Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Sensing Tactile Arrays, Shear Force, and Proprioception of Robot on Texture Recognition

In robotics, tactile perception is important for fine control using robot grippers and hands. To effectively incorporate tactile perception in robots, it is essential to understand how humans use mechanoreceptors and proprioceptors to perceive texture. Thus, our study aimed to investigate the impact of tactile sensor arrays, shear force, and the positional information of the robot’s end effector on its ability to recognize texture. A deep learning network was employed to classify tactile data from 24 different textures that were explored by a robot. The input values of the deep learning network were modified based on variations in the number of channels of the tactile signal, the arrangement of the tactile sensor, the presence or absence of shear force, and the positional information of the robot. By comparing the accuracy of texture recognition, our analysis revealed that tactile sensor arrays more accurately recognized the texture compared to a single tactile sensor. The utilization of shear force and positional information of the robot resulted in an improved accuracy of texture recognition when using a single tactile sensor. Furthermore, an equal number of sensors placed in a vertical arrangement led to a more accurate distinction of textures during exploration when compared to sensors placed in a horizontal arrangement. The results of this study indicate that the implementation of a tactile sensor array should be prioritized over a single sensor for enhanced accuracy in tactile sensing, and the use of integrated data should be considered for single tactile sensing.
An effective method for epidemic suppression by edge removing in complex network

Since the birth of human beings, the spreading of epidemics such as COVID-19 affects our lives heavily and the related studies have become hot topics. All the countries are trying to develop effective prevention and control measures. As a discipline that can simulate the transmission process, complex networks have been applied to epidemic suppression, in which the common approaches are designed to remove the important edges and nodes for controlling the spread of infection. However, the naive removal of nodes and edges in the complex network of the epidemic would be practically infeasible or incur huge costs. With the focus on the effect of epidemic suppression, the existing methods ignore the network connectivity, leading to two serious problems. On the one hand, when we remove nodes, the edges connected to the nodes are also removed, which makes the node is isolated and the connectivity is quickly reduced. On the other hand, although removing edges is less detrimental to network connectivity than removing nodes, existing methods still cause great damage to the network performance in reality. Here, we propose a method to measure edge importance that can protect network connectivity while suppressing epidemic. In the real-world, our method can not only lower the government’s spending on epidemic suppression but also persist the economic growth and protect the livelihood of the people to some extent. The proposed method promises to be an effective tool to maintain the functionality of networks while controlling the spread of diseases, for example, diseases spread through contact networks.
Kumatakenin inhibited iron-ferroptosis in epithelial cells from colitis mice by regulating the Eno3-IRP1-axis

Inhibition of epithelial ferroptosis in colonic tissues relieved clinical symptoms and improved endoscopic presentations in inflammatory bowel disease (IBD). Kumatakenin, the main ingredient of traditional Chinese medicinal cloves and Alpinia purpurata, is reported to possess therapeutic benefits. However, whether kumatakenin could inhibit ferroptosis and further alleviate colitis remains unclear. Here, we measured the effects of kumatakenin on ferroptosis of colonic epithelial cells from colitis mice. The colitis model was induced in mice by oral intake of 2.5% dextran sulfate sodium in drinking water. RNA sequencing was performed to investigate the mechanism underlying kumatakenin-mediated effects on colitis. The results showed that different doses of kumatakenin significantly alleviated symptoms and suppressed intestinal inflammation in the colitis mouse model. Kumatakenin supplementation decreased cellular iron levels and suppressed ferroptosis in epithelial cells from colitis mice. RNA sequencing, qPCR, and pharmacological inhibition assays showed that kumatakenin reduced cellular iron levels and suppressed ferroptosis in epithelial cells from colitis mice at least partially by upregulating expression of enolase (Eno-3). Furthermore, kumatakenin decreased iron levels in epithelial cells by modulating the Eno3-iron regulatory protein (IRP1) axis. Molecular docking results revealed that kumatakenin could bind Eno3 via hydrogen bonding with the amino acid residues Thr208, Val206, and Pro203. This work will provide a scientific basis for the clinical use of kumatakenin in the treatment of colitis.
Childhood Dyslipidemia and Carotid Atherosclerotic Plaque in Adulthood: The Cardiovascular Risk in Young Finns Study

Background Childhood exposure to dyslipidemia is associated with adult atherosclerosis, but it is unclear whether the long‐term risk associated with dyslipidemia is attenuated on its resolution by adulthood. We aimed to address this question by examining the links between childhood and adult dyslipidemia on carotid atherosclerotic plaques in adulthood. Methods and Results The Cardiovascular Risk in Young Finns Study is a prospective follow‐up of children that began in 1980. Since then, follow‐up studies have been conducted regularly. In 2001 and 2007, carotid ultrasounds were performed on 2643 participants at the mean age of 36 years to identify carotid plaques and plaque areas. For childhood lipids, we exploited several risk factor measurements to determine the individual cumulative burden for each lipid during childhood. Participants were categorized into the following 4 groups based on their childhood and adult dyslipidemia status: no dyslipidemia (reference), incident, resolved, and persistent. Among individuals with carotid plaque, linear regression models were used to study the association of serum lipids with plaque area. The prevalence of plaque was 3.3% (N=88). In models adjusted for age, sex, and nonlipid cardiovascular risk factors, the relative risk for carotid plaque was 2.34 (95% CI, 0.91–6.00) for incident adult dyslipidemia, 3.00 (95% CI, 1.42–6.34) for dyslipidemia resolved by adulthood, and 5.23 (95% CI, 2.57–10.66) for persistent dyslipidemia. Carotid plaque area correlated with childhood total, low‐density lipoprotein, and non–high‐density lipoprotein cholesterol levels. Conclusions Childhood dyslipidemia, even if resolved by adulthood, is a risk factor for adult carotid plaque. Furthermore, among individuals with carotid plaque, childhood lipids associate with plaque size. These findings highlight the importance of primordial prevention of dyslipidemia in childhood to reduce atherosclerosis development.
Noise in amorphous magnetic materials

Magnetic noises in rapidly quenched amorphous magnetic materials were examined. Magnetic noises are observed as the fluctuations of the induced voltage wave forms of the magnetic cores on every cycle of the alternating exciting field. Fe, Ni, and Co base alloys were tested, and the following results have been obtained. 1) CoFeSiB amorphous materials show the lowest noise, 2) Water quenching treatment decreased the noise remarkably, 3) The lower Curie temperature leads to the lower magnetic noise. 4) Some Co base amorphous alloys have the performance comparable to or better than that of the conventional polycrystalline material such as Supermalloy.
A search for the Sunyaev–Zeldovich effect at millimetre wavelengths

The Sunyaev–Zeldovich effect is potentially a powerful tool for observational cosmology. We report the results of the first attempt to detect this small effect at a wavelength of 1.15 mm. At this wavelength the effect has a sign opposite to that at the longer wavelengths previously employed in such studies. Observational data on two clusters, 0016+ 16 and Abell 478, are described. Particular attention has been devoted to the position-dependent offsets which are characteristic of these observations. We analyse the spurious systematic contribution such an offset adds to the Sunyaev–Zeldovich signal, and we demonstrate that, provided that chopping and nodding are performed in hour angle, this contribution can be precisely measured from the baseline drift. These analytical techniques have been applied to 16 hr of observational data on these two clusters. We achieved a marginal detection of the effect corresponding to a temperature decrement in the Rayleigh–Jeans region of ΔTRJ=−1.6±1.0mK for the cluster 0016+16 and an upper limit of ΔTRJ=−0.2±1.0mK for the cluster Abell 478.
Climatic stability recorded in speleothems may contribute to higher biodiversity in the Cape Floristic Region

The geography and genesis of diversity remain an enduring topic in ecology and evolution. Mediterranean Climate Ecosystems (MCEs), with their high plant diversities in winter rainfall climates, pose a challenge to popular hypotheses evoking high water availability and temperature as necessary for the evolution of high diversity. We test the hypothesis of environmental stability as a driver for the evolution of regional‐scale floristic diversity using speleothem oxygen (δ18O) and carbon (δ13C) isotopic values as proxies for past climatic variability in the Cape Floristic Region (CFR) and other MCEs.
Strategizing organizational capabilities for industrial digitalization – exploring managers' technological frames

PurposeThis study draws on technological frames to provide an understanding of organizational processes of strategizing by exploring how strategizing organizational capabilities for industrial digitalization could be understood through managers' perceptions of digital technology applications. This study complements earlier research focused on industry outcomes by addressing technological frames to understand how strategizing organizational capabilities within industrial digitalization may provide insight into socio-cognitive aspects which may affect technology-induced organizational change.Design/methodology/approachThe single case study uses 14 in-depth interviews collected over two years (October 2020 to February 2022). The study follows an interpretative research design exploring managers' perceptions of industrial digitalization through a digitalization project.FindingsThe case study contributes to research by emphasizing socio-cognitive aspects through technological frames exploring how and why managers' perceptions of industrial digitalization affect strategizing organizational capabilities. The study contributes to practice by bringing attention to the disparate views of industrial digitalization. By illustrating how socio-cognitive aspects shape organizational capabilities, this study offers managers valuable insight into the relationship between an organization's capabilities, the individual and the shared structures affecting a digitalization project.Research limitations/implicationsThe case study is limited to Swedish manufacturing industries and is not aiming to be transferred or generalized to other industrial contexts or countries.Originality/valueThis study recognizes that strategizing organizational capabilities depends on managers' ability to illuminate the socio-cognitive aspects. Hence, the study contributes to practice by bringing attention to the disparate views among managers on the enhancement efforts made using digital technologies.
Bottom-up Processing to Help Learners with Decoding: Planning, Implementing, and Reflecting on a Lesson

The researcher carried out the action research to experiment and experience the effectiveness of applying the theoretical aspects of bottom-up processing to help learners with decoding in listening lessons. The experimental lesson included six adult intermediate students (n = 6) from different backgrounds at an English language teaching professional development training undertaken at International House, Bangkok, Thailand in July 2021. The researcher planned the lessons after the literature review on bottom-up processing and surveying students’ needs and interests. The pre-lesson preparation included collecting the group profile, setting lesson aims and learning outcomes, analyzing the target language assuming learners’ existing knowledge, anticipating problems and solutions, planning the lesson procedures with appropriate teaching, and learning materials. and considering the timetable fit. The study aimed to find out the effectiveness of a listening lesson focused on bottom-up processing through self-reflection, and feedback from the observer, co-teachers, and students. The study was largely based on the collection of qualitative data through means of the researcher’s post-lesson reflection, and feedback from the observation, co-teachers, and students, which functioned as research tools in the study. The data analysis found that bottom-up processing was learner-engaging and beneficial to students’ positive perception of improving their listening skills. In addition, they provided the researcher with insights into the steps of planning and administering a listening lesson focused on bottom-up processing identifying areas of achievement and improvement.
A HARMONIC BALANCE APPROACH FOR CHAOS PREDICTION: CHUA’S CIRCUIT

The paper proposes a practical engineering approach for predicting chaotic dynamics in an important class of nonlinear systems. The aim of this approach is to provide a heuristic method of analysis which can give reasonably accurate answers but is far simpler to apply than other more rigorous methods based on nonlinear dynamics. Our approach is founded on the harmonic balance principle and uses standard describing function techniques well known to design engineers. Our method consists of a synergism of two independent techniques, each one constituting a possible mechanism for chaos. These two techniques are combined into a single algorithm which is highly efficient computationally, taking only a fraction of the time normally required by other more exact procedures. In order to present and illustrate our algorithm clearly, and in order to compare its predictions with readily available results obtained by rigorous methods, we have chosen Chua’s circuit as a vehicle to demonstrate the effectiveness of this approach. Chua’s circuit was chosen not only for the huge amount of results already published concerning the dynamics of this system, but also because it represents a real physical system easily built in the laboratory, and whose simple mathematical model has proven to be realistic and mathematically tractable. Although our algorithm does not guarantee its prediction is fully reliable, any more than the widely used describing function method does, its significance is based entirely on the empirical evidence that it yields qualitatively correct, though not exact, results for all of the chaotic phenomena that we have investigated in Chua’s circuit, as well as in many other chaotic systems. We hope therefore that our chaos prediction algorithm will find practical uses among engineers and scientists not familiar with more specialized mathematical approaches, in search of a simple and practical, although not rigorous, tool for analyzing systems with complex dynamics.
Severity assessment of wheat stripe rust based on machine learning

Introduction The accurate severity assessment of wheat stripe rust is the basis for the pathogen-host interaction phenotyping, disease prediction, and disease control measure making. Methods To realize the rapid and accurate severity assessment of the disease, the severity assessment methods of the disease were investigated based on machine learning in this study. Based on the actual percentages of the lesion areas in the areas of the corresponding whole single diseased wheat leaves of each severity class of the disease, obtained after the image segmentation operations on the acquired single diseased wheat leaf images and the pixel statistics operations on the segmented images by using image processing software, under two conditions of considering healthy single wheat leaves or not, the training and testing sets were constructed by using two modeling ratios of 4:1 and 3:2, respectively. Then, based on the training sets, two unsupervised learning methods including K-means clustering algorithm and spectral clustering and three supervised learning methods including support vector machine, random forest, and K-nearest neighbor were used to build severity assessment models of the disease, respectively. Results Regardless of whether the healthy wheat leaves were considered or not, when the modeling ratios were 4:1 and 3:2, satisfactory assessment performances on the training and testing sets can be achieved by using the optimal models based on unsupervised learning and those based on supervised learning. In particular, the assessment performances obtained by using the optimal random forest models were the best, with the accuracies, precisions, recalls, and F1 scores for all the severity classes of the training and testing sets equal to 100.00% and the overall accuracies of the training and testing sets equal to 100.00%. Discussion The simple, rapid, and easy-to-operate severity assessment methods based on machine learning were provided for wheat stripe rust in this study. This study provides a basis for the automatic severity assessment of wheat stripe rust based on image processing technology, and provides a reference for the severity assessments of other plant diseases.
On statistical transform methods and their efficiency

General conditions for the asymptotic efficiency of certain new inference procedures based on empirical transform functions are developed. A number of important processes, such as the empirical characteristic function, the empirical moment generating function, and the empirical moments, are considered as special cases. RESUME On developpe des conditions generales garantissant l'efficacit6 asymptotique de certaines nouvelles procedures d'inf6rence fondees sur des transformations empiriques. Ces conditions s'appliquent a un ensemble de processus importants, y compris la fonction caracteristique empirique, la fonction g6neratrice des moments, ainsi que les moments empiriques eux-memes.
MR-Class: A Python Tool for Brain MR Image Classification Utilizing One-vs-All DCNNs to Deal with the Open-Set Recognition Problem

Simple Summary MR-Class is a deep learning-based MR image classification tool for brain images that facilitates and speeds up the initialization of big data MR-based studies by providing fast, robust and quality-assured imaging sequence classifications. Our studies observed up to 10% misclassification rates due to corrupt and misleading DICOM metadata. This highlights the need for a tool such as MR-Class to help with data curation. MR-Class can be integrated into workflows for DICOM inconsistency checks and flagging or completing missing DICOM metadata and thus contribute to the faster deployment of clinical artificial intelligence applications. Abstract Background: MR image classification in datasets collected from multiple sources is complicated by inconsistent and missing DICOM metadata. Therefore, we aimed to establish a method for the efficient automatic classification of MR brain sequences. Methods: Deep convolutional neural networks (DCNN) were trained as one-vs-all classifiers to differentiate between six classes: T1 weighted (w), contrast-enhanced T1w, T2w, T2w-FLAIR, ADC, and SWI. Each classifier yields a probability, allowing threshold-based and relative probability assignment while excluding images with low probability (label: unknown, open-set recognition problem). Data from three high-grade glioma (HGG) cohorts was assessed; C1 (320 patients, 20,101 MRI images) was used for training, while C2 (197, 11,333) and C3 (256, 3522) were for testing. Two raters manually checked images through an interactive labeling tool. Finally, MR-Class’ added value was evaluated via radiomics model performance for progression-free survival (PFS) prediction in C2, utilizing the concordance index (C-I). Results: Approximately 10% of annotation errors were observed in each cohort between the DICOM series descriptions and the derived labels. MR-Class accuracy was 96.7% [95% Cl: 95.8, 97.3] for C2 and 94.4% [93.6, 96.1] for C3. A total of 620 images were misclassified; manual assessment of those frequently showed motion artifacts or alterations of anatomy by large tumors. Implementation of MR-Class increased the PFS model C-I by 14.6% on average, compared to a model trained without MR-Class. Conclusions: We provide a DCNN-based method for the sequence classification of brain MR images and demonstrate its usability in two independent HGG datasets.
BOTDA Sensing System Based on Differential Golay Coding and Deconvolution Algorithm

Performance enhanced BOTDA sensors that allows for simultaneous optimization of sensing range and spatial resolution is proposed and demonstrated in this work by using differential Golay coding and deconvolution algorithm, where pulse coding is used to extend the sensing distance, meanwhile deconvolution processing is utilized to enhance the spatial resolution. Specifically, differential coding pulses are used to eliminate the Brillouin Frequency Shift (BFS) distortion that results from the deconvolution processing. In addition, to mitigate the distortion of Brillouin trace imposed by the erbium-doped fiber amplifier (EDFA) transient effect, pre-distortion is employed for the pulse train to ensure a more accurate decoded Brillouin trace. Different widths of pulse pairs and different sampling rates have been investigated to optimize the performance of sensing system. For proof of concept, 25 km sensing range and 0.2 m spatial resolution are demonstrated by using 60/40 differential Golay coding pulse train and deconvolution algorithm. The proposed technique manages to alleviate the contradiction between sensing range and spatial resolution of BOTDA sensors, which paves the way to enable both long sensing range and high spatial resolution simultaneously.
Continuous Differentiability in the Context of Generalized Approach to Differentiability

Recently, in their paper, the authors generalized the notion of differentiability by defining it for all points of the functional domain (not only interior points) in which the notion of differentiability can be considered meaningful. In this paper, the notion of continuous differentiability is introduced for the differentiable function f:X→Rm with a not necessarily open domain X⊆Rn; i.e., the continuity of the mapping df:X→HomRn,Rm is considered. In addition to introducing continuous differentiability in the context of this generalized approach to differentiability, its characterization is also given. It is proved that the continuity of derivatives at some not necessarily interior points of the functional domain in the direction of n linearly independent vectors implies (continuous) differentiability.
Damping Time Scaling of Coronal Loop Oscillations Deduced from Transition Region and Coronal Explorer Observations

The damping mechanism of recently discovered coronal loop transverse oscillations provides clues to the mechanism of coronal heating. We determine the scaling of the damping time with the parameters of the loops observed in extreme ultraviolet by the Transition Region and Coronal Explorer. We find excellent agreement of the scaling power to the power predicted by phase mixing and poor agreement with the power predicted by the wave leakage or ideal decay of the cylindrical kink mode mechanisms. Phase mixing leads to rapid dissipation of the Alfvén waves due to the variation of the Alfvén speed across the wave front and formation of small scales. Our results suggest that the loop oscillations are dissipated by phase mixing with anomalously high viscosity.