Miracle-dz/UniFAR-FTUs huggingface dataset:
Review of Research on Fault Diagnosis of Rolling Bearings Based on Deep Learning

Deep learning has powerful capabilities in deep feature extraction and expression, and has been successfully applied in equipment fault diagnosis, overcoming the shortcomings of traditional diagnostic methods that rely on expert experience. It can save costs while improving diagnostic accuracy. This article briefly introduces three commonly used neural networks: Deep Belief Networks (DBN), Convolutional Neural Networks (CNN), and Long Short-Term Memory Networks (LSTM), and points out the problems in rolling bearing diagnosis and analyzes future development directions.
Algorithms for superiorization and their applications to image reconstruction

Computational tractability with limited computing resources is a major barrier for the ever increasing problem sizes of constrained optimization models that seek a minimum of an objective function satisfying a set of constraints. On the other hand, there exist efficient and computationally much less demanding iterative methods for finding a feasible solution that only fulfills the constraints. These methods can handle problem sizes beyond which existing optimization algorithms cannot function. To bridge this gap we present a new concept called superiorization, envisioned methodologically as lying between optimization and feasibility seeking. It enables us to use efficient iterative methods to steer the iterates toward a point that is feasible and superior, but not necessarily optimal, with respect to the given objective/merit function. Using efficient iterative methods to do ‘superiorization’ instead of ‘full constrained optimization’ or only ‘feasibility’ is a new tool for handling mathematical models that include constraints and a merit function. The target improvement of the superiorization methodology is to affect the computational treatment of the mathematical models so that we can reach solutions that are desirable from the point of view of the application at hand at a relatively small computational cost. The key to superiorization is our discovery that two principal prototypical algorithmic schemes, string-averaging projections and block-iterative projections, which include many projection methods, are bounded perturbation resilient. While stability of algorithms under perturbations is usually made to cope with all kinds of imperfections in the data, here we have taken a proactive approach designed to extract specific benefits from the kind of stability that we term perturbation-resilience. Superiorization uses perturbations proactively to reach feasible points that are superior, according to some criterion, to the ones to which we would get without employing perturbations. In this work, we set forth the fundamental principle of the superiorization methodology, give some mathematical formulations, theorems and results, and show potential benefits in the field of image reconstruction from projections.
ows over time: A rst step towards solving dynamic packing problems ?

Flows over time (4) generalize classical network ows by in- troducing a notion of time. Each arc is equipped with a transit time that species how long ow takes to traverse it, while ow rates may vary over time within the given edge capacities. In this paper, we extend this concept of a dynamic optimization problem to the more general setting of abstract ows (8). In this model, the underlying network is replaced by an abstract system of linearly ordered sets, called \paths" satisfying a simple switching property: Whenever two paths P and Q intersect, there must be another path that is contained in the beginning of P and the end of Q. We show that a maximum abstract ow over time can be obtained by solving a weighted abstract ow problem and constructing a temporally repeated ow from its solution. In the course of the proof, we also show that the relatively modest switching property of abstract networks al- ready captures many essential properties of classical networks.
Optimal Prediction of Forecast Error Covariances through Singular Vectors

Abstract Optimal perturbations, also referred to as singular vectors (SVs), currently constitute an important guideline for the generation of initial ensembles to be used for ensemble prediction. The optimality of these perturbations refers to their property of maximizing prespecified quadratic measures of error growth, given that tangent-linear error evolution is assumed. The goal of ensemble prediction is the accurate prediction of the uncertainty of forecasts made with dynamical numerical weather prediction models. In the present paper the theoretical justification for the use of SVs in ensemble prediction systems is investigated. It is shown that, in a tangent-linear framework, SVs—constructed using covariance information valid at the initial time—evolve into the eigenvectors of the forecast error covariance matrix valid for the end of the optimization interval. As such, SVs represent the most efficient means for predicting the forecast error covariance matrix, given a prespecified number of allowable.
Photocatalytic Activity of the Blends Based on TiO2 Nanoparticles and Reduced Graphene Oxide for Degradation of Acetaminophen

The aim of this work is to highlight the influence of blends based on TiO2 nanoparticles and reduced graphene oxide (RGO) on the photodegradation of acetaminophen (AC). To this end, the catalysts of TiO2/RGO blends with RGO sheet concentrations equal 5, 10, and 20 wt. % were prepared by the solid-state interaction of the two constituents. The preferential adsorption of TiO2 particles onto the RGO sheets’ surfaces via the water molecules on the TiO2 particle surface was demonstrated by FTIR spectroscopy. This adsorption process induced an increase in the disordered state of the RGO sheets in the presence of the TiO2 particles, as highlighted by Raman scattering and scanning electron microscopy (SEM). The novelty of this work lies in the demonstration that TiO2/RGO mixtures, obtained by the solid-phase interaction of the two constituents, allow an acetaminophen removal of up to 95.18% after 100 min of UV irradiation. This TiO2/RGO catalyst induced a higher photodegradation efficiency of AC than TiO2 due to the presence of RGO sheets, which acted as a capture agent for the photogenerated electrons of TiO2, hindering the electron–hole recombination. The reaction kinetics of AC aqueous solutions containing TiO2/RGO blends followed a complex first-order kinetic model. Another novelty of this work is the demonstration of the ability of PVC membranes modified with Au nanoparticles to act both as filters for the removal of TiO2/RGO blends after AC photodegradation and as potential SERS supports, which illustrate the vibrational properties of the reused catalyst. The reuse of the TiO2/RGO blends after the first cycle of AC photodegradation indicated their suitable stability during the five cycles of pharmaceutical compound photodegradation.
Labour Care Guide implementation as a decision-making tool for monitoring labour among health care providers in Southwestern Uganda: A protocol for a multisite Effectiveness-Implementation study

Background: The new WHO Labour Care Guide, also regarded as the "next-generation partograph" basing on recent evidence has been recorded as a core component of the 2018 consolidated set of guidelines on intrapartum care for positive child birth experience. The Ugandan Ministry of Health is in the process of adopting the new WHO Labour Care Guide (LCG) with no local context specific data to inform this transition. This study will employ evidence-based research frameworks to identify challenges, and potential opportunities that would inform and refine the implementation strategy and scale-up of this highly promising LCG. We will also seek to utilize best practices to evaluate implementation effectiveness of the new LCG, through employing measurable implementation matrices (implementation, service, patient outcomes). Methods: This study will be a multisite effectiveness-implementation study across all basic and comprehensive emergency obstetric and newborn care facilities in Mbarara district and Mbarara City in Southwestern Uganda. We will employ both quantitative and qualitative methods to evaluate the use of the newly recommended WHO Labor Care Guide in monitoring labor among all health care providers actively engaged in deliverying women across all public maternity health facilities in Mbarara district and Mbarara City. No participant has been recruited at hie point in time Results and Discussion: This study will offer an opportunity to ascertain whether the new WHO Labour Care Guide tool is an effective decision-making tool to monitor labor among healthcare providers conducting routine deliveries in publicly funded facilities in Southwestern Uganda. We will also identify practical, context-specific and actionable strategies for achieving optimal implementation effectiveness in a rural low resource setting.
A Novel Method to the Aging Feature Extraction of Viscoelastic Sandwich Structures

The viscoelastic sandwich structure exhibits highly nonlinear dynamic characteristics, and minimal changes in signal caused by aging state alterations, thus posing a challenge for extracting its aging characteristics. A method is proposed for extracting aging features from viscoelastic sandwich structures using variational mode decomposition (VMD) and permutation entropy (PE). First, the original dynamic response signal is decomposed by VMD to acquire several intrinsic mode functions (IMF). Then the permutation entropy characteristics of each IMF are calculated. The research results show that there are obvious differences in the distribution of permutation entropy corresponding to each IMF component, which indicates that the permutation entropy feature extracted based on the variational modal decomposition is more susceptive to the change of the aging state of the structure, thus verifying the availability of this method for feature extraction.
Research on Self-driving Vehicle Path Tracking Adaptive Method Based on Predictive Control

The autonomous vehicles can improve the traffic utilisation and ensure the safe driving. Thus, the relative analysis is one hot research topic nowadays. The path tracking control system is one key link for the quick and stable tracking of target paths of autonomous vehicles. For analyzing the route tracing process of the Self-driving vehicles under complex working conditions, this paper proposes an adaptive model prediction controller. The controller chooses a vehicle dynamics model and incorporates a fuzzy adaptive control method into the model predictive control algorithm. Through the fuzzy adaptive control method, the weight of the objective function in the model predictive control algorithm is adaptively optimized to improve the control effects. The joint simulation tests are applied in the CarSim, MATLAB and Simulink software. Compared with the general model predictive controller, the outcomes indicate that the conceptualized controller can effectively reduce the tracking errors and ensure safe driving.
Un módulo lingüístico emergido por “simbiogénesis” y adaptado para una gramática generativa no transformacional

A linguistic module emerged by “symbiogenesis” and adapted for a non-transformational generative grammar In this paper I will defend TELEs, the theory of language evolution by three emergences and natural selection. The aim is to support the first step, wherein language is the resulting module of an associative emergence from two previous modules that are responsible for conceptual and syntactic aspects, respectively. My strategy will consist of showing the consistency between the latest research in various disciplines (philosophy: multimodularity; biology: symbiogenesis, emergence, exaptation, and linguistics: nontransformational generative grammars) as well as my own theory. Key words: Origin of language, TESE theory, module, multimodularity, symbiogenesis, emergence, exaptation, non-transformational generative grammars.
Study on Hydrophobic Surface of Magnesium Alloy Prepared by Femtosecond Laser and its Corrosion Resistance

Due to their excellent barrier and shielding effects, superhydrophobic magnesium alloy surfaces are increasingly interested in corrosion protection. This paper proposes a method for producing a stable superhydrophobic surface by laser scanning ablation of AZ31B magnesium alloy on its surface. The prepared layered micro-nanoemulsion convex structure with a lotus-like surface exhibited excellent superhydrophobicity after modification by stearic acid, and the static water droplet contact angle reached 163°. After 90 days in outdoor air, droplet pressure-based experiments show that the superhydrophobic surface possesses a long-term stable Cassie state. In addition, The superhydrophobic surface showed excellent corrosion resistance against magnesium alloy in a Neutral 3.5 wt. % NaCl solution. The results of the dynamic potential polarization tests showed that the existence of a gas layer cavity on the lotus-like surface structure caused the corrosion current density of superhydrophobic AZ31B (3.86±1 uA/cm2) to decrease by 2 orders of magnitude compared with that of the extruded AZ31B (103±1 uA/cm2). We believe the proposed method is simple, efficient, and non-polluting, especially for medical magnesium alloys in the field of in vivo implant materials to slow down corrosion and promote clinical applications.
Two-frame Phase-shifting Interferometry by Minimizing the Residual-background

Demodulation of two random phase shift fringe patterns is an ill-posed problem. Existing methods often need to remove the fringe background by pre filtering in the frequency, which takes more time, and have a great influence on the phase reconstruction because of the frequency aliasing, these limits the application of two frame phase-shift interferometry in actual production. In this case, this paper based on the idea of Statistical Average, proposes a Statistical Average technology for phase extraction of two frame randomly phase-shifted interferograms from rough to precision without pre filtering. The proposed methods based on minimizing the residual background, firstly estimates the average background by calculating the average for all pixels, and then adding and subtracting the two frames with the background removed. The phase shift and phase are solved by using the properties of trigonometric functions. Then the amplitude is solved using the obtained phase and phase shift. Finally, the background is solved and the original light intensity is updated. Compared with existing methods, our method has the advantages such as rapid convergence and quick calculation by offering the right initial value. The accuracy of my proposed method is approximately double that of existing methods. Simulation and experimental results show that our method is a practical method of two frame phase shift interferences, both simple and reliable.
Mathematical Analysis of Restoration Techniques

This paper carries out the mathematical analyses and implements linear restoration filters such as Inverse filter, Pseudo Inverse filter, Wiener filter, Constrained Least square filter. Linear filters are used to restore images that are blurred by spatial invariant degradation model and degraded by additive noise. The performance of each filter is considered based on the statistical parameter of the restored image. The analysis is carried out for each filter over a range of noise and blur. The filter transfer function is optimized to get a nearest estimate to the original image. The performance of each filter is compared and conclusions are presented.
Financial reporting in times of crisis: The case of the Serbian developing economy during COVID-19

This study examines the impact of the COVID-19 pandemic on financial reporting in Serbia from the perspective of preparers of financial statements. Notably, the pandemic affected financial reporting in Serbia and preparers faced more professional difficulties during than before the pandemic. Financial reporting problems regarding future expectations and uncertainty are an important source of concern for preparers. The preparers who apply International Financial Reporting Standard (IFRS) for Small and Medium-sized Entities (SMEs) perceive some more difficulties than those who apply full IFRSs. This study contributes to the scarce literature regarding the impact of the COVID-19 pandemic on financial reporting. JEL Classification: M41, G01
Popular Tag Recommendation by Neural Network in Social Media

Although “a picture is worth a thousand words,” this may not be enough to get your post seen on social media. This study's main objective was to determine the best ways to characterize a photo in terms of viral marketing and public appealing. We have to obtain this dataset for this reason from the social media site such as Instagram. A total of 1.4 million hashtags were used in the 570,000 photos that we crawled. Prior to training the text generation module to produce such popular hashtags, we had to determine the components and features of the photo. We trained a multilabel image classification module using a ResNet neural network model for the first section. In order to create hashtags pertaining to their popularity, we trained a cutting-edge GPT-2 language model for the second portion. This work differs from others in that, and it initially offered a cutting-edge GPT-2 model for hashtag generation using a combination of the multilabel image classification module. The popularity issues and ways to make an Instagram post popular are also highlighted in our essay. Social science and marketing research can both be conducted on this subject. Which content can be considered popular from the perspective of consumers can be researched in the social science setting. As a marketing strategy, end users can help by offering such well-liked hashtags for social media accounts. This essay adds to the body of knowledge by demonstrating the two possible uses of popularity. Compared to the base model, our popular hashtag generating algorithm creates 11% more relevant, acceptable, and trending hashtags, according to the evaluation that was carried out.
Stochastic Control of Continuous-Time and Continuous-State Systems via Direct Comparison

The standard approach to stochastic control is dy- namic programming. In this paper, we introduce an alternative approach based on direct comparison of the performance of any two policies, by modeling the state process as a continuous-time and continuous state Markov process. The approach provides a unified framework for stochastic control and other optimization theory and methodologies including Markov decision processes, perturbation analysis, and reinforcement learning. The new insights obtained may lead to new research topics. Control and performance optimization of stochastic sys- tems is a multi-disciplinary subject that has attracted wide attention from many research communities. Many different research areas have been established; these different areas take different perspectives and therefore have different mod- els of the systems and formulations of the problem. The traditional stochastic control theory deals with con- tinuous systems modeled by stochastic differential equations, and the standard approach is dynamic programming (1). The approach is particularly suitable for finite-horizon problems; it works backwards in time. The problem with infinite horizon can be treated as the limiting cases of the finite- horizon problem when time going to infinity, and the long- run average cost problem can be treated as the limiting case of the problems with discounted costs. In the approach, the Hamilton- Jacobi-Bellman (HJB) equation for the optimal policies is first established with the dynamic programming principle, a verification theorem is then proved which states that the solution to the HJB equation indeed provides the value function and from which an optimal control process can be constructed. The HJB equations are usually differ- ential equations, and the concept of viscosity solution is introduced when the value functions are not differentiable (8).
Distinct neural encoding of glimpsed and masked speech in multitalker situations

Humans can easily tune in to one talker in a multitalker environment while still picking up bits of background speech; however, it remains unclear how we perceive speech that is masked and to what degree non-target speech is processed. Some models suggest that perception can be achieved through glimpses, which are spectrotemporal regions where a talker has more energy than the background. Other models, however, require the recovery of the masked regions. To clarify this issue, we directly recorded from primary and non-primary auditory cortex (AC) in neurosurgical patients as they attended to one talker in multitalker speech and trained temporal response function models to predict high-gamma neural activity from glimpsed and masked stimulus features. We found that glimpsed speech is encoded at the level of phonetic features for target and non-target talkers, with enhanced encoding of target speech in non-primary AC. In contrast, encoding of masked phonetic features was found only for the target, with a greater response latency and distinct anatomical organization compared to glimpsed phonetic features. These findings suggest separate mechanisms for encoding glimpsed and masked speech and provide neural evidence for the glimpsing model of speech perception.
Local adaptation and host specificity to copepod intermediate hosts by the tapeworm Schistocephalus solidus

Abstract Host–parasite coevolution may lead to patterns of local adaptation in either the host or parasite. For parasites with complex multi‐host life cycles, this coevolution may be more challenging as they must adapt to multiple geographically varying hosts. The tapeworm Schistocephalus solidus exhibits some local adaptation to its second intermediate host, threespine stickleback, to which the parasite is strictly specialized. However, the tapeworm's adaptation to its first intermediate host (any of a number of copepod species) is not documented. We investigated if there was local adaptation and host specify in the tapeworm Schistocephalus solidus to its copepod first intermediate hosts. We exposed copepods from five lakes in Vancouver Island (BC, Canada) to local (i.e. same lake) and foreign tapeworms in a reciprocal exposure experiment. Results indicate that the tapeworm is not locally adapted to the copepods. Instead, we observed moderate‐effect host specificity, infection rates being higher in certain copepod species than in others. Infection rates also varied among cestode populations. These results show that although S. solidus infects multiple copepod genera, they are not equally competent hosts. Differences in S. solidus epidemiology among lakes is likely to be driven more by this partial specialization, than by local adaptation to first intermediate hosts.
From the One, Judge of the Whole: Typed Entailment Graph Construction with Predicate Generation

Entailment Graphs (EGs) have been constructed based on extracted corpora as a strong and explainable form to indicate context-independent entailment relation in natural languages. However, EGs built by previous methods often suffer from the severe sparsity issues, due to limited corpora available and the long-tail phenomenon of predicate distributions. In this paper, we propose a multi-stage method, Typed Predicate-Entailment Graph Generator (TP-EGG), to tackle this problem. Given several seed predicates, TP-EGG builds the graphs by generating new predicates and detecting entailment relations among them. The generative nature of TP-EGG helps us leverage the recent advances from large pretrained language models (PLMs), while avoiding the reliance on carefully prepared corpora. Experiments on benchmark datasets show that TP-EGG can generate high-quality and scale-controllable entailment graphs, achieving significant in-domain improvement over state-of-the-art EGs and boosting the performance of down-stream inference tasks.