Miracle-dz/UniFAR-FTUs huggingface dataset:
Incipient Fault Detection of Helical Gearbox Based on Variational Mode Decomposition and Time Synchronous Averaging

As the fault detection methods diagnose defects in the earlier stage, the subsequent costs will be reduced. Feature extraction from the vibration signal is the foremost step for incipient fault detection of gearboxes. However, the current statistical features in the time and frequency domains cannot diagnose the early or low intensity faults. In this research, for the first time, besides these features, some other features are extracted by combining variational mode decomposition and time synchronous average (VMD-TSA) to overcome the problem. The combinations have occurred in two ways. First, the Intrinsic Mode Functions (IMFs) of the TSA signal are calculated by VMD, and the Amplitude Energy (AE) and Permutation Entropy (PE) of the first four IMFs are computed. Secondly, the IMFs of vibration signals are calculated, and the TSA features are extracted from the most informative IMF. Moreover, 16 features in the time domain, 13 features in the frequency domain, and 9 features by TSA are extracted from the vibration signals. These features are extracted from healthy and four faulty conditions: crack, spalling, chipping, and wear in three different severities. After feature extraction, the Relief-F algorithm selects the informative features, and selected features are utilized for fault detection by a Feed-Forward Neural network (FNN) classifier. In this study, the ability of the VMD-TSA method is compared with others like Empirical Mode Decomposition-TSA (EMD-TSA) and Ensemble Empirical Mode Decomposition-TSA (EEMD-TSA), which shows that the proposed method is more powerful than others in early fault detection. Besides, the classification accuracy of these methods is compared with some other feature selection methods like Laplacian Score (LS), Principal Component Analysis (PCA), and Minimum Redundancy-Maximum Relevance (MRMR). Also, the performance of the FNN classifier is compared with the Support Vector Machine (SVM). As shown in this study, the VMD-TSA features improve the early fault detection in a positive manner. For instance, the classification accuracy of all features without VMD-TSA in crack fault detection is 93.98%. However, by adding VMD-TSA features, the accuracy grows up to 99.48% in the same conditions. Graphical Abstract
Modeling and simulation of fixed bed regenerators for a multi-tower decoupled advanced solar combined cycle

Two dynamic models of fixed bed regenerators for metallic and ceramic configurations have been developed in Modelica. These models have been both worked out within CAPTURE European project and will serve as design tool for a fixed bed regenerative heat exchange system. The present article describes in detail both models and presents a case study that compares experimental and simulation results for the testing of a ceramic honeycomb regenerative matrix.
Atypically Shaped Cardiomyocytes (ACMs): The Identification, Characterization and New Insights into a Subpopulation of Cardiomyocytes

In the adult mammalian heart, no data have yet shown the existence of cardiomyocyte-differentiable stem cells that can be used to practically repair the injured myocardium. Atypically shaped cardiomyocytes (ACMs) are found in cultures of the cardiomyocyte-removed fraction obtained from cardiac ventricles from neonatal to aged mice. ACMs are thought to be a subpopulation of cardiomyocytes or immature cardiomyocytes, most closely resembling cardiomyocytes due to their spontaneous beating, well-organized sarcomere and the expression of cardiac-specific proteins, including some fetal cardiac gene proteins. In this review, we focus on the characteristics of ACMs compared with ventricular myocytes and discuss whether these cells can be substitutes for damaged cardiomyocytes. ACMs reside in the interstitial spaces among ventricular myocytes and survive under severely hypoxic conditions fatal to ventricular myocytes. ACMs have not been observed to divide or proliferate, similar to cardiomyocytes, but they maintain their ability to fuse with each other. Thus, it is worthwhile to understand the role of ACMs and especially how these cells perform cell fusion or function independently in vivo. It may aid in the development of new approaches to cell therapy to protect the injured heart or the clarification of the pathogenesis underlying arrhythmia in the injured heart.
Development of a Humanized VHH Based Recombinant Antibody Targeting Claudin 18.2 Positive Cancers

Claudin 18.2 (CLDN18.2), a tight junction (TJ) family protein controlling molecule exchange between cells, is frequently over-expressed in gastric cancer, pancreatic adenocarcinomas and in a fraction of non–small cell lung cancer cases. The tumor properties indicate that CLDN18.2 could be an attractive drug target for gastric and pancreatic cancers. In this study, we present effective strategies for developing anti-CLDN18.2 therapeutic candidates, based on variable domain of heavy chain of heavy chain antibodies (VHHs). CLDN18.2-specific VHHs were isolated by panning a phage display library from an alpaca immunized with a stable cell line highly expressing CLDN18.2. Humanized VHHs fused with human IgG1 Fc, as potential therapeutic candidates, exhibited desirable binding specificity and affinity to CLDN18.2. In vitro experiments showed that hu7v3-Fc was capable of eliciting both antibody-dependent cellular cytotoxicity (ADCC) and complement-dependent cytotoxicity (CDC) on CLDN18.2 positive tumor cells. In the mouse xenograft model, the anti-tumor efficacy of hu7v3-Fc was significantly more potent than Zolbetuximab, the benchmark anti-CLDN18.2 monoclonal antibody. Moreover, in vivo biodistribution using zirconium-89 (89Zr) labeled antibodies demonstrated that hu7v3-Fc (89Zr-hu7v3-Fc) exhibited a better tumor penetration and a faster tumor uptake than Zolbetuximab (89Zr-Zolbetuximab), which might be attributed to its smaller size and higher affinity. Taken together, anti-CDLN18.2 hu7v3-Fc is a promising therapeutic agent for human CLDN18.2 positive cancers. Furthermore, hu7v3 has emerged as a potential module for novel CLDN18.2 related therapeutics.
A Maximum-Likelihood-Based Two-User Receiver for LoRa Chirp Spread-Spectrum Modulation

Long Range (LoRa) is an emerging low-power wide-area network technology offering long-range wireless connectivity to Internet of Things (IoT) devices. For energy efficiency reasons, LoRa end nodes implement a nonslotted ALOHA multiple access scheme to transmit packets to the gateway. Due to the lack of synchronization between end nodes, collisions between uplink packets have been identified as the main obstacle to the scaling of dense LoRa networks. To tackle this issue, we present in this article a LoRa receiver that is capable of decoding colliding packets from two interfering end nodes. The proposed two-user detector is derived from the maximum-likelihood principle using a detailed model of two colliding LoRa packets. As the complexity of the maximum-likelihood sequence estimation is prohibitive, complexity-reduction techniques are introduced to enable practical implementations of the receiver. An in-depth performance analysis highlights that the proposed two-user detector inherently leverages the differences in received power, time offsets, and frequency offsets between the users to separate and demodulate their respective signals. To demonstrate the practicality of the proposed detector, an interference-robust synchronization algorithm is then designed and evaluated. Simulation results indicate that a LoRa receiver combining the proposed synchronization algorithm and two-user detector is capable of detecting and demodulating two interfering users with satisfactorily error rates.
Simulation-Based Methods for Markov Decision Processes

Markov decision processes have been a popular paradigm for sequential decision making under uncertainty. Dynamic programming provides a framework for studying such problems, as well as for devising algorithms to compute an optimal control policy. Dynamic programming methods rely on a suitably de ned value function that has to be computed for every state in the state space. However, many interesting problems involve very large state spaces (\curse of dimensionality"), which prohibits the application of dynamic programming . In addition, dynamic programming assumes the availability of an exact model, in the form of transition probabilities (\curse of modeling"). In many practical situations, such a model is not available and one must resort to simulation or experimentation with an actual system. For all of these reasons, dynamic programming in its pure form may be inapplicable. In this thesis we study an approach for overcoming these di culties where we use (a) compact (parametric) representations of the control policy, thus avoiding the curse of dimensionality, and (b) simulation to estimate quantities of interest, thus avoiding model-based computations (curse of modeling). It is not limited to Markov decision processes, but applies to general Markov reward processes for which the transition probabilities and the one-stage rewards depend on a parameter vector . We propose gradient-type algorithms for updating based on the simulation of a single sample path, so as to improve a given performance measure. As possible performance measures we consider the weighted reward-to-go and the average reward. The corresponding algorithms (a) can be implemented online and update the parameter vector either at visits to a certain state, or at every time step. (b) have the property that the gradient (with respect to ) of the performance measure converges to 0 with probability 1. This is the strongest possible result for gradientrelated stochastic approximation algorithms. We illustrate the methodology by considering the call admission control problem where a telecommunications provider sells bandwith of a single communication link to customers so as to optimize the revenue. We use the proposed algorithms to optimize the parameters a heuristic threshold policy for this problem. 2
A Greater Improvement of Intrahepatic Fat Contents after 6 Months of Lifestyle Intervention Is Related to a Better Oxidative Stress and Inflammatory Status in Non-Alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is a disorder characterized by the excessive accumulation of lipids in the liver parenchyma. To date, there is no effective pharmacological treatment against NAFLD. Objective: To assess the relationship between the improvement of the intrahepatic fat content (IFC) in patients with NAFLD and metabolic syndrome and biomarkers of oxidative stress and inflammation after 6 months of lifestyle intervention. Patients diagnosed with NAFLD (n = 60 adults; 40–60 years old) residing in the Balearic Islands, Spain, were distributed in tertiles attending the improvement of IFC calculated by magnetic resonance imaging (MRI). Anthropometrics, blood pressure, maximal oxygen uptake, and pro/antioxidant and inflammatory biomarkers were determined in plasma before and after the lifestyle intervention. The improvement in IFC levels was higher in tertile 3 with respect to tertiles 2 and 1. The greatest improvement in IFC is related to cardiorespiratory fitness and adherence to the Mediterranean diet (ADM). Higher reductions in weight, body mass index (BMI), and alanine aminotransferase (ALT) were observed in tertile 3 with respect to tertile 1 after 6 months of intervention. The improvement in catalase, irisin, and cytokeratin 18 plasma levels were higher in tertile 3, whereas no differences were observed in superoxide dismutase activity. Malondialdehyde and protein carbonyl levels, as biomarkers of oxidative damage, remained unchanged in all groups. The present data show that the reduction of IFC is associated with an improvement in pro/antioxidant and pro-inflammatory status and a better cardiorespiratory fitness in NAFLD patients.
An Analysis of Slang Used on Prawirotaman Street, Yogyakarta

This Paper discusses about the analysis of slang that is used by western tourist in Prawirotaman Street in Yogyakarta. The purpose of this study is to find out the types and meaning of slang primarily used by western tourists. It uses a qualitative description method where it describes slang that is used by western tourists. Theory of Eric Partridge (2004) in types of slang is used to indentify the slang. Three types of slang are commonly used by the western tourists on Prawirotaman Street, Yogyakarta and 15 reasons are found. Therefore, the research has implications for the reader to have and apply their knowledge about slang in informal context of daily communication especially in certain tourism area contexts. It also enriches reader’s linguistic feature that can enhance their communicative skills since the social functions of the language are learnt.
How is that done? Student views on resources used outside the engineering classroom

ABSTRACT While the traditional lecture remains a key feature in the teaching of mathematically intensive disciplines at a tertiary level, what students do outside class, the resources they use, and how they use them are critical factors in their success. This study reports on a survey of students studying a range of engineering subjects, giving their views on the effectiveness of resources that they use outside the classroom. Resource types examined included textbooks, lecturer course notes, in-class developed notes, and other online material, including multimedia. While lecturer-generated material was generally seen as more effective than formal textbooks and social media, external screencasts were rated as most effective where material appropriate to their class was available. It is suggested that student use of screencast resources has the potential to facilitate improved learning outcomes, and with accompanying changes in assessment focus, may enable more substantive pedagogical changes.
On properties of cyberattacks and their nuances

Purpose Several attack models attempt to describe behaviours of attacks with the intent to understand and combat them better. However, all models are to some degree incomplete. They may lack insight about minor variations about attacks that are observed in the real world (but are not described in the model). This may lead to similar attacks being classified as the same type of attack, or in some cases the same instance of attack. The appropriate solution would be to modify the model or replace it entirely. However, doing so may be undesirable as the model may work well for most cases or time and resource constraints may factor in as well. This paper aims to explore the potential value of adding information about attacks and attackers to existing models. Design/methodology/approach This paper investigates used cases of minor variations in attacks and how it may and may not be appropriate to communicate subtle differences in existing attack models through the use of annotations. In particular, the authors investigate commonalities across a range of existing models and identify where and how annotations may be helpful. Findings The authors propose that nuances (of attack properties) can be appended as annotations to existing attack models. Using annotations appropriately should enable analysts and researchers to express subtle but important variations in attacks that may not fit the model currently being used. Research limitations/implications This work only demonstrated a few simple, generic examples. In the future, the authors intend to investigate how this annotation approach can be extended further. Particularly, they intend to explore how annotations can be created computationally; the authors wish to obtain feedback from security analysts through interviews, identify where potential biases may arise and identify other real-world applications. Originality/value The value of this paper is that the authors demonstrate how annotations may help analysts communicate and ask better questions during identification of unknown aspects of attacks faster,e.g. as a means of storing mental notes in a structured manner, especially while facing zero-day attacks when information is incomplete.
Detection of Aflatoxin B1 in Single Peanut Kernels by Combining Hyperspectral and Microscopic Imaging Technologies

To study the dynamic changes of nutrient consumption and aflatoxin B1 (AFB1) accumulation in peanut kernels with fungal colonization, macro hyperspectral imaging technology combined with microscopic imaging was investigated. First, regression models to predict AFB1 contents from hyperspectral data ranging from 1000 to 2500 nm were developed and the results were compared before and after data normalization with Box-Cox transformation. The results indicated that the second-order derivative with a support vector regression (SVR) model using competitive adaptive reweighted sampling (CARS) achieved the best performance, with RC2 = 0.95 and RV2 = 0.93. Second, time-lapse microscopic images and spectroscopic data were captured and analyzed with scanning electron microscopy (SEM), transmission electron microscopy (TEM), and synchrotron radiation-Fourier transform infrared (SR-FTIR) microspectroscopy. The time-lapse data revealed the temporal patterns of nutrient loss and aflatoxin accumulation in peanut kernels. The combination of macro and micro imaging technologies proved to be an effective way to detect the interaction mechanism of toxigenic fungus infecting peanuts and to predict the accumulation of AFB1 quantitatively.
Species Complexes in Capparis (Capparaceae) Resolved with Plastidial Markers

Capparis with about 142 taxa is the largest genus in the angiosperm family Capparaceae, and is of high medicinal and economic importance. Several taxa such as C. cartilaginea, C. cleghornii, C. incanescens, C. moonii, C. roxburghii, C. sepiaria, C. spinosa and C. spinosa subsp. himalayensis are difficult to distinguish from each other based on morphology, and they all have partly overlapping geographic distributions. This study assesses the potential of four plastidial loci (rbcL, matK, trnL-trnF, and psbA-trnH) to resolve three species complexes in Capparis. Successful amplification and sequencing, as well as statistical analyses, revealed matK, trnL-trnF, and their combination to be the most effective DNA markers. With these markers we were able to distinguish different Capparis taxa collected from their natural habitats and from the crude drug market. The species rank of C. himalayensis was reinstated based on morphology and molecular data. The phylogenetic relationships of the poorly known C. danielii and C. incanescens with C. sepiaria were clarified.
Memristor-Based Read/Write Circuit with Stable Continuous Read Operation

In recent years, computation-intensive applications, such as artificial intelligence, video processing and encryption, have been developing rapidly. On the other hand, the problems of “storage wall” and “power consumption wall” for the traditional storage and computing separated architectures limit the computing performance. The computational circuits and memory cells based on nonvolatile memristors are unified and become a competitive solution to this problem. However, there are various problems that prevent memristor-based circuits from entering practical applications, one of which is the memristor state deviation problem caused by continuous reading. In this paper, we study some circuits studied by predecessors on read/write circuit, compare the experimental results, analyze the reason for the resistance state deviation of memristor, and put forward a new parallel structure of memristor based on opposite polarity. The logic “1” and logic “0” are represented by the positive and negative voltage difference of two memristors with opposite polarity, which can effectively alleviate the problem of the resistance state deviation caused by continuous reading. A reading voltage of 2 V is applied to the four circuits at the same time, and continuous reading is carried out until the output voltage becomes stable. The voltage offset of the optimized circuit when reading logic “0” is reduced to 78 mV, which is significantly smaller than that of other circuits. In addition, when reading logic “1”, it has the effect of enhancing the information stored in the memristor.
Value versus growth stocks: The coming reversal of fortunes

Over the last ten years, U.S. growth stocks have outperformed U.S. value stocks by an average 7.8% per year.1 Such eye-watering underperformance of value has been atypical historically. As Figure 1 illustrates, the value factor as defined by Fama-French has on average outperformed growth over ten-year time horizons going back to 1936. This has led some to question the existence of the value premium. While we believe that the rationale for the premium— which is supported by a deep body of academic literature2—is sound, the depth and persistence of value’s recent underperformance is striking.
Inferring Cause and Effect in the Presence of Heteroscedastic Noise

We study the problem of identifying cause and effect over two univariate continuous variables X and Y from a sample of their joint distribution. Our focus lies on the setting where the variance of the noise may be dependent on the cause. We propose to partition the domain of the cause into multiple segments when the noise indeed is dependent. To this end, we minimize a scale-invariant, penalized regression score, finding the optimal partitioning using dynamic programming. We show under which conditions this allows us to identify the causal direction for the linear setting with heteroscedastic noise, for the non-linear setting with homoscedastic noise, as well as empirically confirm that these results generalize to the non-linear and heteroscedastic case. Altogether, the ability to model heteroscedasticity translates into an improved performance in telling cause from effect on a wide range of synthetic and real-world datasets.