different behaviours and present an original classification system highlighting ten self-organisation methods that each allow one or more behaviours to be performed. An analysis, based on this classification system, links the identified mechanisms with behaviours that could be considered as obtainable or not. Finally, we discuss some perspectives on this work, notably from the point of view of an operator or designer.
Psychobiotic Lactobacillus plantarum JYLP-326 relieves anxiety, depression, and insomnia symptoms in test anxious college via modulating the gut microbiota and its metabolism

Introduction Test anxiety is a common issue among college students, which can affect their physical and psychological health. However, effective interventions or therapeutic strategies are still lacking. This study aims to evaluate the potential effects of Lactobacillus plantarum JYLP-326 on test anxious college students. Methods Sixty anxious students were enrolled and randomly allocated to the placebo group and the probiotic group. Both groups were instructed to take placebo and JYLP-326 products twice per day for three weeks, respectively. Thirty unanxious students with no treatments were assigned to a regular control group. The anxiety, depression, and insomnia questionnaires were used to measure students’ mental states at the baseline and the end of this study. 16S rRNA sequencing and untargeted metabolomics were performed to analyze the changes in the gut microbiota and fecal metabolism. Results The questionnaire results suggested that JYLP-326 administration could relieve the symptoms of anxiety, depression, and insomnia in test anxious students. The gut microbiomes of the placebo group showed a significantly greater diversity index than the control group (p < 0.05). An increased abundance of Bacteroides and Roseburia at the genus level was observed in the placebo group, and the relative abundance of Prevotella and Bifidobacterium decreased. Whereas, JYLP-326 administration could partly restore the disturbed gut microbiota. Additionally, test anxiety was correlated with disordered fecal metabolomics such as a higher Ethyl sulfate and a lower Cyclohexylamine, which could be reversed after taking JYLP-326. Furthermore, the changed microbiota and fecal metabolites were significantly associated with anxiety-related symptoms. Conclusion The results indicate that the intervention of L. plantarum JYLP-326 could be an effective strategy to alleviate anxiety, depression, and insomnia in test anxious college students. The potential mechanism underlying this effect could be related to the regulation of gut microbiota and fecal metabolites.
Slip and flow in pastes of soft particles: Direct observation and rheology

Microgel pastes and concentrated emulsions are shown to exhibit a generic slip behavior at low stresses when sheared near smooth surfaces. The magnitude of slip depends on the applied stress. Well above the yield stress, slip is negligible compared to the bulk flow. Just above the yield stress, slip becomes significant and the total deformation results from a combination of bulk flow and slip. At and below the yield stress, the bulk flow is negligible and the apparent motion is entirely due to wall slip. By directly imaging the deformation of pastes and from rheological measurements, we show that slip is characterized by universal scaling properties, which depend on solvent viscosity, bulk shear modulus, and particle size. A model based on elastohydrodynamic lubrication between the squeezed particles and the shearing surface explains these properties quantitatively.
Inference in an Authorship Problem

Abstract This study has four purposes: to provide a comparison of discrimination methods; to explore the problems presented by techniques based strongly on Bayes' theorem when they are used in a data analysis of large scale; to solve the authorship question of The Federalist papers; and to propose routine methods for solving other authorship problems. Word counts are the variables used for discrimination. Since the topic written about heavily influences the rate with which a word is used, care in selection of words is necessary. The filler words of the language such as an, of, and upon, and, more generally, articles, prepositions, and conjunctions provide fairly stable rates, whereas more meaningful words like war, executive, and legislature do not. After an investigation of the distribution of these counts, the authors execute an analysis employing the usual discriminant function and an analysis based on Bayesian methods. The conclusions about the authorship problem are that Madison rather than Hamilton .
A new Rosslor hyperchaotic system and its realization with systematic circuit parameter design

Based on two modified Rosslor hyperchaotic systems, which are derived from the chaotic Rosslor system by introducing a state feedback controller, this paper proposes a new switched Rosslor hyperchaotic system. The switched system contains two different hyperchaotic systems and can change its behaviour continuously from one to another via a switching function. On the other hand, it presents a systematic method for designing the circuit of realizing the proposed hyperchaotic system. In this design, circuit state equations are written in normalized dimensionless form by rescaling the time variable. Furthermore, an analogous circuit is designed by using the proposed method and built for verifying the new hyperchaos and the design method. Experimental results show a good agreement between numerical simulations and experimental results.
Chapman–Enskog solution for diffusion: Pidduck’s equation for arbitrary mass ratio

In several studies of rarefied gas dynamics and particle transport, not only the diffusion coefficient, but also a detailed description of the Chapman–Enskog solution for diffusion is required. The case of diffusion of a trace species in a background gas is considered for arbitrary molecular mass ratio, and accurate results are reported for the Chapman–Enskog solution obtained by solving numerically an integral equation of Pidduck.
Optically switched resonant tunneling diodes

The room‐temperature photoinduced switching of an InGaAs/AlAs resonant‐tunneling diode is demonstrated. When illuminated at an irradiance of greater than 20 W cm−2 using 1.3 μm radiation, the resonant‐tunneling diode switches from a high‐conductance to a low‐conductance electrical state and exhibits a voltage swing of 600 mV. The switching characteristics are reversible and, in the absence of light, the detector returns to its original high conductance operating state. Small‐signal optical measurements performed with the device biased prior to resonance demonstrate a 3 dB bandwidth of ∼1.5 GHz.
A Multi-Level Systems Biology Analysis of Aldrin’s Metabolic Effects on Prostate Cancer Cells

Although numerous studies support a dose–effect relationship between Endocrine disruptors (EDs) and the progression and malignancy of tumors, the impact of a chronic exposure to non-lethal concentrations of EDs in cancer remains unknown. More specifically, a number of studies have reported the impact of Aldrin on a variety of cancer types, including prostate cancer. In previous studies, we demonstrated the induction of the malignant phenotype in DU145 prostate cancer (PCa) cells after a chronic exposure to Aldrin (an ED). Proteins are pivotal in the regulation and control of a variety of cellular processes. However, the mechanisms responsible for the impact of ED on PCa and the role of proteins in this process are not yet well understood. Here, two complementary computational approaches have been employed to investigate the molecular processes underlying the acquisition of malignancy in prostate cancer. First, the metabolic reprogramming associated with the chronic exposure to Aldrin in DU145 cells was studied by integrating transcriptomics and metabolomics via constraint-based metabolic modeling. Second, gene set enrichment analysis was applied to determine (i) altered regulatory pathways and (ii) the correlation between changes in the transcriptomic profile of Aldrin-exposed cells and tumor progression in various types of cancer. Experimental validation confirmed predictions revealing a disruption in metabolic and regulatory pathways. This alteration results in the modification of protein levels crucial in regulating triacylglyceride/cholesterol, linked to the malignant phenotype observed in Aldrin-exposed cells.
Feature Encoding Strategies for Multi-View Image Classification

Machine vision systems can vary greatly in size and complexity depending on the task at hand. However, the purpose of inspection, quality and reliability remains the same. This work sets out to bridge the gap between traditional machine vision and computer vision. By applying powerful computer vision techniques, we are able to achieve more robust solutions in manufacturing settings. This thesis presents a framework for applying powerful new image classification techniques used for image retrieval in the Bag of Words (BoW) framework. In addition, an exhaustive evaluation of commonly used feature pooling approaches is conducted with results showing that spatial augmentation can outperform mean and max descriptor pooling on an in-house dataset and the CalTech 3D dataset. The results of the experiments contained within, details a framework that performs classification using multiple view points. The results show that the feature encoding method known as Triangulation Embedding outperforms the Vector of Locally Aggregated Descriptors (VLAD) and the standard BoW framework with an accuracy of 99.28%. This improvement is also seen on the public Caltech 3D dataset where the improvement over VLAD and BoW was 5.64% and 12.23% respectively. This proposed multiple view classification system is also robust enough to handle the real world problem of camera failure and still classify with a high reliability. A missing camera input was simulated and showed that using the Triangulation Embedding method, the system could perform classification with a minor reduction in accuracy at 98.89%, compared to the BoW baseline at 96.60% using the same techniques. The presented solution tackles the traditional machine vision problem of object identification and also allows for the training of a machine vision system that can be done without any expert level knowledge.
Some properties of line congruences

In a previous paper the writer has obtained various formulae for surfaces in higher space by means of Cayley's functional method; and recently the same method has been applied to surfaces on a quadric form in [5] with the object of investigating the properties of line congruences in [3]. The purpose of the present note is two-fold: to indicate a few applications of the formulae mentioned above, and to give direct and (it is believed) novel proofs of some results obtained elsewhere by the functional process. The demonstrations given here differ, save in two cases, from those based on Schumacher's four-dimensional representation which, as it stands, applies only to congruences without double rays and therefore lacks generality. It was James who first noticed that a congruence without singularities has in general a finite number of double rays and is accordingly defined by five, instead of four, independent characters. These characters have been considered in previous work; but for present purposes it is convenient to describe them afresh.
Axial ratio beamwidth enhancement of a low-profile circularly polarized antenna using defected ground structures

Abstract This article explores the possibilities of incorporating a defected ground structure (DGS) for widening the 3 dB axial ratio beamwidth (ARBW) of a simple low-profile planar circularly polarized (CP) antenna. Two pairs of DGSs are etched symmetrically along the diagonals of the ground plane to overcome the limited 3 dB ARBW performance of a crossed-slotted circular-shaped planar CP antenna. Here, one pair of DGSs is orthogonal to another pair of DGSs. The distance between the pairs of DGSs plays a key role in enhancing the 3 dB ARBW by 40.62%. A gap of 0. 18λ0 is considered between the DGSs to maintain the symmetrical electric field distribution in the substrate. It also helps to reduce orthogonal current components and provides uniformly distributed patch surface currents. These phenomena collectively help the proposed CP antenna to exhibit right-handed circular polarization radiation with the 3 dB ARBW of 186° and 188° in the E-plane and H-plane, respectively. The measured results yield impedance bandwidth (S11 ≤ −10 dB) of 2.6% (2.283–2.342 GHz), CP bandwidth of 0.9% (2.294–2.315 GHz), gain higher than 4.8 dBic, and total antenna efficiency more than 64% across the entire CP band.
Multilayer model for Hall effect data analysis of semiconductor structures with step-changed conductivity

We present a multilayer model for analysis of Hall effect data of semiconductor structures composed of sublayers with different thicknesses and contacts placed on the top surface. Based on the circuit theory we analyze the contributions of the conductivity of every sublayer and derive general expressions for the conductivity and carrier mobility of a multilayer planar sample. The circuit analysis is performed taking into account the fact that the sample sublayers are partially connected in parallel to each other by series resistances formed in areas lying below the contacts from each upper layer. In order to solve the inverse problem of determining the electrical parameters of one of the sublayers, a procedure for analysis of the Hall effect data is proposed. The model is simplified for a structure composed of two layers with the same type of conductivity, and is used to determine the electrical parameters of GaN films grown on relatively thick GaN buffers.
An Efficient Accelerator Based on Lightweight Deformable 3D-CNN for Video Super-Resolution

Deformable convolutional networks (DCNs) have shown outstanding potential in video super-resolution with their powerful inter-frame feature alignment. However, deploying DCNs on resource-limited devices is challenging, due to their high computational complexity and irregular memory accesses. In this work, an algorithm-hardware co-optimization framework is proposed to accelerate the DCNs on field-programmable gate array (FPGA). Firstly, at the algorithm level, an anchor-based lightweight deformable network (ALDNet) is proposed to extract spatio-temporal information from the aligned features, boosting the visual effects with low model complexity. Secondly, to reduce intensive multiplications, an innovative shift-based deformable 3D convolution is developed using low-cost bit shifts and additions, maintaining comparable reconstruction quality. Thirdly, at the hardware level, a dedicated critical processing core, together with a block-level interleaving storage scheme, is presented to avoid dynamic and irregular memory accesses caused by the deformable convolutions. Finally, an overall architecture is designed to accelerate the ALDNet and implemented on an Intel Stratix 10GX platform. Experimental results demonstrate that the proposed design can provide significantly better visual perception than other FPGA-based super-resolution implementations. Meanwhile, compared with the prior hardware accelerators, our design can achieve $2.75\times $ and $1.63\times $ improvements in terms of throughput and energy efficiency, respectively.
Comparing Neural Network and Ordinal Logistic Regression to Analyze Attitude Responses

Many social studies analyze attitude responses using the linear regression model. This model typically treats questionnaire data as continuous scales, although the data is merely ordinal. One type of regression model that is more appropriate to analyze rank-order responses is the Ordinal Logistic Regression (OLR) model. In addition to the use of regression, the Artificial Neural Network (ANN) model has recently been applied in various studies. This paper delivered comparative descriptions of both the ANN and OLR models. The theoretical features and properties, which include parameters, variable selection, and model evaluation, followed by comparisons of the disadvantages and advantages of both models were analytically reviewed. [Service Science, ISSN 2164-3962 (print), ISSN 2164-3970 (online), was published by Services Science Global (SSG) from 2009 to 2011 as issues under ISBN 978-1-4276-2090-3. ]
Data-driven few-shot crop pest detection based on object pyramid for smart agriculture

Abstract. Farmers are often annoyed by crop pests, which can lead to serious losses if not detected in time. Deep learning has gradually penetrated into the agricultural field due to its proven superior performance, promoting the development of smart agriculture. Its real-time performance and effectiveness can help farmers spot pests in time and reduce losses. However, with the increasing application of deep learning technology, its disadvantage of relying heavily on large amounts of data has gradually been exposed. Before training a network, people need to spend a lot of manpower, time, and money to prepare a complete dataset and make the correct labels. To solve this problem, we introduced a less-shot object detection method in crop pest detection. We provide the first few-shot pest detection dataset (FSPD), which contains 20 categories and 2000 samples for this research. We use a few-shot object detection network based on an object pyramid called multiscale positive sample refinement. Numerous experiments are conducted to prove that, with only few (1/2/3/5/10-shot) samples, the accuracy of our method on FSPD is better than that of traditional deep learning methods, such as YOLO and faster R-CNN. After embedding the object pyramid branch, the network performance improves by an average of 5.9% on three splits compared with that without embedding. With input of only 10-shot novel samples, our method can achieve 76%, 79%, and 82% for the mAP on three splits, respectively. As a research study on the detection of crop diseases and pests with less radiation, our work can give some new insight to the future scholars, which is of great significance to the development of smart agriculture.
Hydrogen emission by Nd-YAG laser-induced shock wave plasma and its application to the quantitative analysis of zircalloy

An experiment was carried out to demonstrate the detection of a hydrogen emission line, HI656.2nm (Hα), in a plasma induced by a Q-switched Nd-YAG (YAG, yttrium aluminium garnet) laser in a low pressure gas on various types of samples, such as zinc, a glass slide, and a zircalloy tube. Contribution by surface water could be suppressed by a laser cleaning treatment and the resulting calibration curve obtained for zircalloy tube samples doped with various concentrations of hydrogen (0, 200, 540, and 960) suggest potential applications to the quantitative analysis of hydrogen. A study of the dynamic process represented by the time profiles of the hydrogen emission, in comparison with those for zinc atomic emission, revealed a specific feature that is related to the small mass of hydrogen. This specific feature can be explained by the shock wave excitation mechanism in terms of new hypothetical process, namely, a mismatch between the movement of ablated hydrogen atoms and the formation of the shock wave.
Portfolio Optimization Using Minimum Spanning Tree Model in the Moroccan Stock Exchange Market

Portfolio optimization is a pertinent topic of significant importance in the financial literature. During the portfolio construction, an investor confronts two important steps: portfolio selection and portfolio allocation. This article seeks to investigate portfolio optimization based on the Minimum Spanning Tree (MST) method applied on the Moroccan All Shares Index (MASI) historical stock log returns covering the period from 2 January 2013 to 27 October 2022 allowing us to build two portfolios: MST-Portfolio and MST-Portfolio 2. Portfolio selection was carried out for MST-Portfolio and MST-Portfolio 2, respectively, based on 63 stocks or using the Degree Centrality (DC) measure and portfolio allocation for both portfolios was carried through the use of the Inverse Degree Centrality Portfolio (IDCP). The obtained portfolios were compared with the Minimum Variance Portfolio (MV Portfolio) and Equal Weighting Portfolio (EW Portfolio) using centrality measures, diversification, and backtesting. According to the used indicators analysis, MST-Portfolio and MST-Portfolio 2 are the most well-performed and robust portfolios showing a good performance during the studied period, even during the COVID-19 crisis, and ensuring a good level of diversification. The findings demonstrate that both suggested methods can enhance portfolio performance, evidence that can help investors or active managers when optimizing their portfolios.
Lift force reduction due to body image of vortex for a hovering flight model

Abstract The effect of the body on the lift force in hovering flight is studied here by including the effect of image vortex rings (IVRs) in the inviscid vortex ring model proposed by Rayner (J. Fluid Mech., vol. 91, 1979, pp. 697–730) and used by Wang & Wu (J. Fluid Mech., vol. 654, 2010, pp. 453–472) to study lift force due to wakes. The body is treated simply as an equivalent sphere following the data of Ellington (Phil. Trans. R. Soc. Lond. B, vol. 305, 1984a, pp. 17–40). It is shown that the body image reduces the lift by inducing a further downwash near the wing tip and an additional contraction to the real vortex rings (RVRs). The amount of force reduction due to body image is found to grow cubically with relative body size, defined by the equivalent radius relative to the wing span, and approximately linearly with the feathering parameter. For Apis and Bombus with large relative body size and large feathering parameter, the body images reduce lift by an amount near 8 % according to the present simplified analysis.