Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Graphene on Yield, Grain Quality, 2-AP Biosynthesis and Antioxidant Systems of Fragrant Rice

The application of nanotechnology in agricultural neighborhoods is rapidly developing with the aim of promoting growth and enhancing crop tolerance to environmental stresses. However, there are fewer reports on the application of graphene nanoparticles in practical production, especially in fragrant rice. In early-season and late-season pot experiments conducted in 2022, the effects of graphene on the yield, grain quality, 2-acetyl-1-pyrroline (2-AP) and antioxidant systems of two fragrant rice cultivars (19× and Meixiangzhan) were examined at concentrations of 9 g/hm2, 18 g/hm2 and 27 g/hm2. The results showed that graphene T1 treatment at 9 g/hm2 significantly increased the activity of PDH and P5CS, promoted the synthesis of proline and P5C and significantly increased the 2-AP content of the grains of the two fragrant rice cultivars by 10.33–39.88% and 22.05–65.76%, respectively, in both growing seasons. Meanwhile, the lower concentration of T1 treatment (9 g/hm2) increased the grains per panicle and 1000-grain weight, enhancing the grain yield of both fragrant rice cultivars. The T1 treatment (9 g/hm2) had significant effects on the appearance and nutritional quality of both fragrant rice cultivars. It increased the head rice rate and protein content of the grains while also increasing the amylose content of 19× and reducing the chalkiness degree of 19×. Conversely, the T1 treatment reduced the amylose content and increased the chalkiness degree of Meixiangzhan. In addition, the low concentration of T1 treatment significantly increased the POD and SOD activities, increased the content of photosynthetic pigments and decreased the content of MDA in the leaves. However, 18 g/hm2 and 27 g/hm2 had slightly negative effects on yield, grain quality and fragrance biosynthesis in both fragrant rice cultivars. Furthermore, the results of structural equation modeling showed that antioxidant enzymes had a significant, positive effect on the grain’s 2-AP content and 2-AP synthesis-related enzyme activity, and photosynthetic pigments had a significant, positive effect on yield and grain appearance quality, while rice appearance quality and nutritional quality had significant, positive effects on yield. Overall, this study showed that suitable concentrations of graphene have good potential for use in fragrant rice production, but additional attention should be paid to the concentration of graphene application.
Generalized variational mode decomposition for interlayer slipping detection of viscoelastic sandwich cylindrical structures

The viscoelastic sandwich cylindrical structure (VSCS) is widely applied in aerospace, transportation, etc. Its health is closely related to the security of its own service and the entire set of equipment. Therefore, it is very important to detect its operating state. With a focus on the difficulty of weak feature extraction and the lack of efficient interlayer slipping detection indexes for the VSCS, this paper proposes a generalized variational mode decomposition (GVMD) method to extract the weak feature of the VSCS, and constructs a detection index to identify interlayer slipping fault. To this end, first, a GVMD method is proposed to decompose the original signal into a set of band-limited components (called variational mode functions, VMFs) of interest by taking full advantage of prior information such as spectral locations and bandwidths. Then, the vibration signals from the symmetric positions of the VSCS are decomposed by GVMD to extract the VMFs as expected. Finally, the interlayer slipping detection index, namely the normalized energy of the VMFs, is constructed to identify the interlayer slipping fault. And the interlayer slipping symptoms are explored. The effectiveness of GVMD and the proposed detection index is verified by the simulation and the experiment. The results show that: (1) compared with VMD and complementary ensemble empirical mode decomposition, GVMD can expectedly extract feature components (including weak feature components) owing to its excellent performance. (2) The proposed detection index can effectively identify the interlayer slipping fault of the VSCS, and the frequency components near 2× are sensitive to interlayer slipping fault.
On the evaluation of second order phase statistics in SAR interferogram stacks

During the last decades, time-series interferometric synthetic aperture radar (InSAR) has been emerged as a powerful technique to measure various surface deformation phenomena of the earth. The multivariate statistics of interferometric phase stacks plays an important role in the performance of different InSAR methodologies and also in the final quality description of InSAR derived products. The multivariate phase statistics are ideally described by a joint probability distribution function (PDF) of interferometric phases, whose closed-form evaluation in a generic form is very complicated and is not found in the literature. Focusing on the first two statistical moments, the stack phase statistics can be effectively described by a full (co)variance matrix. Although a closed-form expression of interferometric phase variances has been derived in literature for single-looked pixels, there is no such an expression for neither the variances of the multilooked pixels nor the covariances among interferometric phases. This paper presents two different approaches for evaluation of the full covariance matrix: one based on the numerical Monte-Carlo integration and the other based on an analytical approximation using nonlinear error propagation. We first, clarify on the noise components that are the subject of statistical models of this paper. Then, the complex statistics in SAR stacks and the phase statistics in a single interferogram are reviewed, followed by the phase statistics in InSAR stacks in terms of second statistical moments. The Monte-Carlo approach and the derivation of an analytical closed-form evaluation of InSAR second-order phase statistics are then introduced in details. Finally, the two proposed methods are validated against each other.
Medical image measurement and characterization: extracting mechanical and thermal stresses for surgery

Whatever the type of surgery related to inner organs, traditional or robotic, the contact with them during surgery is a key moment for pursuing the intervention. Contacts by means of surgery instruments namely scalpels, staples, clamps, graspers, etc. are decisive moments. False, and erroneous touching and manoeu-vring of organs operated on can cause irreversible damage as regard morphological aspects (outer impact) and physiological aspects (inner impact). The topic is a great challenge in the eﬀort to measure and characterize damages. In general, electrical instruments for surgery employ the following technologies: ultrasound, radiofrequency (monopolar, and bipolar), and laser. They all result in thermal damages diﬃcult to evaluate. The article proposes a method for a pre-screening of organ features during robotic surgery sessions by pointing out mechanical and thermal stresses. A dedicated modelling has been developed based on experimental activities during surgery session. The idea is to model tissue behaviour from real images to help surgeons to be aware of handling during surgery. This is the ﬁrst step for generalization by considering the type of organ. The measurement acquisitions have been performed by means of an advanced external camera located over the surgery quadrant. The modelling and testing have been carried out on kidneys. The modelling, carried out through Comsol Multiphysics, is based on the bioheat approach. A further comparative technique has been implemented. It is based on computer vision for robotics. The ﬁndings of human tissue behavior exhibit reliable results.
Actuarial Risk Matrices: The Nearest Positive Semidefinite Matrix Problem

The manner in which a group of insurance risks are interrelated is commonly presented via a correlation matrix. Actuarial risk correlation matrices are often constructed using output from disparate modeling sources and can be subjectively adjusted, for example, increasing the estimated correlation between two risk sources to confer reserving prudence. Hence, while individual elements still obey the assumptions of correlation values, the overall matrix is often not mathematically valid (not positive semidefinite). This can prove problematic in using the matrix in statistical models. The first objective of this article is to review existing techniques that address the nearest positive semidefinite matrix problem in a very general setting. The chief approaches studied are Semidefinite Programming (SDP) and the Alternating Projections Method (APM). The second objective is to finesse the original problem specification to consider imposition of a block structure on the initial risk correlation matrix. This commonly employed technique identifies off-diagonal subsets of the matrix where values can or should be set equal to some constant. This may be due to similarity of the underlying risks and/or with the goal of increasing computational efficiency for processes involving large matrices. Implementation of further linear constraints of this nature requires adaptation of the standard SDP and APM algorithms. In addition, a new Shrinking Method is proposed to provide an alternative solution in the context of this increased complexity. “Nearness” is primarily considered in terms of two summary measures for differences between matrices: the Chebychev Norm (maximum element distance) and the Frobenius Norm (sum of squared element distances). Among the existing methods, adapted to function appropriately for actuarial risk matrices, APM is extremely efficient in producing solutions that are optimal in the Frobenius norm. An efficient algorithm that would return a positive semidefinite matrix that is optimal in Chebychev norm is currently unknown. However, APM is used to highlight the existence of matrices close to such an optimum and exploited, via the Shrinking Method, to find high-quality solutions. All methods are shown to work well both on artificial and real actuarial risk matrices provided under collaboration with Tokio Marine Kiln (TMK). Convergence speeds are calculated and compared and sample data and MATLAB code is provided. Ultimately the APM is identified as being superior in Frobenius distance and convergence speed. The Shrinking Method, building on the output of the APM algorithm, is demonstrated to provide excellent results at low computational cost for minimizing Chebychev distance.
A feedback weighted fusion algorithm with dynamic sensor bias correction for gyroscope array

Low-cost Micro-Electromechanical System (MEMS) gyroscopes are known to have a smaller size, lower weight,andlesspowerconsumptionthantheirmoretechnologicallyadvancedcounterparts.However,current low-gradeMEMSgyroscopeshavepoorperformanceandcannotcompetewithqualitysensorsinhigh accuracynavigationalandguidanceapplications.Themainfocusofthispaperistoinvestigateperformance improvementsbyfusingmultiplehomogeneousMEMSgyroscopes.Thesegyrosaretransformedinto avirtualgyrousingafeedbackweightedfusionalgorithmwithdynamicsensorbiascorrection.The gyroscopearraycombineseighthomogeneousgyroscopeunitsoneachaxisanddividesthemintotwo layersofdiﬀerentialconﬁguration. Thealgorithmusesthegyroscopearrayestimationvaluetoremovethe gyroscopebiasandthencorrectthegyroscopearraymeasurementvalue.Thenthegyroscopevarianceis recalculatedinrealtimeaccordingtotherevisedmeasurementvalueandtheweightedcoeﬃcientsand stateestimationofeachgyroscopearededucedaccordingtotheleastsquareprinciple.Thesimulations andexperimentsshowedthattheproposedalgorithmcouldfurtherreducethedriftandimprovetheoverall accuracybeyondtheperformancelimitationsofindividualgyroscopes.Themaximumcumulativeangle errorwas − 2 . 09 degrees after 2000 seconds in the static test, and the standard deviation (STD) of the output fusion value of the proposed algorithm was 0.006 degrees/s in the dynamic test, which was only 1.7% of the STD value of an individual gyroscope.
Identifying Anomalies in Industrial Networks: A Proposed Testbed for Experimental Evaluation

—Not only because of the convergence of Information Technology (IT) and Operational Technology (OT) networks, the pass-through network environment needs to be monitored and adequate security implemented. Due to the occurrence of different types of anomalies and their inconsistency in the literature, three main types of anomalies have been identified in this paper and a testbed has been proposed to serve for further experimental testing. This testbed was created using an anemometer and in the current state using an accelerometer. From the results so far, a correlation between the normal condition and the induced operational anomaly can be observed.
Arbitrary amplitude nucleus-acoustic solitons in multi-ion quantum plasmas with relativistically degenerate electrons

A three component degenerate relativistic quantum plasma (consisting of relativistically degenerate electrons, nondegenerate inertial light nuclei, and stationary heavy nuclei) is considered to model the linear wave and also the electrostatic solitary waves in the light nuclei-scale length. A well-known normal mode analysis is employed to investigate the linear wave properties. A mechanical-motion analog (Sagdeev-type) pseudo-potential approach, which reveals the existence of large amplitude solitary excitations, is adopted to study the nonlinear wave properties. Only the positive potential solitary excitations are found to exist in the plasma medium under consideration. The basic properties of the arbitrary amplitude electrostatic acoustic modes in the light nuclei-scale length and their existence domain in terms of soliton speed (Mach number) are examined. The modifications of solitary wave characteristics and their existence domain with the variation of different key plasma configuration parameters (e.
Feature extraction from point clouds for rigid aircraft part inspection using an improved Harris algorithm

In the process of feature extraction, geometric information about feature points is based on the nearest 1-ring field points, which are usually the edge points of the face adjacent to the feature line. The high structural complexity of rigid aircraft components leads to recognition of false feature points. Therefore, this paper proposes an improved Harris algorithm designed to improve the accuracy of feature extraction. It combines the discrete curvature and the normal vector in a way that is suitable for use with point clouds. Key to this approach is the use of changes in the gradient rather than changes in the curvature to identify corner, plane, and crease points. Our method accurately eliminates pseudo-feature points that are too close to actual feature points in order to refine potential feature points. We demonstrate the efficiency and robustness of the proposed method by validating it against standard models and actual scan data and by comparing it with other feature detection algorithms.
Digital media revitalising colonial heritage: the George Floyd video translocalized in Denmark

ABSTRACT In this paper, we analyse how the colonial spectrality of the George Floyd video translocalises from the United States to Denmark to form heritage assemblages of solidarity, removal and repression that re-vitalize the colonial past and anti-racist protests in contemporary contexts. Through our analysis, we unfold the affective capacities of the Floyd video and its memetic mutations that mobilize publics against asymmetries inherited from colonialism and invent new forms of activism. We show that while protester tactics following Floyd demonstrate a reflexive distribution of subjectivities and positionalities, minority voices and efforts to remove symbols of colonialism are condemned and suppressed in Denmark. Our main contribution is to include digital media into heritage assemblages as an affective device. A second contribution is presenting digital epidemiography as an affective methodology to remotely study transient and unpredictable global events, through an analysis of digital traces found on the internet. A third contribution is the knowledge of how post-Floyd heritage assemblages in Denmark position this Scandinavian country as a former colonizer in the decolonial turn.
Antibiotic Resistance/Susceptibility Profiles of Staphylococcus equorum Strains from Cheese, and Genome Analysis for Antibiotic Resistance Genes

In food, bacteria carrying antibiotic resistance genes could play a prominent role in the spread of resistance. Staphylococcus equorum populations can become large in a number of fermented foods, yet the antibiotic resistance properties of this species have been little studied. In this work, the resistance/susceptibility (R/S) profile of S. equorum strains (n = 30) from cheese to 16 antibiotics was determined by broth microdilution. The minimum inhibitory concentration (MIC) for all antibiotics was low in most strains, although higher MICs compatible with acquired genes were also noted. Genome analysis of 13 strains showed the S. equorum resistome to be composed of intrinsic mechanisms, acquired mutations, and acquired genes. As such, a plasmidic cat gene providing resistance to chloramphenicol was found in one strain; this was able to provide resistance to Staphylococcus aureus after electroporation. An msr(A) polymorphic gene was identified in five strains. The Mrs(A) variants were associated with variable resistance to erythromycin. However, the genetic data did not always correlate with the phenotype. As such, all strains harbored a polymorphic fosB/fosD gene, although only one acquired copy was associated with strong resistance to fosfomycin. Similarly, a plasmid-associated blaR1-blaZI operon encoding a penicillinase system was identified in five ampicillin- and penicillin G-susceptible strains. Identified genes not associated with phenotypic resistance further included mph(C) in two strains and norA in all strains. The antibiotic R/S status and gene content of S. equorum strains intended to be employed in food systems should be carefully determined.
On Local Lyapunov Exponents of Chaotic Hamiltonian Systems

Chaos in conservative systems, particularly in Hamiltonian systems, is different from chaos in dissipative systems. For example, not only the eigenvalues of the symmetric Jacobian, but also the global Lyapunov exponents of Hamiltonian systems occur in pairs (λ,−λ). In this article, we even show that appropriately defined local Lyapunov exponents occur in pairs, and in turn this allows to give a new and easily accessible proof of the pairing property for global Lyapunov exponents. As examples of low dimensional chaotic Hamiltonian systems, we discuss the classical Hénon-Heiles system and a sixth order generalisation. For the latter, there is numerical evidence of two disjoint chaotic seas.
A Comment on “Simple Estimators for Invertible Index Models”

The approach described by Ahn et al. (this issue) (henceforth AIPR) constitutes an elegant contribution to the “control function” literature, which focuses on semiparametric models where endogeneity or nonlinearities of unknown form can be captured by a control function (or “control variable”). Like the rest of this literature, the applicability of the method in AIPR presupposes the ability to identify (perhaps nonparametrically) the control function to have the ability to “match” (asymptotically) pairs of observations in a way that identifies the parameters of interest (see Equations (2.15)– (2.19) in AIPR). While the control function approach has been shown to have aided applicability (Ahn and Powell 1993; Honoré and Powell 1994; Blundell and Powell 2004; Honoré and Powell 2005; Imbens and Newey 2009; Hong and Shum 2010; Aradillas-Lopez, Honoré, and Powell 2007), in this note I argue that it is easy to construct examples of microeconometric models where matching is not possible, either because of interval data, missing data, or incomplete models (e.g, structural models with “multiple equilibria”). In doing this, I also want to argue that, while matching is not possible and the methodology in AIPR cannot be applied, the examples I present produce (conditional) moment inequalities which can be used to do inference on the parameters of interest by using recently developed methods involving conditional moment inequalities. This shows that control function models are powerful vehicles for inference even in partially identified settings.
Modified sequential processing terrain referenced navigation considering slant range measurement

This study proposes a modified terrain referenced navigation (TRN) algorithm considering slant range measurement. The TRN estimates a vehicle's position by comparing the measured terrain information with digital elevation map data. A conventional TRN assumes that a vertical range is measured by a radar altimeter and the measurement error exists only in the vertical direction. Recently, many researches have focused on applying the slant range sensor data, such as light detection and ranging or interferometric radar altimeter data to improve the TRN performance. In such cases, not only the vertical error but also the horizontal error components are generated by slant range measurement errors and the effect of these error components increases significantly as the flight altitude and the slant range measurement errors increase. Therefore, a proper TRN algorithm should be considered to estimate the vehicle position in a stable and accurate manner. In this study, a modified extended Kalman filter [EKF]-based TRN algorithm (ETRN) considering the slant range measurement and the corresponding measurement variance is proposed. The proposed algorithm converts the ETRN measuring the slant range to the conventional ETRN measuring the vertical range. Also, the proposed algorithm provides robust and accurate TRN results by accounting for the vertical and horizontal error components generated by the slant range errors.
A Survey on Digital Twin for Industrial Internet of Things: Applications, Technologies and Tools

Digital twin for the industrial Internet of Things (DT-IIoT) creates a high-fidelity, fine-grained, low-cost digital replica of the cyber-physical integrated Internet for industry. Powered by artificial intelligence (AI) and security technologies, DT-IIoT provides advanced features such as real-time monitoring, predictive maintenance, remote diagnostics, and rapid response for smart IIoT systems. A systematic review of key enabling technologies such as digital twin, AI, and blockchain is essential to develop DT-IIoT and reveal pitfalls. This paper reviews the preliminaries, real-world applications, architectures and models of digital twin-driven IIoT. In addition, advanced technologies for intelligent and secure DT-IIoT are investigated, including state-of-the-art AI solutions such as transfer learning and federated learning, as well as blockchain-based security solutions. Moreover, software tools for high-fidelity digital twin modeling are proposed. A case study on reinforcement learning-based integrated-control, communication, and computing (3C) design is developed to demonstrate the AI-driven intelligent DT-IIoT. Finally, this paper outlines the prospective applications, challenges, and integrations with ABCDE (i.e., AI, Blockchain, cloud computing, big data, edge computing) as the future directions.