Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparse Measurement-Based Modelling Low-Order Dynamics for Primary Frequency Regulation

Developing a low-order model for a target with dynamic analysis and control of the primary frequency regulation (PFR) is critical for power system stability. Measurement-based modelling is an important way to improve the model fit with practical systems. The traditional interpretation of PFR is based on the combined effect of each generator unit on the system frequency, so the response of each unit needs to be measured for modelling. With the integration of a large number, distribution and diversified frequency regulation of units into a power system, it will be a burden to measure the response of all generation units. Instead of revealing the PFR dynamics based on each generation unit, this paper illustrates the impact of all units on the frequency dynamics from the perspective of the system response. Only the system frequency is needed to construct a dataset for identifying the PFR model so that the measurement required is sparse. The effectiveness of the proposed method is verified using a test system containing multiple types of units in numerical simulations and real measurements of an actual power system.
Geochemistry and petrogenesis of ophiolitic rocks from the Indus Suture Zone (ISZ), Ladakh Himalaya: insights for depleted mantle beneath an intra-oceanic island arc complex

ABSTRACT The Indus Suture Zone preserves the ophiolitic remnants of the eastern part of the Neo-Tethys Ocean. Parts of these ophiolitic remnants are exposed along the Khangral-Chiktan and Dras-Kargil road sections of western Ladakh. The observed rock types include ultramafic-mafic cumulates, gabbros, and volcanics emplaced as faulted blocks over the Mesozoic Dras arc complex. Geochemically, these rocks show sub-alkaline tholeiitic characteristics with basalt to basaltic-andesite compositional variation. Based on modal mineralogy, the ultramafic cumulates classify as olivine-websterite, the mafic cumulates as olivine-norite, and the gabbros as norite to hornblende-gabbro. The ultramafic cumulates depict depleted chondrite normalized REE patterns [(La/Yb)N = 0.6–1.1] while the mafic cumulates display depleted to enriched REE patterns [(La/Yb)N = 0.6–3.2]. Fractionated patterns are observed in gabbros [(La/Yb)N = 1.6–4.1], while flat and enriched chondrite normalized REE-patterns in volcanics [(La/Yb)N = 1.0–12.3] similar to NMORB, and EMORB. The multi-element patterns depict subduction-related geochemical characteristics such as enriched LILE and depleted HFSE compared to the primitive mantle. Presence of Mg-rich olivines, orthopyroxenes, and clinopyroxenes while Ti-poor clinopyroxenes in ultramafic-mafic cumulates reflect their derivation from previously depleted mantle sources at high-pressure and temperature comparable to the base of modern intra-oceanic island arc tholeiitic sequences. The studied rocks exhibit close similarity to an intra-oceanic subduction system contemporaneous to the Dras-Shergol-Suru-Thasgam ophiolitic slices of western Ladakh.
Unified theoretical model for calculating laser-induced wavefront distortion in optical materials

Laser-induced thermal lens in optical components causes wavefront distortion of the laser beam and may affect performance and stability of optical systems such as high-power lasers. The bulging of the heated area, the temperature dependence of the refractive index, and the photoelastic effects are responsible for phase shifts damaging beam quality. The theoretical background for laser-induced beam distortion is well understood and applies only for axially symmetric thermal loadings, with the assumptions that the stresses follow thin-disk or long-rod approximations. This, in fact, limits the overall applications of this model. In this work, we developed an unified theoretical model for the optical path change in optical materials regardless of its thickness. The modeling is based on the solution of the thermoelastic equation and has a real description of the surface deformation caused in the optical element. In the appropriated limits, as expected, the model retrieves the thin-disk and the long-rod type distributions. Furthermore, we provided time-dependent radial expressions for the temperature, surface displacement, and stresses. The theory presented in this paper provides simple analytical tools for designing laser systems, and complements previous work allowing one to access optical distortions of materials ranging from thin-disk to long-rod-like distributions.
Global Stereo Reconstruction under

Second-order priors on the smoothness of 3D surfaces are a better model of typical scenes than first-order priors. However, stereo reconstruction using global inference algorithms, such as graph cuts, has not been able to incorporate second-order priors because the triple cliques needed to express them yield intractable (nonsubmodular) optimization problems. This paper shows that inference with triple cliques can be effectively performed. Our optimization strategy is a development of recent extensions to � -expansion, based on the "QPBO" algorithm. The strategy is to repeatedly merge proposal depth maps using a novel extension of QPBO. Proposal depth maps can come from any source, for example, frontoparallel planes as in � -expansion, or indeed any existing stereo algorithm, with arbitrary parameter settings. Index Terms—Stereo, second-order prior, discrete optimization, graph cuts.
Diverting residual biomass to energy use: Quantifying the global warming potential of biogenic CO2 (GWPbCO2)

To calculate the global warming potential of biogenic carbon dioxide emissions (GWPbCO2) associated with diverting residual biomass to bioenergy use, the decay of annual biogenic carbon pulses into the atmosphere over 100 years was compared between biomass use for energy and its business‐as‐usual decomposition in agricultural, forestry, or landfill sites. Bioenergy use increased atmospheric CO2 load in all cases, resulting in a 100GWPbCO2 (units of g CO2e/g biomass CO2 released) of 0.003 for the fast‐decomposing agricultural residues to 0.029 for the slow, 0.084–0.625 for forest residues, and 0.368–0.975 for landfill lignocellulosic biomass. In comparison, carbon emissions from fossil fuels have a 100GWP of 1.0 g (CO2e/g fossil CO2). The fast decomposition rate and the corresponding low 100GWPbCO2 values of agricultural residues make them a more climate‐friendly feedstock for bioenergy production relative to forest residues and landfill lignocellulosic biomass. This study shows that CO2 released from the combustion of bioenergy or biofuels made from residual biomass has a greenhouse gas footprint that should be considered in assessing climate impacts.
High sensitive static fiber Bragg grating strain sensing using lasers locked to relative frequency reference

A novel high sensitive fiber Bragg grating (FBG) strain sensing technique using lasers locked to relative frequency reference is proposed and analyzed theoretically. Static strain on FBG independent of temperature can be measured by locking frequency of diode laser to the mid reflection frequency of matched reference FBG, which responds to temperature similar to that of the sensor FBG, but is immune to strain applied to the same. Difference between light intensities reflected from the sensor and reference FBGs (proportional to the difference between respective pass band gains at the diode laser frequency) is not only proportional to the relative strain between the sensor and reference FBGs but also independent of servo residual frequency errors. Usage of relative frequency reference avoids all complexities involved in the usage of absolute frequency reference, hence, making the system simple and economical. Theoretical limit for dynamic and static strain sensitivities considering all major noise contributions are of the order of 25 pε / √Hz and 1.2 nε / √Hz respectively.
Detecting Cortical Thickness Changes in Epileptogenic Lesions Using Machine Learning

Epilepsy is a neurological disorder characterized by abnormal brain activity. Epileptic patients suffer from unpredictable seizures, which may cause a loss of awareness. Seizures are considered drug resistant if treatment does not affect success. This leads practitioners to calculate the cortical thickness to measure the distance between the brain’s white and grey matter surfaces at various locations to perform a surgical intervention. In this study, we introduce using machine learning as an approach to classify extracted measurements from T1-weighted magnetic resonance imaging. Data were collected from the epilepsy unit at King Abdulaziz University Hospital. We applied two trials to classify the extracted measurements from T1-weighted MRI for drug-resistant epilepsy and healthy control subjects. The preprocessing sequence on T1-weighted MRI images was performed using C++ through BrainSuite’s pipeline. The first trial was performed on seven different combinations of four commonly selected measurements. The best performance was achieved in Exp6 and Exp7, with 80.00% accuracy, 83.00% recall score, and 83.88% precision. It is noticeable that grey matter volume and white matter volume measurements are more significant than the cortical thickness measurement. The second trial applied four different machine learning classifiers after applying 10-fold cross-validation and principal component analysis on all extracted measurements as in the first trial based on the mentioned previous works. The K-nearest neighbours model outperformed the other machine learning classifiers with 97.11% accuracy, 75.00% recall score, and 75.00% precision.
A Novel Edge Preserving Local Linear Stein’s Unbiased Risk Surface Estimator Approach for High Dynamic Range Image

Objective: The main objective of this research is to preserve the edges and also remove the noise from high dynamic range videos during filtering process. This methodology attempts to preserve the original quality of the videos and achieves the better compression ration by compressing it with the concern of the edge preservation details. Methods: The system proposes a novel edge preserving LLSURE (Local Linear Model, Stein’s Unbiased Risk Estimate) surface estimator mechanism which is based on an adaptive local linear model and the principle of Stein’s Unbiased Risk Estimate (SURE). Generally, pixels in the edges and near to the edge are affected during noise filtering. Here the LLSURE filter is extended by using Edge-preserving surface estimator. The proposed estimator is used to leave some noise in the vicinity of edges and improve those edges during denoising process. Finally Weighted Residual Mean Squares (WRMS) is used for compute the quality of estimator. Findings: The performance evaluation was conducted to prove the efficiency of the proposed methodology by comparing it with the existing approach called OCP based video coding technique. The performance evaluation is conducted in terms of the parameters called the Peak Signal to Noise Ratio, Mean Square Error and Maximum error comparison. The experimental tests conducted were proves that the proposed methodology can lead to efficient preservation of the edge details during noise removal than the existing methodologies. Conclusion: The proposed procedure can remove the noise correctly in continuity or surrounding regions of the surface, and preserve discontinuities at the same time.
RANDOM WALKS AND THE ADDITIVE COAGULATION EQUATION

Numerical simulations of hierarchical gravitational clustering in an expanding universe show that, as time evolves, small clusters merge with each other to form larger clusters, whereas fragmentation of clusters is relatively uncommon. Stochastic models of the hierarchical clustering process can provide insight into, as well as useful approximations to, the evolution measured in these simulations. The Poisson random walk excursion model, the Poisson Galton–Watson branching process, and the monodisperse additive coagulation equation are three examples of such stochastic models. When initially identical particles cluster from an initially Poisson spatial distibution, all three approaches give essentially the same description of how clusters grow. This paper shows that clustering from an initially Poisson distribution in which the initial particles do not all have the same mass can be described by simple generalizations of the models above. Such an initial distribution is said to be ‘compound Poisson’. Therefore, excursions of random walks associated with compound Poisson distributions are studied here. In such an excursion set model, clusters grow in essentially the same way as they do in the polydisperse additive coagulation model. Thus, the interrelations between excursion set, branching process and coagulation models of clustering, associated with the Poisson distribution, also apply to compound Poisson distributions. This means that, within the context of these models, when the initial conditions are compound Poisson then merger and accretion rates, and the entire merger history tree, can all be written analytically, just as for clustering from Poisson initial conditions.
A Counterexample in Stochastic Optimum Control

It is sometimes conjectured that nothing is to be gained by using non-linear controllers when the objective is to minimize the expectation of a quadratic criterion for a linear system subject to Gaussian noise and with unconstrained control variables.In fact, this statement has only been established for the case where all control variables are generated by a single station which has perfect memory. Without this qualification the conjecture is false.
Adapting to Unknown Disturbance Autocorrelation in Regression with Long Memory

We show that it is possible to adapt to nonparametric disturbance auto-correlation in time series regression in the presence of long memory in both regressors and disturbances by using a smoothed nonparametric spectrum estimate in frequency-domain generalized least squares. When the collective memory in regressors and disturbances is sufficiently strong, ordinary least squares is not only asymptotically inefficient but asymptotically non-normal and has a slow rate of convergence, whereas generalized least squares is asymptotically normal and Gauss-Markov efficient with standard convergence rate. Despite the anomalous behaviour of nonparametric spectrum estimates near a spectral pole, we are able to justify a standard construction of frequency-domain generalized least squares, earlier considered in case of short memory disturbances. A small Monte Carlo study of finite sample performance is included.
Pulsatility index as a parameter for clinical diagnostics of vascular disease

Arterial wall shear stress (WSS) parameters are widely used as prediction criteria for the initiation and development of atherosclerosis and arterial pathologies. However, clinical measurements of local WSS require complicated and expensive techniques and therefore traditional clinical evaluation of arterial condition relay mainly on flowrate and heart rate measurements. In this manuscript we show that heart rate and flowrate measurements are not sufficient for prediction of local WSS parameters and we suggest a third parameter based on the local pulsatility index (PI). The study includes experimental measurements of the flow profiles and WSS parameters of 16 different physiological flow waveforms in a straight arterial model. For each case, the phase-averaged velocity profiles were measured using digital particle image velocimetry techniques and were used to estimate WSS parameters utilizing the Womersley pulsating flow model. The results show that heart rate and flowrate parameters were not sufficient for prediction local WSS parameters for the 16 cases, while PI parameter exhibited well one-to-one correlation with WSS oscillatory index (OSI) and with the frequency of negative WSS occurrence (P[%]). In addition it provide a lowest limit for the peak-to-peak WSS ratios (min/max) for varying conditions. Therefore, PI was found to be an essential parameter for the complete description of the local WSS quantities for arterial flow.
Refilling of the slot region between the inner and outer electron radiation belts during geomagnetic storms

[1] Energetic electrons (≥50 keV) are injected into the slot region (2 < L < 4) between the inner and outer radiation belts during the early recovery phase of geomagnetic storms. Enhanced convection from the plasma sheet can account for the storm-time injection at lower energies but does not explain the rapid appearance of higher-energy electrons (≥150 keV). The effectiveness of either radial diffusion (driven by enhanced ULF waves) or local acceleration (during interactions with enhanced whistler mode chorus emissions), as a potential source for refilling the slot at higher energies, is analyzed for observed conditions during the early recovery phase of the 10 October 1990 storm. We demonstrate that local acceleration, driven by observed chorus emissions, can account for the rapid enhancement in 200–700 keV electrons in the outer slot region near L = 3.3. Radial diffusion is much less effective but may partially contribute to the flux enhancement at lower L. Subsequent outward expansion of the plasmapause during the storm recovery phase effectively terminates local wave acceleration in the slot and prevents acceleration to energies higher than ∼700 keV. A statistical analysis of energetic electron flux enhancements and wave and plasma properties over the entire CRRES mission supports the concept of local wave acceleration as a dominant process for refilling the slot during the main and early recovery phase of storms. For moderate storms, the injection process naturally becomes less effective at energies ≥1 MeV, due to the longer wave acceleration times and additional precipitation loss from scattering by electromagnetic ion cyclotron waves. However, during extreme events when the plasmapause remains compressed for several days, conditions may occur to allow wave acceleration to multi-MeV energies at locations normally associated with the slot.
Constraining the inertial dissipation method using the vertical velocity variance

[1] The inertial dissipation method (IDM) is commonly used to measure turbulent fluxes over the ocean. It has the advantage over more direct methods in that it depends on the turbulent fluctuations only in the high frequencies of the so-called inertial subrange. These frequencies are above those of typical ship motions and are considered to be relatively unaffected by flow distortion. However, a drawback in applying the method is that the problem is underdetermined: estimation of the fluxes requires knowledge of the Obukhov length L, which is itself a function of the fluxes. The problem is typically solved by iteration, using an initial L estimated from bulk formulae. This introduces a possible dependency on the initial bulk estimate along with problems of convergence. Recently, several authors have proposed improvements to the basic algorithm. For instance, Dupuis et al. [1997] proposed a parameterization of the “imbalance term” in the budget of turbulent kinetic energy (TKE). We explore an alternative approach to the problem. In order to constrain the equations resulting from the IDM we use the vertical velocity variance, σw, measured from the research vessel L'Atalante and an ASIS buoy, both deployed during the 1998 FETCH experiment. These data are compared to several parameterizations of σw on stability derived in experiments. For unstable cases, the data are found to be well described by the Panofsky and Dutton [1984] parameterization, although the scatter of the data is higher for swell conditions than for pure wind sea, indicating a likely sea state effect. Using measured values of σw along with this parameterization, the inertial dissipation problem is fully specified. The convergence of the method is satisfactory, and it offers u* estimates independent of bulk formulae.
Discovery of a High-Efficient Algicidal Bacterium against Microcystis aeruginosa Based on Examinations toward Culture Strains and Natural Bloom Samples

Harmful cyanobacterial blooms occur worldwide and pose a great threat to aquatic ecosystems and public health. The application of algicidal bacteria represents an eco-friendly strategy for controlling harmful cyanobacterial blooms; thus, searching for a high efficiency of algicidal bacteria has been becoming an important and continuous task in science. Herein, we identified a bacterial strain coded Streptomyces sp. HY with a highly algicidal activity, and investigated its algicidal efficiency and mechanism against Microcystis aeruginosa. The strain HY displayed high algicidal activity toward Microcystis aeruginosa cells, with a removal rate of 93.04% within 2 days via indirect attack. Streptomyces sp. HY also showed the ability to lyse several genera of cyanobacterial strains, including Dolichospermum, Pseudanabaena, Anabaena, and Synechocystis, whereas it showed a minor impact on the green alga Scenedesmus obliquus, demonstrating its selectivity specially for targeting cyanobacteria. Its algicidal mechanism involved damages to the photosynthesis system, morphological injury of algal cells, oxidative stress, and dysfunction of the DNA repair system. Furthermore, HY treatment reduced the expression levels of genes (mcyB and mcyD) related to microcystin biosynthesis and decreased the total content of microcystin-leucine-arginine by 79.18%. Collectively, these findings suggested that the algicidal bacteria HY is a promising candidate for harmful cyanobacterial bloom control.
Green credit policy and corporate diversification: evidence from China

ABSTRACT This study draws insights from the New Stakeholder Theory to investigate the influence of green credit policy on firm diversification. Using a sample of publicly listed firms in China from 2006 to 2018, we find that green credit policy has increased the degree of corporate diversification for heavily polluting firms. In addition, the positive impact of green credit policy on industrial diversity is more significant for heavily polluting firms which are state-owned, bigger in size, with institutional ownership of shares and located in provinces with higher level of marketisation. Moreover, financial constraint is an important mediating variable for green credit policy to influence corporate diversification. An important implication of our study is that corporate diversification can be a strategic choice made by heavily polluting firms to overcome financial constraints as a result of the green credit policy. In fact, greater corporate diversity can ensure the survival of polluting firms by allowing them to operate in industries with less environmental regulations.
Infrared acousto-optic materials: applications, requirements, and crystal development

The power requirements for acousto-optic devices increase with wavelength as X2, so it becomes extremely important for infrared operation to have very high figure-of-merit materials available. Potentially important infrared applications include scanners for laser radar, choppers for surveillance satellites, and acousto-optic tunable filters for a variety of systems. Higher efficiency materials are also important as thin films for integrated optic devices, such as the spectrum analyzer, and several bulk acoustic wave-optical waveguide concepts. A large number of materials of the sulfosalt class have been identified as having very desirable acousto-optic properties, and will find use in a number of these applications. More development of these materials is required to make them of suitable quality, and new compositions must be examined to achieve high performance.