Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatiotemporal Dynamics of Remote-Sensed Forel–Ule Index for Inland Waters Across China During the COVID-19 Pandemic

The 2019 coronavirus disease pandemic, called COVID-19, poses a novel research point for water body. Based upon the extracted 199 large lakes and reservoirs across China, this study developed the water color parameters, namely Forel–Ule index (FUI) retrieved from the Moderate Resolution Imaging Spectroradiometer imagery from 2018 and 2022 to explore the spatiotemporal variance of water quality for the first time in the context of COVID-19. The studied results demonstrated marked spatial and temporal patterns. Temporally, 68.8% of the lakes experienced an increase tendency in recent years and there was a noticeable upward trend in water quality during the postepidemic period. Spatially, various changes in lake regions were observed. Water quality of east lakes responded swiftly and showed an obvious improvement after the lockdown, whereas western lakes responded belatedly in the first epidemic year due to fewer human activity and smaller number of confirmed cases and had a clear benefit in the second year owing to the long-term sustaining of lower human activity intensity. This finding emphasized the significant influence of human activities than natural processes. In addition, this work quantitatively demonstrated the association in FUI in terms of new confirmed cases, temperature, precipitation, wind speed, and lake area, consistent with the extensive long-term researches. The study provided sufficient information for water color observation, and revealed the short-term improvement of water quality under epidemic situation. Additional attention of water resource management and sustainable strategies should be paid to inland water in order to maintain the short-term improvement.
Decentralized and Parallelized Primal and Dual Accelerated Methods for Stochastic Convex Programming Problems

We introduce primal and dual stochastic gradient oracle methods for decentralized convex optimization problems. The proposed methods are optimal in terms of communication steps for primal and dual oracles. However, optimality in terms of oracle calls per node takes place in all the cases up to a logarithmic factor and the notion of smoothness (worth case vs average). All the methods for stochastic oracle can be additionally parallelized on each node due to the batching technique.
Enhancement of neural regeneration as a therapeutic strategy for Alzheimer's disease (Review)

Alzheimer's disease (AD), the most common cause of dementia worldwide, has gradually become a global health concern for society and individuals with the process of global ageing. Although extensive research has been carried out on AD, the etiology and pathological mechanism of the disease are still unclear, and there is no specific drug to cure or delay AD progression. The exploration of enhancing nerve regeneration in AD has gradually attracted increasing attention. In the current review, the existing therapeutic strategies were summarized to induce nerve regeneration which can increase the number of neurons, and improve the survival of neurons, the plasticity of synapses and synaptic activity. The strategies include increasing neurotrophic expression (such as brain-derived neurotrophic factor and nerve growth factor), inhibiting acetylcholinesterase (such as donepezil, tacrine, rivastigmine and galanthamine), elevating histone deacetylase levels (such as RGFP-966, Tasquinimod, CM-414 and 44B), stimulating the brain by physiotherapy (such as near-infrared light, repetitive transcranial magnetic stimulation, and transcranial direct current stimulation) and transplanting exogenous neural stem cells. However, further evaluations need to be performed to determine the optimal treatment. The present study reviews recent interventions for enhancing adult neurogenesis and attempts to elucidate their mechanisms of action, which may provide a theoretical basis for inducing nerve regeneration to fight against AD.
Robust Few-Shot Aerial Image Object Detection via Unbiased Proposals Filtration

Few-shot aerial image object detection aims to rapidly detect object instances of novel category in aerial images by using few labeled samples. However, due to the complex background of aerial images, few labeled samples of novel categories, and the model trained with the few-shot learning paradigm is biased toward the base categories, it greatly increases the difficulty of identifying foreground objects of novel categories. In addition to this, tiny object detection is always a hot potato in aerial image object detection, and it is even more difficult for few-shot object detection. To this end, we propose a few-shot aerial image object detection with confidence-iou collaborative proposal filtration and tiny object constraint loss (FsCIT). Specifically, we first introduce a new confidence-iou collaborative proposal filtration scheme to region proposal network (RPN), which combines the unbiased intersection over union (IoU) scores between the two bounding boxes with foreground–background confidence scores to filter redundant region proposals and rescue more foreground proposals for the novel categories in RPN. Then, we design a new tiny object loss constraint term to attempt at overcoming the challenge of tiny object detection in few-shot aerial image object detection. This term considers the central point distance, the size of ground-truth bounding boxes, and the distances between the four edges of the ground-truth bounding box and the predicted bounding box. Experiments on object detection in optical remote sensing images (DIOR), tiny object detection for aerial images (AI-TOD), and high-resolution remote sensing detection (HRRSD) datasets show that FsCIT is effective and can improve the performance of few-shot aerial image object detection.
Metaverse supply chain and operations management

ABSTRACT The metaverse and Web 3.0 have created a new digital world with specific properties and behaviours replicating and influencing the behaviours and processes of physical entities. This study aims to advance our understanding of how the metaverse will impact supply chain and operations management (SCOM). Using elements of a structured literature search and building on the concepts of cyber-physical systems, digital supply chain twins, cloud supply chains, and Industry 4.0/Industry 5.0, we propose a framework for metaverse SCOM encompassing multiple socio-technological dimensions. We conclude that further metaverse developments could result in a co-existence of physical SCOM, metaverse SCOM, and SCOM for coordination of the physical and metaverse worlds. We offer a structured future research agenda pointing to new research questions and topics stemming from metaverse-driven visibility, computational power for data analytics, digital collaboration, and connectivity. New research areas can emerge for the novel metaverse SCOM processes and decision-making areas (e.g. joint demand forecasting for metaverse and physical products, digital inventory allocation in the metaverse, integrated production planning for the metaverse and physical worlds, and pricing and contracting for digital products), as well as new performance measures (e.g. virtual customer experience level, availability of digital products, and digital resilience and sustainability).
Three Near Points Method for Calculating Generalized Curvature and Torsion

The aim of this paper is to establish a new method for calculating generalized curvature and generalized torsion. This method depends on at least three points infinitely close to the considered point. The established method is characterize from other methods by using only dot and cross product of vectors combined from this points, without any parameter that interacts in the defined curve by using some concepts of nonstandard analysis given by Robinson A. and axiomatized by Nelson E. 1- Introduction: - In this paper the problem of obtaining the generalized curvature(6), (7), and generalized torsion(8), is considered and studied by using three points infinitely close to the given point. This method is different from the two methods given in (7), (8) and (9), and it has an advantage that it does not depend on any parameter which interact with the curve.
Dynamic programming with radial basis functions and Shepard's method

In this thesis, we investigate a discretization of the optimality principle in dynamic programming based on radial basis functions and Shepard’s moving least squares approximation method. We prove the convergence of the discrete value function for increasingly dense sets of centres, develop an adaptive version of the algorithm and generalize a Dijkstra-like algorithm of Bertsekas which allows an efficient calculation of the value function. We illustrate the theory with numerous numerical experiments.
Existence of strongly symmetrical weakly pandiagonal graeco-latin squares

Abstract A graeco-latin sauare is a pair of orthogonal latin squares. It is a design of experiment in which the experimental units are grouped in three different ways. In this paper, constructions of a pair of orthogonal latin sauares which are both strongly symmetrical and weakly pandiagonal are investigated. As a result, it is proved that there exists a pair of strongly symmetrical weakly pandiagonal orthogonal latin sauare of order n if and only if n > 4 and n ≡ 0, 1, 3 (mod 4) with only one possible exception for n = 12.
Mutual injection coupling and phase locking of two multiwavelength fiber lasers

Abstract. Passive phase locking of two multiwavelength fiber lasers has been demonstrated by mutual injection coupling and spatial filtering. Since the fiber loop mirrors are employed as two component lasers’ rear reflectors, multiple wavelengths operate simultaneously owing to their broadband reflection and the compound cavity’s mode selection effect. A single-mode filtering fiber is utilized to collect the expected spatial mode’s energy and feed them back into the coupled laser cavity, and stable and high visibility interference fringes have been obtained by the self-phasing process. A contrastive experiment has been made to investigate its coherent output properties when the rear mirrors are replaced by fiber Bragg gratings. Compared with the ordinary coherent combining of fiber lasers lasing at a single-wavelength, higher output power and efficiency have been obtained, and the cost is that the output beams’ coherence and stability decrease slightly.
On the Relation between Valence Weighting and Self-Regulation

ii Acknowledgments. v Vita . vi List of Figures . ix Introduction . 1 Impulse Control and Goal Pursuit . 2 Assessments of Goal Progress and Goal Pursuit . 4 Valence Weighting . 5 Valence Weighting and Impulse Control . 10 Valence Weighting and Assessments of Goal Progress . 13 Current Research . 14 Study 1 . 16 Methods . 17 Results . 21 Study 1 Discussion . 23 Study 2 . 26 viii Methods . 28 Results . 29 Study 2 Discussion . 31 General Discussion . 33 References . 45 Appendix A: Figures . 48
Privacy-Enabled Secure Iterative Learning Control of Networked Systems Subject to Disclosure Attacks

In this brief, a secure iterative learning control architecture is proposed for networked systems, aiming to track the desired output and protect the privacy of signals. The main technique challenges lie in that the homomorphic encryption is integrated into the control scheme without destructing the performance of systems. Therefore, the iterative learning control algorithm based on the quantized feedback and saturated input is presented to support the design of encrypted controller, and the conditions on the parameters of cryptosystem are established to ensure that the inputs obtained by actuator are reliable. As the results, the calculation of secure control law only relies on the encrypted data, thus immunizing the disclosure attacks of adversaries. To the best of our knowledge, there is currently little work to investigate the secure iterative learning control scheme, which can enhance the practicability and security of systems due to the data-driven strategy and encryption mechanism.
Anomaly Detection in Annular Metal Turning Surfaces Based on a Priori Information and a Multi-Scale Self-Referencing Template

To solve the problem of anomaly detection in annular metal turning surfaces, this paper develops an anomaly detection algorithm based on a priori information and a multi-scale self-referencing template by combining the imaging characteristics of annular workpieces. First, the annular metal turning surface is unfolded into a rectangular expanded image using bilinear interpolation to facilitate subsequent algorithm development. Second, the grayscale information from the positive samples is used to obtain the a priori information, and a multi-scale self-referencing template method is used to obtain its own multi-scale information. Then, the phase error and large-size anomaly interference problems of the self-referencing method are overcome by combining the a priori information with its own information, and an accurate response to anomalous regions of various sizes is realized. Finally, the segmentation completeness of the anomalous region is improved by utilizing the region growing method. The experimental results show that the proposed method achieves a mean pixel AUROC of 0.977, and the mean M_IOU of segmentation reaches 0.788. In terms of efficiency, this method is also much more efficient than the commonly used anomaly detection algorithms. The proposed method can achieve rapid and accurate detection of defects in annular metal turning surfaces and has good industrial application value.
EFFECT OF FOOT ROTATION ANGLE ON TRUNK ROTATIONAL STRENGTH AND PHYSICAL QUANTITY TO ROTATE THE BODY

The purposes of this study were to investigate the influence of foot rotation angle on trunk rotational strength and physical quantity to rotate the body. Ten healthy males participated in this study who exerted the maximum trunk rotational strength in a standing posture on the conditions of five different foot rotation angles based on “the central angle” that was defined as the average of the maximum internaland external-rotation of hip joint in a static standing. The force was measured by force gauge sensor. Biomechanical analysis was performed with an optical motion capture system and two force plates. No significant difference was observed in the rotational strength between any conditions, but as the foot rotation angle increased externally, the free moment acting on the right foot decreased and the moment around the center of mass by the ground reaction forces acting on both feet increased.
Dynamic Slope Stability Analysis by a Reliability-Based Approach

The analysis of earth slopes situated in seismic areas has been extensively investigated in literature using deterministic approaches where average values of the input parameters are used and a global safety factor or a permanent displacement at the toe of the slope is calculated. In these approaches, a decoupled analysis is usually performed in which a constant critical seismic acceleration is first calculated based on a pseudo-static representation of earthquake effects. Then, the permanent soil displacement is computed by integration of the real acceleration record above the critical acceleration. A reliability-based approach for the seismic slope analysis is introduced in this paper. This approach is more rational than the traditional deterministic methods since it enables to take into account the inherent uncertainty of the input variables. Furthermore, the deterministic model used in the reliability analysis is based on a rigorous coupled analysis that simultaneously captures the fully non linear response of the soil and history of the real acceleration record. This model is based on numerical simulations using the dynamic option of the finite difference FLAC 3D software. The acceleration time history records used in this analysis is the one registered at the Lebanese Geophysical Center of the National Council for Scientific Research. The performance function used in the reliability analysis is defined with respect to the horizontal permanent displacement at the toe of the slope. The random variables considered in the analysis are the cohesion c and the shear modulus G of the soil since it was shown that these parameters have the most influence on slope displacements. The Stochastic Response Surface Methodology (SRSM) is utilized for the assessment of the probability distribution of the horizontal permanent displacement at the toe of the slope. The results have shown that the mean value of the permanent displacement is highly influenced by the coefficient of variation of the cohesion, the greater the scatter in c the higher the horizontal permanent displacement. Based on the probability distribution of the permanent displacement, the failure probability with respect to the exceedance of an allowable maximal permanent displacement was evaluated and discussed. At the end of the paper, a case study of a typical Lebanese slope is presented and analyzed to illustrate this approach.
A Case Study of Transactional Workload Running in Virtual Machines: The Performance Evaluation of a Flight Seats Availability Service

Much of the research has focused on performance evaluation and, particularly, the response time of clusters in cloud computing. However, one important topic has hardly been addressed: the impact of virtual machine consolidation on real business cases, on companies driven by requirements for high performance in transaction response time, specifically on intermediation trip companies. The ability to provide quality service, guaranteed within several milliseconds, is crucial to the business success of these cluster platforms. We present a case study for evaluating the performance of the seat availability service used by a flight carrier. The case study is the application of the performance evaluation methodology that ranges from monitoring to tuning options of a real-world service running on virtual machines, to understand capacity planning or possible substitution by other configurations of virtualization or containerization of the architecture of the cloud platform. This case study also proposes a workload characterisation using data clusters, allowing the architecture to be modeled as a simple network of multiclass queues of any virtual machine on the platform. Additionally, we estimate the new transaction response time by the possibility of either reducing or incrementing the number of virtual machines and their replacement by containers
Sparse Model Identification of a 4x4 MIMO Channel Measurements in 5 GHz Band

The Least Absolute Shrinkage and Selection Operator has gained attention in the applied mathematics and signal processing communities. The thesis provides us theoretical expressions for solving Compressive Sensing by the LASSO algorithm for Direction of Arrival estimation. The central idea is highlight the fundamental concepts of the complex Least Absolute Shrinkage and Selection Operator (c-LASSO) and give an overview of its application to the Direction Of Arrival estimation.The role of the regularization parameter and suggestions on the selection are exposed. It is found that LASSO can be compared with Conventional beamforming and Least Squares. The presented results in the context of Direction of Arrival (DOA) single snapshot estimation using a 4 antenna linear sensor array.
The Role of STAT3 Signaling Pathway Activation in Subconjunctival Scar Formation after Glaucoma Filtration Surgery

Scar formation resulting from overly active wound healing is a critical factor in the success rate of glaucoma filtration surgery (GFS). IL-6 and TGF-β have been implicated in the pathogenesis of fibrogenesis. In addition, the signal transducer and activator of transcription 3 (STAT3) can be activated by numerous cytokines and growth factors, including IL-6 and TGF-β1. Thus, STAT3 activation may integrate common profibrotic pathways to promote fibrosis. In this study, an increase in p-STAT3 was observed in activated HTFs. Inhibiting STAT3 in cultured HTFs by pharmacological inactivation reversed the fibrotic responses, such as fibroblast migration, the differentiation of resting fibroblasts into myofibroblasts and the deposition of ECM, mediated by IL-6 and TGF-β1. Moreover, the expression of suppressor of cytokine signaling 3 (SOCS3) was decreased in HTFs cultured with IL-6 and TGF-β1, and SOCS3 overexpression rescued ECM deposition, α-SMA expression and migration in IL-6- and TGF-β1-stimulated HTFs by inactivating STAT3. Finally, S3I-201 treatment inhibited profibrotic gene expression and subconjunctival fibrosis in a rat model of GFS. In conclusion, our data suggests that STAT3 plays a central role in fibrosis induced by different profibrotic pathways and that STAT3 is a potential target for antifibrotic therapies following GFS.
Non-Fragile H∞ Control for Piecewise Homogeneous Hidden Semi-Markov Lur’e Systems

In this brief the non-fragile asynchronous control problem is investigated for discrete-time non-homogeneous hidden semi-Markov Lur’e systems subjected to incapability obtained system mode and gain uncertainty, in which each subsystem is composed of mode-independent linear and nonlinear part. The non-homogeneous semi-Markov chain is established by a set of finite consecutive homogeneous semi-Markov chains with different intervals. To make the investigated problem more comprehensive, both the embedded Markov chain and sojourn-time probability density function in semi-Markov chain are considered to be piecewise-homogeneous. Particularly, they are regarded as simultaneously piecewise-homogeneous. Furthermore, with regard to the random occurrence gain uncertainty and mode uncertainty in the execution of actuators, this brief is devoted to designing a non-fragile asynchronous controller that can ensure the mean-square exponentially stability and $H_{\infty }$ performance of the resulting systems. In the final, an illustrative simulation is presented to demonstrate the feasibility of the derived results.
Zero dissipation limit to rarefaction wave with vacuum for a one-dimensional compressible non-Newtonian fluid

In this paper, we study the zero dissipation limit toward rarefaction waves for solutions to a one-dimensional compressible non-Newtonian fluid for general initial data, whose far fields are connected by a rarefaction wave to the corresponding Euler equations with one end state being vacuum. Given a rarefaction wave with one-side vacuum state to the compressible Euler equations, we construct a sequence of solutions to the one-dimensional compressible non-Newtonian fluid which converge to the above rarefaction wave with vacuum as the viscosity coefficient $\epsilon$ tends to zero. Moreover, the uniform convergence rate is obtained, based on one fact that the viscosity constant can control the degeneracies caused by the vacuum in rarefaction waves and another fact that the energy estimates are obtained under some a priori assumption.