Miracle-dz/UniFAR-FTUs huggingface dataset:
The Structure and Vorticity Budget of an Early Summer Monsoon Trough (Mei-Yu) over Southeastern China and Japan

Abstract One of the most persistent rain-making events over East Asia is the development of an early summer monsoon trough (Mei-Yu) which extends from southeastern China to southern Japan. This work studies the structure and vorticity budget of a Mei-Yu system for the period 10–15 June 1975. Subjectively analyzed grid-point data are time composited with respect to the trough axis along three cross sections over southeastern China (western section), southern East China Sea (central section) and southern Japan (eastern section), respectively, during the mature and decaying stages of the trough. The results indicate that the structure of the eastern and central sections resembles a typical midlatitude baroclinic front with strong vertical tilt toward an upper level cold core and a strong horizontal temperature gradient. On the other band, the western section resembles a semitropical disturbance with an equivalent barotropic, warm core structure, a weak horizontal temperature gradient, and a rather strong hor.
Mining High-Quality Pseudoinstance Soft Labels for Weakly Supervised Object Detection in Remote Sensing Images

Weakly supervised object detection in remote sensing images (RSI) is still a challenge because of the lack of instance-level labels, and many existing methods have two problems. First, most of the existing methods usually mine the pseudoground-truth (PGT) instances solely relying on proposal class scores (PCSs). Actually, the reliability of PCS is not enough because of the bird’s eye view imaging and large-scale chaotic background of RSIs, and the instances with high PCS incline to cover the discriminative region, rather than the whole object. Second, the existing methods assign a one-hot label to each instance, and the label of the PGT instance is copied to its neighbor instances, which induces the misclassification problem to some extent. Actually, the probability that the neighbor instances contain the object with the same category is smaller than the PGT instance. For the first problem, the proposal quality score (PQS) is proposed for mining high-quality PGT instances, which contain PCS and dual-context projection score (DCPS). The DCPS is calculated through semantic segmentation and is employed to measure the completeness that each proposal covers an object. For the second problem, a pseudosoft label assignment (PSLA) strategy is proposed to assign a more precise soft label for each instance, where the soft label is determined by the spatial distance between each instance and its nearest PGT instance. The ablation study validates the effectiveness of the PQS and PSLA. The comprehensive comparisons with other WSOD methods on three popular benchmarks show the excellent performance of our method.
Phytochemicals: A Promising Alternative for the Prevention of Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurological condition that worsens with ageing and affects memory and cognitive function. Presently more than 55 million individuals are affected by AD all over the world, and it is a leading cause of death in old age. The main purpose of this paper is to review the phytochemical constituents of different plants that are used for the treatment of AD. A thorough and organized review of the existing literature was conducted, and the data under the different sections were found using a computerized bibliographic search through the use of databases such as PubMed, Web of Science, Google Scholar, Scopus, CAB Abstracts, MEDLINE, EMBASE, INMEDPLAN, NATTS, and numerous other websites. Around 360 papers were screened, and, out of that, 258 papers were selected on the basis of keywords and relevant information that needed to be included in this review. A total of 55 plants belonging to different families have been reported to possess different bioactive compounds (galantamine, curcumin, silymarin, and many more) that play a significant role in the treatment of AD. These plants possess anti-inflammatory, antioxidant, anticholinesterase, and anti-amyloid properties and are safe for consumption. This paper focuses on the taxonomic details of the plants, the mode of action of their phytochemicals, their safety, future prospects, limitations, and sustainability criteria for the effective treatment of AD.
Detoxification of Mahua Seed Cake Using Various Salt Solutions

In order to detoxify saponin (mowrin) and tannins in mahua seed cake, soaking in different salt solutions was carried. The salt solutions used were 0.1 M ammonium dihydrogen phosphate buffer (pH 6), 0.1 M ammonium ferrous sulphate, 0.1 M ammonium acetate, 0.01 M triammonium citrate, 0.01 M diammonium hydrogen citrate, 0.1 M diammonium hydrogen phosphate and 0.1 M ammonium ferric sulphate. All the treatments showed better results compared to water washing. The maximum removal of mowrin and tannins was found with 0.1 M ammonium ferrous sulphate (88.39±3.92 & 52.87±0.46%) among other treatments.
Emotion Recognition via Multiscale Feature Fusion Network and Attention Mechanism

Traditional manual feature-based machine learning methods and deep learning networks have been used for electroencephalogram (EEG)-based emotion recognition in recent years. However, some existing studies ignore the low signal-to-noise ratio and the fact that each subject has unique EEG traits, which suffer from low recognition accuracy and poor robustness. To solve these problems, we propose a novel attention mechanism-based multiscale feature fusion network (AM-MSFFN) that considers high-level features at different scales to improve the generalization of the model for different subjects. Specifically, we first utilize a spatial-temporal convolutional block to extract temporal and spatial features of EEG signals sequentially. Subsequently, considering the sampling rate of EEG signals, the multiscale separable convolutions are designed for capturing emotional state-related information, to better combine and output feature mapping relationships. Convolutional module attention mechanism (CBAM) is applied after point-wise convolution, to better handle EEG variations of different subjects and the key information which facilitates classification. In addition, we adopt a preprocessing module based on data augmentation and data alignment to improve the quality of the training samples. Moreover, ablation studies show that the proposed attention mechanism and multiscale separable convolution contribute significant and consistent gain to the performance of our AM-MSFFN model. To verify the effectiveness of the proposed algorithm, we conducted extensive experiments on the DEAP dataset and SEED. The average accuracies achieve 99.479% and 99.297% for arousal and valence, respectively. The results demonstrated the feasibility of the proposed method.
Whole-Transcriptome Sequencing Reveals a ceRNA Regulatory Network Associated with the Process of Periodic Albinism under Low Temperature in Baiye No. 1 (Camellia sinensis)

The young shoots of the tea plant Baiye No. 1 display an albino phenotype in the early spring under low environmental temperatures, and the leaves re-green like those of common tea cultivars during the warm season. Periodic albinism is precisely regulated by a complex gene network that leads to metabolic differences and enhances the nutritional value of tea leaves. Here, we identified messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), circular RNAs (circRNAs), and microRNAs (miRNAs) to construct competing endogenous RNA (ceRNA) regulatory networks. We performed whole-transcriptome sequencing of 12 samples from four periods (Bud, leaves not expanded; Alb, albino leaves; Med, re-greening leaves; and Gre, green leaves) and identified a total of 6325 differentially expressed mRNAs (DEmRNAs), 667 differentially expressed miRNAs (DEmiRNAs), 1702 differentially expressed lncRNAs (DElncRNAs), and 122 differentially expressed circRNAs (DEcircRNAs). Furthermore, we constructed ceRNA networks on the basis of co-differential expression analyses which comprised 112, 35, 38, and 15 DEmRNAs, DEmiRNAs, DElncRNAs, and DEcircRNAs, respectively. Based on the regulatory networks, we identified important genes and their interactions with lncRNAs, circRNAs, and miRNAs during periodic albinism, including the ceRNA regulatory network centered on miR5021x, the GAMYB-miR159-lncRNA regulatory network, and the NAC035-miR319x-circRNA regulatory network. These regulatory networks might be involved in the response to cold stress, photosynthesis, chlorophyll synthesis, amino acid synthesis, and flavonoid accumulation. Our findings provide novel insights into ceRNA regulatory mechanisms involved in Baiye No. 1 during periodic albinism and will aid future studies of the molecular mechanisms underlying albinism mutants.
Learning by Co-Designing a Board Game to Make Chain Store Knowledge More Robust

The modern student is used to visual information and needs an engaging, stimulating, and fun method of teaching to make learning enjoyable and memorable. Recently, more and more teachers are changing traditional teaching methods and incorporating the concept of learner-centered teaching into their courses. Students must actively identify gaps in self-knowledge, construct clear learning topics, and then integrate relevant information to explain or solve problems. In order to enhance students’ interest in learning and affect their learning effectiveness, the present study introduces students to problem-oriented and game-based learning methods for solving the development problems of chain board games. Students in the third year already possess basic theoretical knowledge and have achieved relevant learning achievements, such as competition awards, industry/academic experience, class cadre experience, community service, etc. Thus, 125 students from two classes participated in this study via quantitative questionnaires. Data analysis with SPSS data revealed significant differences between learning effectiveness and learning methods, social interaction, and subject engagement; students with good learning outcomes were significantly more likely to organize notes and use methods than those with poor learning outcomes.
Design of an Adaptive Algorithm for Feeding Volume–Traveling Speed Coupling Systems of Rice Harvesters in Southern China

We developed an adaptive algorithm to reduce rice loss in harvesting, promote threshing and improve the quality and efficiency of small- and medium-sized rice harvesters operating in southern China’s hilly and mountainous areas. Using a fuzzy PID control algorithm, the harvester adapts to the rice harvesting conditions in southern China, and monitors rice feed volume changes and instantly adjust the traveling speed to optimize feed volume levels and threshing quality. We compared and analyzed the algorithm and the traditional PID control regulation effect in the simulation experiment. The algorithm had a quicker response speed and stable accuracy. In the field trial, the average error rate was 3.4%, and the maximum error rate was 5.1%, with most data points centered around the ideal feeding rate of 3.2 kg/s. Our results showed that the algorithm’s stability, accuracy, and real-time performance met the threshing loss reduction requirements of southern China’s rice harvesting operations.
Implication of gut microbiome in age-related macular degeneration

Age-related macular degeneration (AMD) is the most common cause of blindness in the United States in adults over 55 years of age and is one of the leading global causes of blindness: at least 196 million of the worldwide population have AMD, and prevalence is projected to rise to 288 million by 2040 (Lin et al., 2021). As cases and disease burden increase, improvements in the characterization of AMD pathobiology and exploration of potential therapeutic solutions are necessary first steps in addressing this global health concern.
IMAGE SEGMENTATION USING ACTIVE CONTOURS ON NOISY IMAGES: MATHEMATICAL APPROACHES

In this paper, we apply a Mathematical approached to develop a novel image segmentation method using active contours on noisy images with fifteen percentages of salt and pepper noise. This was implemented by selective binary and Gaussian filtering method to regularized level set evolution. Normally we selectively penalize the level set function to be binary and then use a Gaussian smoothing kernel to regularize it. The benefits of our method is to proposed new region based signed pressure force (SPF) function, which can step effectively the contour at blurred as well as weak edges and automatically detect the interior and exterior boundaries with the initial contour being anywhere in the images affected with noise. Also we demonstrate the advantages of the proposed method over the Chan-vase active contour model. The proposed method can implement by the finite difference scheme with experiment results shown in Matrix laboratory.
Distributed Adaptive Consensus Output Tracking Problem of Nonlinear Multi-Agent Systems with Unknown High-Frequency Gain Signs under Directed Graphs

This paper deals with the consensus output tracking problem for multi-agent systems with unknown high-frequency gain signs, in which the subsystems are connected over directed graphs. The subsystems may have different dynamics, as long as the relative degrees are the same. A new type of Nussbaum gain is first presented to tackle adaptive consensus control of network-connected systems without the knowledge of the high-frequency gains. Adaptive laws and internal models are then proposed to handle the uncertainties and unknown parameters. An integral Lyapunov function based on sufficient conditions is finally introduced to tackle the asymmetry of the Laplacian matrix of directed graphs, into which we incorporate the new Nussbaum gain and the adaptive internal model to design the controller. It is apparent that the control scheme and the adaptive laws are fully distributed, which means that only the relative information of the neighbourhood subsystems’ outputs is used, and the simulation results validate the effectiveness of the control design, whereby they guarantee the asymptotic convergence of errors to zero as well as the boundedness of the state variables.
A hybrid support vector machine regression for exchange rate prediction

Support vector machines (SVMs) have been successfully used to solve nonlinear regression and times series problems. Unlike most conventional neural network models, which are based on the empirical risk minimization principle, SVMs apply the structural risk minimization principle to minimize an upper bound of the generalization error, rather than minimizing the training error. However, one particular model can not capture all data patterns easily. This investigation presents a hybrid SVM model to exploit the unique strength of the linear and nonlinear SVM models in forecasting exchange rate. Furthermore, parameters of both the linear and nonlinear SVM models are determined by Genetic Algorithms (GAs). A numerical example from an existing literature is employed to compare the performance of the proposed model. Experiment results show that the proposed model outperforms the other approaches in the literature.
Drosophila Models Reveal Properties of Mutant Lamins That Give Rise to Distinct Diseases

Mutations in the LMNA gene cause a collection of diseases known as laminopathies, including muscular dystrophies, lipodystrophies, and early-onset aging syndromes. The LMNA gene encodes A-type lamins, lamins A/C, intermediate filaments that form a meshwork underlying the inner nuclear membrane. Lamins have a conserved domain structure consisting of a head, coiled-coil rod, and C-terminal tail domain possessing an Ig-like fold. This study identified differences between two mutant lamins that cause distinct clinical diseases. One of the LMNA mutations encodes lamin A/C p.R527P and the other codes lamin A/C p.R482W, which are typically associated with muscular dystrophy and lipodystrophy, respectively. To determine how these mutations differentially affect muscle, we generated the equivalent mutations in the Drosophila Lamin C (LamC) gene, an orthologue of human LMNA. The muscle-specific expression of the R527P equivalent showed cytoplasmic aggregation of LamC, a reduced larval muscle size, decreased larval motility, and cardiac defects resulting in a reduced adult lifespan. By contrast, the muscle-specific expression of the R482W equivalent caused an abnormal nuclear shape without a change in larval muscle size, larval motility, and adult lifespan compared to controls. Collectively, these studies identified fundamental differences in the properties of mutant lamins that cause clinically distinct phenotypes, providing insights into disease mechanisms.
Investigation of U-shaped plastic optical fiber as refractive index sensor for liquids assessment

In this study, U-shaped fiber optic sensors are fabricated and analyzed to measure the sensitivity of the developed sensor and optimized the detection of the refractive index (RI) of a given liquid. Identifying the authenticity of the RI is very important in food processing, chemical, liquid security and pharmacy. In this research, three types of sensors with different curvature radii (3 mm, 4 mm, and 5 mm) and different angles (30°C and 60°C) with 60 cm length of polymer fibers have been developed to characterize and analyze which type of sensor that will give optimal reading. This sensor is expected to be used for future studies such as in bioengineering, food and liquid security and chemical detection. Each sensor is tested with several types of liquids that have different densities. The development of this RI sensor is also intended to detect a suitable temperature for RI of a liquid that is between 20°C - 55°C. The research analyzed the RI sensitivity using impurity-free liquid (mineral water) and non-impurity liquid (saline water and used cooking oil). The result is measured and collected using Optical Power Meter and a 6500-input light source. The selection of this U-shaped sensor is due to the robustness of this sensor in various environments, high sensitivity, and its simple construction. This work aims to produce a low-cost and highly optimal U-shaped sensor for detecting and measuring the RI of a liquid impurity and security in any environment.
Ark Filter: A General and Space-Efficient Sketch for Network Flow Analysis

Sketches are widely deployed to represent network flows to support complex flow analysis. Typical sketches usually employ hash functions to map elements into a hash table or bit array. Such sketches still suffer from potential weaknesses upon throughput, flexibility, and functionality. To this end, we propose Ark filter, a novel sketch that stores the element information with either of two candidate buckets indexed by the quotient or remainder between the fingerprint and filter length. In this way, no further hash calculations are required for future queries or reallocations. We further extend the Ark filter to enable capacity elasticity and more functionalities (such as frequency estimation and top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> query). Comprehensive experiments demonstrate that, compared with Cuckoo filter, Ark filter has <inline-formula> <tex-math notation="LaTeX">$2.08\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$1.34\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$1.68\times $ </tex-math></inline-formula> throughput of deletion, insertion, and hybrid query, respectively; compared with Quotient filter, Ark filter has <inline-formula> <tex-math notation="LaTeX">$4.55\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$1.74\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$22.12\times $ </tex-math></inline-formula> throughput of deletion, insertion, and hybrid query, respectively; compared with Bloom filter, Ark filter has <inline-formula> <tex-math notation="LaTeX">$2.55\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$2.11\times $ </tex-math></inline-formula> throughput of insertion and hybrid query, respectively.
Cooperative compilation optimization of register allocation and thread management for AMDGPU

Thread parallelism and single-thread’ performance are two important factors affecting the performance of kernel functions, and they are both closely related to register allocation. According to change the thread parallelism to optimize GPU register resource allocation can effectively improve the performance of heterogeneous programs. We obtain the required number of vector registers by counting the number of virtual registers during the compilation of kernel functions, and then combine them with the number of wavefronts used to launch kernel functions for overall performance analysis, proposing a RAW compilation method for collaborative optimization of register allocation and thread management for AMDGPU, which is implemented in the LLVM compiler. It is verified that the method has a speedup ratio of about 1.12x for the Rodinia test set and about 1.4x for the quda application.
Iterative Pseudo-Sparse Partial Least Square and Its Higher Order Variant: Application to Inference From High-Dimensional Biosignals

Partial least square (PLS) regression and its (L1 or L2 norm) regularized versions have been proposed to handle the high-dimensionality aspects of the problem at hand and select relevant features. Addressing these issues improves the generalizability of decoding the unseen data, with the severe challenge of high computational complexity. In order to avoid directly solving the L1 norm optimization problem or performing matrix inversion, this article proposes two PLS-based algorithms, pseudo-sparse PLS (PS-PLS) and iterative pseudo-sparse higher order PLS (iPS-HOPLS). In these proposed methods, we add the Pseudo-Sparsity term to reduce the L1 norm of the regression coefficient vector in a selective scheme for better importance interpretation while keeping the algorithm as simple as possible. Regarding the evaluation of the proposed methods, we investigate three critical high-dimensionality regression problems of 1) the prediction of 3-D trajectory from electrocorticography (ECoG) recordings, 2) decoding continuous fluctuation of the electromyography (EMG) powers from recorded magnetoencephalography (MEG) signals, and 3) continuous decoding of the finger forces from the high-density surface electromyogram (HD-sEMG) signals. As well as providing cognitive-relevant interpretations, the experimental results show significant improvements over the generic methods and competitive performance compared to the state-of-the-art regularized PLS approaches.
Emotional labour job characteristics in compassion work – differentiating exposure, empathy, compassion, and distancing

ABSTRACT Previous research on compassion work has focused on the emotion regulation of human services workers. However, little attention has been paid to the job characteristics of this work. In this paper, we conceptualise a model of compassion work that differentiates the task characteristics (1) exposure and (2) empathy, and the work style requirements (3) compassion and (4) distancing. We tested an operationalisation of our compassion work model for its factor structure and validity in two studies (total N = 985). Our findings confirmed the proposed four-factor structure. Moreover, as expected, exposure was positively related to engagement and burnout, suggesting exposure is a challenge stressor in compassion work; empathy was positively related to burnout, suggesting empathy is a hindrance stressor in compassion work; compassion was positively related to work engagement and work meaningfulness, suggesting it supports self-enhancement in compassion work; and finally distancing was negatively related to burnout, suggesting it supports health and well-being in compassion work. Overall, these findings suggest our model is a promising vantage point for future research, job design, and health prevention in compassion work.