Miracle-dz/UniFAR-FTUs huggingface dataset:
Nodeless superconductivity in the noncentrosymmetric compound ThIrSi

The ThIrSi superconductor, with $T_c = 6.5$ K, is expected to show unusual features in view of its noncentrosymmetric structure and the presence of heavy elements featuring a sizable spin-orbit coupling. Here, we report a comprehensive study of its electronic properties by means of local-probe techniques: muon-spin rotation and relaxation ({\textmu}SR) and nuclear magnetic resonance (NMR). Both the superfluid density $\rho_\mathrm{sc}(T)$ (determined via transverse-field {\textmu}SR) and the spin-lattice relaxation rate $T_1^{-1}(T)$ (determined via NMR) suggest a nodeless superconductivity. Furthermore, the absence of spontaneous magnetic fields below $T_c$, as evinced from zero-field {\textmu}SR measurements, indicates a preserved time-reversal symmetry in the superconducting state of ThIrSi. Temperature-dependent upper critical fields as well as field-dependent superconducting muon-spin relaxations suggest the presence of multiple superconducting gaps in ThIrSi.
Cross-Domain Open Set Fault Diagnosis Based on Weighted Domain Adaptation with Double Classifiers

The application of transfer learning in fault diagnosis has been developed in recent years. It can use existing data to solve the problem of fault recognition under different working conditions. Due to the complexity of the equipment and the openness of the working environment in industrial production, the status of the equipment is changeable, and the collected signals can have new fault classes. Therefore, the open set recognition ability of the transfer learning method is an urgent research direction. The existing transfer learning model can have a severe negative transfer problem when solving the open set problem, resulting in the aliasing of samples in the feature space and the inability to separate the unknown classes. To solve this problem, we propose a Weighted Domain Adaptation with Double Classifiers (WDADC) method. Specifically, WDADC designs the weighting module based on Jensen–Shannon divergence, which can evaluate the similarity between each sample in the target domain and each class in the source domain. Based on this similarity, a weighted loss is constructed to promote the positive transfer between shared classes in the two domains to realize the recognition of shared classes and the separation of unknown classes. In addition, the structure of double classifiers in WDADC can mitigate the overfitting of the model by maximizing the discrepancy, which helps extract the domain-invariant and class-separable features of the samples when the discrepancy between the two domains is large. The model’s performance is verified in several fault datasets of rotating machinery. The results show that the method is effective in open set fault diagnosis and superior to the common domain adaptation methods.
Spatial and temporal variations of net ecosystem productivity in Xinjiang Autonomous Region, China based on remote sensing

Net ecosystem productivity (NEP), which plays a key role in the carbon cycle, is an important indicator of the ecosystem's carbon budget. In this paper, the spatial and temporal variations of NEP over Xinjiang Autonomous Region, China from 2001 to 2020 were studied based on remote sensing and climate re-analysis data. The modified Carnegie Ames Stanford Approach (CASA) model was employed to estimate net primary productivity (NPP), and the soil heterotrophic respiration model was used to calculate soil heterotrophic respiration. Then NEP was obtained by calculating the difference between NPP and heterotrophic respiration. The annual mean NEP of the study area was high in the east and low in the west, high in the north and low in the south. The 20-year mean vegetation NEP of the study area is 128.54 gC·m-2, indicating that the study area is a carbon sink on the whole. From 2001 to 2020, the annual mean vegetation NEP ranged between 93.12 and 158.05 gC·m-2, and exhibited an increasing trend in general. 71.46% of the vegetation area showed increasing trends of NEP. NEP exhibited a positive relationship with precipitation and a negative relationship with air temperature, and the correlation with air temperature was more significant. The work reveals the spatio-temporal dynamics of NEP in Xinjiang Autonomous Region and can provide a valuable reference for assessing regional carbon sequestration capacity.
Dark matter searches with NaI scintillators in the Canfranc underground laboratory: ANAIS experiment

A large mass dark matter search experiment with NaI scintillators at the Canfranc Underground Laboratory is underway. A 10.7 kg prototype with improved light collection efficiency and special low-background improvements has been tested and started taking data underground in summer 2005. Preliminary results and prospects for the experiment are presented.
A Recent Review on Approaches to Design Power System Stabilizers: Status, Challenges and Future Scope

Modern power system networks are complex and subjected to several uncertainties. Due to the complexity and uncertainties involved in power system operation, the networks are prone to instabilities. Rotor-angle instability is one such issue that, if not addressed properly, may lead to system collapse. A power system stabilizer (PSS) is primarily a power oscillation damping (POD) controller used to dampen power oscillations, thereby improving rotor angle stability. The proper design of PSSs is a challenging task and is essential for the secure and reliable operation of power systems. In the past, many power system instability events occurred, resulting in cascading failures and even blackouts. In the literature, several methods are proposed for the efficient design of PSSs or POD controllers. In this contribution, a recent review on the design of PSS is presented, along with challenges and the future scope of research in this field. On the basis of this review, it can be concluded that there is ample scope for designing PSS in a more efficient and robust manner, considering several uncertainties that may occur during the operation of modern power systems.
The structure and appearance of winds from supercritical accretion disks. I - Numerical models

This is the first of two papers that investigate models of radiation-driven winds from super-critical accretion disks. The main assumptions in the models are that the mass and luminosity in the wind emanate from the radius in the disk where accretion becomes supercritical (the critical radius) and that the wind is spherical. In this first paper the equations for disk and wind structure are derived and a steady-state hydrodynamic computer code is develoved and used to solve these equations with the same relaxation techniques used in stellar structure problems. The second paper derives analytic approximations to the results and applies them to various astrophysical systems. The models have four parameters; the mass of the accreting star M, the total accretion rate M/sub T/, . cap alpha. ' (a generalized form of the original disk .cap alpha. parameter including heating by processes other than viscosity), and the ratio of the total luminosity to the Eddington luminosity GAMMA/sub T/. For accretion onto a hard-surfaced star, steady, optically thick winds result for even slightly supercritical accretion. The photosphere is typically approx.10/sup 3/ times the critical radius, hiding much or all of the disk and in some cases a binary companion as well. The object will appearmore » as a supergiant star with a high mass loss rate and a non-blackbody spectrum. Transition from subcritical to supercritical accretion is expected to be a dynamical event, but eventually a stable wind should obtain even if ejection from an unstable disk occurs intermittently. Winds from black hole accretion disks may be somewhat different, but the differences will depend on the form of accretion interior to the critical radius. The nature of these wind indicates they should be applicable to a wide variety of accretion phenomena including cataclysmic and X-ray binaries and perhaps quasars. « less
Epigenetic reprogramming in cancer: From diagnosis to treatment

Disruption of the epigenetic program of gene expression is a hallmark of cancer that initiates and propagates tumorigenesis. Altered DNA methylation, histone modifications and ncRNAs expression are a feature of cancer cells. The dynamic epigenetic changes during oncogenic transformation are related to tumor heterogeneity, unlimited self-renewal and multi-lineage differentiation. This stem cell-like state or the aberrant reprogramming of cancer stem cells is the major challenge in treatment and drug resistance. Given the reversible nature of epigenetic modifications, the ability to restore the cancer epigenome through the inhibition of the epigenetic modifiers is a promising therapy for cancer treatment, either as a monotherapy or in combination with other anticancer therapies, including immunotherapies. Herein, we highlighted the main epigenetic alterations, their potential as a biomarker for early diagnosis and the epigenetic therapies approved for cancer treatment.
A comparison among eight different techniques to achieve an optimum estimation of electrical grounding parameters in two-layered earth

This paper intends to compare the performance of eight techniques, based on five different methods, used to find the electrical grounding parameters of a two-layered earth (resistivities and thickness) corresponding to a specific mathematical model. Parameter estimation is carried out in such a way as to obtain an optimum fitting between the set of resistivity values measured in the field by means of Wenner's Method, and those calculated from the mathematical model using such parameters. Firstly, the paper presents a review of the mathematical basis of five optimization methods used for the implementation of the different techniques subject to comparison. Secondly, the paper defines the implementation of the algorithms for the eight techniques. Three of them are of first order gradient type, one more of second order (Newton-method), the fifth one is based on the Levenberg-Marquardt method, the sixth one on the generalized inverse method, the seventh one on a quasi-Newton method and the last one on a mixed Newton-generalized inverse method. Algorithms are applied to six test cases and comparison of the results is shown. Furthermore, new algorithms to improve existing procedures are presented. >
ELAC2 Functions as a Key Gene in the Early Development of Placental Formation Based on WGCNA

The placenta plays a crucial role in mammalian fetal growth. The most important cell type in the placenta is the trophoblast cell. Many genes have been reported to play important functions in the differentiation of early placental trophoblast cells. Weighted gene co-expression network analysis (WGCNA) is a systematic biological method for describing the correlation patterns among genes across microarray samples. We used WGCNA to screen placental trophoblast development-related genes, and through experimental confirmation, we showed that, among these genes, ELAC2 may play an important regulatory role in the early development of mammalian placental formation. ELAC2 regulates early placental trophoblast differentiation by affecting cell migration and cell proliferation. In addition, ELAC2 may be involved in regulating cell migration processes in a manner that affects epithelial mesenchymal transition (EMT).
Orbital trends in galaxy pairing and merger rates

Low energy collisions are the most frequent, and are characterized by high eccentricities. The orbital trends of such collisions are now studied and the order of magnitude of their frequency is determined. Results support the analytical results of a previous paper that the eccentric orbit is a preferred state, and indicate that the system tends to maintain that state. The merger theory, in its extreme form, postulates that the ellipticals are the product of mergers of spirals. The frequency of the most frequent collisions is found to be extremely low to account for the formation of ellipticals. Results favor the traditional view of the formation of ellipticals by gravitational contraction (involving a single burst of star formation at a high redshift followed by passive evolution) and indicate that the merger process seems to be a sporadic one.
Spin dynamics of a two-dimensional metal in a superconducting state: Application to the high- T c cuprates

We show that a two-dimensional (2D) metal in a superconducting (SC) state with wave-vector-dependent gap behaves as a quantum-spin liquid characterized by a collective mode of resonance type. Formally the mode exists whatever is a form of Fermi surface (FS). However, when the latter is spherical the effect is rather formal: any factor leading to an even weak electron scattering, like finite T, impurity, etc., easily kills the mode. On the contrary, for the type of FS observed in the high-${T}_{c}$ cuprates (characterized by a proximity to the saddle-point wave vectors) the resonance mode exists in a true sense being well separated from the electron-hole continuum and well defined. The effect is due to the effective 1D dimension in a proximity of electronic topological transition quantum critical point. The physical nature of the collective mode varies progressively between two possibilities. In the case of a weak interaction, the resonance mode is a typical effect of a two-particle bound state that appears slightly below the bottom of the two-particle continuum. The effect exists even in the case of infinitesimally weak interaction (of relevant sign). In the case of a strong or intermediate interaction, it is a critical mode existing as a precursor of spin-density wave (SDW) instability. The gap of the collective mode is not related directly to the value of the SC gap (being, however, limited by the latter from above) but rather to a proximity to SDW instability. We predict also a second collective mode lying inside the two-particle continuum. The existence of these two modes and the particular form of their dispersion in the case corresponding to the underdoped cuprates ${(d}_{{x}^{2}\ensuremath{\sim}{y}^{2}}$ symmetry of SC gap, exchange interaction of antiferromagnetic sign, etc.) allows one to understand the main features observed experimentally in the underdoped cuprates, namely the so-called ``41 meV resonance peak'' and ``24 meV incommensurability'' and their two-dimensional wave vector dependences, the doping evolution of the spin dynamics and many other features. The theory also predicts new features to be tested experimentally.
A Clustered Regularly Interspaced Short Palindromic Repeats Knockout Method to Reveal methyl-CpG Binding Domain 4 Function

DNA methylation is an epigenetic mechanism tailored for DNA repression, engineered for regulating genetic expression without direct manipulation of the nucleotide sequence. One component of this process includes methyl-binding proteins (MBD), which have an affinity for methyl groups, and they competitively inhibit transcription factors from binding with genetic promoters. Interestingly, MBD4 is unique because, as opposed to transcriptional repression, it promotes gene repair & demethylation and is associated with various methylation-related diseases, such as Autism. By further studying MBD4, we can identify a potential therapeutic target for MRD and further understand the role of methylation on the epigenome in regards to seasonal plasticity. Therefore, this paper describes a CRISPR Knockout screen to isolate & repress MBD4 from its customary functionality with optimized gRNA targets in Astatotilapia burtoni Cichlid. I expect a morphological change in the Cichlid’s skin color (such change can be identified with computer vision COCO-Style-Dataset-Generator-GUI), which substantiates my belief that MBD4 does, in fact, play a significant role in seasonally-regulated epigenetic switches and can be targeted in methylation treatments. However, the exogenous factors relating to MBD4’s role in methylation remain to be investigated.
Hyperbolic Vector Random Fields with Hyperbolic Direct and Cross Covariance Functions

This article introduces the hyperbolic vector random field whose finite-dimensional distributions are the generalized hyperbolic one, which is formulated as a scale mixture of Gaussian random fields and is thus an elliptically contoured (or spherically invariant) random field. Such a vector random field may or may not have second-order moments, while a second-order one is characterized by its mean function and its covariance matrix function, just as in a Gaussian case. Some covariance matrix structures of hyperbolic type are constructed in this paper for second-order hyperbolic vector random fields.
Event-based feature tracking in a visual inertial odometry framework

Introduction: Event cameras report pixel-wise brightness changes at high temporal resolutions, allowing for high speed tracking of features in visual inertial odometry (VIO) estimation, but require a paradigm shift, as common practices from the past decades using conventional cameras, such as feature detection and tracking, do not translate directly. One method for feature detection and tracking is the Eventbased Kanade-Lucas-Tomasi tracker (EKLT), an hybrid approach that combines frames with events to provide a high speed tracking of features. Despite the high temporal resolution of the events, the local nature of the registration of features imposes conservative limits to the camera motion speed. Methods: Our proposed approach expands on EKLT by relying on the concurrent use of the event-based feature tracker with a visual inertial odometry system performing pose estimation, leveraging frames, events and Inertial Measurement Unit (IMU) information to improve tracking. The problem of temporally combining high-rate IMU information with asynchronous event cameras is solved by means of an asynchronous probabilistic filter, in particular an Unscented Kalman Filter (UKF). The proposed method of feature tracking based on EKLT takes into account the state estimation of the pose estimator running in parallel and provides this information to the feature tracker, resulting in a synergy that can improve not only the feature tracking, but also the pose estimation. This approach can be seen as a feedback, where the state estimation of the filter is fed back into the tracker, which then produces visual information for the filter, creating a “closed loop”. Results: The method is tested on rotational motions only, and comparisons between a conventional (not event-based) approach and the proposed approach are made, using synthetic and real datasets. Results support that the use of events for the task improve performance. Discussion: To the best of our knowledge, this is the first work proposing the fusion of visual with inertial information using events cameras by means of an UKF, as well as the use of EKLT in the context of pose estimation. Furthermore, our closed loop approach proved to be an improvement over the base EKLT, resulting in better feature tracking and pose estimation. The inertial information, despite prone to drifting over time, allows keeping track of the features that would otherwise be lost. Then, feature tracking synergically helps estimating and minimizing the drift.
Impact of dithiol treatment and air annealing on the conductivity, mobility, and hole density in PbS colloidal quantum dot solids

Crosslinking molecules have recently been combined with colloidal quantum dots to build robust, closely packed, conductive solid-state devices. Ethanedithiol (EDT) has been used in PbS quantum dot photovoltaic devices to assist in film formation during fabrication. However, there is evidence that EDT influences the electronic properties of the colloidal quantum dot (CQD) films. We fabricate thin film field-effect transistors and find that EDT treatment increases the majority carrier mobility by a factor of 10. We attribute this increase to a reduction in interparticle spacing which we observe using transmission electron microscopy. However, this increase is accompanied by a decrease in the majority carrier concentration. Using x-ray photoelectron microscopy, we find that EDT reduces the extent of the surface oxidation which is acting as a p-type dopant in these materials. We find that by lightly reoxidizing, we can redope the CQD films and can do so without sacrificing mobility gains.
P3-LINS: Tightly Coupled PPP-GNSS/INS/LiDAR Navigation System With Effective Initialization

P3-LINS, a navigation system tightly coupling precise point positioning (PPP)-global navigation satellite system (GNSS)/inertial navigation system (INS)/light detection and ranging (LiDAR) with the DRANSAC-receiver autonomous integrity monitoring (RAIM) and the doppler iterative closet point algorithm (DICP) as its initialization procedure is established for accurate and reliable positioning in urban environments. The position initialization of P3-LINS is completed by the static PPP with the DRANSAC-RAIM algorithm. The yaw is initialized through the proposed DICP algorithm which relies on the quadratic constraint least-squares problem built by Doppler speed, LiDAR-simultaneous localization and mapping (SLAM) poses, and GNSS positions. After dealing with the initialization issue, the fusion framework of P3-LINS is constructed to handle the heterogeneous sensor data spatial–temporal consistency problem with the three-phase extended Kalman Filter (EKF)-based estimator. The experiments show that the initialization of P3-LINS achieves decimeter-level positioning error in minutes when keeping static and tenths of a degree-level yaw error after the vehicle moves a few seconds on the UrbanLoco. Moreover, P3-LINS outperforms the compared state-of-the-art methods in terms of positioning accuracy and reliability on both the public datasets and self-collected datasets.
The influence of transformational and learning through R&D capabilities on the competitive advantage of firms

PurposeThis study provides theoretical and empirical insights into how firms can enhance their performance strategically and financially by integrating learning and transformational capabilities into research and development (R&D) activities based on the dynamic capabilities approach. To achieve this, the authors propose that transformational capabilities in R&D mediate the relationship between learning capabilities in R&D and competitive advantage.Design/methodology/approachUsing a purposive sampling technique and standardized questionnaires, data were collected from 118 R&D and senior managers. To analyze the data, multiple regression analysis, along with SPSS PROCESS macro (Model 4), was used.FindingsThe results support the theoretical assumption that there are direct and indirect positive and significant effects between learning capabilities in R&D and competitive advantage as mediated by transformational R&D capabilities.Originality/valueThis study explores R&D from a dynamic capabilities perspective and suggests that learning capabilities should come first to enhance businesses’ competitive advantage. Furthermore, transformational R&D capabilities can explain the relationship between learning capabilities in R&D and competitive advantage. The authors recommend that researchers should investigate the contributions of R&D to promote competitive advantage.