Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative effect of different sugars instigating non-enzymatic browning and Maillard reaction products in guava fruit leather

Browning is a major quality deterioration process affecting both visual colour and nutritional value of guava leather. The aim of the study was to determine the role of different sugars viz., sucrose, fructose, glucose and sorbitol in non-enzymatic browning and antioxidant activity of guava fruit leather. The total free amino acids, ascorbic acid and antioxidant activities were at significantly lower levels in glucose and fructose treated guava leather, while the sorbitol added samples had all of above parameters at the highest level; while a reverse trend was observed in browning index and non-enzymatic browning. Among the browning intermediate products, Hydroxymethylfurfural was present at higher concentration (12.80-32.32 ng/g) than furfural (0.29-0.95 ng/g) in guava leather samples. Among the treatments, hydroxymethylfurfural was found lowest in sorbitol (12.8 ng/g) and highest in fructose (32.3 ng/g). In brief, this paper describes a novel effort in bringing the in-vitro studies related to sugars and total free amino acids, influencing the biochemical and nutritional attributes which are responsible for browning in guava fruit leather.
Analysis of Consensus-Based Economic Dispatch Algorithm Under Time Delays

Under consensus-based economic dispatch (ED) algorithm, multiple agents, which control local generation units, cooperatively minimize the total generation cost subject to the balance of the generation and expected demand in smart grids. As ubiquitous time delays on communication links exist in communication networks, studying the effect of delays on the dispatch performance is of both theoretical merit and practical value for the efficient and stable operation of smart grids. In this paper, we consider a well-developed consensus-based ED protocol under constant time delays. We find that there always exists a sufficiently small learning gain parameter under finite constant delays such that the convergence of the consensus-based algorithm is guaranteed. Further, an analytical expression of the upper bound is established for the learning gain parameter, which is determined by the largest delay, the weight matrix and the parameters of generation cost functions. In order to guarantee the optimality of the final solution, we propose the updating rule for iterations when initial states are not received by their neighbors due to time delays. The optimality of the final solution under the proposed updating rule is analyzed. We validate our theoretical results through extensive simulation studies.
Effective Dynamics of Open Systems in Non-Equilibrium Statistical Mechanics

This dissertation investigates effective dynamics of models for open systems, of classical and quantum nature, arising in non-equilibrium statistical mechanics. The evolution of the system's position degree of freedom in these models is described by a stochastic integro-differential equation, whose damping and noise coefficients are dependent on the system's position. The equation is driven by a colored noise process which is quasi-Markov. We study the behavior of the system in the limit as the characteristic time scales tend to zero. In particular, we derive a stochastic differential equation (SDE) describing the system's position in the considered limit. We find that the limiting SDE contains additional drift terms, the so-called noise-induced drifts, in both classical and quantum case. We discuss the implications of these correction drift terms in the context of concrete physical systems.
A Sensitivity‐Based Construction Approach to Variance Minimization of Markov Decision Processes

With a long‐run average performance as the primary criterion for a Markov decision process, variance measures are studied as its secondary criteria. The steady‐state variance and the limiting average variance along a sample path are discussed. The latter one is difficult to handle due to its special form. With a sensitivity‐based approach, the difference formula for the sample‐path variance under different policies is intuitively constructed and then the optimality equation is presented. Moreover a policy iteration algorithm is developed. This work extends the sensitivity‐based construction approach to Markov decision processes with non‐standard performance criteria. The difference between these two types of variance and bias criteria is illustrated with a numerical example.
Looking Back, Looking Forward: A Study Protocol for a Mixed-Methods Multiple-Case Study to Examine Improvement Sustainability of Large-Scale Initiatives in Tertiary Hospitals

Background Hospitals invest extensive resources in large-scale initiatives to improve patient safety and quality at an organizational level. However, initial success, if any, does not guarantee longer-term improvement. Empirical and theoretical knowledge that informs hospitals on how to attain sustained improvement from large-scale change is lacking. Aim The proposed study aims to examine improvement sustainability of two large-scale initiatives in an Australian tertiary hospital and translate the lessons into strategies for achieving sustained improvement from large-scale change in hospital settings. Design and Methods The study employs a single-site, multiple-case study design to evaluate the initiatives separately and comparatively using mixed methods. Semi-structured staff interviews will be conducted in stratified cohorts across the organizational hierarchy to capture different perspectives from various staff roles involved in the initiatives. The output and impact of the initiatives will be examined through organizational documents and relevant routinely collected organizational indicators. The obtained data will be analyzed thematically and statistically before being integrated for a synergic interpretation. Implications Capturing a comprehensive organizational view of large-scale change, the findings will have the potential to guide the practice and contribute to the theoretical understandings for achieving meaningful and longer-term organizational improvement in patient safety and quality.
Subspace System Identification of the Kalman Filter

Some proofs concerning a subspace identification algorithm are presented. It is proved that the Kalman filter gain and the noise innovations process can be identified directly from known input and output data without explicitly solving the Riccati equation. Furthermore, it is in general and for colored inputs, proved that the subspace identification of the states only is possible if the deterministic part of the system is known or identified beforehand. However, if the inputs are white, then, it is proved that the states can be identified directly. Some alternative projection matrices which can be used to compute the extended observability matrix directly from the data are presented. Furthermore, an efficient method for computing the deterministic part of the system is presented. The closed loop subspace identification problem is also addressed and it is shown that this problem is solved and unbiased estimates are obtained by simply including a filter in the feedback. Furthermore, an algorithm for consistent closed loop subspace estimation is presented. This algorithm is using the controller parameters in order to overcome the bias problem.
Spherical beamforming for spherical array with impedance surface

Spherical microphone array beamforming has been a popular research topic for recent years. Due to their isotropic beam in three dimensional spaces as well as a certain frequency range, the arrays are widely used in many applications such as sound field recording, acoustic beamforming, and noise source localisation. The body of a spherical array is usually considered perfectly rigid. A sound field captured by the sensors on spherical array can be decomposed into a series of spherical harmonics. In noise source localisation, the amplitude density of sound sources is estimated and illustrated by mean of colour maps. In this work, a rigid spherical array covered by fibrous materials is studied via numerical simulation and the performance of the spherical beamforming is discussed.
Learning a two-stage CNN model for multi-sized building detection in remote sensing images

ABSTRACT Though tremendous strides have been made in building recognition, to handle multi-sized buildings is fundamental for all building detection pipelines. We explore the reason of the problem in detecting the multi-sized buildings and find that most convolutional neural network (CNN) based recognition approaches aim to be scale-invariant. The cues for recognizing a 3 pixels tall building are fundamentally different than those for recogjnizing a 300 pixels tall building. To tackle this problem, we design a novel two-stage building detection model which contains the region proposal stage and the classification stage. In the region proposal stage, we propose a novel Multi-size Fusion Region Proposal Network (MFRPN) for extracting the feature of various size building and generating wide size range of region proposals. In the classification stage, a deep CNN model is used to distinguish whether the generated region proposals are building regions or not. In order to achieve better performance, we present an improved block voting algorithm by introducing a dynamic weighting strategy which can obtain a more robust classification result increasing the classification accuracy of the region proposals. We attribute this to robust Experimental results on the challenging VHR dataset indicate that our model has a great performance.
Imaging Simulation and Learning-Based Image Restoration for Remote Sensing Time Delay and Integration Cameras

Time delay and integration (TDI) cameras are widely used in remote sensing areas because they capture high-resolution and high signal-to-noise ratio (SNR) images and images in low-light environments. However, the image quality captured by TDI cameras may be affected by many degradation factors, including jitter, charge transfer time mismatch, and drift angle. Moreover, compared with the single-line push-broom cameras and area gaze cameras used in remote sensing, the degraded effect of the TDI camera may accumulate during the charge accumulation process. In this article, we present a fast imaging simulation method for remote sensing TDI cameras based on image resampling that can accurately simulate the degraded image quality affected by different degradation factors. The simulated image pairs can provide a sufficient dataset for modern supervised-learning image restoration methods. In addition, we present a novel network, containing a row-attention block and row-encoder block to help resolve the row-variant blur to resolve the degraded images. We test our image restoration method on the simulated degraded image datasets and real images; the results show that the proposed method can effectively restore degraded images. Our restoration method does not rely on auxiliary information detected by high-frequency sensors or multispectral bands, and it achieves better results than other blind restoration methods.
A Backflow Power Suppression Strategy for Dual Active Bridge Converter Based on Improved Lagrange Method

Dual-active bridge (DAB) converters are receiving increasing attention from researchers as a critical part of the power transmission of energy routers. However, the DAB converter generates a large backflow power in conventional control mode, and when the load is mutated, its output voltage takes longer to return to the reference value accompanied by large fluctuations. To solve the above problems, a hybrid strategy is proposed in this paper to optimize the converter. The mathematical models of the transmitted power and the backflow power were firstly derived through in-depth analysis of the DAB converter under extended-phase-shift (EPS) modulation, and the suppression of the backflow power was performed according to the improved Lagrange method utilized in the obtained results. Moreover, considering the poor dynamic characteristics of DAB converters under PI control, according to the state space average model of output voltage in the paper, a model prediction control equation is established to improve the dynamic response of the converter by predicting the output voltage value at the next moment. The simulation results verify the effectiveness of the optimization strategy presented in the text.
Association between Polymorphism in the Janus Kinase 2 (JAK2) Gene and Selected Performance Traits in Cattle and Sheep

Simple Summary An important element of livestock breeding is the selection that leads to the consolidation and improvement of the traits with utility value. The use of single-nucleotide polymorphism (SNP) markers offers the opportunity to select the appropriate genotype linked to the phenotype, which manifests itself in significant breeding benefits. Polymorphic sites within the genes coding the somatotropic axis are directly and indirectly associated with the phenotype, particularly concerning the production properties of growth and carcass as well as reproductive function. This study analyzed the effect of selected polymorphisms in the Janus Kinase 2 (JAK2) gene on performance characteristics of cattle and sheep. The impact of the mutations on the development of selected traits, which are important from the economic standpoint, was evaluated. Such studies can be utilized to enhance the growth characteristics of cattle and sheep, thereby facilitating the development of more profitable and sustainable breeding methods. Abstract The Janus Kinase 2 (JAK2) tyrosine kinase is an essential component of signal transduction of the class II cytokine receptors, including the growth hormone receptor. Therefore, it may play a crucial role in the signaling pathway of the somatotropic axis, which influences growth, development, and reproductive traits in ruminants. For this purpose, for three breeds of cattle (Hereford, Angus, and Limousin; a total of 781 individuals), two polymorphic sites located in exon 16 (rs210148032; p.Ile704Val, within pseudokinase (JH2)) and exon 23 (silent mutation rs211067160, within JH1 kinase domain) were analyzed. For two breeds of sheep (Pomeranian and Suffolk; 333 individuals in total), two polymorphic sites in exon 6 (rs160146162 and rs160146160; encoding the FERM domain) and one polymorphic site in exon 24 of the JAK2 gene (rs160146116; JH1 kinase domain) were genotyped. In our study, the associations examined for cattle were inconclusive. However, Hereford and Limousin cattle with genotypes AA (e16/RsaI) and AA (e23/HaeIII) tended to have the highest body weight and better daily gains (p ≤ 0.05). No clear tendency was observed in the selected reproductive traits. In the case of sheep, regardless of breed, individuals with the AA (e6/EarI), GG (e6/seq), and AA (e24/Hpy188III) genotypes had the highest body weights and daily gains in the study periods (p ≤ 0.01). The same individuals in the Pomeranian breed also had better fertility and lamb survival (p ≤ 0.01). To the best of our knowledge, these are the first association studies for all these polymorphic sites. Single-nucleotide polymorphisms in the JAK2 gene can serve as genetic markers for growth and selected reproductive traits in ruminants given that they are further investigated in subsequent populations and analyzed using haplotype and/or combined genotype systems.
Minimum Cost Averaging for Multivariate Time Series Using Constrained Dynamic Time Warping: A Case Study in Robotics

In this paper, an innovative algorithm for averaging a set of multivariate time series with different lengths based on Constrained Dynamic Time Warping (CDTW) is proposed. This approach relies on the CDTW to provide the non-linear alignment of the multivariate time series, and employs the proposed Minimum Cost Averaging (MCA) technique to identify the optimum matches and get equal-length time series. MCA-CDTW is a task-agnostic approach that after selecting a reference curve, transforms the rest of the demonstrations in the set to obtain new curves that are time-aligned with the reference. From these transformed curves, not only the mean but also the signal variability can be directly extracted. This technique provides smooth mean curves even when there are large deviations between the demonstrations in the set, and still the complexity of the proposed algorithm is significantly reduced compared to other averaging techniques from the literature. When learning techniques are used to teach a motion to a robotic system, obtaining smooth trajectories is important to achieve good robotic behaviors. The new algorithm MCA-CDTW is tested and compared on two different databases: a literature database where humans move a robotic arm with kinaesthetic teaching, and a set of recordings of a teleoperated robotic arm performing laboratory manipulation. On both datasets, it is demonstrated that the new approach is providing smooth average trajectories.
Special Finslerian generalization of the Reissner-Nordström spacetime

We have obtained Finslerian Ressiner-Nordstrom solution where it is asymptotic to a Finsler spacetime with constant flag curvature while $r\rightarrow\infty$. The covariant derivative of modified Einstein tensor in Finslerian gravitational field equation for this solution is conserved. The symmetry of the special Finslerian Ressiner-Nordstrom spacetime, namely, Finsler spacetime with constant flag curvature, has been investigated. It admits four independent Killing vectors. The Finslerian Ressiner-Nordstrom solution differs from Ressiner-Nordstrom metric only in two dimensional subspace. And our solution requires that its two dimensional subspace have constant flag curvature. We have obtained eigenfunction of Finslerian Laplace operator of "Finslerian sphere", namely, a special subspace with positive constant flag curvature. The eigenfunction is of the form $\bar{Y}_l^m=Y_l^m+\epsilon^2(C_{l+2}^m Y_{l+2}^m+C_{l-2}^m Y_{l-2}^m)$ in powers of Finslerian parameter $\epsilon$, where $C_{l+2}^m$ and $C_{l-2}^m$ are constant. However, the eigenvalue depends on both $l$ and $m$. The eigenvalues corresponded to $Y_1^0$ remain the same with Riemannian Laplace operator and the eigenvalues corresponded to $Y_1^{\pm1}$ are different. This fact just reflect the symmetry of "Finslerian sphere", which admits a $z$-axis rotational symmetry and breaks other symmetry of Riemannian sphere. The eigenfunction of Finslerian Laplace operator implies that monopolar and dipolar terms of multipole expansion of gravitational potential are unchanged and other multipole terms are changed.
Dynamic visual SLAM system based on weighted static features

Existing visual simultaneous localization and mapping (SLAM) systems usually assume that the environment is static, therefore when many mature visual SLAM algorithms are applied to dynamic scenes, the pose errors estimated by the system will increase sharply due to the interference of dynamic objects. To address this problem, this paper proposes a robust visual SLAM system based on ORB-SLAM2 using static features and realizes a two-stage tracking from coarse to fine to improve the system localization accuracy in a dynamic environment. The proposed method uses the yolov5-seg network to implement a semantic thread, then combines semantic prior information and geometric information to accurately extract dynamic feature points, and realizes pose optimization based on the static weight of feature points. In addition, an improved keyframe selection strategy is implemented. Experiments on the public TUM dynamic datasets show that the proposed algorithm performs well on most dynamic datasets, has a great improvement compared with the ORB-SLAM2, and achieves the same accuracy as other advanced dynamic SLAM systems.
Antioxidant and Anti-Inflammatory Activities of Stellera chamaejasme L. Roots and Aerial Parts Extracts

Natural products, mainly plants, have a crucial role in folk medicine. Particularly, Stellera chamaejasme L. has been traditionally used in Mongolian medicine to treat various diseases, including chronic tracheitis, tuberculosis, and psoriasis. In this study, ethanol (EtOH) and dichloromethane (DCM) extracts of its roots (R) and aerial parts (AP) were evaluated for their antioxidant and anti-inflammatory activities. Thin-layer chromatography demonstrated the presence of flavonoids, namely kaempferol and quercetin-3-O-glucopyranoside, only in the EtOH-AP. Conversely, it showed that kaempferol, quercetin-3-O-glucopyranoside, coumarin, luteolin, rutin, morin, and riboflavin were not present in the other three extracts. The S. chamaejasme extracts exhibited strong antioxidant activity. In addition, the roots extracts presented the highest antioxidant activity against peroxyl radicals, with the EtOH-R being the most potent (IC50 = 0.90 ± 0.07 µg/mL). S. chamaejasme extracts also efficiently inhibited the production of one of the main pro-inflammatory cytokines, interleukin (IL)-6, in a dose-dependent manner by lipopolysaccharide-stimulated macrophages. Particularly, DCM-R was the strongest extract, reducing ≈ 91.5% of the IL-6 production. Since this extract was the most effective, gas chromatography–mass spectrometry (GC-MS) analyses were performed and demonstrated the presence of two fatty acids (palmitic acid and 9-octadecenoic acid), one fatty alcohol (1-hexadecanol), and one triterpenoid (squalene) that can contribute to the observed bioactivity. Herewith, S. chamaejasme extracts, mainly DCM-R, exhibit antioxidant and anti-inflammatory activities that could be applied as new and innovative natural formulations for the treatment of chronic inflammatory diseases.
Cataclysmic variables based on the stellar spectral survey LAMOST DR3

Big data in the form of stellar spectra from the spectroscopic survey associated with the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) are important for studying properties of cataclysmic variables (CVs). By cross matching the catalogs of CVs compiled with LAMOST DR3, acquired from October 2011 to July 2015, we obtained the first spectroscopic catalog for CVs observed by LAMOST with high signal to noise ratio, above 8. By integrating line profiles, their equivalent widths (EWs) of the Hα, Hβ, Hγ and Hδ, as well as He I 5876 and 6678 Å lines, were calculated. There were 74 stellar spectra from 48 known CVs and three spectra from three new CV candidates. At the same time, we also collected their previously published EWs. Thirty-three objects had repeated spectra and 30 stars showed spectral variability in the Hα line. Moreover, we carried out photometric follow-up studies for five CVs (UU Aqr, TT Tri, PX And, BP Lyn and RW Tri). We obtained nine new light curves and revised their linear ephemerides. For RW Tri, there is a possible oscillation with an amplitude of 0.0031(2) days and a period of 47.6 ± 0.4 years, which might be caused by a third body (brown dwarf) or magnetic activity cycle.