Miracle-dz/UniFAR-FTUs huggingface dataset:
A Rolling Bearing Fault Diagnosis-Optimized Scale-Space Representation for the Empirical Wavelet Transform

Rolling element bearings have been widely used in mechanical systems, such as electric motors, generators, pumps, gearboxes, railway axles, and turbines, etc. Therefore, the detection of rolling bearing faults has been a hot research topic in engineering practices. Envelope demodulation represents a fundamental method for extracting effective fault information from measured vibration signals. However, the performance of envelope demodulation depends heavily on the selection of the filter band and central frequencies. The empirical wavelet transform (EWT), a new signal decomposition method, provides a framework for arbitrarily segmenting the Fourier spectrum of an analysed signal. Scale-space representation (SSR) can adaptively detect the boundaries of the EWT; however, it has two shortcomings: slow calculation speeds and invalid boundary detection results. Accordingly, an EWT method based on optimized scale-space representation (OSSR), namely, the EWTOSSR, is proposed. The effectiveness of the EWTOSSR is verified by comparisons between the simulation and the experimental signals. The results show that the EWTOSSR can automatically and effectively segment the EWT spectrum to extract fault information. Compared with three well-known methods (the traditional EWT, ensemble empirical mode decomposition (EEMD), and the fast kurtogram), the EWTOSSR exhibits a much better fault detection performance.
Application autofluorescence diagnosis method in endoscopy for investigation mucosal structure in gastrointestinal tract

One of the promising methods for early diagnosis of malignant diseases of the respiratory organs and the gastrointestinal tract (GIT) is now considered a fluorescence method. Application autofluorescence phenomenon in endoscopy allows to obtain a fluorescent image of the mucosa, which shows the difference in the intensity of the autofluorescence of healthy and the affected tissue in the green and red regions of the spectrum. The result of the work is to determine on the basis of scientific research and prototyping capabilities of creating fluorescence video endoscope and the development of fluorescent light (illuminator FLU) for videoendoscopy complex. The solution of this problem is based on the method of studying biological objects in lifetime condition.
Energy and Air Quality Management in a Subway Station using Stochastic Dynamic Optimization

In the Paris subway system, stations represent about one third of the overall energy consumption. Within stations, ventilation is among the top consuming devices; it is operated at maximum airflow all day long, for air quality reasons. In this paper, we present a concept of energy system that displays comparable air quality while consuming much less energy. The system comprises a battery that makes it possible to recover the trains braking energy, arriving under the form of erratic and strong peaks. We propose an energy management system (EMS) that, at short time scale, controls energy flows and ventilation airflow. By using proper optimization algorithms, we manage to match supply with demand, while minimizing energy daily costs. For this purpose, we have designed algorithms that take into account the braking variability. They are based on the so-called Stochastic Dynamic Programming (SDP) mathematical framework. We fairly compare SDP based algorithms with the widespread Model Predictive Control (MPC) ones. First, both SDP and MPC yield energy/money operating savings of the order of one third, compared to the current management without battery (our figure does not include the cost of the battery). Second, depending on the specific design, we observe that SDP outperforms MPC by a few percent, with an easier online numerical implementation.
Efficacy of Paroxetine for the Treatment of Depression in the Context of HIV Infection

Recent studies in the literature point out that HIV-infected subjects are affected by depression with a relatively high frequency. The aim of this study was to assess the efficacy and tolerability of paroxetine for the treatment of depression in the context of HIV infection. 15 HIV-infected subjects (10 patients with a major depressive episode and 5 patients with an adjustment disorder with depressed mood, according to the DSM IV diagnostic criteria) were administered paroxetine at a daily dosage of 20 mg. Depressive symptomatology was monitored by means of the Hamilton Rating Scale for Depression (HAM-D) at the time of enrollment and 2 weeks, 4 weeks, and 6 weeks antidepressants, they lack significant anticholinergic and cardiovascular effects and, moreover, are safe i f taken in overdosage. Up to now, few studies have evaluated the efficacy of these drugs for the treatment of depressed HIV-infected subjects. The aim of this study was to assess the efficacy and tolerability of paroxetine for the treatment of depression in the context of HrV infection.
Computational Grounded Theory: A Methodological Framework

This article proposes a three-step methodological framework called computational grounded theory, which combines expert human knowledge and hermeneutic skills with the processing power and pattern recognition of computers, producing a more methodologically rigorous but interpretive approach to content analysis. The first, pattern detection step, involves inductive computational exploration of text, using techniques such as unsupervised machine learning and word scores to help researchers to see novel patterns in their data. The second, pattern refinement step, returns to an interpretive engagement with the data through qualitative deep reading or further exploration of the data. The third, pattern confirmation step, assesses the inductively identified patterns using further computational and natural language processing techniques. The result is an efficient, rigorous, and fully reproducible computational grounded theory. This framework can be applied to any qualitative text as data, including transcribed speeches, interviews, open-ended survey data, or ethnographic field notes, and can address many potential research questions.
AN APPROACH WITH CAPABLE SECURITY RANKED KEYWORD SEARCH TECHNIQUE

Within this report, a ranked clustering routine is recommended to aid more explore exposition also to provide the earnings in fast count text probe interior a big data aura. Additionally, we appraise looking readiness and freedom lower two rampant peril models. One denounces enterprising that the tie during cites will be regularly secluded bit file encryption, whatever can appear in consequential ransack exactness appearance deterioration. Also, the matched of data in data centers has bedeviled a sudden prosperity. This ready much grimmer to devise count text explore schemes so yield valuable and decent online IR on copious of encrypted data. A preliminary policy enjoys levy the ransack readiness, fidelity, and rank care. The measure appear proves the implied composition not just nicely solves the multi-secret sign weighted inspect headache, brings an obvious discord inspecting readiness, rank confidence, and the importance enclosed by retrieved archives. Within the explore step, this structure can reach a in the direction of computational intricacy in contrast to a mounting size heighten of chronicle lot. Because of the scarce rank system, users need to take a long-winded time for you to choose what they need when towering archives maintain the doubt abraxas. Thus, order-preserving skills work at to reach the rank procedure, to manage find out the truthfulness of ransack engine rises, a formation common as dab hash sub-tree perform not beyond this study. In supplement, the counseled scheme comes with an edge on the measure purpose not outside the rank retreat and applicability of retrieved chronicles.
Strategy for construction of polymerized volume data sets

Strategy for Construction of Polymerized Volume Data Sets. (December 2004) Prathyusha Aragonda, B.Tech., Sri Venkateswara University Chair of Advisory Committee: Dr. Bruce H. McCormick This thesis develops a strategy for polymerized volume data set construction. Given a volume data set defined over a regular three-dimensional grid, a polymerized volume data set (PVDS) can be defined as follows: edges between adjacent vertices of the grid are labeled 1 (active) or 0 (inactive) to indicate the likelihood that an edge is contained in (or spans the boundary of) a common underlying object, adding information not in the original volume data set. This edge labeling “polymerizes” adjacent voxels (those sharing a common active edge) into connected components, facilitating segmentation of embedded objects in the volume data set. Polymerization of the volume data set also aids real-time data compression, geometric modeling of the embedded objects, and their visualization. To construct a polymerized volume data set, an adjacency class within the grid system is selected. Edges belonging to this adjacency class are labeled as interior, exterior, or boundary edges using discriminant functions whose functional forms are derived for three local adjacency classes. The discriminant function parameter values are determined by supervised learning. Training sets are derived from an initial segmentation on a homogeneous sample of the volume data set, using an existing segmentation method. The strategy of constructing polymerized volume data sets is initially tested on synthetic data sets which resemble neuronal volume data obtained by three-dimensional microscopy. The strategy is then illustrated on volume data sets of mouse brain microstructure at a neuronal level of detail. Visualization and validation of the resulting PVDS is shown in both cases.
Fine-Tuning Plant Defence Signalling: Salicylate versus Jasmonate

: Plant defences against pathogens and herbivorous insects form a comprehensive network of interacting signal transduction pathways. The signalling molecules salicylic acid (SA) and jasmonic acid (JA) play important roles in this network. SA is involved in signalling processes providing systemic acquired resistance (SAR), protecting the plant from further infection after an initial pathogen attack. SAR is long-lasting and provides broad spectrum resistance to biotrophic pathogens that feed on a living host cell. The regulatory protein NPR1 is a central positive regulator of SAR. SA-activated NPR1 localizes to the nucleus where it interacts with TGA transcription factors to induce the expression of a large set of pathogenesis-related proteins that contribute to the enhanced state of resistance. In a distinct signalling process, JA protects the plant from insect infestation and necrotrophic pathogens that kill the host cell before feeding. JA activates the regulatory protein COI1 that is part of the E3 ubiquitin ligase-containing complex SCF COI1 , which is thought to derepress JA-responsive genes involved in plant defence. Both synergistic and antagonistic interactions have been observed between SA-and JA-dependent defences. NPR1 has emerged as a critical modulator of cross-talk between the SA and JA signal and is thought to aid in fine tuning defence responses specific to the encountered attacker. Here we review SA-and JA-depen-dent signal transduction and summarize our current understanding of the molecular mechanisms of cross-talk between these defences.
On Some Characteristics of a Simple Random Walk

In most of the probabilistic problems Markov chain models are used and the random walk models are one of the most essential class of the Markov chains. A random walk model appears in many real world problems such as a gambling problem, the motion of a certain particle, the price change in a stock exchange market and the realtime change in a network traffic. In this present study a simple random walk {Xn} is defined and probability distribution function is obtained. After that, the mean, the second moment and the variance of this simple walk is obtained. Also the autocorrelation function RX is given. Furthermore the mean and variance of the increment Xm − Xn are calculated.
Design and simulation of single-photon three-dimensional compressive imaging system based on TCSPC

We propose a single-photon three-dimensional compressive imaging system based on TCSPC. The system uses compressive sensing instead of raster scanning to achieve high spatial resolution, and only two-dimensional reconstructions are required to image a three-dimensional scene. We also propose a system simulation model based on Monte Carlo, which is conduct with double poisson stochastic process model. In the simulation model, we studied the effects of imaging time, optical noise ratio, and gating algorithm on the imaging performance of the system. The results show that the single-photon compressive 3D imaging system based on TCSPC can image in 5 seconds. Noise gating can effectively improve the 3D imaging quality of the system. Our simulation provides a good choice of parameters for subsequent experiments. It has played a theoretical guiding role in the research and application of the actual three-dimensional imaging system.
Characterization of Stochastic Mean‐Field Type 
 H− Index

H− index of mean‐field stochastic differential equations (SDE) is investigated in this paper. For systems with state‐ and input‐dependent noise, we obtain a sufficient condition of H− index larger than some λ>0 via the solvability of differential Riccati equations (DRE). Especially, a necessary and sufficient condition is given for mean‐field SDE with state‐dependent noise, which generalize the corresponding results of classical stochastic systems to the mean‐field stochastic models.
Nonlinear model predictive control of MIMO system with Relevance vector machines and Particle swarm optimization

This paper demonstrates control accuracy andcomputational efficiency of nonlinear model predictive control (NMPC) strategywhich utilizes a probabilistic sparse kernel learning technique calledRelevance vector regression (RVR) and particle swarm optimization withcontrollable random exploration velocity (PSO-CREV). An accurate reliablenonlinear model is first identified by RVR with a radial basis function (RBF)kernel and then the optimization of control sequence is speeded up by PSO-CREV.An improved system performance is guaranteed by an accurate sparse predictivemodel and an efficient and fast optimization algorithm. To compare theperformance, model predictive control (MPC) using a deterministic sparse kernellearning technique called Least squares support vector machines (LS-SVM)regression is done on a highly nonlinear distillation column with severeinteracting process variables. SVR based MPC shows improved tracking performancewith very less computational effort which is much essential for real timecontrol.
Strato-rotational instability without resonance

Strato-rotational instability (SRI) is normally interpreted as the resonant interactions between normal modes of the internal or Kelvin variety in three-dimensional settings in which the stratification and rotation are orthogonal to both the background flow and its shear. Using a combination of asymptotic analysis and numerical solution of the linear eigenvalue problem for plane Couette flow, it is shown that such resonant interactions can be destroyed by certain singular critical levels. These levels are not classical critical levels, where the phase speed $c$ of a normal mode matches the mean flow speed $U$ , but are a different type of singularity where $(c-U)$ matches a characteristic gravity-wave speed $\pm N/k$ , based on the buoyancy frequency $N$ and streamwise horizontal wavenumber $k$ . Instead, it is shown that a variant of SRI can occur due to the coupling of a Kelvin or internal wave to such ‘baroclinic’ critical levels. Two characteristic situations are identified and explored, and the conservation law for pseudo-momentum is used to rationalize the physical mechanism of instability. The critical level coupling removes the requirement for resonance near specific wavenumbers $k$ , resulting in an extensive continuous band of unstable modes.
The “Kluge-Lüttge Kammer”: A Preliminary Evaluation of an Enclosed, Crassulacean Acid Metabolism (CAM) Mesocosm that Allows Separation of Synchronized and Desynchronized Contributions of Plants to Whole System Gas Exchange

: Crassulacean acid metabolism (CAM) is recognized as a photosynthetic adaptation of plants to arid habitats. This paper presents a proof-of-concept evaluation of partitioning net CO 2 exchanges for soil and plants in an arid, exclusively CAM mesocosm, with soil depth and succulent plant biomass approximating that of natural Sonoran Desert ecosystems. We present the first evidence that an enclosed CAM-dominated soil and plant community exposed to a substantial day/night temperature difference (30/20 (cid:2) C), exhibits a diel gas exchange pattern consisting of four consecutive phases with a distinct nocturnal CO 2 uptake. These phases were modulated by plant assimilation and soil respiration processes. Day-time stomatal closure of the CAM cycle during phase III was used to eliminate aboveground photosynthetic assimilation and respiration and thereby to estimate belowground plant plus soil respiration. Rapid changes in temperature appeared to synchronize single plant gas exchange but individual plant gas exchange patterns were desynchronized at constant day/night temperatures (25 (cid:2) C), masking the distinct mesocosm pattern. Overall, the mean carbon budget of this CAM model Sonoran Desert system was negative, releasing an average of 22.5 mmol CO 2 m –2 d –1 . The capacity for nocturnal CO 2 assimilation in this exclusively CAM mesocosm was inadequate to recycle CO 2 released by plant and soil respiration.
Validation of a spatial speech-in-speech test that takes signal-to-noise ratio (SNR) confounds into account

A Spatial Fixed-SNR (SFS) speech-in-speech intelligibility test is presented and the reliability and validity of the test is investigated. As part of the validation the SFS test was used to compare a linear hearing-aid setting to a setting with aggressive compression limiting. Two sub-groups of listeners were tested in a fixed-SNR paradigm at –5 and +5 dB SNR, respectively.
Integrasi Density Based Feature Selection dan Adaptive Boosting (Adaboost) dalam Menangani Ketidakseimbangan Kelas

Ketidakseimbangan kelas (Class Imbalance) dari dataset antara dua kelas yang berbeda yaitu kelas mayoritas dan kelas minoritas, berpengaruh pada algoritma C4.5 yang cenderung menghasilkan akurasi prediksi yang baik pada kelas mayoritas tetapi menjadi tidak konduktif dalam memprediksi contoh kelas minoritas, sehingga nilai hasil akurasi pengklasifikasian (classifier) C4.5 menjadi tidak optimal. Untuk mengurangi pengaruh ketidakseimbangan kelas pada pengklasifikasi C4.5, maka perlu dilakukan dengan menerapkan kombinasi dari metode seleksi fitur yaitu algoritma Adaptive Boosting (Adaboost) dan metode Density Based Feature Selection (DBFS). Penerapan algoritma adaboost dalam seleksi fitur dilakukan untuk memberi bobot pada setiap fitur yang direkomendasikan, sehingga ditemukan fitur yang merupakan classifier yang kuat, sedangkan DBFS berfokus dalam mengidentifikasi kelas minoritas dan mengevaluasi dampak dari sebuah fitur yang bermanfaat berdasarkan rangking fitur agar dapat direkomendasikan pada classifier C4.5 dalam proses pengklasifikasian. Hasil penelitian menunjukkan bahwa, kinerja akurasi pengklasifikasi C4.5 pada dataset mahasiswa lulusan dengan mengkombinasikan DBFS sebelum proses adaboost, dengan pengaturan nilai confidence level 0,50 dan 30 fold cross-validation, menunjukkan tingkat akurasi klasifikasi yang relatif lebih baik dalam penanganan ketidakseimbangan kelas.
WISE : Weighted Imputation for Standard Errors an alternative imputation method /

When faced with missing values, researchers usually resort to ad hoc methods of complete-case analysis or imputation. Both approaches have the advantage of result­ ing in a completed-data set which is then analyzed with the desired statistical method for complete data. However, there are several disadvantages with both approaches. Complete-case analysis could result in loss of efficiency and statistical power if the number of subjects with missing values is substantial. Subsequent analyses of singly imputed data sets treat the missing values as if they are known. Thus, estimates of uncertainty such as standard errors, p-values, and confidence intervals may be too sharp since they do not account for the uncertainty due to the missing values. The method of multiple imputation (MI) addresses the major flaw of single impu­ tation, under-estimation of variability, by imputing m plausible values for each missing value. Special algorithms such as Markov Chain Monte Carlo (MCMC) can be used to generate proper multiple imputations. Unless one has the appropriate computa­ tional tools and statistical expertise, generating the imputations can be challenging and laborious. For example, expertise and time are required to judge convergence of MCMC algorithms. Additionally, there is no agreement on convergence, with the exception of special cases, even among the ecperts. Another disadvantageis that the results are not “reproducible”. That is, consecutively applying the MI to sample data may yield different inferences.
Weighted correlated component analysis for frequency recognition in SSVEP-based BCI.

Correlated component analysis (CORRCA) is a novel method for frequency recognition in steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI), which holds the potential to implement high-performance SSVEP-based BCI systems. CORRCA could provide multiple correlation coefficients to measure the correlation between two signals. However, only the largest one is selected as feature while ignoring all others in previous study, which loses the discriminating information. In current study, we proposed a weighting strategy with nonlinear function to combine all the correlation coefficients to enhance the performance of CORRCA method. Extensive experimental results on a benchmark dataset of thirty-five subjects indicate that the proposed method significantly outperforms the original CORRCA method. This study demonstrates that the proposed method could be a good choice for frequency recognition in SSVEP-based BCI systems.